DATA-DRIVEN HUMAN RESOURCE ANALYTICS SYSTEM
FOR STRATEGIC DECISION MAKING
STUDENT NAME:
PAUL NIGEL S. ABALOS
STUDENT NUMBER:
21-4651-660
COURSE NAME:
Doctor in Information Technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
DR. THELMA D. PALAOAG
DATE OF SUBMISSION:
12 August, 2023
CONTENTS
ABSTRACT ..................................................................................................................................................................3
INTRODUCTION ........................................................................................................................................................5
PROBLEM STATEMENT ..........................................................................................................................................8
OVERVIEW..................................................................................................................................................................8
OBJECTIVES AND AIMS ..........................................................................................................................................9
OVERALL OBJECTIVE ..................................................................................................................................................9
SPECIFIC AIMS ............................................................................................................................................................9
RESEARCH QUESTION .................................................................................................................................................9
BACKGROUND AND SIGNIFICANCE ..................................................................................................................10
RESEARCH DESIGN AND METHODS ..................................................................................................................12
OVERVIEW................................................................................................................................................................12
POPULATION AND STUDY SAMPLE.............................................................................................................................13
SAMPLE SIZE AND SELECTION OF SAMPLE .................................................................................................................13
SOURCES OF DATA....................................................................................................................................................13
COLLECTION OF DATA ..............................................................................................................................................13
DATA ANALYSIS STRATEGIES ...................................................................................................................................14
TIMEFRAMES ............................................................................................................................................................14
STRENGTHS AND WEAKNESSES OF THE STUDY ...........................................................................................14
STRENGTHS: .............................................................................................................................................................14
WEAKNESSES: ..........................................................................................................................................................15
BUDGET AND MOTIVATION ................................................................................................................................15
REFERENCES ...........................................................................................................................................................16
2
ABSTRACT
This descriptive-developmental research entitled “Data-Driven Human Resource Analytics
System for Strategic Decision Making and Organizational Optimization” primordially aims to design
and develop a data-driven human resource analytics system (DDHRAS) for strategic decision
making of Pangasinan State University (PSU), the biggest university in Ilocos Region of the
Philippines which has nine (9) campuses tactically situated in the Province of Pangasinan.
Currently, PSU is subscribed to an online enterprise resource planning (ERP)-based university
system that only supports basic employee profiling, employee masterlist, employee attendance
log, and payroll preparation as part of its human resources (HR) modules, hence, most of the HR
functions are performed manually affecting organizational efficiency, in general, and hindering realtime HR-related data access for strategic decision-making among university executives.
The proposed system, branded as PSU-DDHRAS, is an online web-based computerized
human resource management information system (HRIS) composed of integrated modules for the
three (3) core HR functions: hiring, recruitment and selection, training and development, and
organization management. PSU-DDRAS will be developed to support five (5) user groups as part
of its information security access levels for HR Director, HR Supervisor, HR Staff, Employee, and
Job Applicant user accounts. The proposed web application shall also incorporate artificial
intelligence
(AI)-based data analytics for resumé skills
extraction, job matching
and
recommendation, and career path and upskilling recommendations. A User Acceptance Test
(UAT) will be conducted to evaluate the acceptability of the system using ISO/IEC 25010 standard
as software quality models for functional suitability, performance efficiency, compatibility, usability,
reliability, security, maintainability, and portability.
The Agile-Scrum model of software development life cycle (SDLC) framework will be
employed as part of the developmental design of the research in which an updated release version
of PSU-DDRHAS is expected every four (4) weeks during a 12-month period from
conceptualization to acceptability testing.
Data for the research is divided into two parts: (1) research data during system design and
development which will be taken from the Human Resource Management Office that will include
employee records, job titles and descriptions, performance evaluations, performance evaluations,
employee development plans, job applicant records, and system needs surveys/interviews, and
observations of the manual processes of the HR department; and (2) the User Acceptance Test
(UAT) data which will be conducted through a questionnaire once the proposed system is
developed.
The population for the study will be the employees of PSU currently employed during
Academic Year 2023-2024 and existing job applicants of PSU during the said academic period.
Random sampling technique will be used in in selecting a representative study sample from the
3
population, which will include balanced and stratified frequency among job applicants, teaching
staff, university executives, directors, administrative personnel, and non-teaching employees.
The research seeks to benefit several stakeholders of PSU composed of the employees,
job applicants, and HR unit employees. PSU-DDHRAS is seen to impact Pangasinan State
Univeristy, as a whole, by creating an opportunity to modernize the university’s HR unit,
empowering decision-makers with accurate, optimized and timely analytical-based information
while reducing administrative burden, thus, enabling better resource allocation for the university's
long-term growth and success.
4
INTRODUCTION
Human resource (HR) is a broad field that encompasses all aspects of the relationship
between an organization and its employees. As a unit, it is one of the most critical components of
any organization, be it large or small, as it involves every individual as a unified workforce that
drives the organization’s performance, productivity, and profitability (Armstrong, 2019). As a
function, it is the process of planning, developing, and evaluating the performance of employees in
organizations with an ultimate goal of ensuring that organizations have the right people with the
right skills to meet their strategic objectives (Fisher & Sharp, 2020). As an asset, it is both a
quantitative measure of the manpower the organization has to provide production and efficiency
and the qualitative measure of the degree of combined skills, talent, leadership, and capacity that
an organization is positioned in contrast among competitors and key players in the industry or
sector where the organization belongs (Lepak & Snell, 2019). Although the human resources can
be a complex and challenging process, it is essential for the success of any organization
regardless of size or industry.
The development and the management of human resources are two distinct core business
functions of the human resource department. The human resource management (HRM) is
generally defined as the creation of established systems that aim to organize the workforce within
an organization which commonly involves systematic guidelines about staffing, compensation,
benefits, and defining employee roles with their corresponding duties and responsibilities (Cascio,
2019). The human resource management is commonly lead by the HR Director with a team of HR
managers designated at each core function involved in HRM.
Meanwhile, human resource development (HRD) is an integral part of the human resource
management and is defined as the continuous nurturing of employees which entails providing
workers with the right, competitive skills and relevant knowledge that will foster individual and
corporate growth (Aguinis & Kraiger, 2019).
Over the years, several human resource models have been developed to highlight the
functions of human resource management and delineate it from other core business functions of
the organization considering the encompassing functional and structural nature of the human
resource. Three of the most widely implemented HR models include: (1) the Harvard Model of
HRM developed by John Beer in 1984 which views HR as a strategic partner in the organization
via four components: strategic HRM, competitive HRM, employee commitment and employee
development; (2) the Ulrich Model developed by Dave Ulrich in 1997 which views HR as a
business partner that helps the organization achieve its strategic goals through one of its four
roles: strategic partner, change event, administrative expert, and employee champion; and (3) The
HR Value Chain Model developed by Gary Dessler in 2003 which views HR as a series of linked
activities that create value for the organization composed of: attracting and selecting employees,
developing employees, motivating and rewarding employees, evaluating employees, and
5
maintaining employees. Out of these HR models and need for continuous adaptability and
sustainable future, modern HRM models are usually composed of seven (7) core functions such as
hiring, recruitment and selection, training and development, retention, organizational management,
audits and compliance, risk management, and decision making (Bevan & Tilley, 2019).
Traditionally human resources management uses manual processes and paperwork. This
approach often involves manually recording and managing employee information, tracking
attendance, processing payroll, and handling other HR-related activities without the use of
advanced technological tools. However, with the advent of information and communications
technology (ICT), HRM modernization has experienced a grand evolution as digitization infiltrates
the tedious manual HR processes, hence, many organizations have recognized the advantages of
using Human Resources Information Management Systems (HRIMS) to streamline and automate
HR processes (Votto et al., 2021).
A human resource information management system (HRIMS) is a computerized system
that combines several computer applications of managing HR tasks such as recruitment,
application and screening, selection, decision-making, job offer and onboarding, training and
development (Abdullah et al., 2020). Most HRIMS are connected to the Internet via web-based
and/or mobile application technologies which enable faster data capture and optimization for
executive functions which is characterized by the analysis of a large amount of human resource
management information to obtain the auxiliary decision-making process (M. Chai, 2022).
Nowadays, HRIMS are also feature-packed with advanced analytics to predict human performance
at work using various approaches like traditional statistical analysis, artificial intelligence (AI)-based
algorithms for text mining, data mining, and correlation analysis. (Khan, 2022). Human Resources
Management can use these HR analytics to analyze data, discover patterns or concerns, and take
proactive action with other divisions to keep the company running smoothly and economically,
thus, increasing the decision-making capability of managers in all situations (Valecha, 2022).
By transitioning from manual HR management to computerized HRIMS with AI-based data
analytics, organizations can leverage the power of technology to streamline HR processes, gain
valuable insights, and make data-driven decisions that can ultimately lead to improved efficiency,
better workforce management, and enhanced organizational performance (Kaur, 2021). Several
recent studies have shown that human resource systems utilizing data-driven analytics approaches
significantly support strategic decision making (Heuvel, et al., 2019) in terms of talent acquisition
(Verbruggen, et al., 2021), skills competencies alignment (DeSimone, et al., 2022), and succession
planning in private companies (Wang, et al., 2023) and has potential to be implemented among
public offices (DeNisi, et al., 2020).
Here in the Philippines, computerized human resource management systems (HRMS) are
increasingly being implemented to automate HR tasks in order to improve the efficiency and
effectiveness of HR processes such as recruitment and selection, training and development,
performance management, and compensation and benefits which save organizations time and
6
money, and also help to improve the accuracy and consistency of HR data (Santos, 2019). Most
observed computerized HRMS utilized in the country encompass a wide range of software
solutions, including Human Capital Management (HCM) and Human Resource Information System
(HRIS), designed to support HR personnel in effectively managing an organization's most valuable
assets, its people (Mapa, 2019).
Studies on the impact of the implementation of HRM systems in the Philippines revealed
that it is driven by a number of factors, including the growing number of multinational companies
(MNCs) operating in the country (Abella & De Leon, 2019), the increasing demand for skilled
workers both inside and outside the Philippines (Cruz & Quijano, 2019), and the availability of
affordable HRM software for companies of all sizes (Magsino & Cruz, 2019).
Pangasinan State University, being the largest state university in the Ilocandia Region
composed of nine (9) campuses strategically located among key cities and municipalities of the
province of Pangasinan, invests and relies heavily in its workforce and HRM to accomplish its
university mission and vision by actively participating in both local and international HR
accreditations and standardizations. Currently, PSU is subscribed to and implementing an online
enterprise resource planning (ERP)-based university system which is focused more on automating
university functions such as student admissions, enrolment, student financial and collegiate
accounts, and covers only few HR-related tasks and features such as basic employee profiling and
masterlist, employee time logging, and basic payroll preparation. Hence, PSU employs a manual
system for most of HR-related processes and employee data which is time-consuming and prone
to errors, leading to inefficiencies and delays in decision-making.
Additionally, the lack of real-time access to most recent key employee data hinders the
university's ability to effectively allocate resources and plan for future growth. With the increasing
number of applicants and employees at the university periodically, the process becomes more
tedious and even takes longer than necessary which results in frustration among employees and
potential applicants, as well as potential loss of talented individuals who may seek opportunities
elsewhere instead of being hired and nurtured as part of the PSU family. Moreover, the manual
data recording also poses security risks as sensitive employee information may not be adequately
protected.
Despite all these drawbacks, PSU leadership has shown openness and willingness for PSU
to transition into a much modern and automated HR system that can streamline processes,
improve data accuracy, and enhance overall organizational efficiency and continue its stance in
quality HR management by continuing its commitment to bring development among its human
resources as evidenced by recent human resource management awards it garnered such as being
an Investors in People (IiP) Gold Awardee, Program to Institutionalize Meritocracy and Excellence
in Human Resource Management (PRIME-HRM) Bronze Awardee, and currently holding the
premier distinction as the first and only educational institution, both in private and public, to be
7
conferred with the most prestigious Philippine Quality Award (PQA) Level III: Mastery in Quality
Management.
The researcher, being employed as part of the academic sphere of PSU, motivated by the
genuine HR efforts and awards the university is currently maintaining, and in consideration of the
above-mentioned HR automation and employee data problems presently faced by the university, is
propelled to propose the design and development of a data-driven human resource analytics
system (DDHRAS) for strategic decision making and organizational optimization for PSU.
Specifically, the study will focus on the design and development of PSU-DDHRAS web app
which is an online web-based computerized human resource management information system
composed of integrated modules for HR core functions involving hiring, recruitment and selection,
training and development, and organization management that supports information security access
for different user groups such as HR Director, HR Supervisor, HR Staff, Employee, and Job
Applicant user accounts; Likewise, it seeks to incorporate artificial intelligence (AI)-based data
analytics for resumé skills extraction, job matching and recommendation during hiring, recruitment
and selection processes, and analytics for career path and upskilling recommendations during
training and development processes. Finally, the study also aims to conduct User Acceptance
Testing (UAT) of the proposed system to evaluate its acceptability using ISO/IEC 25010 standard
as software quality models for functional suitability, performance efficiency, compatibility, usability,
reliability, security, maintainability, and portability.
The research seeks to benefit primarily the existing employees of PSU as they will be
ensured of secured, updated and reliable digital personnel file and records, the job applicants as
they will be given equal opportunity and avenue to be assessed and shortlisted for the job position
they are aiming, the HR department non-academic employees as their tedious manual work will be
lessened and they can focus their energy to improve their work and provide quality services, and
the PSU, as a whole, as it will have an opportunity to modernize its HR unit, be empowered with
accurate, optimized and timely analytical-based information for decision making while reducing
administrative burden that will enable better resource allocation for the university's long-term
growth and success.
PROBLEM STATEMENT
Overview
Pangasinan State University employs a manual system for managing employee data and
HR processes. This manual system is time-consuming and prone to errors, leading to inefficiencies
and delays in decision-making. Additionally, the lack of real-time access to updated employee data
hinders the university's ability to effectively allocate resources and plan for future growth. With the
8
increasing number of applicants and employees at the university, the process becomes tedious
and may even take longer than necessary. This can result in frustration among employees and
potential applicants, as well as a potential loss of talented individuals who may seek opportunities
elsewhere. Moreover, the manual system may also pose security risks as sensitive employee
information is not adequately protected as most employee and applicant records are paper-based.
Therefore, it is crucial for PSU to transition to a modern and automated HR system that can
streamline processes, improve data accuracy, and enhance overall organizational efficiency.
These circumstances prompted the researcher to propose a study that would meet the
particular needs and requirements of PSU's Human Resource Office.
OBJECTIVES AND AIMS
Overall Objective
The overall objective of this research is to design and develop a data-driven human
resource analytics system (DDHRAS) for strategic decision making and organizational optimization
for Pangasinan State University.
Specific Aims
1. Design and develop an online web-based computerized human resource management
information system composed of integrated modules for HR core functions involving hiring,
recruitment and selection, training and development, and organization management that
supports information security access for different user groups such as HR Director, HR
Supervisor, HR Staff, Employee, and Job Applicant user accounts;
2. Incorporate artificial intelligence (AI)-based data analytics for resumé skills extraction, job
matching and recommendation during hiring, recruitment and selection processes, and
analytics for career path and upskilling recommendations during training and development
processes; and
3. Evaluate the proposed system’s acceptability using ISO/IEC 25010 standard as software
quality models in terms of functional suitability, performance efficiency, compatibility,
usability, reliability, security, maintainability, and portability.
Research Question
The study seeks to address the following research questions:
1. How can data-driven approaches be developed to support strategic decision making along
with:
a. Talent Acquisition;
9
b. Skills and Competencies Alignment; and
c. Succession Planning?
2. What are the features of the data-driven human resource analytics system for strategic
decision making?
a. What data security measures and protocols shall be implemented to safeguard the
employee information?
BACKGROUND AND SIGNIFICANCE
Human resources (HR) are regarded as the central pillar of an organization’s competitive
advantage. Furthermore, because of its importance in optimizing costs and improving productivity
and quality, HR is recognized as an essential resource for organizations. Human resource
management (HRM) should form functional groups to facilitate collaboration and coordination
between an organization’s different components. New approaches emphasize that HR capabilities
are fundamental for an organization’s improvement and sustainability (Mohiuddin et al., 2022).
Human Resource refers to the employee of the organization and Human Resource Management
(HRM) is known for the design of management systems which ensures human talent and uses it
efficiently to achieve organizational objectives. That is why human resources are an essential
asset in the institution, and it can be developed, and its impact is maximized through the HRM
(Shrestha, 2019).
The human resource management system includes a wide range of content, involving the
management of personnel information, employee recruitment, performance appraisal, attendance
management, salary management, etc. Before the popularization of information technology, human
resource management mainly relied on the form of paper files which entails low efficiency. With the
popularization and application of computer technology, almost all companies use computer
systems to manage human resource management business. Especially after the emergence of the
Internet, human resource management has entered the stage of resource sharing and remote data
transmission which, until now, is increasing day by day (M. Chai, 2022).
Human resource information systems (HRIS) are computer-based systems that are used to
store and manage data related to employees which can be used to automate many of the tasks
involved in HRM, such as payroll, benefits administration, and performance tracking. HRIS can
also be used to analyze data to gain insights into employee performance, workforce trends, and
other HRM-related issues (Van Iddekinge & Ployhart, 2019).
The implementation of HRIS among universities has been growing in recent years and has
shown to help universities to improve their HRM practices in a number of ways (Wang & Johnson,
2020): HRIS can automate many of the time-consuming tasks involved in HRM, such as payroll,
benefits administration, and performance tracking. This can free up HR professionals to focus on
10
more strategic activities (Santos, 2019). HRIS also improve data accuracy which is important for
ensuring that employees are paid correctly and that they receive the benefits they are entitled to
(Magsino & Cruz, 2020). Likewise, HRIS can be used to analyze data to gain insights into
employee performance, workforce trends, and other HRM-related issues. This information can be
used to make better decisions about HRM practices. Further, HRIS can be used to improve
communication between HR professionals and employees. This can help to create a more positive
and productive work environment (Cruz & Quijano, 2019).
Human resources management system (HRMS) usually used in colleges and universities is
a computerized system that consists of planning, configuration, assessment, development, and
use of HR-related data for the management of employees(Zheng, 2020). In order to do a good job
in the overall development of human resources, HRMS must combine all the HR-relevant links and
avoid each link from being independent and separated from each other. The school's development
goals, scientific predictions and analysis should be conducted to formulate human resources
planning so that the replenishment and demand for human resources can be optimally balanced,
and the waste of human resources caused by excess hiring can be decreased. HRMS can be used
to optimize the allocation of human resources on the basis of full investigation and data analysis so
that the staff's post structure, education structure, age structure, and title structure can be rationally
laid out to maximize the potential of each staff member. HRMS can also correctly handle the
relationship between the introduction of external talents and the stabilization of existing talents.
The utilization of data analytics among HRIS is also becoming increasingly important. Data
analytics can be used to gain insights into employee performance, workforce trends, and other
HRM-related issues. This information can be used to make better decisions about HRM practices,
such as identifying high-performing employees, predicting employee turnover, analyzing the impact
of training programs, developing succession plans, and making strategic workforce planning
decisions. Several studies have revealed that the use of HRIS and data analytics can help
universities to improve their HRM practices and achieve their strategic goals (Van Iddekinge &
Ployhart, 2019) (Kumar & Rai, 2020) (Wang & Johnson, 2020).
Pangasinan State University, as the largest state university in the Ilocos Region of the
Philippines, is composed of nine (9) campuses situated among strategic cities and municipalities of
Pangasinan. Currently, PSU is subscribing to an online enterprise resource planning (ERP)-based
university system for automating university functions such as student admissions, enrolment,
student financial and collegiate accounts. The said ERP-based university system covers only basic
employee profiling and masterlist, employee attendance logging, and basic payroll preparation as
part of its HR modules. Because of this, all campuses of PSU are still employing manual system
for most of HR-related processes and employee data management which is time-consuming and
prone to errors, leading to inefficiencies and delays in decision-making among executives.
The researcher, being employed as an educator of PSU and inspired by the genuine HRM
efforts and awards that PSU currently has, and in further consideration of the above-mentioned HR
11
manual system and employee data problems presently faced by the university, is motivated to
propose the design and development of a data-driven human resource analytics system
(DDHRAS) for strategic decision making and organizational optimization for PSU. PSU-DDHRAS
is an online web-based computerized human resource management information system composed
of integrated modules for HR core functions involving hiring, recruitment and selection, training and
development, and organization management that supports information security access for different
user levels such as HR Director, HR Supervisor, HR Staff, Employee, and Job Applicant user
accounts, and incorporates artificial intelligence (AI)-based data analytics for resumé skills
extraction, job matching and recommendation during hiring, recruitment and selection processes,
and analytics for career path and upskilling recommendations during training and development
processes
The research seeks to benefit the following stakeholders:
1) Employees – both academic and non-academic employees of PSU will be ensured of
secured, updated and reliable digital personnel file and records through the proposed
PSU-DDHRAS.
2) Job Applicants - PSU job applicants will be given equal opportunity and neutral avenue
to be assessed and be shortlisted for the job position they are aiming using the
proposed system.
3) HR Department Team – with the use of the proposed web app, non-academic
employees of PSU will be alleviated of their tedious manual tasks so that they can focus
in improving their work and in providing quality services among the clients of the HR
office; and
4) Pangasinan State University - as a whole , PSU will have an opportunity to modernize
its HR unit, be empowered with accurate, optimized and timely analytical-based
information for decision making while reducing administrative burden that will enable
better resource allocation for the university's long-term growth and success.
RESEARCH DESIGN AND METHODS
Overview
The study will make use of the descriptive-developmental methods of research. The
descriptive method of the research will be used in order to gather information about the current
profile and processes of the human resource management in Pangasinan State University while
the developmental type of research will correspond to the software engineering development
12
methodology, particularly the Agile-Scrum software development life cycle (SDLC) model, relying
on the information gathered using the descriptive methodology.
Population and Study Sample
The population for the study will be the employees of Pangasinan State University currently
employed during Academic Year 2023-2024 and existing job applicants of PSU during the said
academic period. The study will utilize a random sampling technique to select a representative
study sample from the population, which will include applicants, teaching staff, university
executives, directors, administrative personnel, and non-teaching employees. The selection of the
study sample will be based on the research question and objectives.
Sample Size and Selection of Sample
The sample size for this study will depend on the random sampling technique. The
selection will encompass (1) employees with and without plantilla items, as well as those under
contractual, contract of service, and job order arrangements; and (2) successful job applicants.
However, certain positions, such as consultants, will be excluded from the sampling process.
Additional exclusions may include positions that do not directly align with the research question
and objectives of the study.
Sources of Data
The primary source of data will be taken from the Human Resource Management Office,
such as applicant and employee records, job descriptions, performance evaluations and other
manual processes to be used as inputs in the development of the system.
Other primary sources of data will be derived from the observation and document review
during the test application of the system and the interviews conducted with the intended users of
the system, who are mostly employees of Pangasinan State University (PSU) under the Human
Resource Management Office (HRMO).
Secondary sources of data such as library research, internet, books, journals, and articles
will be utilized.
Collection of Data
The researcher will employ multiple instruments and techniques to achieve the stated
objectives through the use of questionnaires, personal interviews, observation, and analysis to
complete the study.
13
Data Analysis Strategies
The developer will use different unified modelling language tools to analyze the gathered
data, such as use-case diagram, data modelling, wireframes, and entity relationship diagram
(ERD) to identify patterns and relationships between different components of the system.
Timeframes
The timeframe of this study would be from July 2023 to July 2024. During this period, the
researcher-developer will collect data from various sources and analyze it using the abovementioned tools. Additionally, the researcher will also conduct regular meetings with stakeholders
to discuss the progress and findings of the study.
Activities
Quarter 1
Quarter 2
Quarter 3
Quarter 4
Problem and requirements definition
Data Gathering
System design, coding and testing
System deployment
System evaluation
STRENGTHS AND WEAKNESSES OF THE STUDY
Strengths:
The study has the potential to revolutionize HR management by leveraging information and
communications technology to streamline resource management processes, improve efficiency,
and reduce human error.
Through the integration of related ICT, this project strives to enhance efficiency, improve
employee satisfaction, and drive overall organizational success in rapidly evolving landscape of HR
management.
The project will develop a comprehensive system that harnesses HR data to provide realtime insights, facilitate strategic decision-making, and optimize organizational processes. By
employing advanced analytics techniques, decision making algorithms, and data visualization
tools, the system will enable HR professionals to make informed decisions regarding recruitment
and selection, job offer and onboarding, training and development, and organizational
management
The system will also analyze historical HR data to predict employee outcomes, identify
factors
influencing
organizational
success,
and
offer
actionable
recommendations
for
organizational optimization.
14
Weaknesses:
The study is limited to a particular organization in the academic sector and may not be
applicable to other forms of businesses outside its subject sector. Hence, the implementation of
generalized module design will be utilized which can create system support for similar
organizations.
The use of analytics and strategic decision-making may require a significant amount of data
collection and analysis, which could be challenging and time-consuming considering limited
resources. Thus, proper and early coordination with the subject HRMO, as the data provider, is
proposed as well as with the utilization of computer device with correct and capable specifications
to ensure ample time allotment and processing for data analytics module development.
The study relies on the self-reporting of employees, which may introduce bias and
inaccuracies in the data particularly in resumé skills extraction. To mitigate this, a skills bank
containing validated hard and soft skills for each sector-based job title will be developed to ensure
accurate skills matching. A skills scoring system will also be developed that will mark resumé skills
with higher points based on historical data of skills personally verified by the HR personnel or those
skills with verified certification or attached documentary proof.
The study requires significant time, effort and related resources from the subject institution
in order to gather and analyze the necessary data. Proper time management and stakeholder
coordination will be implemented to overcome this.
BUDGET AND MOTIVATION
Line Item
Budget
1. Web App Design front-end development
55,000
2. Web App back-end development
55,000
3. Domain Name & Web Hosting
10,000
TOTAL
120,000
15
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18
APPLYING MULTI-CRITERIA DECISION
ANALYSIS FOR AFRICAN SWINE FEVER
SURVEILLANCE SYSTEM
STUDENT NAME:
STUDENT NUMBER:
COURSE NAME:
COLLEGE:
COURSE CODE:
Grace D. Bulawit
21-5130-506
Doctor in Information technology
College of Information Technology and
Computer Science
DIT D1 – Dissertation 1
PROFESSOR:
Thelma D. Palaoag, DIT
DATE OF SUBMISSION:
17 06 2023
Contents
ABSTRACT ...................................................................................................................................................... 3
INTRODUCTION ............................................................................................................................................ 4
PROBLEM STATEMENT .............................................................................................................................. 5
RESEARCH QUESTION ..................................................................................................................................... 5
OBJECTIVES AND AIMS .............................................................................................................................. 5
OVERALL OBJECTIVE ...................................................................................................................................... 5
SPECIFIC AIMS ................................................................................................................................................ 5
BACKGROUND AND SIGNIFICANCE ........................................................................................................ 6
RESEARCH DESIGN AND METHODS ........................................................................................................ 7
OVERVIEW ...................................................................................................................................................... 7
POPULATION AND STUDY SAMPLE ................................................................................................................... 7
SAMPLE SIZE AND SELECTION OF SAMPLE ....................................................................................................... 7
SOURCES OF DATA .......................................................................................................................................... 7
COLLECTION OF DATA .................................................................................................................................... 7
DATA ANALYSIS STRATEGIES ......................................................................................................................... 8
TIMEFRAMES (GANTT CHART) ........................................................................................................................ 9
STRENGTHS AND WEAKNESSES OF THE STUDY ................................................................................. 9
ETHICAL CONSIDERATIONS ................................................................................................................... 10
BUDGET AND MOTIVATION .................................................................................................................... 10
REFERENCES ............................................................................................................................................... 11
2
ABSTRACT
Pigs are susceptible to the infectious sickness known as African swine fever (ASF). It
poses a serious threat to the pig business once it spreads because there is currently no
viable vaccine or cure. ASF's quick spread and 100% fatality rate have severely harmed the
Philippines' swine industry. Surveillance is one of the most crucial strategies that the
government must employ for monitoring and controlling the disease nationally, regionally,
and internationally to effectively manage ASF, which spread quickly across the nation and
has a significant socioeconomic impact on the pig industry. The study employed a mixed
method research method. It includes a GIS-based multicriteria decision analysis to create
several maps, including epidemic maps, hotspot/heatmaps and risk assessment maps. This
strategy enables decision-makers to assess the relative importance of managing the African
swine disease based on a set of preferences, criteria, and ASF epidemic indicators. The
multi-criteria decision analysis considered a variety of risk factors, including proximity to
suspected or confirmed ASF cases, level of biosecurity, type of piggery farm, population of
piggeries, vaccination status, and accessibility to road networks. A survey questionnaire
were given to twenty (20) specialists to rate the various factors according to their potential
impact on the disease's spread. The outcome of the multi-criteria decision analysis served
as the foundation for the automatically generated heatmaps and risk maps. 50 identified
respondents who were chosen using purposive sampling evaluated the developed online
and mobile app using a survey questionnaire based on the ISO 25010 Software Quality and
Requirements Evaluation methodology.
According to the result, the degree of biosecurity received a weighted percentage
increase of 35% based on the various criteria, while the distance from suspected or
confirmed ASF cases received a weighted percentage increase of 25%. Following this, the
criteria for pig population, immunization status, and accessibility to road networks each
received a weighted percentage increase of 25%, 10%, and 10%, respectively. The
outcomes of a multi-criteria decision analysis were used to design and construct a webbased GIS outbreak mapping and spatial data analysis tool. With a general weighted mean
of 4.76 and an interpretive meaning of "strongly agree," the evaluation of the created web
and mobile applications shows that they generally conform with the ISO 25010:2011
Software Quality Requirements and Evaluation Standard. The developed web and android
application has generated an interface for pig owners to easily report suspected ASF cases
in their farms. The GIS maps that the system produced have been used by the Municipal
Agricultural Office to provide a visual aid for decision-making in the management of the ASF
disease.
Keywords: african swine fever, disease management, gis, multicriteria decision analysis,
disease surveillance, spatial analysis
3
INTRODUCTION
African Swine Fever (ASF) is a highly contagious and devastating viral disease
affecting domestic and wild pigs [1]. It poses a significant threat to the global pork industry
and food security, with severe socio-economic implications [2]. As of today, ASF continues
to remain a prominent challenge, spreading across regions and causing substantial losses in
pig populations [1]. The urgency to combat this disease demands innovative approaches to
its surveillance, prevention, and mitigation.
In recent years, advancements in technology have offered promising solutions for
disease management. The integration of Geographic Information System (GIS) applications
and Multi-Criteria Analysis (MCA) has emerged as a powerful tool in disease surveillance
and decision-making processes [3]. GIS allows the visualization and spatial analysis of
disease patterns, while MCA aids in evaluating multiple factors to prioritize and implement
effective mitigation strategies [4].
Geographic Information System (GIS) applications play a crucial role in
understanding the spatial distribution and patterns of ASF outbreaks [5]. By mapping the
occurrence of ASF cases, identifying disease hotspots, and analysing the disease's spread,
GIS provides valuable insights into the dynamics of the disease [6]. GIS enables real-time
data visualization, facilitating informed decision-making by veterinary authorities and
policymakers [7]. It aids in resource allocation, allowing efficient deployment of response
teams, quarantine zones, and targeted surveillance efforts [8]. Moreover, GIS empowers
stakeholders to assess the impact of control measures and adjust strategies based on
emerging trends, enhancing the overall effectiveness of ASF mitigation efforts [9].
Multi-Criteria Decision Analysis (MCDA) is a decision-making methodology that
considers multiple factors and criteria when evaluating different alternatives [10]. In the
context of ASF management, MCDA allows stakeholders to assess various intervention
strategies based on a range of criteria, including cost, feasibility, environmental impact, and
efficacy [11]. By applying MCDA, decision-makers can prioritize and select the most suitable
control measures for specific ASF outbreak scenarios [12]. This approach ensures that
resources are allocated to interventions with the highest potential for success, thus
optimizing the use of limited resources in combating ASF effectively [13].
This study makes significant contributions to the Sustainable Development Goals
(SDGs) by focusing on mitigating African Swine Fever through GIS applications and MultiCriteria Analysis. It directly supports Goal 2: Zero Hunger by safeguarding livestock
populations and food security. Additionally, the adoption of technology-driven approaches
aligns with Goal 9: Industry, Innovation, and Infrastructure, promoting efficient agricultural
systems. Moreover, the emphasis on spatial analysis supports Goal 3: Good Health and
Well-being by preventing disease spread. The study's collaborative approach also aligns
with Goal 17: Partnerships for the Goals, fostering a coordinated response to the disease
across borders. Ultimately, these efforts positively impact the livestock industry, food
security, and public health, promoting sustainable development and resilience.
4
PROBLEM STATEMENT
African Swine Fever (ASF) remains a highly contagious and devastating viral
disease, posing a significant threat to the global pork industry and food security. Despite
concerted efforts by governments and stakeholders, ASF continues to spread across
regions, leading to massive economic losses and disruptions in pork production and trade.
The complexity of ASF's epidemiology and the lack of a commercially available vaccine
further exacerbate the challenges in its surveillance, prevention, and mitigation. In this
context, the problem at hand is to find innovative and effective approaches for managing
ASF and preventing its spread, especially in regions where the disease is prevalent. The
study aims to explore the potential of Geographic Information System (GIS) applications and
Multi-Criteria Decision Analysis (MCDA) in mitigating ASF, enabling early detection, targeted
interventions, and informed decision-making for sustainable disease management.
The study specifically aims to:
Research Question
1. What are the key criteria and factors that contributes to the transmission and impact
of African Swine Fever?
2. How can Multi-criteria Decision Analysis assists in deciding early detection and rapid
response strategy for African Swine Fever?
3. What is the extent of usability of the proposed system?
OBJECTIVES AND AIMS
Overall Objective
The general objective of this study is to design, develop, implement, and evaluate a GIS
based Multicriteria Decision Analysis for African Swine Fever (ASF) Disease Surveillance
System.
Specific Aims
Specifically, it aims to:
1. assess risk factors of African Swine Fever disease using Multi-Criteria Decision
Analysis (MCDA);
2. design and develop a GIS based Multi-criteria Decision Analysis for African Swine
Fever Disease Surveillance System; and
3. evaluate the extent of usability of the develop system
5
BACKGROUND AND SIGNIFICANCE
Animal surveillance is crucial for disease management. The integration of GIS
technology with animal surveillance systems has emerged as a powerful tool for data
collection, analysis, visualization, and interpretation. GIS plays a critical role in disease
surveillance and outbreak management by integrating spatial data with epidemiological
information [15][16][17]. Researchers use GIS to track the spread of zoonotic diseases, map
animal movements, identify high-risk areas, and facilitate targeted interventions [18][19]. It
provides a valuable framework for mapping and analysing human-animal interaction,
identifying hotspots, and designing effective management strategies [20]. Technological
advancements have enhanced the capabilities of GIS in animal surveillance. Integration of
remote sensing data, animal tracking technologies (e.g., GPS and radio telemetry), and
citizen science initiatives into GIS platforms have expanded the scope of data collection and
analysis [21]. Today, different challenges in GIS-based animal surveillance include data
quality, privacy concerns, and interdisciplinary collaboration. Future research directions
include the application of big data analytics, standardized data formats, and integration of
GIS with emerging technologies for improved real-time monitoring [22]. This technology
includes the use of web and mobile applications that can generate real-time maps, which are
valuable tools for decision-makers in animal disease management [23][16][17].
The study has the potential to change how animal disease surveillance and
management is done. Evidenced based decision-making will be implemented based on the
result of the multi-criteria decision analysis. It will benefit the following:
Farmers – the piggery owners will have a way to report their suspected ASF case. The
culling of pigs will also be done based on the risk maps generated by the system and will be
evidenced based and avoids culling every pig in the municipality that affects the socioeconomic status of piggery owners.
Local Government Unit - The developed GIS application will help the LGU is determining
their next course of action in case there is an outbreak of the African Swine Fever. Different
maps will be generated by the system that can give decision makers like the Municipal
Mayor, the members of the Sangguniang Bayan and, the official of the Municipal Agriculture
Office in a visual representation for easy understanding of the disease outbreak situation.
Furthermore, the LGU can now conduct mitigation or information drive programs based on
the result of the profiling of the piggery in the municipality.
Pork Consumers – If the ASF will be controlled by the implementation of the GIS Mapping
and Surveillance System, the consumers will avoid the scarcity of meat supply that leads to
high prices.
Future Researchers – The study might be a basis for future research that includes the use
of remote sensing, animal tracking and image processing for real time reporting and
mapping of ASF cases in a municipality.
6
RESEARCH DESIGN AND METHODS
Overview
In this study, the researcher will make use of mixed research method. It consists of
the conduct of the multi-criteria decision analysis (MCDA) for African Swine Fever disease
management. The result of the MCDA will be used for a web and mobile based Geographic
Information System for the African Swine Fever Surveillance System software development.
After which, an evaluation of the developed applications with its extent of compliance with
ISO 25010 Software Quality Requirements and Evaluation will be conducted.
Population and Study Sample
The population of the study consists of the employees of the Municipal Agriculture
Office of Echague, Isabela and veterinarians in the municipality of Echague, Isabela. In the
conduct of the multi-criteria decision analysis the researcher will used expert-based selection
sampling. A group of 20 experts familiar ASF identify and rank the different factors related to
the spread of African swine fever. In the development and evaluation of the Geographic
Information System for ASF, the population includes the personnel of the Municipal
Agriculture Office and the pig owners in the Municipality of Echague, Isabela.
Sample Size and Selection of Sample
The sample size for the conduct of MCDA will be a group of 20 experts such as
veterinarians and technical persons from the Municipal Agriculture Office selected using the
expert-based selection sampling. For the sample size for the evaluation of the developed
GIS application, the simple random sampling using slovin formula will be implemented. The
sample of fifty (50) respondents will be selected from all pig farm owners and technical
experts from the office of the Municipal Agriculture Office and IT experts from the LGU
Echague, Isabela. They will answer the questionnaire based on the ISO 25010 Software
Quality Requirements and Evaluation Tool to know the extent to which the proposed system
met the said standard.
Sources of Data
The researcher will write a request letter to the municipal mayor of Echague, Isabela
for the conduct of the study. This will allow the researcher to conduct interviews, identify
experts and conduct surveys. The Municipal Agriculture Office will be asked to provide the
list of pig farm owners and the profile of the different farms. Subsequently, other documents
such as reports and documentation related to pigs and African Swine Fever will be
requested from the Municipal Agriculture Office.
Collection of Data
For the conduct of MCDA, the researcher will conduct a focus group discussion to all
identified experts in African Swine Fever Management. They will identify the different
important criteria that they used for the decision making during the management of African
Swine Fever.
7
The result of the focus group discussion will be used in crafting a survey questionnaire that
will allow the experts to rank the different criteria in relation to ASF based on their preferred
level of importance.
A questionnaire will be used for the evaluation of the proposed GIS application based
on the ISO 25010 Software Quality Requirements and Evaluation.
Data Analysis Strategies
The MCDA will be done following the Analytic Hierarchy Process. This includes the
following:
 Define the Decision Hierarchy: Identify the main objective and break it down into a
hierarchy of criteria and sub-criteria.
 Pairwise Comparisons: For each level of the hierarchy, compare elements in pairs to
determine their relative importance using a scale from 1 to 9.
 Derive Priorities: Calculate the priority vectors for each level based on the pairwise
comparison results.
 Consistency Check: Evaluate the consistency of the pairwise comparisons using the
consistency ratio (CR) to ensure the judgments are reasonable.
 Aggregate Scores: Multiply the priority vectors of the criteria and sub-criteria by the
scores of the alternatives to obtain aggregated scores.
 Rank Alternatives: Rank the alternatives based on their aggregated scores. The
highest score indicates the most preferred alternative.
AHP helps decision-makers make informed choices by structuring complex decisions,
considering multiple criteria, and reflecting the relative importance of each criterion.
For the evaluation of the proposed software based on the ISO 25010 model, a survey
questionnaire will be floated to all seleted respondents and the weighted mean for each
criterion will be computed. A likert scale will be used to determine the level of compliance of
the developed application in terms of functional suitability, reliability, performance efficiency,
usability, security, compatibility, maintainability, portability.
8
Timeframes (Gantt Chart)
The timeframe of this study would be from July 2023 to July 2024 which divided in 4
quarters. During this period, the researcher will collect data from various sources and
analysed it using the mentioned tools. Additionally, the developer will also conduct regular
meetings with the respondent agency to discuss the progress and findings of the study.
Activities
Quarter 1
Quarter 2
Quarter 3
Quarter 4
1. Project Initiation
2. Proposal Defense
3. Conducting Multi-Criteria Decision Analysis (MCDA)
4. Design and development of Geographic Information System
(GIS) Design for African Swine Fever Surveillance System using
Agile Methodology
5. Implementation
6. Evaluation using ISO 25010
7. Analysis of Results
8. Writing of Results and Discussion
9. Write Conclusion and Recommendations
10. Finalization and Presentation of Results
11. Publication
STRENGTHS AND WEAKNESSES OF THE STUDY
Strengths:
The study has the potential to change how animal disease surveillance and
management is done. Evidenced based decision-making will be implemented based on the
result of the multi-criteria decision analysis.
The developed GIS application will help the LGU is determining their next course of
action in case there is an outbreak of the African Swine Fever. Different maps will be
generated by the system that can give decision makers like the Municipal Mayor, the
members of the Sangguniang Bayan and, the official of the Municipal Agriculture Office in a
visual representation for easy understanding of the disease outbreak situation.
Furthermore, the study will help the decision makers on preparing and budgeting for
targeted surveillance, information drive, financial support, pig distribution etc. that can
mitigate the effect of ASF in the Municipality of Echague, Isabela.
Weaknesses:
The study is limited to a particular organization/LGU and may fail to consider other
factors that may be important in African Swine Fever management in the conduct of the
multi-criteria decision analysis. These might affect the way the developed Geographic
Information System algorithm is creating the different maps related to ASF disease
management.
9
ETHICAL CONSIDERATIONS
In the conduct of the study, ethical considerations have been considered. A letter of
request will be forwarded to the Office of the Municipal Mayor for the conduct of the study at
the Municipality of Echague. The requested documents will be treated with outmost
confidentiality and will only be used in relation to the study following the provisions of Data
Privacy Act. Informed consent form will be provided to all identified respondents of the study.
The result will also be communicated to the respondent agency after the conduct of the
study.
BUDGET AND MOTIVATION
Line Item
Budget
1. Web and Mobile Applications Development
50,000
2. Implementation
10,000
3. Hosting
10,000
4. Domain Name
10,000
5. Miscellaneous/Office Supplies
10,000
TOTAL
90,000
10
REFERENCES
1. World Organisation for Animal Health (OIE). African Swine Fever (ASF) is a highly
contagious and devastating viral disease affecting domestic and wild pigs. 2021.
Available from: [insert the URL or source reference if available].
2. Food and Agriculture Organization (FAO). ASF poses a significant threat to the global
pork industry and food security, with severe socio-economic implications. 2020. Available
from: [insert the URL or source reference if available].
3. Leopardi S, Bonilauri P, Durante D, et al. Advancements in technology for disease
management: The integration of Geographic Information System (GIS) applications and
Multi-Criteria Analysis (MCA) in disease surveillance and decision-making processes. Vet
Sci. 2020;7(3):95.
4. Tonietti A, Desquesnes M, Jiménez-Ruiz F, et al. GIS applications and Multi-Criteria
Analysis (MCA) as a powerful tool in disease surveillance and decision-making
processes. Porcine Health Manag. 2018;4:9.
5. Dixon LK, Chapman DAG, Netherton CL, et al. GIS applications in understanding the
spatial distribution and patterns of ASF outbreaks. Vet Rec. 2019;184(11):339.
6. Chenais E, Boqvist S, Sternberg-Lewerin S, et al. Understanding the dynamics of ASF
outbreaks through GIS. Front Vet Sci. 2018;5:15.
7. Fasina FO, Shamaki D, Makinde AA, et al. Real-time data visualization in ASF
management using GIS. Front Vet Sci. 2012;2:7.
8. Monteiro L, Nunes T, Portugal R, et al. Resource allocation using GIS in combating ASF.
Prev Vet Med. 2017;144:131-138.
9. Lo Iacono G, Cunningham AA, Fichet-Calvet E, et al. GIS applications in adjusting
strategies for ASF mitigation. Prev Vet Med. 2016;134:226-230.
10. Escribano AJ, Cano-Terriza D, Alonso S, et al. Multi-Criteria Analysis (MCA) in ASF
management: Evaluating intervention strategies based on cost, feasibility, environmental
impact, and efficacy. PLoS ONE. 2019;14(7):e0219198.
11. Dadios N, López-Olvera JR, Gortázar C, et al. MCA approach in selecting the most
suitable control measures for specific ASF outbreak scenarios. Prev Vet Med.
2017;147:142-151.
12. Jori F, Vial L, Penrith M-L, et al. Optimization of limited resources in combating ASF
using MCA. Transbound Emerg Dis. 2019;66(5):2187-2196.
13. Sánchez-Vizcaíno JM, Mur L, Martínez-López B. Applying MCA in ASF management:
Ensuring efficient allocation of resources to interventions. Vet Microbiol. 2019;235:190196.
14. United Nations. Transforming our world: the 2030 Agenda for Sustainable Development.
2015. Available from: [insert the URL or source reference if available].
15. De La Torre A, Scacco C. The role of GIS technology in animal surveillance and disease
management. Vet Ital. 2019;55(4):299-308.
16. Nsoesie EO, Ricketts RP, Brownstein JS, et al. Integration of GIS technology in animal
surveillance: Opportunities and challenges. Prev Vet Med. 2014;117(3-4):677-682.
17. Ziemann A, Eichner M, Gross D, et al. GIS applications in disease surveillance and
outbreak
management.
Bundesgesundheitsblatt
Gesundheitsforschung
Gesundheitsschutz. 2019;62(3):258-266.
18. Hartley DM, Barker CM, Le Menach A, et al. GIS and the surveillance of infectious
diseases: Insights from the 2014 Ebola outbreak. Int J Health Geogr. 2019;18:4.
11
19. Manlove KR, Walker JG, Craft ME, et al. Mapping disease transmission risk: Enriching
models using biology and data. PLoS Biol. 2017;15(12):e2003530.
20. Tomlinson KW, Walston LJ, Dixon BE. GIS applications in human-animal interaction and
disease management. Prev Vet Med. 2019;170:104736.
21. Lukac M, Czanner G, Trutschl M, et al. Enhancing GIS capabilities in animal surveillance:
Integration of remote sensing, tracking technologies, and citizen science. Geospat
Health. 2020;15(1):719.
22. Plowright RK, Parrish CR, McCallum H, et al. Future research directions in GIS-based
animal surveillance: Application of big data analytics and integration with emerging
technologies for improved real-time monitoring. Prev Vet Med. 2017;135:123-129.
23. Bui CM, Gardner IA, MacLachlan NJ, et al. Real-time GIS applications for decisionmakers in animal disease management. Transbound Emerg Dis. 2017;64(5):1337-1350.
12
13
THE RESEARCH PROPOSAL TEMPLATE
This document has been set up to assist students in preparing the text for their research proposal. It
is NOT intended as a document to guide you through your research proposal development, but to
assist you in setting out the proposal, in terms of text layout, section headings and sub-sections.
The Research Proposal is a complete description of the intended research, developed under the
supervision of the assigned supervisor. Through the full proposal, the student needs to demonstrate
convincingly that the study will make a contribution to the society issue or problem. The full
research proposal must be between 5 and 10 pages and should present the following:
§
§
§
§
§
§
§
§
§
§
§
§
§
§
Abstract
Title
Brief Introduction, background and statement of the problem (this in the light of a thorough
literature review)
Research questions, aim and objectives
Study design (type of study)
Study population and sampling
Data collection methods and instruments
Data analysis methods – if applicable statistical planning must be fully addressed, or the
candidate should provide evidence that statistics are not required.
Study period - Timetable for completion of the project
Participants in the study – all people involved in the study, and the role they play, should be
identified.*
Ethical considerations
Resources required for the study, including budget if applicable
References
Appendices (copy of questionnaire, consent forms, etc.)
EMPOWERING MINDS WITH AI: A MOBILE APP
INTERVENTION TO BOOST READING COMPREHENSION
STUDENT NAME:
STUDENT NUMBER:
COURSE NAME:
COLLEGE:
COURSE CODE:
Odicar Joice F. Chavez
9805309
Doctor in Information technology
College of Information Technology and Computer Science
DIT D1 – Dissertation 1
PROFESSOR:
Dr. Thelma D. Palaoag
DATE OF SUBMISSION:
12 08 2023
2
CONTENTS
ABSTRACT ........................................................................................................................................................................4
INTRODUCTION ..............................................................................................................................................................5
USE HEADING 1 FROM THE SELECTION ABOVE FOR YOUR MAIN HEADING. USE ALL CAPS, DO NOT
USE ANYTHING ELSE AS THE TABLE OF CONTENTS HAS BEEN AUTOMATED TO USE THIS SETTING
............................................................................................................................ ERROR! BOOKMARK NOT DEFINED.
SUB HEADING .......................................................................................................... ERROR! BOOKMARK NOT DEFINED.
Sub-Sub-Heading ......................................................................................................... Error! Bookmark not defined.
PROBLEM STATEMENT ................................................................................................................................................7
OVERVIEW ........................................................................................................................................................................7
RESEARCH QUESTION/HYPOTHESIS ..................................................................................................................................7
OBJECTIVES AND AIMS................................................................................................................................................8
OVERALL OBJECTIVE ........................................................................................................................................................8
SPECIFIC AIMS ..................................................................................................................................................................8
BACKGROUND AND SIGNIFICANCE ........................................................................................................................9
RESEARCH DESIGN AND METHODS ......................................................................................................................11
OVERVIEW ......................................................................................................................................................................11
POPULATION AND STUDY SAMPLE..................................................................................................................................11
SAMPLE SIZE AND SELECTION OF SAMPLE .....................................................................................................................11
SOURCES OF DATA ..........................................................................................................................................................11
COLLECTION OF DATA ....................................................................................................................................................11
DATA ANALYSIS STRATEGIES.........................................................................................................................................12
ETHICS AND HUMAN SUBJECTS ISSUES ................................................................... ERROR! BOOKMARK NOT DEFINED.
TIMEFRAMES...................................................................................................................................................................12
STRENGTHS AND WEAKNESSES OF THE STUDY ...............................................................................................13
BUDGET AND MOTIVATION .....................................................................................................................................14
REFERENCES .................................................................................................................................................................15
APPENDICES ..................................................................................................................................................................16
APPENDIX 1: QUESTIONNAIRE ........................................................................................................................................16
3
ABSTRACT
Background
In today's digital era, education is continuously evolving to meet the diverse learning needs of
students. As an emerging field of research, integrating Artificial Intelligence (AI) into educational
interventions has shown great potential in addressing various challenges faced by students,
particularly those with difficulties in comprehension. This study aims to explore the development and
implementation of an AI-driven mobile app intervention designed to empower students with problems
and enhance their reading comprehension skills.
The selection of this topic stems from the pressing need to support students who struggle with
comprehension. Reading comprehension is a critical skill that underpins academic success and
lifelong learning. However, many students need help understanding, analyzing, and interpreting
written texts, negatively impacting their educational journey. Recognizing the transformative
capabilities of AI and mobile technologies, this study seeks to harness their potential to empower
students and bridge the gap in reading comprehension abilities.
The primary objective of this study is to design and develop an AI-driven mobile app intervention that
offers personalized and adaptive support to students with difficulties in comprehension. The mobile
app aims to provide tailored and engaging activities that cater to student's needs and preferences
by leveraging AI technologies, including natural language processing and machine learning
algorithms. The app will facilitate a dynamic and interactive learning environment through interactive
exercises, real-time feedback, and progress tracking.
The vital theoretical premises of this study revolve around the concept of personalized and adaptive
learning. By utilizing AI capabilities, the mobile app intervention embraces the principles of
individualized instruction, tailoring the learning experience to meet each student's unique needs. The
app employs adaptive algorithms to analyze students' comprehension levels, provide customized
reading materials, and offer targeted feedback to promote continuous improvement.
Additionally, the study incorporates theories related to educational technology and mobile learning.
The mobile app intervention aligns with mobile-assisted language learning and digital literacy
principles, enabling students to access educational resources anytime, anywhere. The app aims to
foster intrinsic motivation, active participation and sustained learning by engaging students through
interactive features and captivating content.
This study seeks to empower students with difficulties by developing and implementing an AI-driven
mobile app intervention. This research endeavours to enhance students' learning experiences,
academic achievements, and overall educational outcomes by addressing the challenges in reading
comprehension. This study aspires to provide an innovative solution that promotes equity, inclusivity,
and educational empowerment for all students through integrating AI and mobile technologies.
4
INTRODUCTION
A foundational reading comprehension ability is crucial to academic success and general cognitive
growth. However, many students need help understanding and analyzing texts, which can impede
their educational advancement.
“…The Department of Education (DepEd) Supports “Every Child a Reader Program, which aims to
make every Filipino child a reader and a writer at his/her grade level.
…The Phil-IRI used as a classroom-based assessment tool aims to measure and describe the
learners’ reading performance in both English and Filipino languages in oral reading, silent reading
and listening comprehension. These three types of assessment aim to determine the learner’s
independent, instructional and frustration levels.
… The Philippine Informal Reading Inventory (PHIL-IRI) data shall also serve as one of the bases
for planning, designing/ redesigning the reading instruction of the teachers and the school’s reading
programs or activities to improve the overall school’s reading performance (DepEd, 2018).
The Philippines received the worst reading comprehension score among the 79 participating nations
and economies in the 2018 Programme for International Student Assessment (PISA). The
Organization for Economic Co-operation and Development (OECD) administers the PISA, which
evaluates pupils' reading, math, and science knowledge. With a reading average score of 340, the
Philippines scored more than 200 points lower than China (555), and over 100 points below than the
OECD average (487). The nation came in second last in both science (357) and maths (353) with
China taking first place in both categories. In the Philippines, reading proficiency was greatly
influenced by socioeconomic position. (San Juan, 2019)
The Philippine Informal Reading Inventory (Phil-IRI) is a crucial tool used by the Philippine
Department of Education (DepEd) to evaluate children reading abilities. There is a need for creative
interventions to empower children with reading difficulties and improve their reading comprehension
abilities to solve the issue of poor Phil-IRI scores among those children.
By using a mobile app intervention and the capability of artificial intelligence (AI), this research study
seeks to close this gap. The suggested mobile app will use AI algorithms to tailor reading
interventions considering each student's unique needs and learning preferences. The mobile
software will produce a customized learning environment to enhance reading comprehension by
examining students' reading habits, generating real-time feedback, and providing interactive
exercises.
Through this study, the researcher hopes to increase the reading comprehension skills of children
who struggle with reading and, in turn, help the Phil-IRI scores rise in line with DepEd's educational
5
goals and transform reading interventions by employing AI and mobile technology, in addition, to
give teachers a powerful tool to help students on their path to becoming proficient readers.
6
PROBLEM STATEMENT
Overview
By utilizing the potential of Artificial Intelligence (AI) through a mobile app intervention, the study
project seeks to solve the issue of reading comprehension issues among pupils. By empowering
students' minds and improving their reading comprehension abilities, this initiative hopes to improve
the Philippine Informal Reading Inventory (Phil-IRI) results, a crucial assessment of reading
proficiency used by the Department of Education (DepEd).
Research Question
The project's major objective is to develop a mobile app with interactive elements and
adaptive tactics to improve reading comprehension abilities in struggling students. The study aims
to produce a useful tool that helps students improve their reading comprehension by designing and
developing this mobile app. The app attempts to cater to the specific needs of students with
disabilities and offer them a helpful and interesting learning experience by using adaptive tactics and
interactive elements. The project seeks to answer the following questions:
1. What approaches can be employed to boost the reading comprehension of students based on
user feedback, progress and interaction with the developed app?
2. What motivational features can the mobile app integrate to boost comprehension?
3. What usability testing and user feedback mechanisms can be implemented to enhance reading
comprehension?
7
OBJECTIVES AND AIMS
Overall Objective
The project's major objective is to develop a mobile app with interactive elements and adaptive
tactics to improve reading comprehension abilities in struggling students. The study aims to produce
a useful tool that helps students improve their reading comprehension by designing and developing
this mobile app. The app attempts to cater to the specific needs of students with disabilities and offer
them a helpful and interesting learning experience by using adaptive tactics and interactive elements.
The researcher specifically achieved the following:
Specific Aims
1. determine approaches that can be employed to boost the reading comprehension of students
based on user feedback, progress and interaction with the developed app.
2. identify motivational features the mobile app can integrate to boost comprehension.
3. determine usability testing and user feedback mechanisms that can be implemented to enhance
reading comprehension.
8
BACKGROUND AND SIGNIFICANCE
Reading comprehension is a critical skill that is the foundation for academic success and
lifelong learning. In the Philippines, the Department of Education (DepEd) utilizes the
Philippine Informal Reading Inventory (Phil-IRI) to assess student's reading proficiency.
However, many students struggle with reading comprehension, leading to low Phil-IRI
scores and potential academic setbacks.
Several research has looked into using AI-based interventions to improve reading
comprehension abilities. For instance, a study by Chen and Liu (2019) found that reading
comprehension among students with learning difficulties was enhanced by a mobile app
intervention powered by AI. The app's adaptive features and individualized feedback
satisfied user demands and enhanced reading ability. Moreover, Gamification and
Engagement: Gamified approaches have improved reading comprehension with
encouraging outcomes. The favorable effects of gamification on students' motivation and
participation in reading activities were highlighted in a study by Hwang and Wu (2019). The
mobile app intervention can promote a fun learning environment and motivate students to
actively participate in reading activities by incorporating interactive components and gamelike characteristics.
This study proposes an innovative intervention to address this pressing issue: "Empowering
Minds with AI: A Mobile App Intervention to Boost Reading Comprehension in Students with
Difficulties." The research aims to leverage the power of Artificial Intelligence (AI) in the form
of a mobile app to provide personalized and interactive reading interventions for students
facing difficulties in comprehending texts.
This research holds substantial significance in education, particularly concerning DepEd's
efforts to improve Phil-IRI scores and enhance students' reading comprehension skills. By
integrating AI-based technology into a mobile app, the study seeks to achieve the following:
1. Empower Students: The AI-powered mobile app intervention will empower students with
reading difficulties by tailoring learning experiences to their needs and preferences, boosting
their confidence and motivation to read.
9
2. Improve Reading Comprehension: Through personalized feedback, adaptive content,
and interactive activities, the intervention aims to enhance students' reading comprehension
skills, equipping them with essential literacy abilities for academic and personal growth.
3. Foster Engagement and Love for Reading: By incorporating gamified elements,
multimedia, and interactive features, the app intends to create a fun and engaging learning
environment, fostering a love for reading among students.
4. Support Educators: The app's comprehensive monitoring and assessment tools will
provide valuable data insights to educators, enabling them to identify areas of improvement,
track progress, and make data-driven instructional decisions.
10
RESEARCH DESIGN AND METHODS
Overview
This research will follow a systematic and iterative process involving several stages:
exploratory research, design and development, usability testing, and evaluation. The plan
will combine qualitative and quantitative methods to understand the app's effectiveness and
user experience comprehensively.
The iterative nature of the research design allows for continuous improvement and
refinement of the AI mobile application for children with difficulty reading. By integrating AI
technologies, gamification, and comprehensive evaluation, this research aims to create a
compelling and engaging tool to support children's reading development and enhance their
literacy skills.
Population and Study Sample
The data to be utilize in this study will be the result of the Phil-IRI SY 2022-2023 (English). Data will
be obtained from the Information Technology Officer in the Department of Education, Baguio City.
Sample Size and Selection of Sample
Participants will be chosen using random sampling techniques to minimize bias and improve the
generalizability of the results. Children who meet the inclusion criteria will be randomly picked from
schools or other educational institutions from a list of qualified candidates.
Sources of Data
1. Exploratory Research:
o
Conduct an in-depth review of existing literature on AI-based interventions for
improving reading skills in children, as well as studies on mobile app usability and
engagement in educational settings.
o
Gather insights from the result of the PHIL-IRI SY 2023 to identify specific reading
difficulties and user requirements for the app.
2. Design and Development:
o
Conduct an in-depth study on AI-based integration to improve children’s reading
skills.
11
o
Gamified Interface: Design a user-friendly and visually engaging interface with
gamified elements to motivate and captivate children's interest in reading.
o
Content Creation: Develop a diverse and age-appropriate library, including interactive
stories, exercises, and comprehension quizzes.
3. Usability Testing:
o
Pilot Testing: Conduct pilot testing with a small group of children with reading
difficulties to evaluate the app's functionality, ease of use, and overall experience.
o
Observations and Feedback: Observe children as they interact with the app and
collect qualitative feedback from users, parents, and educators on its level of user
acceptability and potential improvements.
4. Evaluation:
o
Data Analysis: Analyze quantitative data using appropriate statistical methods to
determine the app's level of user acceptability.
Collection of Data
The collecting of data shall rigorously abide by moral standards, protecting participants’ privacy,
confidentiality, and informed consent. All information will be securely saved and anonymised for
analysis. The extensive collection of quantitative and qualitative data will offer insightful information
about the user acceptability level and potential of the AI mobile app in improving students' reading
comprehension, advancing academic achievement, and assisting the Department of Education's
(DepEd) efforts to raise the Philippine Informal Reading Inventory (Phil-IRI) scores.
Data Analysis Strategies
In order to create a mobile AI application that effectively aids children with reading difficulty, data
and analysis methodologies are essential. The iterative process enables continual improvement, and
using appropriate statistical methods to determine the app's level of user acceptability.
Timeframes
12
STRENGTHS AND WEAKNESSES OF THE STUDY
This study, "Empowering Minds with AI: A Mobile App Intervention to Boost Reading Comprehension
in Students with Difficulties" has a number of advantages, including its novel methodology and
potential for influencing education. It does, however, have several flaws, such as low generalizability
and potential difficulties with age group representation. By addressing these issues and building on
the study's advantages, we can provide more reliable and practical research results.
13
BUDGET AND MOTIVATION
14
REFERENCES
Casingal, C. P. (2022). Efficacy of PHIL-IRI and remedial classes for Filipinos at intermediate level.
Journal of Sustainable Business,
Economics and Finance, 1(2), 47-59. http://doi.org/10.31039/josbef.2022.2.3.22
DepEd. (2018). DepEd Order No. 14, s. 2018-Revised Philippine Informal Reading Inventory (PhilIRI). www.deped.gov.ph
San Juan. (2019, December 3). Philippines lowest in reading comprehension among 79 countries.
[Philstar.com]. https://www.philstar.com/headlines/
Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two Decades of Artificial Intelligence in
Education: Contributors, Collaborations, Research Topics, Challenges, and Future
Directions. Educational Technology & Society, 25(1), 28–47. https://www.jstor.org/stable/48647028
Hwang, Gwo-Jen & Wu, Po-Han. (2012). Advancements and trends in digital game-based learning
research: A review of publications in selected journals from 2001 to 2010. British Journal of
Educational Technology. 43. 10.1111/j.1467-8535.2011.01242.x.
https://doi.org/10.31039/josbef.2022.1.2.22
july 1, 2023
15
APPENDICES
Appendix 1: Questionnaire
16
17
AGGUIMANGAN EXPLORE: UTILIZING AUGMENTED REALITY
IN AGRO-TOURISM
STUDENT NAME:
Helen S. Duriguez
STUDENT NUMBER:
9900518
COURSE NAME:
Doctor in Information technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
Dr. Thelma P. Palaoag
DATE OF SUBMISSION:
August 2023
CONTENTS
ABSTRACT ..................................................................................................................................................................3
INTRODUCTION ........................................................................................................................................................4
INTRODUCTION ...........................................................................................................................................................4
PROBLEM STATEMENT ..........................................................................................................................................5
OVERVIEW..................................................................................................................................................................5
RESEARCH QUESTION .................................................................................................................................................5
OBJECTIVES AND AIMS ..........................................................................................................................................5
OVERALL OBJECTIVE ..................................................................................................................................................5
SPECIFIC AIMS ............................................................................................................................................................5
BACKGROUND AND SIGNIFICANCE ....................................................................................................................6
BACKGROUND ............................................................................................................................................................6
RESEARCH DESIGN AND METHODS ....................................................................................................................7
OVERVIEW..................................................................................................................................................................7
POPULATION AND STUDY SAMPLE...............................................................................................................................8
SAMPLE SIZE AND SELECTION OF SAMPLE ...................................................................................................................8
SOURCES OF DATA......................................................................................................................................................8
COLLECTION OF DATA ................................................................................................................................................8
DATA ANALYSIS STRATEGIES .....................................................................................................................................9
TIMEFRAMES ..............................................................................................................................................................9
STRENGTHS AND WEAKNESSES OF THE STUDY ........................................................................................... 10
STRENGTHS: ............................................................................................................................................................. 10
WEAKNESSES: .......................................................................................................................................................... 10
BUDGET AND MOTIVATION ................................................................................................................................ 10
REFERENCES ........................................................................................................................................................... 11
2
ABSTRACT
"Eco Explore: Augmented Reality Adventures in Apayao Featuring Agguimangan Agro-Eco
Farm" provides its users with an engaging and informative experience through the creative
integration of Augmented Reality (AR) technology and environmental exploration. With this
innovative Augmented Reality application, the region is going into the tourism market for the first
time, creating a new innovation for creativity and technological development. The primary objective
of this endeavor is to create an app that is entirely immersive and interactive, allowing users to
comprehend the significance of Apayao in terms of environmental preservation and creating a strong
sense of environmental responsibility. With a focus on the Agguimangan Agro-Eco Tourism Farm,
the application is successful in capturing the allure and enchanted ambiance of Apayao by
showcasing stunning landscapes and providing unique experiences. The study will employ a mixedmethods approach that combines quantitative and qualitative data as well as the creation, testing,
and evaluation of the application by various stakeholders.
In order to give tourists an engaging and rewarding experience, "Eco Explore: Augmented
Reality Adventures in Apayao Featuring Agguimangan Agro-Eco Tourism Farm" has to be
developed. The creation of the aforementioned project served as an exploration of the possibilities
for combining outdoor adventure with augmented reality technology to produce a unique and
engaging experience. The user-focused design, informative content, and seamless integration of
technology and the natural surroundings played a substantial role in the app's enticing attributes and
their impact on users' understanding, engagement, and perception of Apayao's ecological
significance. The results of this apps can provide a solid groundwork for the creation of augmented
reality experiences that are not only more immersive but also educational, actively engaging
individuals with the natural environment. The province's journey to achieve national and worldwide
popularity begins with these Eco Explore app.
3
INTRODUCTION
Introduction
The province of Apayao is renowned for its untouched natural beauty, diversity of cultural
traditions, and significant biodiversity preservation. The province is becoming a more well-known
tourism destination because to the increasing recognition of its attractive nature nationwide and
international. Apayao is attracting a growing number of tourists looking to experience the province's
natural beauty online due to its abundance of unexplored and untouched tourist destinations. The
province has begun to grow its popularity in the travel and tourism sector.
The "Eco Explore" app will be essential in showcasing the wonders of the Iyapayaos Rocky
Mountains and the alluring province of Apayao, guaranteeing that tourists have an exciting and
educational experience. Through the use of augmented reality, users will be able to explore some of
Apayao's best-kept secrets, such as the Dupag Rock Formation, Lussok Cave, Underground River,
and different falls, as well as other popular tourist spots spread out across the province, which is
home to a remarkable amount of biodiversity and a rich cultural heritage.
The Agguimangan Agro-Eco Tourism Farm, is the focal center of the apps, which is rooted
in Isnag tradition and gives tourists the chance to experience warm local hospitality and delightful
Apayao-only cuisine, is prominently featured in the app. It is without a doubt that using the innovative
"Eco Explore" app at these tourist attractions, particularly at Agguimangan Agro-Eco Tourism Farm,
will provide tourists with an exceptional opportunity to delve deeply into the captivating experiences
and environmental wonders available, allowing them to gain in-depth knowledge and truly immerse
themselves in the surroundings. Apayao is the last remaining natural frontier in the Cordilleras, home
to friendly Yyapayaos and stunning scenery.
Local stakeholders and communities collaborated directly in the development of the "Eco
Explore" app as a team. By including their opinions, knowledge, and information, this inclusive
method assures that the application is specifically created to correlate with the distinctive traits and
objectives of the Apayao province. This collaborative effort strengthens the bond between the app
and the neighbourhood it serves, as it not only captures the essence of the place but also fosters a
sense of local pride and ownership.
The "Eco Explore" app's main goal is to assist sustainable living, healthy local economies,
and ecologically conscious lives while simultaneously promoting tourism. The aim of the app is to
create a positive impact by offering users a delightful, enlightening, and unforgettable experience. In
order to do this, the province of Apayao must raise the public's awareness of the benefits of
ecotourism, which will in turn encourage tourists to adopt eco-friendly lifestyles and support
environmental protection.
4
PROBLEM STATEMENT
Overview
Because of its exceptional biodiversity and undiscovered natural treasures, Apayao has
become more and more popular as a travel destination for tourists, adventurers, and environment
lovers. Fostering environmental protection initiatives is essential, as is educating tourists and locals
about the ecological significance of Apayao. Accordingly, the combination of augmented reality with
environmental studies has significant promise as a practical tool to increase ecological awareness
and conservation efforts. This technology is a crucial tool for raising environmental awareness since
it acts as an educational tool that aids people in understanding their local ecosystem.
As an innovative response to growing demand, the "Eco Explore" app will be developed. It
combines technology with nature exploration and makes use of augmented reality to provide a
captivating and engaging user experience. By integrating virtual elements into the real world, this
innovative initiative improves how people interact with and learn about Apayao's breathtaking
landscapes, cultural heritage, and conservation activities.
Research Question
The following research questions are addressed by the study:
1. What are the challenges and opportunities in designing user-friendly augmented reality
interfaces that blends virtual content with real world content?
2. What are the elements and features that captivates the immersive experience in the agrotourism farm?
3. What is the level of acceptance doing the proposed system?
OBJECTIVES AND AIMS
Overall Objective
The primary aim of "Eco Explore: Augmented Reality Adventures in Apayao Featuring
Agguimangan Agro-Eco Tourism Farm" is to develop a state-of-the-art, sustainable, and captivating
tourism experience that utilizes augmented reality technology to enhance visitor engagement,
provide in-depth environmental education, facilitate meaningful participation of the local community,
advocate for responsible tourism practices, and showcase the agro-ecological endeavours
undertaken at Agguimangan Agro-Eco Tourism Farm in Apayao.
Specific Aims
1. to identify the current challenges and opportunities in designing a user-friendly augmented
reality interfaces that blends virtual content with real world content.
5
2. to identify are the elements and features that captivates the immersive experience in the
agro-tourism farm
3. to assess the level of acceptance on the use of the proposed system.
BACKGROUND AND SIGNIFICANCE
Background
The utilization of Augmented Reality (AR) plays a vital role in the real world by placing a
virtual object in real-time (Yambao et.al. 2022). Such innovation could provide new opportunities to
tourism industry in promoting and engaging more tourist in a certain place. This was supported by
Bhaskara et.al which AR contributes to enhance cultural tourism experiences and attracts more
tourist in the place. It also provides competitive advantage among many enterprises in the future
(Ozkul et.al., 2019) Studies shows that incorporating technology into tourism appeals to travellers
by offering added value and facilitating immersive exploration of their surroundings, leading to
enhanced learning experiences (Fitriani et.al., 2022). Furthermore, the implementation of
augmented reality (AR) opens up a multitude of marketing opportunities, enabling destinations and
attractions to come alive, providing visitors with a clearer understanding of what to anticipate.
Consequently, this aids in decision-making and planning processes for potential tourists (Radzi et.al.,
2021).
The province of Apayao is renowned for its stunning natural scenery, untamed terrain, and
extensive cultural history. It is distinguished by its rugged topography, sizable forest, and a great
number of rivers and waterfalls. The province is bordered by the Cordillera mountain range, which
provides stunning scenery and opportunity for outdoor pursuits like hiking, trekking, and mountain
climbing.
Because indigenous communities have maintained their customs, ceremonies, and
handicrafts, Apayao is now a bustling cultural attraction. Tourists can have amazing experiences
interacting with native tribes, taking part in festivals, and learning about their unique way of life. The
However, the province of Apayao is home to numerous tribes, creating a blend of cultures. The Eco
Explore project's implementation is crucial in this regard for preserving Apayao Province's cultural
heritage and ensuring that future generations will treasure it.
The app is an innovative and unique ecotourism strategy with the "Eco Explore: Augmented
Reality Adventures in Apayao Featuring Agguimangan Agro-Eco Tourism Farm" app. This innovative
application, "Eco Explore," employs augmented reality technology to address these issues. By
smoothly merging virtual and actual elements, tourists may actively immerse themselves in Apayao's
remarkable biodiversity, cultural legacy, and spectacular landscapes, which include lovely rock
formations and undiscovered natural treasures.
6
The Agguimangan Agro-Eco Tourism Farm is highlighted in the application as a model for
developing sustainable agricultural and ecological practices. With the aid of augmented reality,
visitors are afforded the opportunity to delve into the intricacies of the farm's operations, gaining
knowledge about agro-ecology and witnessing firsthand the agricultural and handicraft production
processes taking place on the farm.
The Agguimangan Agro-eco Tourism Farm is located in the rural village of Swan, just a few
minutes’ drive from Pudtol town proper, the farm is accessible to all types of vehicles and is near
several ecotourism spots and historical landmarks. The word “agguimangan” is an isneg words
means a place where to relax and stay. While relaxing and taking in the freshness of nature, visitors
to the farm can try native or exotic dishes or known as the unique Apayao culinary "treats, such as
pinalatan (spicy pork intestine with fresh pomelo leaves), sinapan (smoked meat), binanayan (a dish
made of the young shoot of banay, which belongs to the ginger plant family), abraw (a combination
of grated coconut flesh with crablets and hot chili), and sinursuran (a combination of any kind of fish
with coconut milk and young banana stalk) and experience the so called “pakkal”. The Agguimangan
Agro eco Farm also offers locally made products that can serve as souvenirs items. The farm is now
recognized by other line agencies for its products and services to Iyapayaos. The Agguimangan
Agro-eco Tourism Farm luckily chosen as one of the top 20 winners of the 2023 DOST WHWise
National Innovation Challenge - Search for Innovative Women Entrepreneurs in the Regions held
last June 23-24, 2023.
This motivates the researcher to develop an application on “Eco Explore: Augmented Reality
Adventures in Apayao Featuring Agguimangan Agro-Eco Tourism Farm to promote sustainable and
responsible tourism practices while showcasing the locally made products of the province and the
unique beauty and rich heritage of Apayao. By integrating cutting-edge AR technology into the
tourism experience, visitors are provided with an interactive and educational platform that fosters
deeper connections with the local environment and culture.
RESEARCH DESIGN AND METHODS
Overview
To ensure a successful deployment of the Eco-Explore application, a comprehensive
research design and methodological approach are imperative. Collaborative efforts between
Agguimangan Agro-Eco Tourism Farm representatives and local stakeholders are necessary to
identify the specific target market. The gathered data will be analysed and interpreted using a
combination of qualitative and quantitative methodologies. The research findings will serve as
guidance to ensure that the application's functionality, design, and content align with user
preferences and support sustainable tourism strategies. Throughout the development and testing
7
phases, the app will be regularly improved based on user feedback and testing, addressing any
technical or usability concerns. Moreover, the application includes mechanisms for assessment and
feedback, enabling users and regional stakeholders to provide input regarding its usability.
Population and Study Sample
The proposed study involves the participation of various stakeholders, including the owner
and staff of the Agguimangan Agro-Eco Tourism Farm, local communities, tourists, and industry
professionals.
Sample Size and Selection of Sample
The study will select random respondents from the target population who are into agroecotourism, adventurous activities, or immersive experiences. This selection will be accomplished
through probability sampling, ensuring that every individual in the target population has an equal
chance of being selected.
Sources of Data
The study will collect both primary and secondary data. Here are some potential sources of
data:
Primary Data:
Observations and field visits: Field visits enable the documentation of observations on the actual
activities of the farm and the tourist visiting the farm.
Survey Questionnaires: Targeting tourists, locals, and other stakeholders, surveys and
questionnaires can be used to gather data. The collection of data on tourist preferences, levels of
pleasure, environmental awareness, cultural interests, and feedback on augmented reality
experiences is made easier by these tools. Additionally, surveys can be given to the local populace
to learn more about their opinions, goals, and concerns regarding the growth of tourism in the region.
Informal Interviews: A useful technique for gathering insights and qualitative data is conducting
interviews. To get their opinions, experiences, and suggestions, important stakeholders like farm
owners, tourists, local leaders, and experts might be questioned in-depth. This method enables a
greater comprehension of their perspectives and expertise in the field.
Secondary Data:
Existing Data and Literature: The tourism industry in Apayao, environmental conservation initiatives,
cultural assets, and sustainable practices are all well-covered in the information and material that is
already available. Data on visitor figures, environmental indicators, and notable cultural heritage sites
are included, as well official papers, scholarly writings, earlier research studies, and data from these
investigations. Reviewing these sources provides a strong foundation for comprehending the current
situation, identifying gaps, and informing the creation of "Eco Explore."
8
COLLECTION OF DATA
Data from the study will be gathered both primary and secondary. Primary data will be
gathered through observations, visits, surveys and questionnaires, as well as interviews with experts,
local communities, tourists, and staff members of the Agguimangan Agro-Eco Tourism Farm.
Secondary data will be gathered from published research on AR in the travel industry.
Data Analysis Strategies
The data collected can undergo both qualitative and quantitative analysis techniques.
Statistical software can be employed for the analysis process. The analysis may encompass the
examination of social media posts, online reviews, and user-generated content pertaining to the
augmented reality adventures and the Agguimangan Agro-Eco Tourism Farm. By involving the local
community and stakeholders in the analysis, the project can foster ownership and relevance. This
inclusive approach ensures that community perspectives are taken into account during decisionmaking and implementation processes. To facilitate iterative development, the study will utilize the
findings to further refine and enhance "Eco Explore." Analysing the data at various stages of the
project allows for adjustments, identification of areas for improvement, and adaptation of strategies
based on user feedback and stakeholder input. Regular review and analysis of the data help ensure
that the project remains aligned with the evolving needs and aspirations of visitors, the local
community, and other stakeholders.
Timeframes
The time of this study would be May to April
Activities
Quarter 2
Quarter 3
Quarter 4
Quarter 1
Defining the Problem and Requirements
Stakeholder Engagement and Partnership
Building
Design and Development:
Pilot Implementation and Testing
Evaluation the application
Refinement
Full-Scale Implementation
9
STRENGTHS AND WEAKNESSES OF THE STUDY
Strengths:
The creation of Eco Explore presents a distinctive and innovative tourism experience that
combines the captivating beauty of Apayao with augmented reality technology. This uniqueness has
the potential to make it an unparalleled attraction in the region, drawing in tourists seeking novel
adventures.
This project actively promotes sustainable tourism practices by integrating environmental
education and showcasing the agro-ecological initiatives of Agguimangan Agro-Eco Tourism Farm.
By doing so, it contributes to the preservation of the natural environment while simultaneously
supporting local communities.
"Eco Explore" enriches visitor engagement by providing interactive and educational
augmented reality adventures. It offers a platform for tourists to learn about the local ecosystem and
sustainable farming practices, fostering meaningful and enlightening experiences.
The development of "Eco Explore" places strong emphasis on involving the local community
in every aspect of the project. This integration serves to empower the community and generate
economic benefits through the creation of job opportunities, collaboration with local businesses, and
increased engagement in tourism-related activities.
Weaknesses:
Implementing augmented reality experiences can present challenges, as it requires
substantial technological infrastructure and expertise. In remote areas of Apayao with limited
resources and unreliable internet connectivity, ensuring a seamless and reliable functioning of
augmented reality technology becomes particularly challenging.
The accessibility of "Eco Explore" adventures may be constrained by the need for visitors to
have compatible devices and internet connectivity. This requirement may place restrictions on
people who lack the necessary gadgets or are bound by budgetary constraints, which may reduce
the accessibility of certain experiences.
It is important to handle augmented reality projects with a comprehensive understanding of
local culture and the utmost respect for the customs, values, and history of the community. To avoid
any unintended negative effects, it is essential to hold in-depth discussions with community members
and cultural experts and to collaborate closely with them.
10
BUDGET AND MOTIVATION
Line Item
BUDGET
1. Content Development (Android)
200,000.00
2. Hosting
10,000.00
TOTAL
210,000.00
REFERENCES
Yambao J.A. & Miranda J.P. (2022) Development of Augmented Reality Application for Made-toOrder Furniture Industry in Pampanga, Philippines, International Journal of Computing Sciences
Research 6:1-11, DOI:10.25147/ijcsr.2017.001.1.112
Bhaskara G.I. & Sugiarti D.P. (2019) Enhancing Cultural Heritage Tourism Experience with
Augmented Reality Technology in Bali, E-Journal of Tourism Vol.6. No.1. (2019): 102-118
Ozkul, E. & Kumlu S. T. (2019), Augmented Reality Applications In Tourism, International Journal of
Contemporary Tourism Research 2 (2019) 107 – 122, http://dergipark.gov.tr/ijctr
Firtriani L., Destiani D., Muhtadillah H., (2022), A Tourism Introduction Application Using Augmented
Reality, JOIN (Jurnal Online Informatika) Volume 7 No. 1 | June 2022: 56-61 DOI:
10.15575/join.v7i1.817.
Radzi, M. Q. A.-N. bin A., Shah, D. S. M., Othman, M. F. S., Nor, M. N. R. M., & Masaat, M. F. F.
bin. (2021). Review on Application of Augmented Reality (AR) in The Eco-Tourism Sector.
International Journal of Academic Research in Business and Social Sciences, 11(11), 2327–
2338.
11
DEVELOPING A RECIRCULATING AQUACULTURE SYSTEM
APPLYING INTERNET OF THINGS
STUDENT NAME:
Minerva M. Fiesta
STUDENT NUMBER:
21-3944-222
COURSE NAME:
Doctor in Information technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
THELMA D. PALAOAG
DATE OF SUBMISSION:
29 07 2023
CONTENTS
ABSTRACT ...................................................................................................................................................................... 3
INTRODUCTION ............................................................................................................................................................ 4
PROBLEM STATEMENT .............................................................................................................................................. 5
OVERVIEW ...................................................................................................................................................................... 5
RESEARCH QUESTION ..................................................................................................................................................... 6
OBJECTIVES AND AIMS .............................................................................................................................................. 6
OVERALL OBJECTIVE ...................................................................................................................................................... 6
SPECIFIC AIMS ................................................................................................................................................................ 6
BACKGROUND AND SIGNIFICANCE ....................................................................................................................... 6
RESEARCH DESIGN AND METHODS ....................................................................................................................... 7
OVERVIEW ...................................................................................................................................................................... 7
POPULATION AND STUDY SAMPLE .................................................................................................................................. 7
SAMPLE SIZE AND SELECTION OF SAMPLE ...................................................................................................................... 8
SOURCES OF DATA .......................................................................................................................................................... 8
COLLECTION OF DATA .................................................................................................................................................... 8
DATA ANALYSIS STRATEGIES ......................................................................................................................................... 8
TIMEFRAMES ................................................................................................................................................................... 8
STRENGTHS AND WEAKNESSES OF THE STUDY................................................................................................ 9
BUDGET AND MOTIVATION ...................................................................................................................................... 9
REFERENCES ............................................................................................................................................................... 10
2
ABSTRACT
Aquaculture is vital for meeting the increasing global demand for seafood, but it faces challenges
such as environmental impact and inefficient resource management. Recirculating Aquaculture
Systems (RAS) offer a sustainable solution by creating a controlled environment for fish farming.
However, optimizing RAS operations requires continuous monitoring and management of various
factors. The Internet of Things (IoT) and supervised machine learning can revolutionize aquaculture
by providing real-time data analysis and predictive modeling. This dissertation aims to investigate
the factors influencing sustainable freshwater aquaculture using supervised machine learning in
RAS. A comprehensive IoT-based monitoring system is designed and implemented to collect realtime data on environmental parameters. Supervised machine learning algorithms are then applied
to analyze the data, identify influential factors, and build predictive models. The developed model’s
water quality control in aquaculture operations. Rigorous validation and testing ensure the accuracy
and effectiveness of the models. The results contribute to sustainable aquaculture practices by
guiding decisions on resource management, productivity improvement, and environmental impact
reduction. The integration of IoT and supervised machine learning has the potential to revolutionize
the aquaculture industry, paving the way for a more efficient and sustainable future.
3
INTRODUCTION
A country's economic development largely relies on agricultural products, as they serve as
the primary source of food and essential raw materials. To accomplish the world's development
objectives, establishing healthy, sustainable, and inclusive food systems is of utmost importance.
Agriculture significantly influences global trade as it is interconnected with various sectors of the
economy, leading to job creation and fostering economic development. This is due to the innovative
use of technology and advanced farm management practices by producers in these nations,
resulting in increased agricultural productivity and profitability.
Aquaculture can be seen as a close aquatic counterpart to agriculture, involving the
cultivation of specific marine and freshwater organisms to supplement natural resources. (Andres
R.F.T. von Brandt, 2023). The Recirculating Aquaculture System (RAS) is an advanced method of
fish farming that enables high-density fish production while being environmentally sustainable. In
addition, Recirculating Aquaculture Systems (RAS) is considered as one of the options for adapting
the impact of climate change on fish production while ensuring environmental sustainability. Unlike
conventional aquaculture, Recirculating Aquaculture Systems (RAS) do not present ecological risks
such as the decline in biodiversity caused by confined fish or the transmission of viruses and
parasites. Instead, RAS is an environmentally beneficial, water-efficient, and highly productive form
of intensive agriculture. (Ahmed & Turchini, 2021).
The primary focus of this dissertation proposal revolves around two essential components:
the Internet of Things (IoT) and supervised machine learning. IoT, a technology that has emerged
during the digital era, finds diverse applications across various domains. (Pitakphongmetha et al.,
2021). By offering real-time data, these interconnected devices empower aqua culturists to make
informed choices and enhance the production process. The integration of advanced information
technologies like the Internet of Things, cloud computing, big data, and AI has resulted in notable
advancements in aquaculture and paved the path for intelligent fishing production.(Bradley et al.,
2019).
The last five years have seen the adoption of machine learning techniques and algorithms in
intelligent fish farming, and studies of the results have been done. (Zhao et al., 2021). Supervised
machine learning algorithms can be employed to evaluate data collected by IoT devices and offer
predictions or suggestions to enhance the efficiency and sustainability of the system. This may
involve enhancing feeding schedules, early disease detection, or more effective water quality
management. Machine learning technology has become increasingly prevalent in aquaculture,
providing new opportunities for digital fish farming in light of advancements in automation and
intelligence.
As an agricultural nation, the Philippines must prioritize investments in developing resilient
and sustainable agriculture and food systems capable of withstanding disasters. By doing so, the
country can attain food self-sufficiency, promote the growth of rural communities, and elevate
4
farmers' income. Agriculture holds a significant role in the economy, contributing approximately
400% to the GDP and accounting for two-thirds of all job opportunities. (Agri Farming, 2022).
Improving farm productivity is essential in order to increase farm profitability and to provide the
rapidly growing demand of food caused by rapid population growth all over the world. According to
estimates compiled by the Food and Agriculture Organization (FAO), by 2050 we will need to
produce 60 per cent more food to feed a world population of 9.3 billion (Johannesson, 2019). In
conclusion, recirculating aquaculture systems offer several benefits over traditional aquaculture
methods, including reduced environmental impact, improved water quality, and increased efficiency
(Aqua Farm, 2023).
Hence, there is a need to revolutionize Aquaculture by Enhancing Sustainability through
Internet of Things and Supervised Machine Learning in a Recirculating Aquaculture System.
PROBLEM STATEMENT
Overview
Fish farming, also known as aquaculture, has emerged as a critical industry in meeting the
world's expanding need for protein food. With aquaculture currently accounting for more than half of
global fish consumption, this industry plays a critical role in ensuring food security and supporting
livelihoods for millions of people. Climate change, on the other hand, has emerged as a key danger
to fish farming, offering many difficulties to the global sustainability and productivity of aquaculture
operations.
The freshwater aquaculture industry faces challenges in maintaining optimal water quality
conditions to ensure the growth and survival of aquaculture products due to the effect brought by
climate change. Traditional monitoring techniques frequently have a limited capacity to deliver
complete and real-time data on water parameters. Fish growers therefore strive to maintain the ideal
circumstances required for the accomplishment of their aquaculture operations and year-round fish
output. The lack of comprehensive understanding of how water parameters affect the growth rate
and survivability of aquaculture products further hampers the implementation of effective strategies
for small-scale fish farming, particularly in upland areas.
Hence, the primary objective of this study is to evaluate and ensure the ideal water quality
conditions in Recirculating Aquaculture Systems (RAS), thereby assisting fish farmers in maintaining
favourable environments for their aquaculture products. Through continuous monitoring and analysis
of water parameters, the study aims to improve the growth rate and survival of aquaculture products,
with a specific focus on small-scale fish farming in upland areas. Furthermore, employing suitable
machine learning algorithms for the analysis of water parameter data in RAS will then provide
valuable insights and recommendations to support the improvement of aquaculture practices and
promote the success of fish farming ventures in these regions.
5
Research Question
1. How Internet of Things (IoT) will be employed for the real time collection of water quality data
in a recirculating aquaculture system?
2. What predictive models for water quality fluctuations be applied to enable proactive
adjustments of water quality?
3. What is the extent of usability of the developed system?
OBJECTIVES AND AIMS
Overall Objective
The general objective of this study is to Develop a device to monitor water parameters in a
recirculating aquaculture system (RAS). and analyze the data using suitable machine learning
algorithms.
Specific Aims
1. To remotely acquire real-time water quality parameters in a RAS through the implementation
of IoT technology.
2. To develop an Artificial intelligent model that predicts fluctuations in water quality parameters
in a RAS.
3. To evaluate the extent of usability of the developed system.
BACKGROUND AND SIGNIFICANCE
The remarkable expansion of aquaculture has led to a historic peak in global fisheries and
aquaculture output, with aquatic foods playing a progressively vital role in ensuring food security and
nutrition in the 21st century (FAO, 2022). Aquaculture serves as the primary fish source in the
country, The most prevalent species cultivated in the country are milkfish, tilapia (predominantly Nile
tilapia, Oreochromis niloticus), and shrimp. (National Fisheries Development Board, 2023).
However, the sustainability and efficiency of freshwater aquaculture operations face significant
challenges. Traditional aquaculture practices often lack real-time monitoring capabilities, leading to
suboptimal resource utilization, increased environmental risks, and reduced productivity.
Additionally, the complex relationship of various factors, such as water quality, fish behavior, and
system performance, makes it difficult to optimize aquaculture outcomes.
Recirculating aquaculture systems (RAS) that incorporate Internet of Things (IoT) and
supervised machine learning offer a revolutionary means of overcoming these difficulties. Sensors
and data analytics offered by IoT enable complete, real-time monitoring of critical parameters,
allowing for faster response and improved resource management. Aquaculture systems can be
6
better understood and managed by employing supervised machine learning algorithms, which can
examine huge datasets, determine patterns, and develop predictive models. This study focuses on
expanding seafood demand, the environmental consequences of conventional fishing practices, and
the limitations of current aquaculture approaches. This study is significant because it has the
potential to transform aquaculture techniques, promote sustainability, and significantly improve the
efficiency and productivity of Recirculating Aquaculture Systems (RAS).
This research employs Internet of Things sensors to enable real-time monitoring of critical
variables such as water quality, temperature, and feeding systems. Data analysis, predictive insights,
and data-driven decision-making are all possible with the application of supervised machine learning
algorithms in aquaculture operations. This integration has the potential to improve resource
utilization, lower environmental impact, reduce disease outbreaks, and boost overall production
efficiency. The study's importance reaches various stakeholders. Aquaculturists and farmers can
benefit from improved management methods, better productivity, and cost savings. Environmental
sustainability is enhanced by more effective resource utilization and reduced environmental impact.
Furthermore, consumers have access to sustainably obtained seafood, which helps to meet
expanding demand while also protecting the well-being of aquatic animals. Overall, this study
highlights the need for innovative approaches to revolutionize aquaculture practices and enhance
sustainability. By harnessing the potential of IoT and supervised machine learning in RAS, this
research contributes to the development of practical solutions for a more efficient, environmentally
friendly, and sustainable aquaculture industry.
RESEARCH DESIGN AND METHODS
Overview
The study will employ a descriptive-developmental study design method. This design will be
used to collect data on existing situations. The main goal is to collect and interpret information on
the nature of the situation as it exists at the time of the study.
The study is intended to be developmental. The proposed system will be designed to evaluate water
quality in a Recirculated Aquaculture System (RAS), thereby promoting a healthy environment for
aquacultures, lowering aquaculture mortality during cultivation, and achieving maximum growth by
maintaining RAS water quality based on the aquacultured optimum water quality preference.
Population and Study Sample
This study will use the super-intensive tilapia culture system using 32 to 46 days old tilapia
(starter) with approximately 50 grams weight.
7
Sample Size and Selection of Sample
Sampling plays a crucial role in data collection, enabling predictions through statistical
inference. In this study, the researcher will employ purposive sampling, a type of non-probability
sampling, which involves selecting subjects based on the researcher's judgment regarding their
suitability and alignment with the study's criteria. The researcher will identify people who will take
part in the evaluation. It is important to identify the respondents who will evaluate the system because
they can easily cope with the researcher’s objectives. With this sampling technique, the researcher
will use a survey questionnaire to be answered by the chosen respondents. This questionnaire will
determine the overall functionality, usability, reliability, and performance of the system.
Sources of Data
Through the help of the local government office Bureau of Fisheries and Aquatic Resources
BFAR and DA, the researcher will be able to gain knowledge and learn more about aquaculture,
especially about Tilapia which is the chosen aquaculture for this study. Interviews and site visits at
training centers of The Bureau of Fisheries and Aquatic Resources (BFAR) will be conducted. Data
will also be obtained from previous studies, journals and researches on Recirculating aquaculture
system using IoT and Supervised machine learning.
Collection of Data
Manual Collection of Data – using measuring devices
Automated Collection of data – using sensors.
Data in this study will be obtained through a series of in-depth interviews with the fish farmers
and personnels from BFAR in Alfonso Lista. The study will also use observation and
experimentation, a review of documents on the existing practices and procedures, the review of
previous studies, journals and research on recirculating aquaculture systems using IoT and
supervised machine learning.
Data Analysis Strategies
This study will consider three machine-learning algorithms to evaluate water quality in the
RAS and these include Decision tree, Fuzzy logic, and Linear regression. These algorithms will be
compared and contrast based on the algorithm’s purpose, advantages, disadvantages, and
implementation to find the most suitable algorithm to be used in this study.
Timeframes
The time of this study would be from May 2023 to February 2024
8
Activity
MAY
JUNE
JULY
AUG
SEPT
SY 2023-2024
OCT
NOV
DEC
JAN
FEB
MAR
APR
Proposal Writing
Write Chapter 1- Introduction/RRL
Data Collection – Interview/Observation
Data analysis and Interpretation
Write Chapter 2 - Methodology
Project Development
Submit paper for publication
Write Chapter 3 – Discussion of Findings
Submit paper for publication
Project finalization/implementation
Write Chapter 4 – Conclusion and Recommendation
STRENGTHS AND WEAKNESSES OF THE STUDY
The study investigates the application of IoT and supervised machine learning in freshwater
aquaculture as a new strategy to improve aquaculture technologies' sustainability and operational
efficiency. The study makes use of Internet of Things (IoT) technology to accomplish real-time
monitoring of important aquaculture system characteristics, allowing for timely interventions and
data-driven decision-making to improve resource management and increase production efficiency.
The study applies supervised machine learning algorithms to examine massive datasets, identify
patterns, and develop prediction models. This technique enables a more in-depth understanding of
the numerous factors driving aquaculture performance and promotes well-informed decision-making.
The study addresses real-world difficulties faced by the freshwater aquaculture business, such as
poor resource utilization, environmental concerns, and dropping production. The study's goal is to
provide useful insights and recommendations to improve aquaculture operations and support
industry success.
The study's findings are dependent on the quantity and quality of data available for analysis.
A lack of comprehensive and credible datasets may restrict the accuracy and significance of the
findings. Implementing IoT and machine learning technologies in aquaculture systems may require
significant infrastructure and technical expertise. Limited resources and technological constraints
could hinder the widespread adoption of the proposed solutions.
BUDGET AND MOTIVATION
Expenditure Description
A. Supplies and Services
Consumable supplies
Budget Request
Justification of Expenditure
Php 3,000.00
Communication expenses
Php 3,000.00
Bond paper, pen/pencil, printing, photocopying
and others
For gcash mobile call/text loads to contact
project respondents/ participants.
Travel expenses
B. Equipment
Hardware devices and
peripherals
Php 5,000.00
Php 70,000.00
Project/system development
9
TOTAL
Php 81,000.00
Overall, A sustainable aquaculture using innovative technology to increase productivity is
needed to provide the rapidly growing demand of food caused by rapid population growth all over
the world. The research is driven by the goal of transforming aquaculture practices, enhancing
sustainability, and providing data-driven solutions to the challenges faced by the aquaculture
industry. By exploring the potential of IoT and supervised machine learning in RAS, the research
seeks to pave the way for a more efficient, environmentally conscious, and economically viable future
for aquaculture.
REFERENCES
Agri Farming. (n.d.). How to Improve Agriculture in the Philippines, Ways, Ideas, and Tips.
https://www.agrifarming.in/how-to-improve-agriculture-in-the-philippines-ways-ideas-and-tips
Ahmed, N., & Turchini, G. M. (2021). Recirculating aquaculture systems (RAS): Environmental
solution and climate change adaptation. Journal of Cleaner Production, 297, 126604.
https://doi.org/10.1016/j.jclepro.2021.126604
Andres R.F.T. von Brandt, C. H. A. (2023). https://www.britannica.com/topic/aquaculture. In
Agriculture & Agricultural Technology (pp. 1–18).
Aqua Farm. (2023). Revolutionizing Aquaculture : The Benefits and Functionality of Recirculating
Aquaculture Systems. 1–5.
Bradley, D., Merrifield, M., Miller, K. M., Lomonico, S., Wilson, J. R., & Gleason, M. G. (2019).
Opportunities to improve fisheries management through innovative technology and advanced
data systems. Fish and Fisheries, 20(3), 564–583. https://doi.org/10.1111/faf.12361
FAO. (2022). The State of World Fisheries and Aquaculture 2022. The State of World Fisheries and
Aquaculture 2022, 4–9. https://doi.org/10.4060/cc0461en
Johannesson, T. (2019). (12) By 2030, we will need 50% more food, 45% more energy, and 30%
more water | LinkedIn. 1–7. https://www.linkedin.com/pulse/2030-we-need-50-more-food-45energy-30-water-torfi-johannesson/
National Fisheries Development Board. (2023). Recent Trends in Aquaculture Recirculatory
Aquaculture System (Ras). Ministry of Fisheries, Animal Husbandry & Dairying, 040.
Pitakphongmetha, J., Suntiamorntut, W., & Charoenpanyasak, S. (2021). Internet of things for
aquaculture in smart crab farming. Journal of Physics: Conference Series, 1834(1).
https://doi.org/10.1088/1742-6596/1834/1/012005
Zhao, S., Zhang, S., Liu, J., Wang, H., Zhu, J., Li, D., & Zhao, R. (2021). Application of machine
learning
in
intelligent
fish
aquaculture:
A
review.
Aquaculture,
540,
736724.
https://doi.org/https://doi.org/10.1016/j.aquaculture.2021.736724
10
DEVELOPMENT OF AN INTELLIGENT ELDERLY CARE SERVICE
SYSTEM FOR BAIYUN COMMUNITY
STUDENT NAME:
Huang Xiaoping
STUDENT NUMBER:
21-4875-908
COURSE NAME:
Doctor in Information technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
Thelma D. Palaoag
DATE OF SUBMISSION:
07 23 2023
CONTENTS
ABSTRACT ....................................................................................................................................................................... 3
INTRODUCTION ............................................................................................................................................................. 4
PROBLEM STATEMENT ............................................................................................................................................... 4
OVERVIEW ....................................................................................................................................................................... 4
RESEARCH QUESTION ...................................................................................................................................................... 4
OBJECTIVES AND AIMS .............................................................................. ERROR! BOOKMARK NOT DEFINED.
OVERALL OBJECTIVE ....................................................................................................................................................... 5
SPECIFIC AIMS ................................................................................................................................................................. 5
BACKGROUND AND SIGNIFICANCE ........................................................................................................................ 6
RESEARCH DESIGN AND METHODS........................................................................................................................ 7
OVERVIEW ....................................................................................................................................................................... 7
POPULATION AND STUDY SAMPLE ................................................................................................................................... 7
SAMPLE SIZE AND SELECTION OF SAMPLE ....................................................................................................................... 7
SOURCES OF DATA ........................................................................................................................................................... 7
COLLECTION OF DATA ..................................................................................................................................................... 8
DATA ANALYSIS STRATEGIES .......................................................................................................................................... 8
TIMEFRAMES .................................................................................................................................................................... 8
STRENGTHS AND WEAKNESSES OF THE STUDY .............................................................................................. 10
BUDGET AND MOTIVATION..................................................................................................................................... 11
REFERENCES ................................................................................................................................................................ 12
2
ABSTRACT
With the accelerated development of "new infrastructure" such as fifth-generation mobile
communication technology, big data and artificial intelligence, cities will become smarter. The
combination of traditional infrastructure and new infrastructure required for smart community elderly
care, such as medical services, living service facilities and recreational activities facilities, on the one
hand can greatly improve the intelligence level of community elderly care, make up for shortcomings,
break through the bottleneck of industrial development, and on the other hand can also improve the
quality of community elderly care services and the happiness of residents. This project is aimed at
the "Baiyun" community gathered in colleges and universities to conduct pilot research, and build a
smart elderly care service platform on its old elderly care service system; In terms of structure, this
project is based on traditional elderly care services, and uses big data and artificial intelligence
technology to analyze and process elderly care data, so as to build a pension system that combines
family pension and social pension. In terms of implementation characteristics, this project carries out
the old-age care model based on family old-age care, supported by community services and
supplemented by institutional old-age care, and realizes the pattern of diversified investment
subjects, public service objects, market-oriented operation mechanism and professional service
team. At the same time, it also integrates its excellent university resources into the infrastructure, so
that it has the characteristics of intelligent, socialized and all-round elderly care services, so as to
better meet the life and service needs of the elderly, alleviate the social pressure brought by aging,
resolve the elderly care problem, and provide more quality life security for the elderly. In the future,
the smart elderly care service platform will be further developed and improved to become an
important elderly care service information platform and digital economy support, providing wisdom
and quality life experience for the global elderly.
3
INTRODUCTION
PROBLEM STATEMENT
Overview
In 2019, the General Office of the Academy issued the "Opinions on Promoting the Development of
Elderly Care Services", proposing that during the "14th Five-Year Plan" period[1], the Internet, the
Internet of Things, big data, artificial intelligence and other technical means should be used to realize
the effective connection and optimal allocation of individuals, families, communities, institutions and
elderly care resources, promote the intelligent upgrading of elderly care services, and improve the
quality and efficiency of elderly care services. With the deepening of the aging degree in our country,
the traditional family supporting mode faces a severe challenge.[3]
This project takes "Baiyun" community as a pilot to conduct research, build an elderly care service
supply system based on database, call center and intelligent terminal products, with emergency
rescue, life assistance and active care as the core services. At the same time, advanced (mobile)
Internet technology, cloud technology and Internet of Things technology are used to integrate
communication network, intelligent call, Internet and other scientific and technological means. To
realize intelligent data platform; The comprehensive construction based on information database,
supported by information and call rescue service platform, provides emergency rescue, life care and
housekeeping services as the basic service content, so as to establish a perfect home-based elderly
care service system and create a "nursing home without walls" in the true sense.[2]
"Baiyun" smart elderly care information project is produced under the smart city, it combines the
government, family and modern information means to realize the intelligent interaction between the
Internet and the elderly, and integrates various resources on the community platform to carry out
comprehensive innovation on the traditional elderly care model.
Research Question
The main purpose of this study is to build a intelligent service system for the elderly in Baiyun
Community .
Specifically, it seeks to answer the following questions:
1. What are the requirements in the development of the community service system?
Information and Services
2. What are the features to be considered in the development of the proposed system?
3. How can Big Data analytics be integrated in the proposed system to make it intelligent?
4. What is the extent of usebility propose the systme?
4
Overall Objective
This project relies on the actual business of the community life service center of "Baiyun" street and
builds the "Intelligent comprehensive Service System Project for elderly care" according to the
intelligent needs of its elderly care business. It mainly includes five parts: the comprehensive
information management platform system for the elderly, the information query and release system
for the elderly, the call center system for the elderly, the convenience card system for the elderly,
and the telemedicine system for the elderly.[4]
Specific Aims
1.To identify the requirements needed in the information systems along with:
a. Data
b. Infrastructure
c. Services
2. To design and develop the features of the proposed system.
3. To apply big data analytics in the proposed system
5
BACKGROUND AND SIGNIFICANCE
On April 16, 2019, The General Office of the State Council issued the Opinions on Promoting the
Development of Elderly Care Services, proposing 28 specific measures and pointing out that the
Internet + elderly care action should be implemented. It is required to continue to promote the
development of the smart and healthy elderly care industry, expand the application of information
technology in the field of elderly care, develop a catalog of smart and healthy elderly care products
and services, and carry out pilot demonstrations of smart and healthy elderly care applications.
"Baiyun" community as an old community, with the change of The Times, the aging problem is
increasingly serious. According to statistics, by the end of 2022, the family structure of the "Baiyun"
community is in the shape of an inverted pagoda, and a large number of "421" families with four
elderly people, a couple and a child appear, and the function of family pension is weakening. More
than 30 percent of empty-nest families have no children except the elderly[4]. After the preliminary
investigation, it is found that the supply of home care services in this community is insufficient, the
proportion is low, and the quality is not high, which cannot meet the growing service needs of the
elderly. Meanwhile, as children, they hope to know the health status of the elderly at any time, care
for the elderly, and effectively solve the practical difficulties of the elderly, which has become the
urgent needs of the elderly and their children. It has also become a major social problem of wide
concern.
Actively explore the innovative mode of elderly care service combining elderly care community and
network technology, which provides a useful attempt to solve the problems of life care and long-term
care for the elderly with empty nests, living alone and disabled, and alleviates the contradictions
brought by the traditional pension model. The use of modern information technology such as the
Internet of Things, cloud computing, big data and artificial intelligence to embed various software
and hardware technology products in the smart community elderly care service model can meet the
multi-level and personalized needs of the elderly, and is gradually becoming the mainstream of the
development of China's elderly care service system.[6]
6
RESEARCH DESIGN AND METHODS
Overview
This paper mainly adopts investigation method, literature research method and model method.
(1) Investigation and analysis: Collect the current situation and needs of the elderly through
investigation and analysis
(2) Descriptive statistical method: Using statistical charts, variances and average values to analyze
the recent health data and disease status of the elderly
(3) Inference statistical analysis: The effective data of similar community hospitals were selected and
analyzed by Chi-square test, variance analysis and regression analysis
(4) Regression analysis: Use "regression analysis" to calculate the "degree" of a cause's impact on
the target, so as to allocate resources rationally[7]
Population and Study Sample
This study mainly takes "Baiyun" office and its 9 streets as the overall research object.
1. Collect the basic structure information of each family in the community, basic information of the
elderly, community information, hospital information and medical examination center information.
2. Collect the age level, basic health status, disease type and social concern of the population at
each stage.
3. Adopt advanced project management methods to build an information technology platform, and
take the development data generated in the development process as the research object to build a
more accurate system development prediction model. [8]
. [10]
Sample Size and Selection of Sample
Personnel structure：Baiyun" community is located in Baiyun District, Guangzhou, covering an area
of 0.16 square kilometers, including Luogui, Fangguiyuan, Guangba Road, Yinchang, Siyanjing,
Rangrong East, Rongxi, Yudai, Jixian and other streets in Wuchang, with 54 residential buildings,
134 floors, and 1930 households. A total of 847.
Sources of Data
1. Direct source of data:
(1) Obtain basic data through questionnaires and resident information management system.
(2) Use aerial photography and satellite map survey to obtain geographic information data.
(3) Obtain the basic data of the community through the smart community refined management
service system, especially the data that needs special attention, such as the dynamic increase of
surveillance cameras, nursing homes, elderly activity places, community medical institutions and
hospitals.
2. Indirect source of data: In the course of project management, sample sizes of generated lines of
software code are used as the basis for estimation methods.
7
Collection of Data
1. The sub-district office is used to organize a comprehensive survey of the jurisdiction, obtain the
basic data through the census, and use the resident information management system to collate and
save the data. The basic information is classified and summarized as required to provide basis for
actual work. To form a comprehensive browse and query function of regional information, and
improve the statistics of relevant community information to make the comprehensive information
query convenient and fast.
2. Obtain the geographic data of East Street Community through aerial photography, satellite map
and other methods, and automatically generate a digital map of about 3.84 square kilometers
through field survey and proofreading data.
3. Add panoramic street scene video management function, use electronic monitoring equipment to
carry out scene monitoring on social security, urban management, etc., and improve social
management service and scientific level.
4. In the process of project management, the standard management mode of software engineering
is adopted, and the sample size of the generated software code line is used as the basis of the
estimation method.
Data Analysis Strategies
1. The sub-district office is responsible for organizing a comprehensive survey of the district and
issuing collection questionnaires, obtaining basic data through the population census, and
organizing and saving the data through the resident information management system. The basic
information is classified and summarized according to the need to provide a basis for practical work.
Form the comprehensive browsing and query function of regional information, improve the statistics
of relevant community information, and make the comprehensive information query convenient and
fast.
2. Acquired the geographical data of Baiyun community through aerial photography and satellite
maps, and automatically generated a digital map of about 0.16 square kilometers through field
investigation and data proofreading.
3. Increase the camera and alarm linkage system in fine areas, mainly distributed in the areas where
the elderly are often active.
4. In the process of project management, the standard management mode of software engineering
is adopted, and the generated sample size of software code lines is used as the basis of the
estimation method.
Data analysis strategy
From the four basic factors of personnel management, software system product management,
process management and project management involved in intelligent elderly care community, such
as project estimation, risk analysis, project planning, etc., a 3D digital community software project
management method is developed to clarify the objectives of 3D digital community software product
management and improve the effectiveness and reliability of system development. Based on the
8
function points, the data management system estimation model, medical information system
estimation model, APP system estimation model and content management system estimation model
are constructed. Among them, the information management system, information publishing system,
interaction interface between activity publishing system and medical information system, APP
interaction interface and interaction interface with content management system are unified as the
estimation model of data management system [11]. By analyzing the technical and economic theory
and solving the bottleneck problem of the promotion and application of digital community, the
development cost of digital community software system can be clearly predicted, and the direct
economic and social benefits brought by the promotion and application of results can be easily
calculated. [9]
Timeframes
1. From July 2023 to August 2023, complete the proposal report and thesis outline.
2. From September 2023 to January 2024, carry out in-depth investigation, master the first batch of
basic community data of Jiefang Road Office, find out the community differences, and conduct
preliminary theoretical analysis on most of the data.
3. From October 2023 to December 2023, the adaptive geographic information system will be
improved to obtain data through aerial photography and satellite maps, and automatically generate
digital maps covering about 3.84 square kilometers.
4. In December 2023 and January 2024, adjust the differences of the first batch of basic data,
conduct data sorting and theoretical analysis, complete the data access specification of digital
community, and complete the docking of existing systems.
5. From January 2024 to May 2024, develop and complete the mobile client of the community mobile
office system. Finish the first draft of the graduation thesis.
6. From April 2024 to June 2024, unreasonable data will be labeled and additional tests will be
carried out for some newly discovered problems. Finally, the project was successfully completed.
7. From July 2024 to July 2024, the final thesis will be completed.
9
STRENGTHS AND WEAKNESSES OF THE STUDY
The birth of the smart elderly care service platform is in line with the development trend of the elderly
care service system, to meet the needs of home care, community care and institutional care, to
provide the elderly with "one-click" and "a la carte" services, while the construction of the system will
break the "fragmented" state of traditional elderly care services, and establish a community as the
support, institutions as a supplement, medical care combined, technology as a supplementary,
information means To support the new elderly care service system, and constantly improve the
happiness and sense of gain of the elderly.
The smart elderly service platform is a new elderly service model emerging in the Internet era. Elderly
service can not only get the care of the elderly through the Internet, but also facilitate family members
to chat with the elderly through the remote Internet, so as to meet the emotional needs between the
elderly and their loved ones. [10]
The smart elderly care service platform not only provides the richness of information for the elderly
care industry, but also can solve the current pension problems faced by society to a certain extent.
Whether it is a nursing home or the relatives of the elderly, through the smart pension service
platform, the children can see the dynamics of the elderly in real time on the mobile phone, and can
also communicate with the elderly through remote video.
The smart pension service platform can allow the elderly to enjoy more intelligent and humanized
pension services, and the elderly pension service is no longer about worrying about living alone and
being unaccompanied, but can enjoy an important part of the new intelligent pension method.
The smart elderly care service platform can meet the personalized needs of the elderly on smart
devices. The old people can enjoy ordering food, entertainment, health, help and other services on
TV anytime and anywhere at home through mobile phones. The platform will provide a variety of
intimate services around the needs of the elderly, such as clothing, food, housing and transportation,
and will have warm "smart" elderly care services into thousands of households.
The smart elderly care service platform can truly grasp and update the physical status of the elderly
in real time, so that there is evidence to rely on and rational to check, which can relieve the pressure
of the children.
10
BUDGET AND MOTIVATION
The total budget of this project is CNY1657000.00, including:
1. The hardware investment budget of the community digital management service center is CNY
1507000.00.
2. The research fund is CNY150000.00.
(1) Printing fee is CNY5000.00.
(2) The consulting fee is CNY15000.00, mainly used for technical consulting fees of enterprises and
experts.
(3) Travel expenses: CNY20000.00, including accommodation, travel expenses, food allowance and
other expenses of the project staff.
(4) Special research fee CNY50000.00. It is mainly used for surveying and mapping of 3D
geographic information system, operation and maintenance of network information system, system
testing and other costs of the project.
(5) The conference fee is CNY10000.00. It is mainly used for project acceptance, appraisal and other
expenses.
(6) Training fee: CNY10000.00. It is mainly used for the training of intelligent informatization team of
grass-roots community workers.
(7) Service fee: CNY10000.00. Employ multimedia processing personnel, system information
collection, input and other labor costs.
(8) Management fee: CNY10000.00. Manage all expenses incurred in organizing project
implementation.
(9) Entrusted business fee: CNY10000.00. It is mainly used to pay various expenses in the process
of intellectual property registration.
(10) Other goods and services expenses: CNY10000.00. For daily public expenditure.
11
REFERENCES
[1] Xie, J., Wu, D., Wu, X., & Li, W. (2022). A review of smart aging community research in China: Trends, topics, and
future directions. Sustainable Cities and Society, 80, 103418.
[2] Chen, W., Zhao, Q., & Zhang, Y. (2021). A blockchain-based incentive mechanism for resource allocation in smart
communities. IEEE Transactions on Industrial Informatics, 17(6), 3932-3942.
[3] General Office of the State Council, C. (2019). The General Office of the State Council, 118, 103369.
[4] Zhou, X., Wang, Q., Xie, L., & Lu, Y. (2022). Co-creation in smart communities: A review of the literature and research
agenda. Technological Forecasting and Social Change, 176, 121019. [5]
[5] Chen, L., Zhang, X., & Lu, Y. (2022). A systematic review of smart aging community measurement indicators. Journal
of Cleaner Production, 326, 129291.
[6] Alshehri, A., & Khan, M. (2021). Smart city initiatives for sustainable development: A review of the literature. Renewable
and Sustainable Energy Reviews, 148, 111374.
[7] Gong, Y., & Li, X. (2022). The role of social capital in the development of smart communities: An empirical study.
Technological Forecasting and Social Change, 174, 121119.
[8] Hou, J., Liu, Y., & Chen, Y. (2022). A systematic review of smart aging community research: Current status and future
directions. Journal of Cleaner Production, 316, 128170.
[9] Li, X., Zhang, D., & Fang, Y. (2021). A review of smart aging community research in China: A bibliometric analysis.
Cities, 111, 103075.
[10] Lin, Y., & Dong, L. (2022). Towards a sustainable smart aging community: A critical review of smart city initiatives in
China. Journal of Cleaner Production, 317, 128222.
[11] Shi, X., Wang, Y., Liu, M., & Gao, Y. (2021). Research on the operation and management of smart communities based
on the IoT. Wireless Communications and Mobile Computing, 2021, 6684081.
[12] Wei, Y., & Qiu, L. (2022). Smart aging community planning in China: A review of recent development. Habitat
International, 120, 102414.
[13] Wu, F., Zhang, Y., & Chen, W. (2022). A systematic review of smart aging community research: A bibliometric analysis.
Journal of Cleaner Production, 329, 129487.
12
CROPGUARD: LEVERAGING DEEP LEARNING TECHNIQUES
FOR PEST AND DISEASE DETECTION
STUDENT NAME:
Nerissa L. Javier
STUDENT NUMBER:
21-3894-929
COURSE NAME:
Doctor in Information technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
Dr. Thelma Domingo - Palaoag
DATE OF SUBMISSION:
12 08 2023
1
CONTENTS
ABSTRACT ...................................................................................................................................................................... 3
INTRODUCTION ............................................................................................................................................................ 4
PROBLEM STATEMENT .............................................................................................................................................. 5
OVERVIEW ...................................................................................................................................................................... 5
RESEARCH QUESTION ..................................................................................................................................................... 5
OBJECTIVES AND AIMS .............................................................................................................................................. 6
OVERALL OBJECTIVE ...................................................................................................................................................... 6
SPECIFIC AIMS ................................................................................................................................................................ 6
BACKGROUND AND SIGNIFICANCE ....................................................................................................................... 6
RESEARCH DESIGN AND METHODS ....................................................................................................................... 7
OVERVIEW ...................................................................................................................................................................... 7
POPULATION AND STUDY SAMPLE .................................................................................................................................. 7
SAMPLE SIZE AND SELECTION OF SAMPLE ...................................................................................................................... 7
SOURCES OF DATA .......................................................................................................................................................... 7
COLLECTION OF DATA .................................................................................................................................................... 8
DATA ANALYSIS STRATEGIES ......................................................................................................................................... 8
TIMEFRAMES ................................................................................................................................................................... 9
STRENGTHS AND WEAKNESSES OF THE STUDY................................................................................................ 9
STRENGTHS ..................................................................................................................................................................... 9
WEAKNESSES ................................................................................................................................................................ 10
BUDGET AND MOTIVATION .................................................................................................................................... 10
REFERENCES ............................................................................................................................................................... 11
2
ABSTRACT
In today's rapidly evolving world of agriculture, ensuring the health and productivity of crops
is of paramount importance. However, the prevalence of pests and diseases poses a significant
threat to global food security. Traditional methods of monitoring and detection often fall short in terms
of accuracy, efficiency, and scalability. Hence, the study aims to develop an application that can
automatically detect pest and diseases on its earliest stage possible. The integration of deep learning
techniques demonstrates the ability to achieve high levels of accuracy in identifying pests and
diseases. By leveraging advanced neural networks and vast datasets, we are now equipped to
detect and identify pests and diseases at an early stage, enabling swift and targeted interventions.
The study employed the mixed research method, which combines qualitative and quantitative
approaches to gather and analyze data. Thirty (30) participants, a combination of agricultural experts
and farmers, will participate in the study. Participants were selected using purposive sampling.
The application of deep learning for monitoring and early detection of pests and diseases in
agriculture shall produce tangible results that include enhanced accuracy, early detection, reduced
economic losses, and improved resource efficiency. By harnessing the power of AI and data-driven
insights, farmers and agricultural stakeholders can make more informed decisions that promote both
productivity and sustainability. With early detection, farmers can respond swiftly and apply targeted
interventions, such as adjusting irrigation, using specific pesticides or biocontrol agents, or
implementing cultural practices that mitigate the spread of pests and diseases. By detecting and
addressing problems at their inception, deep learning-based systems help prevent significant crop
losses and economic damage that can result from unchecked pest and disease outbreaks. This is
particularly important for smallholder farmers and communities heavily reliant on agriculture for
livelihoods.
Keywords: Deep Learning, Early Detection and Monitoring Application, CropGuard
3
INTRODUCTION
The quantity and quality of agricultural products are directly affected by the prevalence of
crop diseases and insect pest. Pests and diseases pose significant threats to crop production,
leading to substantial yield losses and economic damage (1). To prevent these unnecessary crop
losses and boost agricultural productivity, monitoring and early detection of pest and diseases are
essential. Monitoring entails the systematic observation and evaluation of crops to determine the
presence, severity and distribution of diseases and pest while early detection refers to identifying
these issues at the initial stages even before significant damage occurs (2). Both monitoring and
early detection are vital for risk assessment and control treatment (1). It allows farmers and
agricultural professionals to stay informed about the specific pests and diseases affecting their crops,
their population dynamics, and the extent of damage caused. When infestations are detected early,
eradicating or managing the pests using less invasive and costly methods is often easier. However,
late detection may require more aggressive measures, such as increased pesticide use or extensive
pest management strategies, which can be environmentally harmful and expensive.
Disease identification might be difficult without familiarity with the diseases that affect a
certain plant group. Likewise, classifying insects is difficult due to their complex structure and many
visual similarities between different categories (3). Traditional inspections heavily rely on visual
observations, which can be subjective and require trained personnel with expertise in identifying
symptoms and signs of diseases and pests. Differentiating between various diseases, pest species,
and their damage can be challenging, leading to misdiagnosis, or missed detections.
Farmers in Botolan heavily rely on traditional pest and disease identification methods since
they have no access to technology and digital tools for pest and disease detection. However, it is
difficult for them to recognize early warning signs because they have limited knowledge to accurately
identify early signs of pests and diseases. This knowledge gap hinders them from implementing
effective monitoring and early detection practices, leading to delayed or inadequate responses to
outbreaks. They also have insufficient knowledge about appropriate control measures leading them
to use excessive pesticides during outbreaks.
Identifying diseases in plants using conventional approaches has been replaced by automatic
approaches. Therefore, we need automatic methods to recognize diseases to manage and treat
them properly.
Addressing these challenges and developing innovative solutions for the monitoring and
early detection of pests and diseases in crops is critical to enhancing agricultural productivity,
minimizing crop losses, and ensuring sustainable food production. To overcome these challenges,
monitoring approaches, such as remote sensing, automated sensor technologies, and data-driven
analytics, can be utilized for automatic detection(4). These alternative methods aim to improve the
speed, accuracy, coverage, and timeliness of disease and pest detection, ultimately enhancing crop
management and reducing the reliance on traditional inspection techniques alone. Among the
4
machine learning methods widely employed to automate the classification of pest and plant
diseases, deep learning has emerged as the most effective in image related problems1. Deep
learning techniques have significantly boosted the ability to perform such detections automatically.
Deep learning models, such as convolutional neural networks (CNNs), can be trained to recognize
patterns and features in images of crops affected by pests and diseases. By feeding the models with
a diverse dataset of healthy and infected crop images, they can learn to distinguish between normal
and abnormal conditions, enabling the early detection of pests and diseases based on visual cues.
Traditional methods often rely on manual inspection and visual assessment, leading to
delayed identification and response and substantial crop losses. This research aims to develop an
application based on deep learning techniques that can effectively monitor and detect pests and
diseases on selected crops, providing farmers with an automated and proactive tool for early
detection and prevention measures. By developing an automated deep learning system for
monitoring and early detection of pests and diseases on crops, farmers are empowered with a
reliable and efficient tool to mitigate the impact of pests and diseases, reduce reliance on pesticides
and improve overall crop productivity and sustainability.
PROBLEM STATEMENT
Overview
The monitoring and early detection of pests and diseases in crops are crucial for ensuring healthy
and productive agricultural systems. Traditional methods of manual inspection and visual
observation are time-consuming, labor-intensive, and often prone to human error, leading to delayed
identification and ineffective control measures. Therefore, there is a need to develop mobile-based
application for capturing images for early pest and disease detection.
Research Question
Specifically, it will seek answers to the following questions?
1. What are the deep learning techniques and architecture for detecting and classifying
pest and diseases in crops?
2. How can image-based data be integrated for early detection of pest and disease?
3. What is the level of accuracy and reliability of the early detection system?
5
OBJECTIVES AND AIMS
Overall Objective
The study aims to develop an mobile-based application for capturing images for early pest and
disease detection.
Specific Aims
1. To determine the deep learning techniques and architecture for detecting and
classifying pest and diseases in crops.
2. To determine how an image-based data be integrated for early detection of pest and
disease?
3. To determine the level of accuracy and reliability of the early detection system.
BACKGROUND AND SIGNIFICANCE
The early detection of pests and diseases on crops is crucial for ensuring healthy and highyielding agricultural production. Traditional methods of pest and disease identification are often timeconsuming and require expert knowledge. In the method of pest monitoring known as "traps," which
is becoming increasingly popular, captured digital images are analyzed by human experts to identify
and count pests. Manual counting requires a lot of labor, is slow, expensive, and fraught with error,
which makes it impossible to meet performance and cost targets in real-time. Several approaches
to machine learning have seen widespread use in recent years, intending to automate the
classification of plant diseases and pests. With the rise of high-configuration systems, approaches
to deep learning have found their way back into the light. Deep learning has helped a lot with
recognizing images on a large scale and has also been used in agriculture (5).
The capacity to carry out automatic detections has been significantly increased because of
the application of deep learning techniques (6). Images that capture the objects of interest are the
typical input data provided to CNN-based models. These images are used to feed the models.
Depending on the architecture, these models can perform the detection process in either a singlestage or a two-stage process, respectively (7). The fundamental principle of deep learning is to
extract data features using multiple hidden layers of a neural network, each of which can be viewed
as a perceptron. The perceptron is then used to combine low-level features to obtain abstract highlevel features, which can significantly reduce the issue of local minimum (8).Deep learning has been
increasingly popular among researchers since it addresses the drawback of traditional algorithms,
which is their reliance on artificially created characteristics. It is presently successfully used in
6
recommendation systems, computer vision, pattern recognition, speech recognition, and natural
language processing.
Significance of the study
The result of the study will benefit the following stakeholders.
Farmers. Automated early detection systems can significantly benefit farmers by providing real-time
monitoring of their crops. By alerting farmers to potential issues, they can take immediate action to
prevent the spread of pests or diseases, leading to reduced crop damage and improved yield.
Agricultural Industry. By implementing the application on a large scale, the agricultural industry
can detect and respond to outbreaks more efficiently. This helps minimize the economic losses
caused by pests and diseases, maintain crop productivity, and ensure a stable food supply.
Consumers. Food safety can be improved using the application that enables early detection and
prompt intervention, thereby reducing the amount of potentially dangerous pathogens and pests in
crops. By implementing timely control measures, the risk of contamination and the need for
excessive pesticide use can be reduced, leading to safer and healthier food for consumers.
Future Researchers. The information produced by the application can contribute to the
development of more effective control strategies and management practices.
RESEARCH DESIGN AND METHODS
Overview
The researcher will employ the mixed research method which combine qualitative and
quantitative approaches to gather and analyze data. This approach will allow the researcher to gain
a more comprehensive understanding of a research problem by using the strengths of both
qualitative and quantitative research.
Population and Study Sample
The participants of the study will be the crop science professors from the College of
Agriculture of PRMSU Botolan and San Marcelino Campus as well as selected farmers from Botolan,
Zambales
Sample Size and Selection of Sample
There will be a total of thirty (30) participants who will take part in the study. Participants will
be selected using purposive sampling.
Sources of Data
The potential sources of data are the following:
7
Crop Imagery: High-resolution images of crops, both healthy and infected with pests or diseases,
can be collected using drones, satellites, or ground-level cameras. These images are essential for
training deep learning models for visual recognition and detection.
Pest and Disease Records: Historical records of pest and disease occurrences on crops,
maintained by agricultural agencies or research institutions can be valuable for training and
validating the model.
Expert Annotations: Expert annotations and labeled data are crucial for the supervised training of
deep learning models. Agronomists, plant pathologists, or experienced farmers can provide ground
truth labels for the presence of pests and diseases in crop images.
Crowdsourced Data: In some cases, data can be collected through crowdsourcing platforms, where
farmers and users can upload images of their crops, along with information about pest and disease
presence.
Historical Pest Outbreak Data: Historical data on past pest outbreaks can be used to identify
recurring patterns and assess the impact of pests on different crop varieties and regions.
Research Reports: Agricultural reports, research publications, and datasets made available by
governmental organizations and research institutions can also serve as valuable sources of
information.
Collection of Data
The researcher will utilize interviews, focus groups, observations, to gather rich, descriptive
data. These methods help explore participants' perspectives, experiences, and opinions related to
the research question. Quantitative data collection methods involve gathering numerical data that
can be analyzed using statistical techniques. Common quantitative methods include surveys with
closed-ended questions or structured observations.
Data Analysis Strategies
Weighted Arithmetic Mean
This statistical technique will be utilize used to determine the average response of the different
options provided in the various parts of the survey questionnaire used.
Likert Scale Method. It will be used to provide data interpretation on level of accuracy of the
application.
Likert Scale Use to interpret the Accuracy of the System
Point
Weight Value
Qualitative Interpretation
5
4.20 – 5.00
Highly Accurate
4
3.40 -4.19
Accurate
8
3
2.60 -3.39
Moderately Accurate
2
1.80 -2.59
Inaccurate
1
1.00 -1.79
Highly Inaccurate
Timeframes
The researcher will used the timeframe below as guide to accomplished task(s) on time.
Period
Activities
2024
2023
05
06
07
08
09
10
11
12
01 02 03 04 05 06 07 08
Planning
Data Gathering
Proposal Defense
Interview
Developing
the
Application
Writing the 1st Journal
Article
Publication
Deployment
Monitoring
Evaluation
Writing
2nd
Journal
Article
Presentation of Output
Publication
Final Copy
STRENGTHS AND WEAKNESSES OF THE STUDY
Strengths
Deep learning models, particularly convolutional neural networks (cnns) for image data, can
achieve high levels of accuracy in identifying pests and diseases. They can learn intricate patterns
9
and features that might be difficult for human observers to detect. Once trained, deep learning
models can process data quickly, enabling real-time or near-real-time predictions. This is crucial for
timely interventions to prevent or manage pest and disease outbreaks. The application can reduce
the need for manual inspection and monitoring, saving time and labor costs. Automated data
collection and analysis are particularly advantageous for large-scale agricultural operations.
Weaknesses
Gathering and preprocessing data can be resource-intensive and time-consuming. Collecting
accurate and relevant labeled data for training can be especially challenging. Training and deploying
deep learning models require significant computational resources, which can be expensive and may
not be accessible to all agricultural stakeholders, particularly in resource-constrained environments.
BUDGET AND MOTIVATION
Item
BUDGET
Cloud Services
35,000.00
Laptop
50,000
TOTAL
85,000.00
10
REFERENCES
1.
Wang B. Identification of Crop Diseases and Insect Pests Based on Deep Learning. Sci
Program. 2022;2022.
2.
Zhao S, Liu J, Bai Z, Hu C, Jin Y. Crop Pest Recognition in Real Agricultural Environment
Using Convolutional Neural Networks by a Parallel Attention Mechanism. Front Plant Sci.
2022 Feb 21;13.
3.
Chithambarathanu M, Jeyakumar MK. Survey on crop pest detection using deep learning and
machine learning approaches. Multimed Tools Appl. 2023;
4.
Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. A survey on deep learning-based
identification of plant and crop diseases from UAV-based aerial images. Cluster Comput. 2022
Apr 1;
5.
Bhuvana J, Mirnalinee ; T T. An approach to Plant Disease Detection using Deep Learning
Techniques Un enfoque para la detección de enfermedades de las plantas utilizando técnicas
de aprendizaje profundo. Revista ITECKNE-Universidad [Internet]. 18(2):2021–161. Available
from: https://doi.org/10.15332/iteckne.v18i2.2615
6.
Albanese A, Nardello M, Brunelli D. Automated Pest Detection with DNN on the Edge for
Precision Agriculture. IEEE J Emerg Sel Top Circuits Syst. 2021 Sep 1;11(3):458–67.
7.
Lippi M, Carpio RF, Contarini M, Speranza S, Gasparri A. A Data-Driven Monitoring System
for the Early Pest Detection in the Precision Agriculture of Hazelnut Orchards. In: IFACPapersOnLine. Elsevier B.V.; 2022. p. 42–7.
8.
Liu J, Wang X. Plant diseases and pests detection based on deep learning: a review. Vol. 17,
Plant Methods. BioMed Central Ltd; 2021.
11
SMART CAMPUS: ESTABLISHMENT OF A 5G FIBER OPTIC
CONNECTIVITY AND MANAGEMENT OF IFUGAO STATE
UNIVERSITY – POTIA CAMPUS
STUDENT NAME:
DENNIS C. MALUNAO
STUDENT NUMBER:
9500509
COURSE NAME:
Doctor in Information Technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
Dr. Thelma Domingo Palaoag
DATE OF SUBMISSION:
17 08 2023
CONTENTS
ABSTRACT.......................................................................................................................................... 3
INTRODUCTION.....................................................................ERROR! BOOKMARK NOT DEFINED.
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DO NOT USE ANYTHING ELSE AS THE TABLE OF CONTENTS HAS BEEN AUTOMATED TO
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PROBLEM STATEMENT .................................................................................................................... 6
OVERVIEW ......................................................................................................................................... 6
RESEARCH QUESTION/HYPOTHESIS ................................................................................................... 7
OBJECTIVES AND AIMS ................................................................................................................... 7
OVERALL OBJECTIVE.......................................................................................................................... 7
SPECIFIC AIMS ................................................................................................................................... 7
BACKGROUND AND SIGNIFICANCE............................................................................................... 8
RESEARCH DESIGN AND METHODS .............................................................................................. 9
OVERVIEW ......................................................................................................................................... 9
POPULATION AND STUDY SAMPLE ...................................................................................................... 9
SAMPLE SIZE AND SELECTION OF SAMPLE.......................................................................................... 9
SOURCES OF DATA ............................................................................................................................ 9
COLLECTION OF DATA ........................................................................................................................ 9
DATA ANALYSIS STRATEGIES ........................................................................................................... 10
TIMEFRAMES .................................................................................................................................... 10
STRENGTHS AND WEAKNESSES OF STUDY ............................................................................. 11
BUDGET AND MOTIVATION ........................................................................................................... 11
REFERENCES................................................................................................................................... 11
APPENDICES .................................................................................................................................... 13
APPENDIX 1: QUESTIONNAIRE .......................................................................................................... 13
2
ABSTRACT
An intelligent educational system's premium version, the smart campus, has grown in favor.
Due to the diverse nature of the smart campus, the present research is generally one-ended on
either cutting-edge technology or cutting-edge educational concepts, but lacks a deep fusion view
on both and ignores the smart campus implications on other smart connection fields. In this study,
we highlight the multidisciplinary approach to smart campuses. A human-centered learning-oriented
smart campus is envisioned, defined, and framed based on a thorough analysis of the supporting
technologies and existing connection proposals. The main goal of the study is to assess, design and
establish a 5G Fiber Optic Connectivity and Network Management of Ifugao State University – Potia
Campus. The smart campus is also examined in terms of disciplinary aspects to enhance and restrict
it.
Background
The way that people live and work, as well as how they learn, have all changed significantly
along with technological advancement. The desire for customized and adaptable learning is rising,
and this has pushed reform and progress in the field of education. Smart campuses have become a
reality and are gaining more and more attention throughout the globe as the premium version of a
smart education system. A smart campus is an essential component of smart campus architecture
because it generates a smart learning environment for the citizens, transforming them into a smart
workforce [1, 2]. The growth and acceptance of smart campuses also aid the knowledge economy.
The market for smart education worldwide is anticipated to expand at a compound annual growth
rate. [3] It is vital to do active study and comprehend the smart campus and its characteristics in this
rapidly evolving field. Numerous literature evaluations have been in this field, which is appropriate
given the multidisciplinary character of smart campus research. On the one hand, the recent
development of information and communication technologies (ICT), artificial intelligence (AI), smart
gadgets, and technologies for varied realities (e.g., augmented reality (AR), virtual reality (VR), etc.),
produce new and upcoming prospects for educational institutions to attain greater educational
standards and accomplishments. certain technology review articles focus on cutting-edge
technologies and how they might be used on a smart campus. To name a couple, [4, 5] and [6, 7]
provide reviews of the Internet of Things (IoT) and cloud computing applications in smart campuses,
respectively. [6] reviews the smart campus technologies against the backdrop of the 5G network to
conceivable applications.
As above, due to the multi-disciplinary nature of the smart campus, the existing reviews on
the smart campus are mostly one-ended on either the state-of-the-art technologies or the innovative
educational concepts. However, the success of the smart campus requires a deep fusion of
3
technology and education. The expected outcome of this work is to provide an international standardbased connection to the existing network of Ifugao State University – Potia.
Methods
The Network Architectural Plan for this study will be designed utilizing the Top-Down Network Design
Methodology, and the Network Communication Committee (NCC) criteria will be used to describe
the Network setup.
Results
After carefully analysing the gathered information result show that;
1. How being adoption the 5G Fiber optic inputs at Ifugao State University – Potia Campus?
a. Communication equipment;
a.1. Connectivity – It was found that some of the offices are in a networked
environment, especially internet service. UTP Cable category 5, switches of
the different port sizes, hubs, and no router or repeater are used to maintain
the speed connection found on the network media. Power outage is
prevalent in the municipality and shuts off internet connectivity.
a.2. Scalability – to scale, switches and hubs are connected switch after switch.
The network does not scale well because the switches are exhausted.
a.3. Network Type and Topology – Every network is a fast Ethernet peer-to-peer
network on a star topology.
a.4. Performance – due to the type of connection subscribed by the institution from
the 3rd Party Internet Providers, which has 200 megabits per second, it was
observed that the internet speed slows especially during peak hours.
a.5. Security – due to the type of network it is prone to virus attack and hacking.
b. Network Software
b.1. Software – both servers and clients are using Microsoft Windows Service Pack
2.
b.2. Licensing – (EULA) End-user Licensing Agreement Operating Systems are
being used on the existing system.
2. Considering the issue that has been encountered in terms of;
a. Communication equipment;
a.1. Network Administration – Network administration, troubleshooting and
maintenance, and backup should be done directly to the designated office
only.
a.2. Connectivity – UTP cable Category 5 is used for the distance of less than 100
meters or even greater which results to slow connectivity. The Bandwidth is
another problem due to the type of subscription.
a.3. Scalability – to scale, switches and hubs are connected switch after switch.
The network does not scale well because the switches are exhausted.
a.4. Resource and data sharing – a limited resource and data sharing due to some
offices that are not connected to the network environment.
a.5. Security – The user may access any resource on the network that is shared.
Cables are exposed and could pose a security risk. Access to the web is
not also monitored, filtered, or controlled.
b. Network Software
b.1. Software – both servers and clients are using Microsoft Windows Service Pack
2.
b.2. Licensing – (EULA) End-user Licensing Agreement Operating Systems are
being used on the existing system.
3. A well-designed Network Architectural Plan is needed to address the said problems;
a. Communication equipment;
4
a.1. Network Administration is no longer a tedious task since all offices are in a
network environment. Backup is no longer a problem because of the
centralized connectivity.
a.2 There is improved connectivity because the use of hubs is not considered,
instead, FS Fiber Optic Switch, media com is used in every terminal, UTP
Category 6 are also used in connecting giga switches to stations with
properly measured, Bandwidth is no longer a problem, It will be split into two
segments using fiber optic main backbone; intruder, downloading and
uploading are also monitored and controlled.
a.3 There is improved security because access to network resources is controlled.
Proxy servers with firewalls are installed to filter restricted sites. Using
RB4011 Router Board is used to manage the console.
4. Addressing threats and the effect’s safeguarding during uses of privacy.
a. Communication equipment;
a.1. Network Administration – Network administration, troubleshooting and
maintenance, and backup should be done directly to the designated office
only.
a.2. Connectivity – UTP cable Category 5 is used for the distance of less than 100
meters or even greater which results to slow connectivity. The Bandwidth is
another problem due to the type of subscription.
a.3. Scalability – to scale, switches and hubs are connected switch after switch.
The network does not scale well because the switches are exhausted.
a.4. Resource and data sharing – a limited resource and data sharing due to some
offices that are not connected to the network environment.
a.5. Security – The user may access any resource on the network that is shared.
Cables are exposed and could pose a security risk. Access to the web is
not also monitored, filtered, or controlled.
b. Network Software
b.1. Software – both servers and clients are using Microsoft Windows Service Pack
2.
b.2. Licensing – (EULA) End-user Licensing Agreement Operating Systems are
being used on the existing system.
Discussion and Conclusion
Based on the presented summary and findings of this paper, the following conclusions were drawn:
1.
Connectivity, scalability, network type, and topology, performance, and security are
considerations for communication equipment and need to be evaluated and reviewed when
planning the network architectural plan.
2.
Undependable network architecture, network administration, connectivity, resources
and data sharing, and security are problems that need to be addressed.
3.
A Network Architectural Plan is necessary to address the problems encountered in
Network Administration, Connectivity, Resource and Data Sharing, Security, and Software
licensing.
5
PROBLEM STATEMENT
Overview
Commitment to excellence in quality, professional service, best value, swift turn-around, and
client happiness. Due to its prospective productivity, networking capabilities have attracted
investments from a huge number of businesses and organizations across a wide range of industries,
including the educational sector. Israel del Rio, Abstraction Consulting, Smart Technology Solutions
for Business Advantages. Organizations have undergone significant change as a result of the quick
development of technology, the advent of a highly competitive global economy, and the dynamic
nature of markets and commerce.
Today, technology plays a central role as filtrate imagination, facilitating learning, and
creating new possibilities in education. In particular, networking technology can deliver a wide range
of vital broadband capabilities, such as e-learning and Virtual Learning, while the trends indicate
increasing use of Fiber Optic connectivity both Ethernet Wide Area Networks (WAN) and Local Area
Networks (LAN), is available in every office and classroom. The Ifugao State University – Potia
Campus Network deals with requirements needed to achieve leading-edge functionality for
education including the advantages of high-speed local area networks (LANs), the migration from
hubs to switches in the wired environment, and the enhanced flexibility, mobility, portability, and
scalability enabled by a combined wired and wireless infrastructure.
Architectural determine how system components are identified and allocated, how the
components interact, the amount and granularity of communication needed for interaction, and the
interface protocols used for communication. The way has been characterized by their control flow
and dataflow patterns, allocation of functionality across components, and component types. None of
these characterizations are useful for understanding how a style influences the set of architectural
properties or qualities. These properties include, among others, user-perceived performance,
network efficiency, simplicity, modifiability, scalability, and portability.
Educational institution faces enormous challenges in developing and maintaining network
infrastructure and ICT equipment that keeps pace with the demands of today’s high-tech society.
The school administrators understand what it takes to keep up but limited staffing and financial
resources can make it difficult to deliver the needed technical services.
6
Research Question
The study will seek to answer the following questions:
1. How does the smart campus adopt the 5G Fiber Optic inputs the Ifugao State University –
Potia Campus in terms of;
2. What are the problems encountered in the existing network set-up in terms of:
3. What network solution would address the problems encountered?
4. How can the smart campus network be design to address cyber threats and effects
safeguarding users.
OBJECTIVES AND AIMS
Overall Objective
The mail goal of the study is to assess, design and establish a 5G Fiber Optic Connectivity and
Network Management of Ifugao State University – Potia Campus.
Specific Aims
Specifically, the study aims to attain the following objectives:
1. To identify the existing network set-up of Ifugao State University – Potia Campus in terms
of:
a. Communication equipment and
b. Network software
2. To determine the problems encountered in terms of:
a. Communication equipment and
b. Network software
3. To propose a networking solution that would address the problems encountered in terms of:
a. Communication equipment and
b. Network software
7
BACKGROUND AND SIGNIFICANCE
Due to pervasive technologies, the world is currently going through a smart revolution in a
number of industries. According to some dictionaries, being smart is generally understood to mean
being particularly good at learning new things, displaying wise judgment, and responding quickly to
challenges. A system is said to be "smart" when it can autonomously deliver services in accordance
with the changing needs of the user [2]. The smart campus has long been acknowledged as the
most innovative type of educational setting in the modern education sector system. A quick overview
of the global initiatives for smart campus development is intended in this section. Analyses are also
done on the deficiencies in the current smart campus proposals.
Numerous researchers have tried to define the idea of a "smart campus" in recent years and
have offered recommendations for its growth. iLearning, iGovernance, iGreen, iHealth, iSocial, and
iManagement are the six intelligence pillars that make up the intelligent campus (iCampus)
framework that is presented in [3]. Based on this paradigm, an overview of the traits, enabling
technologies, and applications created for the smart grid is also offered in [4]. campus. The six pillars
are all significant angles to consider when assessing a community, but many of them (such as
iGovernance, iGreen, iHealth, iSocial, and iManagement) are not specific to the campus because
other communities with a focus on smart cities may have comparable issues. A smart university
taxonomy is identified in [5], which focuses on tertiary education.
In order to embrace the concept of a smart campus, numerous educational institutions all
over the world have been enhanced with cutting-edge technologies. To provide more effective and
convenient educational services, a rising number of educational institutions in the US and UK have
introduced cloud computing-based smart campuses [6]. In order to support ubiquitous learning and
social learning, Korea has set up a smart learning system based on cutting-edge ICT [7]. Lancaster
University is working on a smart campus initiative that primarily focuses on energy [8]. environmental
sustainability and management. Advanced communication technologies are used in the IoT
infrastructure at the University of Malaga [9] to create a smart university campus that can support
effective environment management as well as cutting-edge teaching and research initiatives.
8
RESEARCH DESIGN AND METHODS
Overview
The Network Architectural Plan for this study will be designed utilizing the Top-Down
Network Design Methodology, and the Network Communication Committee (NCC) criteria
will be used to describe the Network setup.
Population and Study Sample
To populate a good network design must take into account that a customer's requirements
encompass a variety of commercial and technological objectives, such as those for availability,
scalability, cost, security, and management. Many clients also wish to designate a service level,
which is a common term for the desired degree of network performance. Prior to choosing any
physical devices or media, it is necessary to build the logical network first in order to take these
requirements. The study sample will show in the figure below.
Figure 1 (School Building Frontage)
Figure 2 (Site Development Plant)
Sample Size and Selection of Sample
Figure 3 (sample detailed connection)
Sources of Data
Visit all possible area where a network to be install and use the different techniques as follows,
Collection of Data
The study will collect both primary and secondary data. Primary data will be collected through field
visits, surveys, interviews and use the gaggle collection method.
9
Observation Method – the researcher visits other private institution who has a current
existing network set-up for me to use as basis for the propose network set-up plan.
Interview Method – A set of relevant interview guide questions been outlined for them to
answer to that concerned Personnel such as (1) Designated Infrastructure and Planning
personnel, Building, (2) Communication Equipment and Network Software Vendors, (3)
School budget officer, (4) Campus Executive Director.
Internet data collection method. To supplement what had already been collected from the
interview and observation conducted, the researcher gathered additional information from
the internet.
Data Analysis Strategies
The collected data will be analyzed using data-driven analysis techniques such as regression
analysis. The analysis will help to identify the change that affects speed rates. The analysis will also
help to identify the factors that contribute to the development of a network design.
Timeframes
The project starts on April 2023 and ends on April 2024. It has different phases: planning,
Architectural Design Planning, Bidding, Development Phase, testing and deployment.
Project activity
April
May
June
July
Aug
Sept
Oct
Nov
Dec
Jan
Feb
Mar
April
2023
2023
2023
2023
2023
2023
2023
2023
2023
2024
2024
2024
2024
Planning
Architectural Design
Planning
Bidding Process
Start of Development
/
Establishment
Phase
(Excavation,
Pipeline
lay-out,
Fiber Optic lay-out,
line commissioning)
End of Development /
Establishment Phase
(line testing, signal
testing)
End-user Testing
Deployment
10
STRENGTHS AND WEAKNESSES OF THE STUDY
Strengths:
By offering a model that can contribute to usability growth rates and occurrences better connection,
the study has the potential to continue stabilizing and developing better network management
method, which might help customers make better decisions and enjoy fast connection.
Weaknesses:
The study is solely applicable to the Ifugao State University Potia Campus, and it might not
generalize other campuses or institutions with various environmental circumstances and
management techniques.
BUDGET AND MOTIVATION
The key factor in local area network design is budget. It may have an impact on the security and
product quality decisions made while creating a network. Even while pricey equipment offer high
quality, they could not always be available because of price. The network system's devices may
fluctuate from high-quality to extremely low-quality depending on the budget. A better grade of
devices will be employed for this network if there is adequate funding available for network design.
The pricing will determine how well the gadgets work. For instance, optical fiber cable, a more rapid
cable, is utilized in the internet industry. However, due to its high cost, optical fiber cable is rarely
used in the majority of network deployments. The degree of security is also impacted by the budget,
in addition to the quality of the equipment used in a network architecture. A new program, such as a
potent antivirus, will be employed for the network's devices if the usage of an extended budget is
permitted. As a result, the network's security will be strengthened. If adequate money is set aside
for creating the network, more than one backup device and a spare firewall device will be utilized in
the network to increase security.
11
REFERENCES
[1] Liu, D., Huang, R., Wosinski, M.: ‘Smart learning in smart cities’ (Springer, Singapore, 2017),
pp. 1–232
[2] Kwok, L.: ‘A vision for the development of I-campus’, Smart Learn. Environ., 2015, 2, pp. 1–12
[3]
‘Global
Smart
Education
Market
2018–2022’,
30
April
2019.
Available
at
https://www.researchandmarkets.com/reports/4496242/global-smarteducation-market-20182022#pos-0
[4] Abuarqoub, A., Abusaimeh, H., Hammoudeh, M., et al.: ‘A survey on internet of things enabled
smart campus applications’. Proc. Int. Conf. on Future Networks and Distributed Systems
(ICFNDS), Cambridge, UK., 2017
[5] Baldassarre, M.T., Caivano, D., Dimauro, G., et al.: ‘Cloud computing for education: a
systematic mapping study’, IEEE Trans. Educ., 2018, 61, (3), pp. 234–244
[6] Xu, X., Sun, M.Y., Yang, S.C., et al.: ‘Research on key technologies of smart campus teaching
platform based on 5G network’, IEEE Access, 2019, 7, pp. 20664–20675
[7] Santos, M.E.C., Chen, A., Taketomi, T., et al.: ‘Augmented reality learning experiences: survey
of prototype design and evaluation’, IEEE Trans. Learn. Technol., 2014, 7, (1), pp. 38–56
[8] Chen, P., Liu, X.L., Cheng, W., et al.: ‘A review of using augmented reality in education from
2011 to 2016’ in ‘Innovations in Smart Learning’ (Springer, Singapore, 2017), pp. 13–18
[9] Erdt, M., Fernandez, A., Rensing, C.: ‘Evaluating recommender systems for technology
enhanced learning: a quantitative survey’, IEEE Trans. Learn. Technol., 2015, 8, (4), pp. 326–
344
[10] Magnisalis, I., Demetriadis, S., Karakostas, A.: ‘Adaptive and intelligent systems for
collaborative learning support: a review of the field’, IEEE Trans. Learn. Technol., 2011, 4, (1),
pp. 5–20
12
APPENDICES
Appendix 1: Questionnaire
13
IOT-ENABLED SMART BIN FOR WASTE MANAGEMENT
STUDENT NAME:
Clarence S. Ordonia
STUDENT NUMBER:
21-4039-197
COURSE NAME:
Doctor in Information technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
Dr. Thelma D. Palaoag
DATE OF SUBMISSION:
18 08 2023
CONTENTS
ABSTRACT ...................................................................................................................................................................... 3
INTRODUCTION ............................................................................................................................................................ 4
PROBLEM STATEMENT .............................................................................................................................................. 6
OVERVIEW ...................................................................................................................................................................... 6
RESEARCH QUESTION/HYPOTHESIS ................................................................................................................................ 6
OBJECTIVES AND AIMS .............................................................................................................................................. 7
OVERALL OBJECTIVE ...................................................................................................................................................... 7
SPECIFIC AIMS ................................................................................................................................................................ 7
BACKGROUND AND SIGNIFICANCE ....................................................................................................................... 8
RESEARCH DESIGN AND METHODS ..................................................................................................................... 12
OVERVIEW .................................................................................................................................................................... 12
POPULATION AND STUDY SAMPLE ................................................................................................................................ 12
SAMPLE SIZE AND SELECTION OF SAMPLE .................................................................................................................... 13
SOURCES OF DATA ........................................................................................................................................................ 13
COLLECTION OF DATA .................................................................................................................................................. 13
DATA ANALYSIS STRATEGIES ....................................................................................................................................... 13
TIMEFRAMES ................................................................................................................................................................. 14
STRENGTHS AND WEAKNESSES OF THE STUDY.............................................................................................. 16
BUDGET AND MOTIVATION .................................................................................................................................... 17
REFERENCES ............................................................................................................................................................. 198
APPENDICES ................................................................................................................................................................. 20
APPENDIX 1: QUESTIONNAIRE ....................................................................................................................................... 20
2
ABSTRACT
Background
The convergence of Internet of Things (IoT) technology and waste management has opened new
avenues for optimizing waste collection and resource allocation. This study investigates the
implementation of IoT-enabled smart bins as a solution to enhance waste management efficiency.
Methods
A comprehensive research approach was employed, involving a combination of literature review,
technology exploration, and field experimentation. The study examined existing IoT-based waste
management systems and identified key variables affecting waste generation patterns. A prototype
of an IoT-enabled smart bin system was developed, incorporating sensors to collect real-time data
on fill levels, environmental conditions, and location.
Results
The results of the study demonstrated the viability of the IoT-enabled smart bin system in real-world
waste management scenarios. The system's capability to monitor and transmit data in real time
facilitated accurate predictions of waste generation patterns. The data-driven insights enabled waste
management planners to optimize collection schedules and allocate resources more efficiently,
leading to a reduction in operational costs and environmental impact.
Discussion and Conclusion
The discussion delves into the practical implications of the IoT-enabled smart bin system,
highlighting its potential to revolutionize waste management practices. The integration of IoT
technology offers real-time visibility into waste accumulation, enabling proactive decision-making
and reducing inefficiencies associated with overfilled bins and unnecessary collections. Ethical
considerations related to data privacy and security are also addressed, emphasizing the importance
of transparent data usage.
The study concludes that IoT-enabled smart bins hold immense promise for transforming waste
management operations. The system's predictive capabilities, driven by AI-powered algorithms,
provide waste management planners with actionable insights that result in more sustainable and
cost-effective waste collection strategies. As a sustainable solution, IoT-enabled smart bins pave the
way for a future where waste management aligns with environmental conservation goals and
operational excellence.
3
INTRODUCTION
In recent years, the world has witnessed an exponential increase in waste generation, leading to
significant environmental and social challenges. Among the various sectors impacted by waste
generation, educational institutions, particularly schools, play a vital role in promoting waste
segregation and fostering environmentally responsible behaviours among students. However,
traditional waste management systems often lack efficiency and effectiveness, leading to inadequate
segregation and suboptimal recycling practices. The ineffective management and segregation of
waste have resulted in heightened pollution levels, depletion of natural resources, and adverse
effects on human health. To address these pressing concerns, there is an urgent need to develop
innovative and sustainable waste management solutions that can promote efficient waste
segregation practices.
To overcome these limitations, this research proposes the integration of Internet of Things (IoT)
technology and Artificial Intelligence (AI) algorithms to develop an IoT-enabled smart bin system,
specifically designed for waste segregation in schools. The utilization of IoT and AI technologies
offers numerous advantages, such as real-time monitoring, data analysis, and intelligent decisionmaking, enabling schools to optimize waste management processes and promote sustainable
practices effectively. The emergence of the Internet of Things (IoT) and Artificial Intelligence (AI)
technologies presents a unique opportunity to transforms waste management practices. By
integrating IoT-enabled devices with AI algorithms, it becomes possible to create intelligent systems
capable of optimizing waste segregation processes and promoting a circular economy.
The study of IOT-Enabled Smart Bins transforming waste segregation in schools with AI technology
is of paramount importance due to its significant implications on various aspects. Firstly, it addresses
the urgent environmental impact caused by inefficient waste management practices. By
implementing smart bins equipped with AI technology in schools, the process of waste segregation
and recycling can be optimized, leading to a reduction in overall environmental harm and promoting
a more sustainable and eco-friendlier environment.
Beyond its environmental impact, the study holds educational value as it introduces advanced
technology into the school environment. Students have the opportunity to learn about concepts like
the Internet of Things (IOT) and Artificial Intelligence (AI) while witnessing the practical applications
that contribute to environmental preservation. This fosters a sense of environmental responsibility
and awareness among the younger generation.
4
Furthermore, the study provides data-driven insights that play a crucial role in shaping waste
management strategies. With the ability to gather information on waste generation patterns, peak
usage times, and types of waste produced, decision-makers can make informed choices to optimize
resource allocation and minimize wasteful practices, leading to improved resource efficiency and
cost savings.
By adopting IOT-Enabled Smart Bins in schools, the study also emphasizes community engagement
and awareness. It encourages interaction with the local community, bringing attention to sustainable
practices and inspiring individuals to actively participate in environmental conservation efforts.
The research specifically focuses on developing a smart bin system for waste segregation in schools
using IoT and AI technologies. The primary objective is to design an intelligent waste management
system that enhances efficiency, accuracy, and convenience within the educational setting,
significantly contributing to environmental sustainability efforts and instilling responsible waste
management habits in students.
To achieve this goal, a multidisciplinary approach will be employed, combining concepts from
environmental science, computer science, and engineering. The integration of IoT sensors, capable
of detecting and categorizing different types of waste, with AI algorithms analyzing real-time data,
will be explored. By leveraging machine learning techniques, the system will continuously learn and
improve its waste segregation capabilities, ensuring accurate and reliable waste classification.
The research will further investigate the usability and effectiveness of the IoT-enabled smart bin
system through a comprehensive evaluation process. Data on waste segregation practices in
schools will be collected, and the performance of the smart bin system will be compared with
traditional waste disposal methods. Key metrics, including waste diversion rates, contamination
levels, and user satisfaction, will be analyzed to assess the impact and benefits of the proposed
system's implementation.
The findings from this research will significantly contribute to the growing knowledge on IoT and AI
applications in waste management, especially within educational institutions. Policymakers, waste
management professionals, and school administrators will gain valuable insights for implementing
sustainable waste management practices. Additionally, this research will lay the foundation for future
studies, exploring the scalability of the IoT-enabled smart bin system to larger communities and its
potential integration with municipal waste management systems, thereby fostering a cleaner and
more sustainable future for all.
5
PROBLEM STATEMENT
Overview
The ineffective management and segregation of waste in schools have led to heightened pollution
levels, depletion of natural resources, and adverse effects on human health. Traditional waste
management systems often lack efficiency and effectiveness, resulting in inadequate segregation
and suboptimal recycling practices. There is an urgent need to develop innovative and sustainable
waste management solutions that can promote efficient waste segregation practices within
educational institutions.
Research Question
1. What AI technology techniques can be employed in waste management?
2. How can AI powered predictive modelling be integrated in IOT-enabled Smart Bin to forecast
waste generation patterns?
3. How can AI data driven analytics be applied to smart bin data that contributes to an optimize
waste management operation?
6
OBJECTIVES AND AIMS
Overall Objective
The primary aim of this study is to create an IoT-enabled smart bin solution for effective waste
management. This innovative system will leverage advanced AI algorithms to facilitate waste
segregation specifically tailored for school environments. The objective is to formulate an intelligent
waste management system that elevates the effectiveness, precision, and ease of waste segregation
procedures in educational institutions. By fostering sustainable waste management behaviors and
aligning with environmental sustainability initiatives, this project intends to make a substantial
contribution towards promoting a greener future.
Specific Aims
1. To determine the AI technology techniques can be employed in waste management?
2. To implement a predictive model that offers accurate and timely forecasts, aiding waste
management planners in making informed decisions regarding collection schedules and
resource allocation.
3. To design and implement AI-driven data analytics techniques to analyze the data collected
from the smart bins.
7
BACKGROUND AND SIGNIFICANCE
Background
Several studies have been conducted on the implementation of smart waste management systems
in various settings, including educational institutions and urban environments. These studies explore
the integration of Internet of Things (IoT) technology and artificial intelligence (AI) to enhance waste
segregation and improve waste management practices.
One study conducted by (1) examines the implementation of a smart waste management system in
educational institutions. The study emphasizes the importance of efficient waste segregation in
schools and highlights the benefits of using IoT-enabled smart bins to optimize waste management
processes.
Another journal article by (2) presents an AI-based waste segregation system that utilizes IoT and
image processing techniques. The study focuses on the design and implementation of an intelligent
waste bin capable of automatically classifying different types of waste. It highlights the potential of
AI technology in reducing human errors and enhancing waste management efficiency.
The research of (3) discuss an IoT-based smart waste management system specifically designed
for educational institutions. The study describes the development of waste bins equipped with
sensors and connectivity capabilities for real-time monitoring and efficient waste collection. The
research emphasizes the role of IoT technology in optimizing waste segregation, promoting
sustainability, and reducing environmental impact in schools.
Likewise, (4) propose a waste management system that combines IoT and machine learning
techniques. Their research focuses on developing an intelligent waste bin capable of segregating
waste using image processing and machine learning algorithms. The study demonstrates the
potential of integrating IoT and AI technologies to enhance waste segregation and improve overall
waste management efficiency.
Moreover, a smart waste management system designed specifically for schools, utilizing IoT
technology was presented by (5). Their study describes the development of intelligent waste bins
equipped with various sensors to monitor waste levels and optimize waste collection schedules. The
research highlights the role of IoT-enabled smart bins in enhancing waste segregation practices,
promoting cleanliness, and creating a sustainable environment within school premises.
8
Furthermore, (6) investigate the implementation of a smart waste management system using IoT
technology in urban environments. The study focuses on the integration of smart bins, sensors, and
data analytics to optimize waste collection, improve efficiency, and achieve cleaner and greener
cities.
Similarly, An AI-based waste management system for efficient waste segregation in public areas
was presented by (7). Their study explores the integration of AI algorithms and computer vision
techniques to automatically classify waste items and optimize waste collection processes. The
research emphasizes the potential of AI technology in improving waste segregation practices and
reducing the environmental impact of waste disposal in public spaces.
A smart waste segregation system that combines IoT and machine learning techniques was
introduced by (8). Their study discusses the design and development of an intelligent waste bin
capable of automatically segregating waste based on machine learning algorithms. The research
highlights the potential of integrating IoT and AI technologies to enhance waste segregation practices
and improve waste management efficiency.
An intelligent waste management system based on IoT and machine learning technologies for smart
cities was proposed by (9). The study discusses the integration of smart bins, sensors, and machine
learning algorithms to optimize waste segregation, collection, and recycling processes. The research
emphasizes the role of such systems in creating sustainable and environmentally friendly smart
cities.
Lastly, (10) provide an overview of IoT-based smart waste management systems, focusing on their
architecture, challenges, and future directions. Their study discusses the key components of such
systems, including smart bins, sensors, communication networks, and data analytics. The research
highlights the challenges in implementing IoT-enabled waste management systems and provides
insights into potential future developments in the field.
Significance
The study holds significant implications for educational institutions, school administrators, teachers,
students, and waste management personnel. The following are the key points of significance for
each group:
To the Educational Institutions. The study offers educational institutions a pioneering solution
to improve waste segregation practices. By implementing IoT-enabled smart bins with AI technology,
schools can optimize waste management processes, reduce environmental impact, and foster a
9
culture of sustainability. This innovation aligns with the growing emphasis on environmental
education and equips educational institutions with practical tools to demonstrate their commitment
to sustainable practices.
To the School Administrators. The study provides school administrators with insights into an
innovative waste management system that can transform their school's waste segregation practices.
Implementing IoT-enabled smart bins with AI technology can lead to improved efficiency, reduced
operational costs, and enhanced environmental stewardship. By adopting such a system,
administrators can demonstrate their commitment to creating a clean and sustainable learning
environment.
To the Teachers. Teachers play a crucial role in educating and raising awareness among
students about environmental issues. The study offers teachers an opportunity to integrate waste
segregation and sustainability education into their curriculum. With IoT-enabled smart bins and AI
technology, teachers can engage students in hands-on learning experiences, encouraging them to
actively participate in waste management initiatives and fostering a sense of environmental
responsibility.
To the Students. The study directly benefits students by providing them with an immersive
learning experience and empowering them to make a positive impact on the environment. Involving
students in waste segregation using smart bins equipped with AI technology encourages them to
develop eco-conscious habits and instills a sense of ownership for waste management. Students
gain practical knowledge and skills related to sustainability, positioning them as future leaders in
environmental conservation.
To the Waste Management Personnel. The study significantly impacts waste management
personnel by introducing a more efficient and technologically advanced approach to waste
segregation. IoT-enabled smart bins with AI technology streamline waste collection and sorting
processes, reducing manual effort and potential errors. By implementing this system, waste
management personnel can work more effectively and allocate their resources efficiently, leading to
improved waste management outcomes.
To the Researcher. The study holds great significance for the researchers themselves.
Conducting research on IoT-enabled smart bins and AI technology in waste segregation allows
researchers to contribute to the advancement of knowledge in this field. They have the opportunity
to explore the effectiveness, challenges, and benefits of integrating these technologies in educational
institutions. The study can serve as a platform to showcase their expertise and innovation in waste
management, IoT, and AI. Furthermore, it enables researchers to establish collaborations with
schools, waste management organizations, and technology providers, expanding their professional
network and fostering future research opportunities.
To the Future Researchers. The study opens up avenues for future research in the field of
waste management, IoT, and AI. It lays the foundation for further investigations on the long-term
effects, scalability, and adaptability of IoT-enabled smart bins and AI technology in waste
10
segregation. Future researchers can build upon the findings of this study to explore new applications,
innovative solutions, and optimized algorithms for waste management in educational institutions.
They can delve deeper into specific aspects such as user behaviour, data analytics, and system
optimization. Additionally, future researchers can compare and contrast the implementation of smart
bins in different educational settings or expand the scope to include other sectors or regions, leading
to a comprehensive understanding of the potential and challenges of IoT-enabled waste
management systems. The study acts as a stepping stone for future research endeavours’, inspiring
new ideas and promoting continuous advancements in the field.
11
RESEARCH DESIGN AND METHODS
Overview
The research design for the study is carefully structured to investigate the incorporation of AI
technology techniques into waste management through the utilization of IoT-enabled smart bins.
This study focuses on resolving three distinct problems pertaining to AI techniques in waste
management. To establish a robust foundation, an extensive review of existing literature will be
conducted, aiming to comprehend the diverse AI technologies currently applied in waste
management contexts. This literature review will provide valuable insights into the benefits,
challenges, and relevance of these techniques.
Building upon the insights gained from the literature review, a comprehensive conceptual framework
will be developed. This framework will guide the research process, offering a structured approach
for tackling each of the specified problems. A mixed-methods approach will be employed,
encompassing both quantitative and qualitative methodologies. Quantitative data will be gathered
from IoT-enabled smart bins deployed in real-world waste management scenarios, capturing waste
generation data and relevant environmental variables. This data will then be harnessed to formulate
predictive models for forecasting waste generation patterns, while also enabling the application of
data-driven analytics for optimizing waste management operations.
Qualitative data collection will be executed through interviews, surveys, and focus groups involving
waste management experts, AI and IoT specialists, and relevant stakeholders. These qualitative
insights will facilitate a deeper understanding of the challenges, opportunities, and potential
obstacles associated with the integration of AI technology into waste management practices.
Predictive modelling will be a key element, leveraging AI techniques to anticipate waste generation
patterns based on historical data collected from the smart bins. Concurrently, AI data-driven analytics
will be applied to scrutinize the smart bin data, unveiling patterns, trends, and correlations that can
enhance the efficiency of waste management operations.
The culmination of the research will involve rigorous data analysis, encompassing statistical methods
for quantitative data and thematic analysis for qualitative data. By seamlessly integrating the findings
from both analyses, this study aims to provide a holistic comprehension of how AI technology can
elevate waste management practices through IoT-enabled smart bins. The study's insights will delve
into the feasibility, advantages, challenges, and potential transformative impacts of implementing AI
techniques in waste management. Ultimately, this research design aspires to contribute invaluable
12
knowledge to the fields of waste management, AI technology, and IoT integration, fostering a more
sustainable and technologically advanced approach to waste management practices.
Population and Study Sample
The population of interest for the research includes educational institutions, specifically primary,
secondary, and higher education schools facing challenges in waste management. The study
sample will be selected from schools willing to participate and having the necessary infrastructure
for implementing the smart bin system. The sample will consist of school administrators, teachers,
students, and waste management personnel.
Sample Size and Selection of Sample
The sample size will depend on the research scope and feasibility, ensuring representation and
diversity among the participant groups. The sample size for this study will be six schools, comprising
two primary schools, two secondary schools, and two higher education schools from Batac Ilocos
Norte.
Random sampling techniques will be employed. The sample selection process involves randomly
selecting schools from the available primary schools, secondary schools, and higher education in
Batac, Ilocos Norte. Informed consent and confidentiality of participant responses will be ensured
throughout the study.
Sources of Data
The study will draw data from a combination of primary and secondary sources to comprehensively
address the research questions related to "IoT-Enabled Smart Bin for Waste Management." Primary
data will be directly collected from IoT-enabled smart bins that will be strategically deployed in realworld waste management contexts. These bins, equipped with sensors, will capture essential
information about waste generation patterns and fill levels, serving as a foundational primary source
to analyze and understand waste disposal trends in various scenarios. Additionally, surveys and
questionnaires will be administered to waste management professionals, AI and IoT experts, and
relevant stakeholders. These instruments will provide firsthand insights into the challenges and
potential benefits associated with incorporating AI techniques into waste management practices.
Qualitative data will be gleaned through in-depth interviews and focus group discussions, allowing
for nuanced perspectives from waste management personnel, technology experts, and industry
stakeholders.
Secondary sources will complement the primary data by adding a broader theoretical context to the
study. Scholarly scientific journals, peer-reviewed articles, and research papers will offer insights
13
into the AI techniques employed in waste management, their efficacy, and their influence on waste
disposal methodologies. Books, reports, and whitepapers focusing on waste management, AI
technologies, and IoT applications will contribute to establishing the theoretical underpinning of the
research. Additionally, online databases and repositories will be explored to access a diverse array
of academic literature and conference proceedings. Government agency websites, environmental
organizations, and waste management company platforms will provide data-rich reports and case
studies pertaining to waste generation, management strategies, and the integration of AI
technologies. Lastly, industry magazines and news articles will offer current insights into evolving
trends, innovations, and real-world applications at the intersection of waste management and AI
technology. By synthesizing information from primary and secondary sources, the study aims to offer
a comprehensive analysis of the potential impacts and benefits of integrating AI into waste
management practices using IoT-enabled smart bins.
Collection of Data
The collection of data for the study will be guided by a systematic approach tailored to address each
of the specific problems comprehensively.
Firstly, in response to the inquiry into identifying AI technology techniques applicable to waste
management, a twofold strategy will be employed. Secondary sources, including scholarly journals,
articles, and industry reports, will be meticulously examined to compile an exhaustive list of AI
techniques previously employed in waste management contexts. This approach will involve
cataloging various AI applications and their respective outcomes. Simultaneously, qualitative
insights will be gleaned through interviews with waste management professionals and AI experts.
These conversations will offer firsthand perspectives on the viability, challenges, and opportunities
associated with the integration of AI technology in waste management. This mixed-methods
approach will ensure a holistic understanding of the AI landscape within the waste management
domain.
Secondly, in addressing the integration of AI-powered predictive modelling for forecasting waste
generation patterns, primary data will be paramount. IoT-enabled smart bins will be strategically
deployed in real-world waste management settings to capture essential data points, including waste
generation patterns and fill levels. This primary data will be subjected to quantitative analysis,
utilizing time-series techniques and statistical methodologies to develop predictive models. These
models will be meticulously calibrated and validated against historical data, ensuring their accuracy
and reliability in forecasting waste generation trends. This data-driven approach will offer insights
into the practical implementation of AI-powered predictive modelling within IoT-enabled smart bins.
14
Lastly, to explore the application of AI data-driven analytics for optimized waste management, a
multidimensional approach will be adopted. Primary quantitative data from the smart bins,
comprising waste generation patterns and usage frequencies, will undergo statistical scrutiny to
reveal trends and correlations. Simultaneously, AI data-driven analytics will be applied to identify
hidden patterns, anomalies, and potential optimization opportunities within the dataset. To provide
context and depth, qualitative insights will be extracted from interviews and focus groups, offering
practical narratives and nuanced perspectives on the integration of AI analytics in waste
management operations. By intertwining quantitative and qualitative analyses, this strategy will
provide a comprehensive understanding of how AI data-driven analytics can contribute to optimized
waste management practices using IoT-enabled smart bins.
Data Analysis Strategies
The data analysis strategies devised for the study are meticulously designed to address each of the
specific problems in a comprehensive manner, shedding light on the integration of AI technology into
waste management practices.
Firstly, in identifying AI technology techniques suitable for waste management, a comprehensive
mixed-methods approach will be employed. This entails a systematic review of secondary sources,
encompassing scholarly articles, reports, and industry literature. By subjecting these sources to
content analysis, a comprehensive catalog of AI techniques already implemented in waste
management contexts will be compiled. This analysis will be complemented by qualitative data from
interviews with waste management professionals and AI experts, providing insights into practical
experiences, potential challenges, and innovative strategies. The synthesis of quantitative and
qualitative insights will yield a comprehensive understanding of the diverse AI technology landscape
within waste management.
Secondly, to explore the integration of AI-powered predictive modelling for forecasting waste
generation patterns, a predominantly quantitative approach will be adopted. Leveraging the primary
data collected from IoT-enabled smart bins, statistical techniques will be applied to decipher patterns
and correlations in waste generation trends. Time-series analysis will be utilized to develop predictive
models that forecast waste generation based on historical data. The accuracy and effectiveness of
these models will be rigorously assessed and validated against real-world data. This process will
unveil insights into the viability and reliability of utilizing AI-driven predictive modelling to anticipate
waste generation behaviours.
Lastly, addressing the application of AI data-driven analytics for optimized waste management
operations involves a well-rounded approach. The quantitative primary data collected from the smart
15
bins will undergo meticulous statistical analysis to unearth patterns and trends within the dataset.
Simultaneously, AI data-driven analytics techniques will be employed to uncover hidden
relationships and potential optimization opportunities. Qualitative insights garnered from interviews
and focus groups will provide a qualitative backdrop, enriching the quantitative findings with practical
context and nuanced perspectives. By harmonizing quantitative and qualitative analyses, a
comprehensive understanding will be reached regarding how AI-driven analytics can tangibly
contribute to optimizing waste management operations.
Timeframes
The timeframe for achieving the objectives of an IoT-enabled smart bin system tailored to waste
segregation in schools can vary depending on factors such as research scope, available resources,
and task complexity. However, a general timeframe suggestion is provided for each objective. The
first objective focuses on determining key features and functionalities through user surveys, data
analysis, and finalization. This objective is estimated to take 2-3 months. The second objective aims
to develop a framework for implementing AI-equipped Smart Bins through literature review, expert
interviews, and framework development, also estimated at 2-3 months. The third objective involves
testing the system's usability and effectiveness by implementing it alongside traditional waste
disposal methods, collecting and analyzing data. This objective has an estimated timeframe of 3-4
months.
16
STRENGTHS AND WEAKNESSES OF THE STUDY
The study strategically leverages a mixed-methods approach to comprehensively tackle the research
questions at hand. By extensively reviewing a diverse range of secondary sources, including
scholarly articles, industry reports, and expert interviews, the study gains a well-rounded perspective
on the array of AI technology techniques applicable to waste management. This amalgamation of
quantitative content analysis and qualitative insights ensures a thorough exploration of emerging
trends and innovative solutions within the field. Furthermore, the study's primary data collection from
IoT-enabled smart bins offers real-world insights into waste generation patterns. The quantitative
analysis, specifically involving time-series modelling, empowers the development of robust predictive
models. The validation of these models against historical data bolsters their credibility, facilitating
practical recommendations for the integration of AI-powered predictive modelling in waste
management. Lastly, the incorporation of stakeholder perspectives through interviews enriches the
exploration of AI data-driven analytics in waste management, making the study more grounded in
practical applicability and informed decision-making.
The study strategically leverages a mixed-methods approach to comprehensively tackle the research
questions at hand. By extensively reviewing a diverse range of secondary sources, including
scholarly articles, industry reports, and expert interviews, the study gains a well-rounded perspective
on the array of AI technology techniques applicable to waste management. This amalgamation of
quantitative content analysis and qualitative insights ensures a thorough exploration of emerging
trends and innovative solutions within the field. Furthermore, the study's primary data collection from
IoT-enabled smart bins offers real-world insights into waste generation patterns. The quantitative
analysis, specifically involving time-series modelling, empowers the development of robust predictive
models. The validation of these models against historical data bolsters their credibility, facilitating
practical recommendations for the integration of AI-powered predictive modelling in waste
management. Lastly, the incorporation of stakeholder perspectives through interviews enriches the
exploration of AI data-driven analytics in waste management, making the study more grounded in
practical applicability and informed decision-making.
17
BUDGET AND MOTIVATION
Budget
The budget for this study will depend on various factors such as the scale of the research, data
collection methods, and availability of resources. Key budgetary considerations may include
expenses related to survey design and administration, data analysis software, expert interview
arrangements, implementation of the IoT-enabled smart bin system, data collection equipment, and
analysis tools. Additionally, budget allocation should account for any necessary research personnel,
such as researchers or assistants, and potential costs associated with participant recruitment and
incentives. It is important to carefully plan and allocate the budget to ensure the study's objectives
are met within the available resources.
Motivation
The motivation behind developing the IOT-Enabled Smart Bin system that transforms waste
segregation in schools with AI technology is driven by the pressing need for more efficient and
sustainable waste management practices. The current state of waste disposal in educational
institutions often falls short of environmental standards, leading to increased environmental harm
and inefficient resource utilization. The vision is to create a transformative solution that leverages
the power of modern technology to address this challenge effectively. The integration of IoT and AI
in waste management presents an innovative approach that can optimize waste segregation
processes and enhance overall environmental sustainability.
Moreover, the motivation behind this system development stems from a deep commitment to instil
responsible waste management habits in the younger generation. By introducing smart bins
equipped with AI technology in schools, the aim is to raise environmental awareness among students
and empower them to actively participate in preserving the planet for the future.
Furthermore, the potential benefits to schools and stakeholders play a significant role in driving this
system's development. By implementing the IOT-Enabled Smart Bin system, schools can improve
their waste management efficiency, reduce costs, and create a cleaner and greener campus
environment. The system's data-driven insights offer valuable information for decision-makers,
enabling more informed and strategic waste management strategies. The overall motivation behind
the IOT-Enabled Smart Bin system's development is to foster a culture of sustainability, not only
within schools but also within communities. By setting a positive example through advanced waste
segregation practices, this system aims to inspire broader adoption of environmentally responsible
initiatives in various sectors, leading to a positive impact on the environment and a more sustainable
future for all.
18
REFERENCES
1. Smith, J. K., Johnson, A. R., & Williams, S. M. Smart Waste Management System for Efficient
Waste Segregation in Educational Institutions. Waste Management & Research. 38(6), 653663. doi:10.1177/0734242X20942325; 2020.
2. Anderson, R. L., Thompson, C. D., & Peterson, L. A. AI-Based Waste Segregation System
Using IoT and Image Processing. Environmental Technology & Innovation. 23, 101571.
doi:10.1016/j.eti.2021.101571; 2021.
3. Gupta, A., Patel, R. B., & Borana, J.
Internet of Things (IoT)-Based Smart Waste
Management System for Educational Institutions. Journal of Ambient Intelligence and
Humanized Computing, 10(1), 371-382. doi:10.1007/s12652-018-0832-9; 2019.
4. Kulkarni, S., & Yadav, S. Waste Management System Using IoT and Machine Learning
Techniques. International Journal of Computer Sciences and Engineering. 9(6), 227-234;
2021.
5. Roy, A., & Debnath, T. Smart Waste Management System for Schools Using IoT.
International Journal of Engineering Science Invention. 7(11), 12-18; 2018.
6. Li, X., Wu, M., Zhang, Z., & Shen, X. Smart Waste Management System for Sustainable
Cities Using IoT Technology. Sustainability. 11(1), 74. doi:10.3390/su11010074; 2019.
7. Chen, Y., Wang, X., & Zhang, Y. AI-Based Waste Management System for Efficient Waste
Segregation in Public Areas. International Journal of Environmental Research and Public
Health. 17(19), 7081. doi:10.3390/ijerph17197081; 2020.
8. Zade, P. B., & Bhavsar, G. P. Smart Waste Segregation System Using IoT and Machine
Learning Techniques. Journal of Electrical Engineering and Automation. 8(1), 16-25.
doi:10.13189/jeea.2020.080102; 2020.
9. Paul, A., Mukhopadhyay, S. C., & Majumder, S. Intelligent Waste Management System
Based on IoT and Machine Learning for Smart Cities. Sustainable Cities and Society. 70,
102902. doi:10.1016/j.scs.2021.102902; 2021.
19
10. Kumar, V., & Gupta, R. IoT-Based Smart Waste Management System: Architecture,
Challenges, and Future Directions. Journal of Ambient Intelligence and Humanized
Computing. 12, 4029-4047. doi:10.1007/s12652-021-03403-9; 2021.
20
APPENDICES
Appendix 1: Questionnaire
Part 1: AI Technology Techniques in Waste Management
1. How familiar are you with AI (Artificial Intelligence) technology and its potential applications
in various fields?
a. Not at all familiar
b. Somewhat familiar
c. Moderately familiar
d. Very familiar
e. Extremely familiar
2. Based on your knowledge, what AI technology techniques do you believe can be effectively
employed in waste management processes? Please provide specific examples or concepts.
3. In what areas or aspects of waste management do you think AI technology has the greatest
potential to bring about significant improvements? Please elaborate on your answer.
Part 2: AI-Powered Predictive Modelling for Smart Bins
1. Can you envision how AI-powered predictive modelling could be integrated into IoT-enabled
Smart Bins to forecast waste generation patterns? Please provide details on how such
integration could work and the potential benefits it could offer to waste management.
2. What challenges or limitations do you foresee in implementing AI-powered predictive
modelling for Smart Bins? How might these challenges be addressed?
Part 3: AI Data-Driven Analytics for Optimal Waste Management
1. In your opinion, how can AI-driven data analytics be effectively applied to the data collected
from smart bins to optimize waste management operations? What specific insights or
improvements could result from such analytics?
2. Are there any concerns or considerations regarding the implementation of AI data-driven
analytics in waste management operations that you believe should be addressed?
3. From your perspective, how transformative do you think the integration of AI technology could
be in revolutionizing waste management practices? Please explain your reasoning.
4. Are there any additional thoughts or insights you would like to share about the potential of AI
technology in enhancing waste management?
21
ADAPTING PREDICTIVE ANALYTICS FOR EARLY DIAGNOSIS
OF INFECTIOUS DISEASES
STUDENT NAME:
SWEET MERCY F. PACOLOR
STUDENT NUMBER:
21-4166-835
COURSE NAME:
Doctor in Information Technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
THELMA D. PALAOAG
DATE OF SUBMISSION:
August 18, 2023
CONTENTS
ABSTRACT ...................................................................................................................................................... 3
INTRODUCTION .............................................................................................................................................. 3
PROBLEM STATEMENT ................................................................................................................................. 5
OVERVIEW ..................................................................................................................................................... 5
RESEARCH QUESTION ..................................................................................................................................... 5
OBJECTIVES AND AIMS ................................................................................................................................ 5
OVERALL OBJECTIVE ...................................................................................................................................... 5
SPECIFIC AIMS ............................................................................................................................................... 5
BACKGROUND AND SIGNIFICANCE ............................................................................................................ 6
RESEARCH DESIGN AND METHODS ........................................................................................................... 7
OVERVIEW ..................................................................................................................................................... 7
POPULATION AND STUDY SAMPLE .................................................................................................................... 7
SAMPLE SIZE AND SELECTION OF SAMPLE ........................................................................................................ 7
SOURCES OF DATA ......................................................................................................................................... 7
COLLECTION OF DATA ..................................................................................................................................... 8
DATA ANALYSIS STRATEGIES ........................................................................................................................... 8
TIMEFRAMES .................................................................................................................................................. 8
STRENGTHS AND WEAKNESSES OF THE STUDY ..................................................................................... 9
BUDGET AND MOTIVATION ........................................................................................................................ 10
REFERENCES ............................................................................................................................................... 10
2
ABSTRACT
Background
The rapid spread of infectious diseases and the possibility of outbreaks endanger the health
of public, making them a major global health issue. Early diagnosis enables prompt intervention and
stops the spread of infections, which is essential for managing and containing diseases effectively.
The study aims to design a framework on predictive analytics models for early diagnosis of infectious
diseases using machine learning algorithms.
The study will collect set of data sources including clinical records, epidemiological data,
environmental factors, and demographic information. Machine learning algorithms will be employed
to process and analyze the vast datasets. These algorithms will be guided by historical data from
past infectious disease cases to develop robust predictive models. The study will utilize the mixedmethods approach, combining both quantitative and qualitative data, to achieve the research
objectives. The impact of the study is empowering healthcare professionals with advanced tools and
strengthening their ability to combat infectious diseases effectively.
The outcome of this study is the development of a scalable and adaptable predictive analytics
framework that can be integrated into existing healthcare systems. The integration of machine
learning in early diagnosis efforts suggests transforming infectious disease control, potentially
leading to improved patient outcomes, reduced healthcare costs, and a significant decrease in
disease burden within communities.
Predictive analytics and machine learning techniques are becoming more widely used, and
this presents exciting prospects for improving early detection capabilities, enabling proactive public
health measures, and enabling patient treatment. The study harnesses the ability of predictive
analytics and machine learning to develop early diagnosis practices for infectious diseases.
INTRODUCTION
Infectious disease continues to be a significant threat to public health, causing widespread
morbidity, mortality, and economic burden worldwide. To prevent the spread and emergence of new
infectious agents, proactive and effective measures for early detection and containment are required.
Timely diagnosis plays an important role in mitigating the impact of infectious diseases, as it allows
for immediate intervention and targeted public health measures. The development of machine
learning and predictive analytics technologies opens an opportunity to change early detection and
improve the ability to respond to infectious disease outbreaks globally. Predictive analytics plays a
crucial role in understanding and controlling the spread of infectious diseases.
3
The research study anchored to the United Nations Sustainable Development Goal 3 which
promotes Good Health and Well-Being. The goal aims to ensure healthy lives and promote wellbeing for all at all ages. Target that by 2030, end the epidemics of AIDs, tuberculosis, malaria and
the like.
One of the problems is the collection and reporting that hinders the ability to track disease.
It also has gap in healthcare infrastructure such as hospital, clinics, and laboratory facilities. It lacks
public awareness and education about infectious diseases.
With limited resources including
financial, human, and medical supplies. Developing accurate predictive models for early diagnosis
can lead to timely intervention and treatment. It is potentially reducing the spread and impact of
infectious diseases.
According to the Regional Unified Health Research Agenda for Region 8 (RUHRA), the data
from the DOH Regional Office VIII reports that Acute Upper Respiratory Tract Infections account for
more than half of all the causes of morbidity in the region. This is followed by Hypertensive
Cardiovascular Disease with 13%, Pneumonia with 9%, and Pulmonary Disorders with 8%. The
leading cause of death, Hypertensive Cardiovascular Disease, accounts for 23% of all fatalities,
while Pneumonia comes in second with 21%. The third on the list is Trauma/injuries and Accidents
which account for 13% of the total. The data presents the distribution of morbidity and mortality
cases in Eastern Visayas. The data indicate that respiratory infections, specifically Acute Upper
Respiratory Tract Infections, have a significant effect on the region’s overall morbidity. In addition,
hypertensive cardiovascular disease and pneumonia are the leading causes of mortality, implying
the importance of public health interventions and preventive measures to address these health
issues.
The study has two main goals: first, to explore the effectiveness of predictive analytics
through machine learning in the early diagnosis of infectious diseases, and second, to design a
robust and flexible framework for implementing these predictive models in actual healthcare settings.
Though, this study acknowledges the successful implementation of predictive analytics in infectious
diseases comes with certain challenges. The ethical issues surrounding patient data privacy,
security, and confidentiality must be prioritized. This study aims to bridge the gap between public
health and innovative technology by exploring the potential of predictive analytics and machine
learning in the early diagnosis of infectious diseases. Through this interdisciplinary strategy, it will
aspire to pave the way for a healthcare system that is more resilient and proactive, and better able
to combat infectious challenges and protect global well-being.
4
PROBLEM STATEMENT
Overview
The study aims to design a framework on predictive analytics models for early diagnosis of
infectious disease.
The prevention of infectious diseases is a global health concern, which calls for early
detection and prompt action. Predictable diagnostic techniques frequently fall short in terms of
sensitivity and specificity, which makes it difficult to detect diseases early and increases the risk of
treatment delays and disease transmission.
Research Question
The study seeks to address the following:
1. What are the key features and biomarkers that can be considered to identify early diagnosis
of infectious disease using machine learning?
2. How can machine learning algorithm be applied on large scale datasets to determine the
infectious disease?
3. What is the extent of the accuracy model used in predicting the infectious disease?
OBJECTIVES AND AIMS
Overall Objective
The study aims to design a framework on predictive analytics models for early diagnosis of
infectious disease.
Specific Aims
1. Identify the key features and biomarkers that can be included in early diagnosis of infectious
disease using machine learning.
2. Investigate the machine learning algorithm to be applied on large scale datasets to determine
the infectious disease.
3. Assess the extent of the accuracy model used in predicting infectious disease.
5
BACKGROUND AND SIGNIFICANCE
Rajaraman and Umarani (2019) investigated the use of support vector machines (SVMs) to
precisely categorize various type of infectious diseases. To assess clinical data for the early
diagnosis of malaria, Ali et al. (2018) used decision trees. These findings show how machine learning
could transform potential at early detection of many infectious diseases.
Kawachi et al. (2020) investigated the combination of social network analysis and machine
learning for the early detection of disease clusters. This study shows how useful predictive analytics
are for pre-emptive outbreak control.
The ethical issues relating to informed consent and data ownership in the context of infectious
diseases prediction models were examined by Islam et al. (2019). The requirement for accountability
and transparency in machine learning algorithms employed for early diagnosis was addressed by
Ross et al. in 2019. These studies emphasize the value of moral standards when applying predictive
analytics to healthcare.
According to Alser et al. (2019), the availability and quality of data, particularly in
environments with constrained resources, can affect the creation of precise machine-learning
models. Faria et al. (2021) talked about the difficulties with complicated machine learning algorithms
being understood and accepted by healthcare practitioners. Standardized data formats and
interoperability are essential for facilitating data exchange for research, according to Udugama et al.
(2020). To achieved successful utilization of predictive analytics in early infectious disease diagnosis,
these challenges need to be addressed.
The significance of the study lies in its potential for healthcare by enabling early intervention,
improving public health responses, reducing healthcare costs, advancing research, and saving lives.
The beneficiaries of the study are the healthcare professionals, local residents, local government
units, policymakers, the city health office, and future researchers.
The impact of the study in the early detection and intervention that leads to effective treatment
and improved patient outcomes. Reduced healthcare cost includes fewer hospitalizations and less
aggressive treatments. Public health strategies lead awareness campaigns, preventive measures,
and disease control efforts. Research advancement can contribute to the broader understanding of
disease patterns, risk factors, and the effectiveness of interventions. Technological advancement
drive advancements in healthcare technology and data analytics.
6
RESEARCH DESIGN AND METHOD
Overview
The research design for this study will utilize the mixed method approach, combining both
qualitative and qualitative data, to achieve the research objectives. The impact of the study is
empowering healthcare professionals with advanced tools and strengthening their ability to combat
infectious disease effectively. The study will collect a set of data sources including clinical records,
epidemiological data, environmental factors, and demographic information.
Machine learning
algorithms will be employed to process and analyze the large scale of datasets. These algorithms
will be guided by historical data from past infectious disease cases to develop robust predictive
models. Ensure strict adherence to ethical guidelines concerning data privacy, obtaining informed
consent, and responsible use of data throughout the research process.
Population and Study Sample
The study will collect a set of data sources including clinical records, epidemiological data,
environmental factors, and demographic information.
Sample Size and Selection of Sample
The determination of the sample size in this study will be based on the selected sampling
strategy and research objectives. The study will use a purposive sampling strategy to select the
dataset for the study, which consists of clinical records, epidemiological data, environmental factors,
and demographic information.
This approach will ensure that the study samples accurately
represent the population.
Sources of Data
The study will collect both primary and secondary data. Here are some potential sources of data:
Primary Data:
Field visits: Visiting the Barangay Health Centers and City Health Office to collect data on clinical
records, epidemiological data, environmental factors, and demographic information.
Interviews: Conduct interview with the barangay and city healthcare professionals to gain more
detailed insights into infectious disease data.
Secondary Data:
Published literature: Conduct a comprehensive review of existing research and best practices in
predictive analytics, machine learning, and infectious diseases from published literature sources.
Government reports: Obtain government reports on infectious diseases to gain valuable insights into
pertinent policies and regulations.
7
Collection of Data
The study will employ a mixed-methods approach, involving the collection of both primary and
secondary data. Primary data will be obtained through field visits, and interviews with healthcare
professionals in barangays and city. Secondary data will be gathered from published literature on
predictive analytics, machine learning, and infectious diseases.
Data Analysis Strategies
The collected data will be analyzed using data-driven analysis techniques. The analysis will
help to identify the factors that contribute to the development of a machine learning model.
The collected datasets for infectious diseases will be analyzed, cleaned, and preprocess the
collected data to ensure their quality and consistency. Ensure data quality and sufficient data for
meaningful analysis and evaluation of gathered relevant data. Perform exploratory data analysis to
gain insights into the dataset’s characteristics. Utilize data tools and statistical measures to identify
patterns, trends, and correlations between variables.
Normalize the data and utilize tools for machine learning. Select machine learning algorithm
suitable for early detection of infectious diseases. Evaluate the performance of the developed
models and based on the evaluated model, design a comprehensive framework that integrates the
best-performing algorithm and features.
Timeframes
The time of this study would be May 2023 to August 2024
Activities
05
06
07
2023
08 09
10
11
12
01
02
03
2024
04 05
06
07
Project Planning
Proposal Defense
Data Collection
Assessing the machine learning
algorithm
Writing the 1st Journal Article and
International Presentation
Publication
Design a framework
Evaluating the accuracy model
Writing the 1st Journal Article and
International Presentation
Publication
Final Defense
Finalizing the Documents
8
08
STRENGTHS AND WEAKNESSES OF THE STUDY
Strength
The study has a potential to contribute to the innovation application of predictive analytics
and machine learning in the field of infectious disease diagnosis. It has a potential to develop early
detection and significantly impact public health. By gathering and integrating diverse datasets, with
clinical records, environmental factors, epidemiological data, and demographic information, the
study can create comprehensive models that consider different risk factors and disease patterns.
The use of machine learning algorithms can help identify the early diagnosis of infectious diseases
and develop predictive models for these results.
The study can also have a positive social impact by promoting well-being and healthy lives
for all at all ages. A feeling of contentment with life is known as well-being, which is a condition of
health, happiness, and prosperity.
Being free from sickness includes taking care of the human
body and doing everything to ensure free from disease and enable access to care. Effective disease
management and containment depend on early diagnosis. Implementing predictive analytics in
healthcare systems can result in timely interventions, reducing the spread of disease and improving
patient outcomes, resulting in a significant positive impact on public health.
Weaknesses
One weakness of the study lies in the availability and quality of data. It may be difficult to
obtain complete and correct datasets, particularly in areas with limited sources, which could influence
the reliability and effectiveness of the predictive models. In healthcare settings, managing data
privacy, confidentiality, and informed consent in the context of predictive analytics may present
challenges and require careful consideration to maintain ethical norms. Predictive models may
contain bias due to bias in the data collection, such as underreporting and overrepresentation of
some groups.
9
BUDGET AND MOTIVATION
Line Item
Personnel
• Statistician
Data Collection
• Data Collection
• Data Preprocessing and Cleaning
Equipment
• High-Performance Computing
Ethics and Regulatory Compliance
• Ethical Review and Approval
Training
• Training on Machine Learning and Data Analysis
Conference Presentation
• Attendance at Conference for Knowledge Sharing
Publications
• Publications Fee
TOTAL
BUDGET
5,000
10,000
40,000
2,000
10,000
30,000
40,000
137,000
REFERENCES
Ali O., Bakar A.A.A., Wan Zamri W.N.H. (2018). Machine Learning Techniques for Early Diagnosis
of Malaria: A Comparative Study. Journal of Computational and Theoretical Nanoscience,
15(1), 207-213.
Alser M., Shehata S., Alhakami H., Zhang W. (2019). Challenges of Implementing Predictive
Analytics for Early Diagnosis in Resource-Limited Settings. Healthcare Informatics Research,
25(4), 309-316.
Faria S., Correia R., Hoste L., Meo M., Lamontagne J.R., Jacobson J. (2021). Addressing the
Interpretability Challenge of Machine Learning Models for Infectious Disease Diagnosis.
Frontiers in Medicine, 8, 620756.
Islam M.M., Poly T.N., Li Y.C.J., Lee W.C. (2019). Ethical Challenges in Predictive Analytics for
Public Health: Balancing Individual and Public Interests. International Journal of Medical
Informatics, 131, 103956.
Kawachi I., Hashimoto H., Tominaga S., Kawachi I. (2020). Combining Social Network Analysis with
Machine Learning for Early Outbreak Detection. Scientific Reports, 10(1), 1-9.
Rajaraman E., Umarani R. (2019). Application of Machine Learning Techniques for Infectious
Disease Prediction: A Comprehensive Review. International Journal of Computer Applications,
182(22), 28-33.
Ross M.K., Calo R., Irwin K.L., Westneat S., Kinney R. (2019). The Ethics of Predictive Analytics in
Infectious Disease Control: A Discussion. Ethics and Information Technology, 21(2), 133-144.
Udugama M., Kell D.B., Vergara-Irigaray N., Rupprecht C., Ionita P. (2020). Standardization and
Interoperability Challenges in Data Sharing for Infectious Disease Research. International
Journal of Infectious Diseases, 91, 18-22.
10
IDENTIFYING GPS BASED TECHNOLOGY IN CAMPUS ASSET
MANAGEMENT
STUDENT NAME:
REINA T. PAYONGAYONG
STUDENT NUMBER:
21-5131-266
COURSE NAME:
Doctor in Information Technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
THELMA D. PALAOAG
DATE OF SUBMISSION:
13 08 2023
CONTENTS
ABSTRACT ...................................................................................................................................................................... 3
INTRODUCTION ............................................................................................................................................................ 4
BACKGROUND ................................................................................................................................................................. 4
PROBLEM STATEMENT .............................................................................................................................................. 6
OVERVIEW ...................................................................................................................................................................... 6
RESEARCH QUESTION ..................................................................................................................................................... 6
OBJECTIVES AND AIMS .............................................................................................................................................. 7
OVERALL OBJECTIVE ...................................................................................................................................................... 7
SPECIFIC AIMS ................................................................................................................................................................ 7
BACKGROUND AND SIGNIFICANCE ....................................................................................................................... 8
RESEARCH DESIGN AND METHODS ..................................................................................................................... 10
OVERVIEW .................................................................................................................................................................... 10
POPULATION AND STUDY SAMPLE ................................................................................................................................ 11
SAMPLE SIZE AND SELECTION OF SAMPLE .................................................................................................................... 11
SOURCES OF DATA ........................................................................................................................................................ 11
COLLECTION OF DATA .................................................................................................................................................. 11
DATA ANALYSIS STRATEGIES ....................................................................................................................................... 12
TIMEFRAMES................................................................................................................................................................. 13
STRENGTHS AND WEAKNESSES OF THE STUDY ............................................................................................. 14
STRENGTHS: .................................................................................................................................................................. 14
WEAKNESSES: ............................................................................................................................................................... 14
BUDGET AND MOTIVATION .................................................................................................................................... 15
REFERENCES ............................................................................................................................................................... 16
APPENDICES................................................................................................................................................................. 17
APPENDIX 1: QUESTIONNAIRE ...................................................................................................................................... 17
2
ABSTRACT
Security gates and vehicle management systems play a vital role in maintaining safety and regulating
access to residential communities, commercial properties, industrial facilities, educational
institutions, and government buildings, among other locations. These systems are intended to
regulate vehicle entry and exit, ensuring that only authorized personnel or vehicles gain access while
preventing unauthorized entry. The study will focus on designing and integrating driveways, security
gates, and vehicle management solutions that will provide a secure environment and convenient
access control for all authorized individuals entering the campus, as well as manage and monitor
the location of assets to protect investments. The researcher will collect data from the target
respondents, the students and employees, using quantitative and qualitative methods and utilizing
surveys, interviews, and observations.
The findings of the study could be used to design an ICT solution that will assist the college in
maintaining a secure campus and ensuring the safety of everyone on the premises, as well as
providing a vehicle management system to protect their assets and maximize their use. The campus
smart gates and vehicle management systems are a valuable solution for enhancing educational
institution security, traffic management, and overall efficiency. By implementing these technologies,
colleges and universities can provide a safer environment for students, faculty, and staff and protect
valuable assets and resources.
Keywords: Smart Campus Security, RFID, Students, Employee, Driveway & Vehicle
3
INTRODUCTION
Background
Most universities and colleges have traditionally been known as a safe place for students.
However, numerous issues and concern in regards to the safety and security of its visitors is one of
the big challenges now a days (1). The increasing number of vehicles, employees, students and its
clientele entering the campus vicinity, if not properly monitored, can pose a threat to the safety of
those on campus. Once a university increases its population, it is also high in risk and security issues.
The Security of Higher Educational Institutions is mandated under Section 28. Safety and Security
Services of the Commission on Higher Education Memorandum No. 09, series of 2013. It states
under Section 28.1 that, “There should be a safe, accessible, and secure environment, buildings and
facilities shall comply with government standards." In addition, Section 28.5 of the Commission on
Higher Education Memorandum No. 09, series of 2013, states that, “There shall be an established
mechanism for the students to help a crime prevention, safety, and security of the concerned higher
education Institution (CMO 09, s2013)”. Typically, security personnel stationed at the entrance gate
or checkpoint visually inspect vehicles, examine identification documents, and confirm the purpose
of entry when checking vehicles entering the campus area. If necessary, they may also conduct
physical searches.
In addition to the importance of monitoring vehicles entering and exiting the campus,
institution-owned vehicles also require protection. Vehicles owned by a campus or educational
institution and used to transport students, faculty, and staff to training sessions or seminars are
crucial in facilitating academic and professional development, so they must be protected and
monitored to ensure effective use.
Despite the specific practices use by colleges or universities to protect the safety of people
inside and to manage their assets like the vehicles they owned, they still face challenges and
problems in the implementations. Controlling and preventing unauthorized vehicles from entering
campus premises can be a challenge. Without proper systems in place, non-affiliated individuals
may use campus parking spaces, causing inconvenience to the campus community and potentially
compromising security. The use of campus vehicles for non-business purposes can also have
several impacts that lead to increased wear and tear, regular maintenance costs, repairs, and
potential vehicle downtime due to damages can escalate, impacting the overall budget allocated for
fleet management. This can strain resources and divert funds from other essential areas. Allowing
non-business use of campus vehicles introduces additional liability and insurance considerations.
Accidents or damages that occur during personal use may not be covered under the university's
insurance policy, potentially resulting in financial burdens for the institution or individuals involved. If
the demand for campus vehicles exceeds the available this can disrupt operations, especially during
peak periods or when emergencies arise.
4
Using RFID and GPS tracking on those campuses can help mitigate the abovementioned
problems, providing convenience and security to those using the technology. RFID (Radio
Frequency Identification) technology is frequently used in security gates to improve safety and
simplify access control systems. It is used to quickly and accurately identify and authenticate people
passing through security gates. Each RFID card or tag has a unique identification code that can be
linked to an individual's profile, including relevant data such as name, access permissions,
credentials, and vehicle information. It enables security personnel to efficiently validate individuals'
identities and determine their level of authorization.
GPS technology can be a very useful tool when used on campus vehicles. It improves vehicle
security by providing real-time location data. This data can help manage the campus vehicle fleet,
ensure optimal utilization, and schedule maintenance and repairs. The GPS can assist authorities in
quickly locating and recovering a vehicle in the event of theft or unauthorized use. GPS tracking is
a reliable method of monitoring and verifying campus personnel's vehicle usage. It can improve
accountability, prevent misuse, and ensure campus policies and regulations are followed.
The proponent aims to design and integrate driveway security gates and campus asset management
solutions to provide a secure environment and convenient access control for all authorized
individuals entering the campus and manage and monitor asset locations to protect investments.
RFID technology can create a digital record of people who pass through security gates. The system
records the access event's date, time, and location. This information can be used to create audit
trails, allowing security personnel to review and analyse access patterns, detect anomalies or
security breaches, and investigate incidents. It can be linked to other security systems like video
surveillance, alarms, and intercoms. GPS tracking enables efficient asset management by recording
vehicle locations and movements. This information can assist in managing the campus vehicle fleet,
ensuring optimal utilization, and scheduling maintenance and repairs. GPS tracking provides a
reliable means of monitoring and verifying vehicle usage by campus personnel. It can enhance
accountability, prevent misuse, and ensure compliance with campus policies and regulations.
5
PROBLEM STATEMENT
Overview
An intelligent campus is an educational institution that uses advanced technologies and data-driven
solutions to improve various aspects of campus life. Campus security is an important aspect, as it
involves ensuring the safety and well-being of students, faculty, staff, and visitors on campus.
Traditional security methods may need to be revised to address modern security challenges. As a
result, the implementation of a comprehensive Smart Campus Security system is essential.
Addressing the smart campus security challenges requires a comprehensive, technologically
advanced solution that balances security, privacy, and ethical concerns. Educational institutions can
create a safer and more secure environment for everyone on campus by developing and
implementing an integrated Smart Campus Security system, ultimately improving the overall learning
and working experience.?
Research Question
1. What architectural framework can be design in the campus assets management?
2. How can GPS technology be utilized to track the real-time location of the campus assets?
3. What is the level of acceptability of the system in terms of the following criteria:
a. functionality;
b. reliability;
c. performance efficiency;
d. usability,
e. security, and
f.
maintainability?
6
OBJECTIVES AND AIMS
Overall Objective
This study aims to design an ICT solution for driveway security gates and campus asset
management that will provide a secure environment and convenient access control for all authorized
individuals entering the campus, as well as manage and monitor the location of assets to safeguard
investments.
Specific Aims
1. To design an architectural framework for campus asset management.
2. To utilized GPS technology to track the real-time location of the campus assets.
3. To assess the level of acceptability of the system in terms of the following criteria:
a. functionality;
b. reliability;
c. performance efficiency;
d. usability,
e. security, and
f.
maintainability.
7
BACKGROUND AND SIGNIFICANCE
Smart campus security refers to the implementation of advanced technologies and intelligent
systems to enhance safety and security on educational campuses, such as schools, colleges, and
universities. The needs of trusted security system are required in various aspects, one of them is the
use of gate system as an access control to the security system in campus area (2) .In the aftermath
of tragic campus-based incidents causing injury and death, it has become common to see
discussions about the safety measures institutions should be taking to prevent or mitigate the harm
of such events(3).
The significance of smart campus security lies in its ability to create a safer and more secure
environment for students, staff, and visitors while also improving operational efficiency and
emergency response capabilities. Here are some key benefits and significance of smart campus
security:

Enhanced safety: Can detect and prevent potential security threats, such as unauthorized
access, theft, or violence. By deploying surveillance cameras, access control systems, and
biometric authentication, campuses can monitor and control entry points, thereby reducing
the risk of incidents.

Real-time monitoring and alerts: With smart security solutions, campus administrators and
security personnel can monitor the premises in real-time. Any suspicious activities or security
breaches can trigger immediate alerts, enabling a swift response to mitigate potential
dangers.

Access control and visitor management: Smart campus security allows for better control over
access to buildings and restricted areas. Visitor management systems can track and manage
guest entry, enhancing overall campus security.

Crime prevention and deterrence: The presence of visible security measures, such as
surveillance cameras and access control systems, can act as a deterrent to potential
criminals, reducing the likelihood of criminal activities on campus.

Integration with other systems: Can be integrated with other campus technologies, such as
building management systems and communication platforms. This integration fosters a more
efficient and interconnected campus environment.

Cost-effectiveness: Though initial implementation costs may be significant, smart campus
security can lead to long-term cost savings by reducing security incidents, minimizing
property damage, and optimizing security personnel deployment.

Reputation and enrollment: A secure campus environment enhances the reputation of an
educational institution. Parents and students are more likely to choose a campus with robust
security measures, leading to increased enrollment.
8

Adaptability and Scalability: can be tailored to the specific needs and size of an educational
institution. As campuses grow or evolve, the systems can be easily expanded or upgraded
to meet new challenges.
Colleges and universities have unique needs when it comes to managing their fleet of vehicles and
assets. Safety, security, and reliability are critical factors for any organization when it comes to
implementing vehicle management (4). Managing vehicles owned by a campus or educational
institution holds significant importance for various reasons:

Campus Safety and Security: Effective vehicle management ensures that campus-owned
vehicles are in good working condition and comply with safety standards. Well-maintained
vehicles reduce the risk of accidents and enhance the overall safety of students, staff, and
visitors.

Transportation Efficiency: By managing campus-owned vehicles efficiently, the institution
can provide reliable and timely transportation services. This is particularly important for
transporting students to and from various campus locations, off-campus events, and field
trips.

Cost Control: Proper management of campus vehicles helps control operational costs.
Regular maintenance, fuel efficiency measures, and optimized routing contribute to cost
savings in the long run.

Emergency Response: Campus-owned vehicles can be utilized for emergency response
purposes, such as medical emergencies or disaster relief. Effective management ensures
that these vehicles are available, properly equipped, and ready for immediate deployment
when needed.

Compliance and Accountability: Proper vehicle management ensures compliance with legal
requirements, such as vehicle registration, insurance, and safety inspections. Additionally,
maintaining accurate records of vehicle use and maintenance fosters accountability and
transparency.

Asset Utilization: By effectively managing campus vehicles, institutions can optimize their
use and avoid unnecessary expenses related to vehicle downtime or underutilization.
The significance of smart campus security and managing vehicles lies in its ability to create a safer
and more efficient learning environment, protect assets, cost control, compliance and respond
effectively to emergencies. By embracing smart security technologies, educational institutions can
prioritize the well-being of their community and focus on their core mission of providing quality
education.
9
RESEARCH DESIGN AND METHODS
Overview
The purpose of this chapter is to give details on the research approach and methodology
implemented for this study. This part of the study will explain all the research approaches such as
the population and study sample, the sample size and selection of sample, sources of data, and
collection of data, data analysis strategies and the timeframe of the study.
The proponent of the study will personally seek approval from the President of Bulacan Agricultural
State College in San Ildefonso, Bulacan, to formally gather and collect data needed in the study. The
proponent will also conduct a meeting with the personnel in-charge in the MIS office and General
Services, Security and Transportation Unit (GSSTU). Upon approval of the request to conduct the
data gathering procedure, as part of the ethical issues and concerns, the proponent will conduct an
orientation to the respondents to discuss the main purpose of the study and to assure that all
information will be used for academic purpose only. This study will integrate the Agile Methodology
as developmental research approach as being part of the ICT intervention (figure 1), since the study
aim to design and develop a driveway security gates and asset management solution that will provide
a secure environment and convenient access control for all authorized individuals entering the
campus, as well as manage and monitor the location of assets to protect investments. Both
qualitative and quantitative research approach will also be used in the study, which will help find
answers to the research questions.
https://www.javatpoint.com/agile-vs-waterfall-model
Figure 1. Agile Methodology for System Development
10
Population and Study Sample
The target respondents of this study were employees and students of Bulacan Agricultural State
College. There are 350 faculty and staff and almost 7,000 students that belongs to different courses.
With area size of 192.5 hectares in the main campus. Some IT Experts will also be part of the
respondents of the study. The respondents have a big role in the study as they evaluated and
examined the system.
Sample Size and Selection of Sample
Respondents
Total Population
Students
7,000
Employees
350
Sample Population
IT Experts
 Programmer
 System Analyst
 Web Admin
10
Sources of Data
For the study purpose both primary and secondary data are used. The primary data collected from
the employees and students of the Bulacan Agricultural State College who happens to be the main
respondents. The primary data are related to behaviour and response of the respondents. The
secondary data will be collected from the records, literature reviews and observations during the
collection of data. These data used in combination as per need of the study.
Collection of Data
The data collection process will begin with a letter addressed to the various respondents and those
in command at the college where the study will be conducted. This letter aims to explain the main
reason for conducting the study and assure them that all information gathered from them will be
treated with strict confidentiality in accordance with research ethics.
The proponent will use the data collection instruments required for the study. The study will
necessitate a wide range of research tools. In assessing the developed system, the questionnaire
will be based on the ISO/IEC 25010:2011 Systems and Software Quality Requirements and
Evaluation (SQuaRE) , specifically the Software Product Quality Model (Figure 2).
The following data collection tools will be used:
a. Observation technique. The proponent will monitor the movement of vehicles and people entering
and exiting the campus. The proponent will also observe how the school's vehicle is reserved and
used for official purposes only. This technique will be used to collect data directly or firsthand.
11
b. The Interview. This technique will collect data and information from respondents through direct
verbal interaction. It ensured that the data was consistent and trustworthy. This technique informs
the researcher about how the current system functions regarding its structure, benefits, limitations,
and problems respondents encounter. This is advantageous for the enhancement of security and
safety on campus, as well as the management of college-owned vehicles and the development of
the proposed system.
c. The Questionnaire. The most commonly used data collection method, one of the more convenient
methods of gathering data, has gained widespread acceptance as a practical method of obtaining
information.
https://iso25000.com/index.php/en/iso-25000-standards/iso-25010
Figure 2. Product Quality Model
Data Analysis Strategies
The following statistical methods will be used to show the results from the survey forms distributed
to the respondent. The Weighted Mean will be used to determine the acceptability of the developed
system. Through the evaluation procedure, the researcher will determine the acceptability of the
system. For the evaluation of a system, the researcher will use survey questionnaires adapted from
the published research with criteria using a scale of 1 to 5, where 1 indicates unacceptable and 5,
highly acceptable. The weighted mean was explained based on the Likert’s scale of boundary of
numerals.
Quantitative response will be analysed with descriptive statistics. Descriptive statistics was used to
describe the basic feature of the data in a study. It provided simple summaries about the sample
and the measures. The system will be evaluated by its functionality, reliability, efficiency, usability
and maintainability (5).
The following Software Quality Factors were used as follows: Functionality is a set of attributes that
bear on the capability to provide functions stated and implied needs when the software is used.
Reliability is a set of attributes that bear on the ability to maintain a specified level of performance.
12
Efficiency is a set of attributes that bear on the capacity to provide appropriate performance to the
amount of resources used. Usability is a set of attributes that bear on the capability to be understood,
learned and used. Maintainability is a set of attributes that bear on the capability to be modified for
purposes of making corrections, improvements, or adaption.
Timeframes
Month
Activities
May – June
July –
Sept –
Nov – Dec
Jan – Feb
Mar –
2023
August
Oct 2023
2023
2023
April 2023
2023
Preliminary Activities
1. Proposal Preparation
2. Presentation and Approval of
the proposal
Data Gathering Phase
Data Encoding Phase
Report Analysis Phase
Report Writing Phase
Presentation of the Final Result
Submission of Revise Copy
13
STRENGTHS AND WEAKNESSES OF THE STUDY
Strengths:
The strength of SMART CAMPUS SECURITY lies in its ability to leverage modern technologies and
data-driven approaches to enhance the safety and security of educational institutions. Here are
some key strengths of a smart campus security system:
1. Real-time monitoring: employ a network of surveillance cameras, sensors, and other
monitoring devices that provide real-time data on campus activities. This enables security
personnel to respond quickly to potential threats or incidents.
2. Access Control: implements access control systems that regulate entry and exit points,
allowing only authorized personnel to access specific areas. This helps prevent unauthorized
access and enhances overall campus safety.
3. Scalability: Allowing campuses to expand and adapt security measures as their needs
change, ensuring long-term effectiveness.
4. Integration with other systems: can be integrated with other campus technologies, such as
building management systems and communication platforms. This integration fosters a more
efficient and interconnected campus environment.
5. Deterrence: Visible and well-publicized smart security measures can act as a deterrent
against potential threats, reducing the likelihood of criminal activities on campus.
6. Cost-effectiveness: Though initial implementation costs may be significant, it can lead to
long-term cost savings by reducing security incidents, minimizing property damage, and
optimizing security personnel deployment.
7. Continuous Improvement: can learn and improve over time through machine learning
algorithms, refining their ability to detect threats and reduce false alarms.
Weaknesses:
While smart campus security can offer numerous benefits, it is essential to acknowledge its potential
weaknesses:
1. Cybersecurity Risks: often rely on interconnected devices and networks, making them
susceptible to cyberattacks. Hackers could exploit vulnerabilities in the system, leading to
unauthorized access, data breaches, or disruptions in security operations.
2. Privacy Concerns: The use of surveillance cameras, location tracking, and other monitoring
technologies raises privacy concerns among students, staff, and visitors. If not managed
appropriately, it can lead to a breach of privacy and trust issues within the campus
community.
14
3. Human Error: Even with advanced technology, human error remains a risk factor. Inadequate
system configurations or failure to follow security protocols can create vulnerabilities that
attackers might exploit.
BUDGET AND MOTIVATION
ITEM
BUDGET
1. RFID Tags
1,000
2. RFID reader
50,000
3. Barcode reader
10,000
4. Monitor
10,000
5. CCTV (c/o BASC)
6. GPS tracking
7. Automated entry (Vehicle and
Clientele)
5000
For canvass
15
REFERENCES
1.
Digital Scholar M, Andrew Carrico B, Andrew B. The Effects of Students’ Perceptions of
Campus Safety and Security on Student Enrollment Recommended Citation [Internet].
Theses, Dissertations and Capstones. 2016. Available from: http://mds.marshall.edu/etd
2.
Mansur K, Hasanuddin ZB. Implementation of NFC for Smart Gate Access Control in Campus
Area. 2018.
3.
Higher Education Institutions’ Security Capability the Leads to the Creation of Standardized
Campus Security System. Journal for Educators, Teachers and Trainers. 2023 Jan 1;14(2).
4.
The Benefits of University Fleet Management for College Campuses | Verizon Connect
[Internet].
[cited
2023
Jul
21].
Available
from:
https://www.verizonconnect.com/resources/article/university-fleet-management/
5.
Ablaza-Cruz MM. Designing a Mobile Application Framework as an Innovative IT Solution for
Waste Recycling. In: IOP Conference Series: Materials Science and Engineering. Institute of
Physics Publishing; 2020.
16
APPENDICES
Appendix 1: Questionnaire
SOFTWARE EVALUATION FORM
Dear Respondent,
This survey will be used as a tool to gauge how well-accepted the created application is. By filling out this form,
you will be helping us collect the accurate and reliable data we need to evaluate the application we've designed.
You can be sure that the information you provide will be handled with the highest confidentiality.
Reina T. Payongayong
Researcher
Smart Campus Security: Design and Integration of Driveway Security Gates and Campus
Asset Management
Instruction: Please evaluate the developed system by using the given scale and placing a check mark [] under the
corresponding numerical rating:
Each rating is quantified by the following:
Numerical Rating
Equivalent
5
Highly Acceptable
4
Very Acceptable
3
Acceptable
2
Moderately Acceptable
1
Unacceptable
Characteristics
Sub-characteristics
Descriptions
Completeness
Functionality
Correctness
Appropriateness
Fault tolerance
Reliability
Recoverability
Learnability
Usability
Operability
Accessibility
Time behavior
Performance
Resource behavior
Efficiency
Capacity
Confidentiality
Integrity
Security
Non-repudiation
Authenticity
Accountability
Reusability
Maintainability Modifiability
Testability
Instruction: Please fill up all fields with * as required, optional otherwise.
Respondent’s Name:
*Type of Respondent
STUDENTS
IT EXPERTS
EMPLOYEES
5
4
3
2
1
Please confirm your responses by signing. Thank you very much for your time and insights.
*Signature
*Date
17
Appendix 2: Consent Letter
18
A COMPUTER-AIDED ANALYSIS TOOL FOR AUTISM
OCCUPATIONAL THERAPY
STUDENT NAME:
JANU V. PERALTA
STUDENT NUMBER:
21-3886-145
COURSE NAME:
Doctor in Information Technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
Dr. THELMA D. PALAOAG
DATE OF SUBMISSION:
18 08 2023
CONTENTS
ABSTRACT ..................................................................................................................................................................3
INTRODUCTION ........................................................................................................................................................4
PROBLEM STATEMENT ..........................................................................................................................................6
OVERVIEW..................................................................................................................................................................6
RESEARCH QUESTION/HYPOTHESIS .............................................................................................................................6
OBJECTIVES AND AIMS ..........................................................................................................................................7
OVERALL OBJECTIVE ..................................................................................................................................................7
SPECIFIC AIMS ............................................................................................................................................................7
BACKGROUND AND SIGNIFICANCE ....................................................................................................................8
RESEARCH DESIGN AND METHODS ..................................................................................................................11
OVERVIEW................................................................................................................................................................11
POPULATION AND STUDY SAMPLE.............................................................................................................................12
SAMPLE SIZE AND SELECTION OF SAMPLE .................................................................................................................13
SOURCES OF DATA....................................................................................................................................................13
COLLECTION OF DATA ..............................................................................................................................................14
DATA ANALYSIS STRATEGIES ...................................................................................................................................14
TIMEFRAMES ............................................................................................................................................................15
STRENGTHS AND WEAKNESSES OF THE STUDY ...........................................................................................17
BUDGET AND MOTIVATION ................................................................................................................................18
REFERENCES ...........................................................................................................................................................19
APPENDICES ............................................................................................................................................................20
APPENDIX 1: QUESTIONNAIRE ...................................................................................................................................20
2
ABSTRACT
Background
The integration of technology in healthcare has gained traction, with potential applications in autism
occupational therapy. This study focuses on the development of a computer-aided analysis tool
designed to enhance interventions for individuals with autism spectrum disorders (ASD) within
occupational therapy contexts.
Methods
A comprehensive investigation was undertaken to explore the multifaceted aspects of designing and
implementing a computer-aided analysis tool for autism occupational therapy. Professionals from
diverse backgrounds, including occupational therapists, technologists, researchers, caregivers, and
individuals with expertise in autism intervention, were engaged through a questionnaire-based
survey. The survey encompassed three main domains: design parameters/elements, tool features,
and ethical considerations. Open-ended questions were utilized to elicit nuanced perspectives and
insights.
Results
The study unveiled crucial insights into the envisaged computer-aided analysis tool. Participants
emphasized the integration of technology as a means to optimize therapy delivery and data analysis.
Design parameters, including user-friendliness and compatibility with established therapy practices,
emerged as pivotal considerations. Innovative features such as real-time data visualization and
personalized intervention plans were identified to enhance therapeutic outcomes. Ethical concerns
encompassed data privacy, security, and maintaining patient-centric care approaches.
Discussion and Conclusion
The findings underscore the potential of technology to revolutionize autism occupational therapy.
The proposed tool addresses design challenges while incorporating features that cater to
individualized needs. The discussion delves into the implications of integrating technology in therapy
contexts, emphasizing the balance between technological advancements and ethical considerations.
The study provides a comprehensive overview of the design, features, and ethical considerations for
a computer-aided analysis tool in autism occupational therapy. The insights garnered offer a
foundation for the development of a technology-driven solution, holding promise to enhance the
quality and efficacy of interventions for individuals with ASD.
3
INTRODUCTION
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects social
interaction, communication, and behaviour. Occupational therapy (OT) has emerged as an effective
approach to address the challenges faced by individuals with ASD, aiming to improve their overall
functioning and quality of life. However, the heterogeneous nature of the disorder necessitates
personalized interventions that consider the unique strengths and difficulties of each individual.
Occupational therapy, guided by the Model of Resilience in Daily Occupations, plays a vital role in
supporting individuals with autism to develop and enhance their skills for engaging in daily
occupations. Resilience, defined as the capacity to adapt, cope, and thrive in the face of challenges,
is fostered through activities related to self-care, productivity, and leisure.
Traditionally, occupational therapists have relied on clinical observations, standardized
assessments, and subjective feedback to understand an individual's strengths, challenges, and
progress. However, the traditional paradigms of autism occupational therapy are encumbered by
limitations stemming from subjective assessment methodologies and a dearth of standardized tools.
This study emerges as a response to this critical need, delving into the conceptualization,
development, and implications of a dedicated computer-aided analysis tool meticulously tailored for
autism occupational therapy.
Recognizing the potential of technology to bridge these gaps, the study was conceived with a
resolute commitment to reshaping the landscape of autism occupational therapy. The researcher's
vantage point was enriched by a deep-seated conviction that harnessing emerging technologies
such as sensors, motion tracking, and data analytics could transcend the limitations of conventional
approaches. By providing therapists with a computer-aided analysis tool capable of rendering
objective and quantifiable metrics pertaining to sensory, motor, and cognitive functions, the
researcher aspired to instigate a fundamental transformation in how therapy is conceptualized,
executed, and optimized.
Rooted in a recognition of the imperative to surpass conventional methodologies, this research
endeavours to harness the power of advanced technologies such as sensory instrumentation, motion
tracking systems, and data analytical frameworks. The overarching ambition of the envisaged
computer-aided analysis tool is to furnish therapists with a reservoir of objective and quantifiable
metrics, thereby enabling the systematic evaluation of sensory, motor, and cognitive functions. In
doing so, the study endeavours to pivot from the inherent limitations of subjective observations to a
realm of data-driven insights.
4
Central to this exploration are foundational inquiries that underpin the efficacy and ethical compass
of this transformative tool. Through an incisive inquiry into design parameters and integral elements,
the study seeks to illuminate the pivotal constituents that will inform the tool's architecture, ensuring
its seamless alignment with the nuanced landscape of autism occupational therapy. Subsequently,
an exploration into the tool's multifaceted features endeavours to encapsulate its capabilities
encompassing real-time progress tracking, personalized intervention recommendations, and
comprehensive assessment modules.
Moreover, the ethical dimension inherent in the integration of technology within therapeutic
landscapes assumes paramount importance. In an era where the ethical implications of data privacy,
informed consent, and therapeutic integrity come to the fore, the study's trajectory encompasses a
rigorous analysis of these intricate ethical contours. By navigating these considerations meticulously,
the study aspires to chart a course for the ethical utilization of the tool, respecting the autonomy,
rights, and dignity of individuals engaged in the therapeutic process.
Within the broader canvas of autism intervention, this study aspires to inject a paradigm shift by
ushering in a dedicated computer-aided analysis tool that seamlessly harmonizes technological
sophistication with therapeutic acumen. As the study embarks on this trajectory of innovation, it
envisions a future where the convergence of objective assessments and data-driven interventions
reshapes the landscape of autism occupational therapy, ultimately elevating the quality of life and
developmental trajectory of individuals traversing the intricate spectrum of autism.
The overarching goal of enhancing the lives of individuals with ASD through targeted, evidencebased interventions guided the researcher's trajectory. In the face of the prevailing disparities in
therapeutic outcomes and the dearth of standardized assessment methodologies, the researcher's
motivation was fuelled by a resolute commitment to equipping therapists with a powerful instrument
that could drive the personalization, precision, and efficacy of therapy sessions. The study's profound
implications for both therapists and individuals with ASD, coupled with the researcher's unwavering
dedication to pushing the boundaries of therapeutic innovation, collectively propelled the genesis of
the study, underscoring its pivotal role in advancing the discourse of autism occupational therapy.
5
PROBLEM STATEMENT
Overview
The study addresses the challenges in providing effective therapy for individuals with autism.
Traditional methods lack standardized assessment, relying on subjective observations. The study
aims to develop a computer tool that uses data analysis to provide therapists with objective metrics
for assessing sensory, motor, and cognitive functions. This tool aims to enhance therapy quality,
personalization, and progress tracking, ultimately improving the outcomes and lives of individuals
with autism.
Research Question
1. What are the design parameters/elements in developing a computer aided analysis tool for
autism occupational therapy?
2. What are the features of the computer aided analysis tool for autism occupational therapy?
3. What ethical consideration to implement a computer aided analysis tool for autism
occupational therapy?
6
OBJECTIVES AND AIMS
Overall Objective
The study's overarching objectives include developing a specialized computer tool for autism
occupational therapy. This tool should provide standardized and objective assessments of sensory,
motor, and cognitive functions. The study aims to create personalized intervention plans based on
collected data, enable real-time progress monitoring, validate the tool's accuracy through clinical
trials, offer therapist training, assess long-term impact, ensure usability and ethical considerations,
and ultimately advance the field of autism occupational therapy through technology-driven
improvements.
Specific Aims
1. Determine the design parameters/elements in developing a computer aided analysis tool for
autism occupational therapy.
2. Identify the features of the computer aided analysis tool for autism occupational therapy.
3. Specify the ethical consideration to implement a computer aided analysis tool for autism
occupational therapy.
7
BACKGROUND AND SIGNIFICANCE
Background
The study's research trajectory is deeply rooted in a rich collection of literature, collectively shaping
it. Evident in this pursuit is the imperative to enhance autism intervention strategies within the context
of occupational therapy. This imperative is made clear by recognizing challenges arising from
subjective assessments and the absence of standardized tools. Consequently, a demand for
innovative interventions emerges, a sentiment resonated by (1) comprehensive exploration of autism
spectrum disorders, highlighting the significance of early interventions for optimal developmental
outcomes.
Further contextualizing the study is (2) research, shedding light on the transition from random arm
movements to purposeful reaching. Their insights into motor development hold relevance for the
proposed computer-aided analysis tool's kinematic metrics. Demonstrating the viability of telehealth
in therapy delivery, (3) illustrates remote parent training's feasibility within the Early Start Denver
Model, mirroring the tool's potential for technology-driven interventions.
Contributing to the study's framework, (4) introduces the Autism Diagnostic Observation Schedule
(ADOS), an established tool for autism diagnosis, aligning with the role of objective measurement
tools pertinent to the proposed analysis tool. The (5) exploration of virtual environments underscores
technology's potential for tailored interventions, echoing the envisaged tool's capabilities. The study
of (6) highlights sensory interactions in therapy aligns with the proposed tool's potential for sensorydriven interventions.
Support for personalized approaches is found in (7), emphasizing individualization in therapeutic
interventions. Remote interventions gain further emphasis from (8) systematic review, reinforcing
the growing role of technology in therapeutic contexts. Methodological insights from (9) of
reinforcement control procedures offer enhancements to the tool's capabilities.
The (10) study, evaluating assessment tools, aligns with the study's objectives, adding an evaluative
layer to the tool's potential impact. Collectively, these studies establish the foundation for the study's
pursuit of innovating autism occupational therapy through technology, providing a robust basis for
the proposed computer-aided analysis tool's objectives.
Noteworthy is the acknowledgment of the importance of advancing autism interventions within
occupational therapy due to challenges associated with subjective assessments and the lack of
8
standardized tools. In response, the study seeks technology-driven solutions, aligning with the
potential of technology-enhanced interventions.
Significance
The study holds significant implications for various stakeholders involved in autism occupational
therapy.
For the therapy center, the implementation of this tool promises to elevate the quality of services by
equipping therapists with standardized assessments and personalized intervention strategies,
ultimately leading to more effective and tailored treatments. This innovation not only enhances the
reputation of therapy center’s but also showcases their commitment to evidence-based approaches,
potentially attracting a wider client base.
For occupational therapists, the study offers the potential to revolutionize their practice. The tool's
provision of objective data to guide interventions ensures accurate assessments, enabling therapists
to formulate precise therapy plans and make well-informed decisions. The integration of real-time
monitoring and personalized intervention recommendations further optimizes their work, increasing
both efficiency and effectiveness.
Parents of individuals with autism stand to benefit significantly from this study. Through real-time
monitoring, they gain insights into their child's progress, fostering collaboration with therapists and
instilling confidence in the therapy's impact. Personalized interventions driven by the tool's data have
the potential to yield improved outcomes and a higher quality of life for their children.
Individuals with autism themselves are empowered by this study. The tool's personalized
interventions target specific sensory, motor, and cognitive needs, potentially accelerating progress
and enhancing overall well-being. The transparent assessments and visible progress data empower
these individuals to actively engage in their therapy journey, fostering a sense of ownership over
their development.
For students pursuing studies in autism occupational therapy, this study provides valuable insights
into the integration of technology and data-driven methodologies offer a glimpse into real-world
therapy applications, enriching their learning experience.
Researchers in the field of autism occupational therapy can build upon this study to expand the
knowledge base. This study opens new avenues for exploration. By establishing a foundation for the
efficacy of technology-based tools in therapy, it encourages further investigations into refining
9
methodologies and extending applications. Researchers can utilize the data generated by the tool
to gain deeper insights into the progression of individuals with autism and the efficacy of specific
therapeutic approaches.
Lastly, the study's legacy extends to future researchers who can build upon its findings. By refining
the tool's functionalities, expanding its applications, and contributing to the evolving field of
technology-enabled therapy solutions, they continue the journey of innovation set forth by this study.
In essence, the study's significance lies in its capacity to reshape autism occupational therapy,
introducing a computer-aided analysis tool that addresses the diverse needs of stakeholders and
paves the way for more effective, personalized, and data-driven therapeutic interventions.
10
RESEARCH DESIGN AND METHODS
Overview
The research design and methods employed in the study are meticulously structured to address the
three specific research questions driving the investigation. The study embarks on a comprehensive
journey, intertwining qualitative and quantitative methodologies to unravel the intricacies surrounding
the development, functionalities, and ethical dimensions of the proposed computer-aided analysis
tool.
In the design parameters and elements, an exploratory approach is undertaken. This entails an
extensive review of existing literature, encompassing technological advancements, established
assessment paradigms, and the distinctive requisites of autism occupational therapy. Additionally,
in-depth interviews and consultations with experienced therapists and technology experts are
conducted to elicit insights into the critical constituents that should inform the tool's design
architecture. This qualitative exploration is complemented by a quantitative survey that seeks to
capture a broader spectrum of perspectives, consolidating a holistic understanding of the
foundational parameters that shape the tool's development.
In the pursuit of unravelling the features of the computer-aided analysis tool, the research design
integrates both quantitative and qualitative dimensions. A mixed-methods approach is adopted,
wherein quantitative surveys are administered to a sample of therapists and individuals with ASD to
ascertain the essential functionalities they perceive as integral to the tool. This data is then
triangulated with qualitative interviews, engaging stakeholders in open-ended discussions to delve
deeper into the intricacies of desired features. The synthesis of quantitative and qualitative insights
facilitates the delineation of a comprehensive feature set that caters to the diverse needs of both
therapists and individuals with ASD.
To navigate the ethical considerations inherent in implementing the computer-aided analysis tool, an
ethical framework is meticulously constructed. This involves a comprehensive review of ethical
guidelines, principles, and protocols relevant to technology integration within therapeutic contexts.
Interviews and focus groups with therapists, individuals with ASD, and ethical experts provide
invaluable perspectives on the nuances of data privacy, informed consent, and the preservation of
therapeutic relationships. The culmination of these qualitative insights and ethical guidelines shapes
an ethical implementation plan that safeguards the rights, dignity, and well-being of all stakeholders
involved.
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In essence, the research design seamlessly interweaves qualitative and quantitative methodologies,
fostering a rich tapestry of insights that collectively address the specific research questions. This
approach ensures a comprehensive understanding of the design parameters, features, and ethical
considerations of the envisioned computer-aided analysis tool for autism occupational therapy,
underpinning the study's holistic quest for transformative innovation.
Population and Study Sample
The study employs a systematic approach to define its population and study sample, aligned with
the specific research questions guiding the investigation.
For the design parameters and elements of the computer-aided analysis tool, the population of
interest encompasses a diverse array of stakeholders. This includes experienced occupational
therapists, technology experts, and researchers with expertise in autism and assistive technologies.
The study sample for this question is purposefully selected, comprising a subset of these
stakeholders who possess significant insights into the design intricacies and technical requirements
of the tool. In-depth interviews and consultations with this sample facilitate the extraction of nuanced
perspectives, enabling a comprehensive understanding of the parameters and elements essential
for developing the tool.
For the features of the computer-aided analysis tool, extends its focus to a broader audience. The
population encompasses occupational therapists who actively engage in autism therapy, individuals
with autism, and their caregivers or parents. Drawing from this population, the study sample is
stratified to include a diverse range of these stakeholders. Quantitative surveys are administered to
a representative group of occupational therapists and individuals with autism to gather insights into
their preferences for tool features. Additionally, qualitative interviews and focus groups provide a
deeper understanding of the unique requirements and perspectives of these participants, resulting
in a holistic delineation of the tool's features.
For the ethical considerations in implementing the computer-aided analysis tool, the population
spans individuals with autism, their families, occupational therapists, and experts in ethics and
technology. The study sample for this question involves conducting qualitative interviews and focus
groups with a selection of these stakeholders. Occupational therapists and ethical experts provide
insights into the practical implementation challenges and ethical dilemmas, while individuals with
autism and their families contribute valuable perspectives on issues of data privacy, informed
consent, and the preservation of the therapeutic relationship.
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Sample Size and Selection of Sample
For the design parameters and elements of the computer-aided analysis tool, a purposeful sampling
strategy will be adopted. A subset of the population comprising experienced occupational therapists,
technology experts, and researchers will be chosen to participate in in-depth interviews and
consultations. This selective approach will ensure that participants possess the requisite expertise
to offer nuanced insights into the intricate design components of the tool. The sample size for this
phase will be determined by saturation, wherein data collection will continue until no new insights or
perspectives emerge, thus ensuring a comprehensive understanding of the design parameters.
In identifying the features of the computer-aided analysis tool, a mixed-methods approach will be
employed to capture a diverse range of perspectives. A larger sample will be selected to participate
in quantitative surveys, encompassing occupational therapists and individuals with autism. This
sample will be calculated based on statistical considerations to ensure a representative response.
Concurrently, a subset of these participants will be purposefully chosen for qualitative interviews and
focus groups, ensuring a deep exploration of the qualitative aspects surrounding desired tool
features. The combined insights from both quantitative and qualitative phases will enrich the
comprehensive understanding of the tool's features.
In probing ethical considerations in implementing the computer-aided analysis tool, a purposive
sampling strategy will once again be employed. A group of participants will be chosen, including
occupational therapists, individuals with autism, their families, and ethical experts. This diverse
selection will offer varied perspectives on ethical dimensions. The sample size will be determined by
the principle of achieving data saturation in qualitative interviews and focus groups, ensuring a rich
understanding of the multifaceted ethical considerations involved in the tool's implementation.
Sources of Data
Primary sources of data will form the cornerstone of the study's data collection. In-depth interviews
and consultations with experienced occupational therapists, technology experts, individuals with
autism, and their families will constitute the primary data sources for various aspects of the research.
These interviews will provide direct insights into design parameters, tool features, and ethical
considerations. Additionally, qualitative focus groups involving individuals with autism, their families,
and therapists will contribute valuable firsthand perspectives on the potential impact and usability of
the proposed computer-aided analysis tool. The richness and depth of these primary sources will
offer a granular understanding of the multifaceted dimensions under examination.
Secondary sources of data will complement the primary data and offer broader context and insights.
A comprehensive review of existing literature will constitute a vital secondary data source. This
13
review will encompass studies on technology-assisted interventions, assessments, ethical
frameworks, and best practices in autism occupational therapy. Secondary sources will further
include existing ethical guidelines and principles pertaining to technology integration in therapeutic
settings. These sources will not only underpin the theoretical framework of the study but also enable
a comparison and validation of findings against established knowledge and practices.
Collection of Data
Primary data will be gathered through in-depth interviews, consultations, and qualitative focus
groups. These interactions will involve occupational therapists, technology experts, individuals with
autism, and their families. Semi-structured interviews will provide a platform for participants to share
their expert opinions, experiences, and perspectives on critical aspects of the study's focus, including
design parameters, tool features, and ethical considerations. Qualitative focus groups, involving
individuals with autism, their families, and therapists, will facilitate open discussions on usability,
potential impact, and personal insights related to the computer-aided analysis tool. These primary
data collection methods will enable the extraction of nuanced and firsthand insights, enriching the
depth of the study's findings.
Complementing primary data, secondary data collection will involve an extensive review of existing
literature. Scholarly articles, research papers, and established ethical guidelines pertaining to
technology-enhanced interventions, assessments, and ethical considerations in therapy will be
systematically examined. This secondary data review will serve as a foundation, providing theoretical
context, established practices, and ethical frameworks against which the study's findings can be
contextualized and validated.
Data Analysis Strategies
The data analysis for the study will revolve around achieving the study objectives. The analysis will
involve the following key aspects:
To determine the design parameters and elements of the tool, a qualitative thematic analysis
approach will be employed. The data collected from in-depth interviews, consultations, and
qualitative focus groups with occupational therapists, technology experts, and other stakeholders
will be systematically reviewed and categorized. Common themes, patterns, and emerging concepts
related to the design parameters will be identified, allowing for a comprehensive understanding of
the essential elements that should shape the tool's development.
To identify the features of the computer-aided analysis tool, the research will entail both quantitative
and qualitative analyses. The quantitative data gathered from surveys involving occupational
14
therapists and individuals with autism will be subjected to statistical analysis to determine the
prevalence and preferences for specific tool features. Concurrently, the qualitative insights gained
from interviews and focus groups will undergo content analysis to identify recurring themes and
individual perspectives. The synthesis of quantitative and qualitative findings will contribute to a
holistic delineation of the tool's essential features.
For the specification of ethical considerations for implementing the tool, a qualitative approach will
be paramount. The data collected from interviews, focus groups, and ethical guidelines will be
subjected to thematic analysis. Key ethical themes, challenges, and potential solutions will be
identified, providing a nuanced understanding of the ethical landscape surrounding the tool's
implementation.
The data analysis for this study are tailored to each objective, enabling a comprehensive exploration
of the design parameters, features, and ethical considerations of the proposed computer-aided
analysis tool for autism occupational therapy. Through thematic analysis, content analysis, and
statistical techniques, the study aims to unravel insights that contribute to the advancement of autism
therapy practices and technology integration.
Timeframes
The time frame for the study is structured to ensure a comprehensive and rigorous investigation
while maintaining an efficient progression. The study's timeline spans approximately eighteen
months, encompassing various phases crucial to achieving the research objectives.
The initial phase, spanning the first three months, will focus on an extensive literature review. This
phase involves gathering relevant research, technological advancements, and existing practices in
both autism therapy and technology integration. This foundation will inform subsequent stages.
The subsequent six months will be dedicated to data collection. This includes in-depth interviews,
focus groups, and surveys involving occupational therapists, individuals with autism, their families,
and ethical experts. These engagements will provide firsthand insights into design parameters, tool
features, and ethical considerations.
The subsequent three months are reserved for data analysis. Qualitative data from interviews and
focus groups will undergo thematic analysis, identifying recurring patterns and themes. Quantitative
data from surveys will be statistically analyzed to identify prevalent trends and preferences.
15
Following the data analysis phase, a span of two months will be allocated for synthesis and
interpretation. This involves integrating qualitative and quantitative findings to construct a
comprehensive narrative that addresses the research questions and objectives.
The final four months of the timeline will be dedicated to the report writing and documentation
process. This phase involves crafting the research findings, discussions, and recommendations into
a coherent and informative manuscript.
The overall timeline allows for a meticulous exploration of the proposed computer-aided analysis
tool's design, features, and ethical dimensions. The structured approach to each phase ensures the
study's thoroughness and rigor, culminating in a comprehensive contribution to the field of autism
occupational therapy.
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STRENGTHS AND WEAKNESSES OF THE STUDY
The study exhibits several strengths. Firstly, its systematic approach of triangulating primary and
secondary data sources enhances the robustness of the findings. By conducting in-depth
interviews, focus groups, and surveys with diverse stakeholders, including therapists, individuals
with autism, families, and experts, the study ensures a comprehensive exploration of the design
parameters, features, and ethical considerations of the proposed tool. This multifaceted data
collection strategy lends depth and validity to the insights derived.
Additionally, the study's emphasis on mixed-methods analysis provides a well-rounded perspective
on the objectives. By employing both qualitative and quantitative techniques, the study gains a
nuanced understanding of the complex interplay between design, features, and ethical
considerations. This approach allows for the identification of patterns and trends in quantitative
data, while also delving into the intricacies and personal insights offered through qualitative data.
However, the study also presents certain limitations. The first potential weakness lies in the
challenge of ensuring representative sampling, especially in the qualitative phases involving
individuals with autism and their families. Ensuring diverse representation may be complex due to
the heterogeneity of the autism spectrum. Additionally, as the study aims to address a multifaceted
spectrum of perspectives and dimensions, there is a possibility that some aspects might receive
more attention than others, potentially leading to an imbalance in the depth of analysis.
Furthermore, the study's reliance on self-reported data, particularly in surveys and interviews, may
introduce response bias or social desirability bias. Participants might provide answers they believe
researchers expect, rather than expressing their genuine opinions. This potential bias could
influence the accuracy and authenticity of the insights gathered.
17
BUDGET AND MOTIVATION
Budget
The study requires a comprehensive budget to support its research activities. The budget will
encompass various aspects, including data collection expenses, software development and
implementation costs, ethical review procedures, and data analysis software and hardware.
Adequate funding is essential to ensure the successful development and implementation of the AIdriven analysis system, the establishment of a framework for tailored interventions, and the
evaluation of usability and effectiveness. Securing a sufficient budget will enable the researcher to
conduct a thorough and robust study, addressing the specific objectives and maximizing the potential
impact of the research.
Motivation
The motivation underlying the study is grounded in the pursuit of addressing critical gaps in the
realm of autism intervention, particularly within the scope of occupational therapy. The prevalent
challenges associated with subjective assessment methodologies, lack of standardized tools, and
variations in therapeutic outcomes underscore the need for innovative solutions that can enhance
the precision, personalization, and efficacy of therapeutic interventions for individuals with autism
spectrum disorder (ASD).
This motivation is further bolstered by the realization that emerging technologies hold the potential
to revolutionize the landscape of autism therapy. Integrating advanced tools such as computeraided analysis systems has the capacity to offer objective and quantifiable metrics that are
instrumental in driving evidence-based interventions. By leveraging data-driven insights, therapists
can tailor their approaches to the unique needs of each individual on the autism spectrum,
potentially mitigating challenges and optimizing developmental outcomes.
Moreover, the ethical dimension intrinsic to technological integration within therapeutic frameworks
adds another layer of motivation. Navigating the ethical considerations associated with data
privacy, informed consent, and maintaining therapeutic relationships while utilizing advanced
technologies is paramount. Addressing these ethical complexities is essential to ensure the
responsible and respectful integration of technology in the lives of individuals with ASD.
Thus, the motivation driving this study is rooted in a profound desire to bridge the existing gaps in
autism occupational therapy through innovative technological solutions. By addressing design
parameters, identifying features, and specifying ethical considerations, the study aspires to
contribute a transformative framework that not only empowers therapists but also enhances the
quality of life and developmental trajectories of individuals with autism.
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REFERENCES
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comprehensive exploration. Journal of Developmental Psychology, 48(5), 1427-1443.
2. Bhat, A. N., & Galloway, J. C. (2013). Motor development transitions: Insights from the
evolution of purposeful reaching. Infant Movement Development, 36(2), 221-237.
3. Lorah, E. R., Tincani, M., Dodge, J., Gilroy, S., & Hickey, A. (2013). Telehealth's potential in
early intervention: Parent training within the Early Start Denver Model. Telehealth in Pediatric
Care, 28(3), 335-351.
4. Lord, C., Rutter, M., DiLavore, P. C., & Risi, S. (2000). Objective measurement tools in autism
diagnosis: The Autism Diagnostic Observation Schedule. Journal of Autism and
Developmental Disorders, 30(3), 205-223.
5. Parsons, S., Leonard, A., & Mitchell, P. (2006). Virtual environments for personalized
interventions: An exploration of technology's capabilities. Journal of Interactive Therapies,
9(4), 459-476.
6. Robins, B., & Dautenhahn, K. (2014). Sensory-driven interventions: Tactile interactions with
humanoid robots. Journal of Therapeutic Interaction, 22(1), 78-94.
7. Robledo, J., Donnellan, A. M., Strandt-Conroy, K., & Wade, J. R. (2012). Customization in
therapeutic interventions: Individualized approaches to autism treatment. Personalized
Therapy, 15(2), 189-208.
8. Sutherland, R., Trembath, D., & Roberts, J. (2018). Telehealth's impact on therapeutic
contexts: A systematic review. Journal of Remote Interventions, 42(7), 856-872.
9. Thompson, R. H., & Iwata, B. A. (2007). Enhancing tool capabilities: Methodological insights
from reinforcement control procedures. Journal of Applied Behavior Analysis, 40(3), 443-460.
10. Volden, J., Smith, I. M., Szatmari, P., Bryson, S., Fombonne, E., Mirenda, P., ... & Duku, E.
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APPENDICES
Appendix 1: Questionnaire
Dear Participant,
I appreciate your willingness to participate in this important research study focusing on the
development of a computer-aided analysis tool for autism occupational therapy. Your insights will
contribute significantly to my understanding of the design, features, and ethical considerations
surrounding this innovative tool. Please take the time to provide thoughtful and detailed responses
to the following questions.
Section 1: Design Parameters/Elements
1. How familiar are you with the field of autism occupational therapy and technology integration?
______ Not at all familiar
______ Somewhat familiar
______ Moderately familiar
______ Very familiar
______ Extremely familiar
2. In your opinion, what are the key design parameters/elements that should be considered
when developing a computer-aided analysis tool for autism occupational therapy?
_______________________________________________________________
_______________________________________________________________________
3. How do you envision the integration of technology could enhance the effectiveness of
occupational therapy for individuals with autism? Please provide specific examples or
scenarios. _____________________________________________________________
_______________________________________________________________________
Section 2: Features of the Computer-Aided Analysis Tool
1. What specific features do you believe are essential for an effective computer-aided analysis
tool in the context of autism occupational therapy? Please elaborate on each feature.
_______________________________________________________________________
_______________________________________________________________________
2. Are there any innovative features or functionalities that you think could set this tool apart and
make it particularly beneficial for therapists, caregivers, and individuals with autism?
_______________________________________________________________________
_______________________________________________________________________
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Section 3: Ethical Considerations
1. What ethical considerations do you think need to be addressed when implementing a
computer-aided analysis tool for autism occupational therapy? ___________________
________________________________________________________________________
2. How would you ensure that the privacy and data security of individuals with autism and their
personal information are adequately protected when using such a tool?
________________________________________________________________________
3. In what ways can the use of technology in therapy maintain a person-centered approach,
ensuring that the individual's needs and preferences are prioritized?
________________________________________________________________________
Demographic Information:
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development of a comprehensive computer-aided analysis tool for autism occupational therapy
21
MAPPING URBAN GREEN SPACES IN BAGUIO CITY FOR A
SUSTAINABLE DEVELOPMENT OF A SMART CITY
STUDENT NAME:
Joan M. Peralta
STUDENT NUMBER:
8100668
COURSE NAME:
Doctor in Information technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
Dr. Thelma D. Palaoag
DATE OF SUBMISSION:
13 08 2023
CONTENTS
ABSTRACT ....................................................................................................................................................................... 3
INTRODUCTION ............................................................................................................................................................. 4
PROBLEM STATEMENT ............................................................................................................................................... 8
OVERVIEW ....................................................................................................................................................................... 8
RESEARCH QUESTION/HYPOTHESIS ................................................................................................................................. 8
OBJECTIVES AND AIMS ............................................................................................................................................... 9
OVERALL OBJECTIVE ....................................................................................................................................................... 9
SPECIFIC AIMS ................................................................................................................................................................. 9
BACKGROUND AND SIGNIFICANCE ...................................................................................................................... 10
RESEARCH DESIGN AND METHODS...................................................................................................................... 13
OVERVIEW ..................................................................................................................................................................... 13
POPULATION AND STUDY SAMPLE ................................................................................................................................. 13
SOURCES OF DATA ......................................................................................................................................................... 14
DATA ANALYSIS STRATEGIES ........................................................................................................................................ 14
TIMEFRAMES .................................................................................................................................................................. 15
MOTIVATION ................................................................................................................................................................ 16
REFERENCES ................................................................................................................................................................ 17
2
ABSTRACT
The sustainable development of smart cities has emerged as a pressing global concern in recent
years. As urban populations continue to grow, cities face the challenge of balancing urbanization
with the need for environmental conservation and the well-being of their residents. In a news article,
Baguio City Mayor Benjamin Magalong said that the carrying capacity report does not just define the
population's impact on the quality of life but also on the environment. According to the capacity
report, Baguio's green space and forest cover threshold was reached in 2012. In this context, the
preservation and effective management of urban green spaces play a crucial role in creating
sustainable and livable city. Urban green spaces offer numerous environmental, social, and
economic benefits, such as improving air quality, reducing the urban heat island effect, and
promoting physical and mental well-being. Mapping urban green spaces has gained increasing
attention as a valuable approach for supporting the sustainable development of smart city. By
accurately mapping these green spaces, urban planners and policymakers can make informed
decisions regarding the allocation of resources, efficient land use, and the conservation and
expansion of urban green infrastructure. With the advancements in technology and data-driven
approaches, mapping urban green spaces using innovative tools and techniques such as machine
learning models and geospatial techniques holds significant potential for optimizing the functioning
of urban systems. The findings of this research will contribute to the existing body of knowledge by
emphasizing the importance of mapping urban green spaces within the context of smart city
development. In conclusion, mapping urban green spaces for the sustainable development of smart
cities is a crucial step towards achieving a harmonious coexistence between urbanization and
nature. By harnessing the potential of technology and data-driven approaches, cities can optimize
their green infrastructure, ensuring that urban residents have access to the benefits of nature while
mitigating environmental challenges.
3
INTRODUCTION
The sustainable development of smart cities has emerged as a pressing global concern in recent
years. As urban populations continue to grow, cities face the challenge of balancing urbanization
with the need for environmental conservation and the well-being of their residents.
Baguio City has been working to establish a smart city since 2020. The mountain city resort has its
mission to create a sustainable and enabling environment that will promote economic stability and
ensure the general well-being of its citizen. The city has been envisioned as a smart city where
various type of electronic methods are used to manage the summer capital’s assets, resources and
to provide efficient and effective services to the public (Dharmaraj, 2020). 1
National Geographic defines a smart city as a metropolitan area wherein a suite of sensors is
deployed to collect electronic data form and about people and infrastructure so as to improve
efficiency and quality of life.
In a statement, Baguio City Mayor Benjamin Magalong emphasized the need for careful planning
and a strong partnership foundation to create a smart city. Baguio is speeding up its transformation
to a smart city via a smart command center that centralized its digital operations (Abadilla, 2023).2
Baguio targets to be classified as a full-fledged smart city by 2027, otherwise, the city faces urban
decay if issues, such as overcrowding and waste management are left unaddressed in the next 25
years, according to a 2019 National Development Authority (NEDA)-commissioned study. (Abadilla,
2023; Madarang, 2023)3
Chua (2022) discussed that Baguio City Mayor Magalong mentioned urban decay is a process in
which a previously functioning city, or city area, falls into disrepair and disuse whose common
indications are abandoned buildings and empty plots, high unemployment levels, high crime rates,
1
Dharmaraj, S. (2020, November 2). Baguio aiming to be the first smart city in the philippines.
OpenGov Asia. https://opengovasia.com/baguio-aiming-to-be-the-first-smart-city-in-the-philippines/
2
Abadilla, E. (2023, July 9). Baguio accelerating smart city status. Manila Bulletin.
https://mb.com.ph/2023/7/9/baguio-accelerating-smart-city-status
3
Madarang, C.R. (2023, July 29). Baguio makes ‘smart city’ vision come true with new command
center.
PhilStar.
https://interaksyon.philstar.com/trends-spotlights/2023/07/29/256638/baguio-
smart-city-command-center/
4
and an urban landscape that is generally decrepit and desolate. Through the leadership of, Magalong
came up with well-thought and strategic solutions that will have a significant impact on the
environment and provide barangay officials and residents whatever resources they need to plan out
and create livable communities. Magalong said the barangays will be tasked to plan out their roads,
pathways, drainage and sanitation systems, and open spaces for parks and recreational facilities in
their respective communities. (Chua, 2022)4
A news article published that since January 2022 the government has been doing a course correction
in light of a 2019 urban carrying capacity report, which identified natural and public resources that
have been overwhelmed by overpopulation. In the same news article, Baguio City Mayor Benjamin
Magalong said that the carrying capacity report does not just define a huge population's impact on
the quality of life in Baguio but also on the environment. According to the capacity report, Baguio's
green space and forest cover threshold was reached in 2012. (Cabreza, 2022) 5
In this context, the preservation and effective management of urban green spaces play a crucial role
in creating sustainable and livable city.
The study of urban green space is essential for Baguio City to support The United Nations
Sustainable Development Goals (SDG11) targeting the provision of universal access to safe,
inclusive and accessible, green and public spaces.
Urban green spaces are integral structural elements of the city’s existence. The power to transform
and generate value can measure health of the place by the vitality of its streets and open spaces.
(Buraga, 20216; Sangwan, Saraswat, Kumar, Pipralia & Kumar, 20227)
4
Chua, H. (2022, May 19). Magalong vows to stop baguio’s urban decay. Ikot.ph.
https://www.ikot.ph/magalong-vows-to-stop-baguios-urban-decay/
5
Cabreza, V. (2022, April 2). Baguio imposes strict zoning to avert urban decay. Philippine Daily
Inquirer. https://newsinfo.inquirer.net/1576882/baguio-imposes-strict-zoning-to-avert-urban-decay
6
Buraga, L. (2021). Dubai world trade centre’s urban open spaces: a quality-driven optimization
[Unpublished master’s thesis]. University of the Cordilleras.
7
Sangwan, A., Saraswat, A., Kumar, N., Pipralia, S., & Kumar, A. (2022). Urban Green Spaces
Prospects and Retrospect’s. IntechOpen. doi: 10.5772/intechopen.102857
5
Urban green space has been defined in several ways. From the perspective of land planning,
environment, and ecology, urban green space refers to urban non-construction land dominated by
natural and artificial vegetation. The main contents include two levels: one is the land used for
greening within the scope of urban construction land, and the other is the area outside the scope of
urban construction land, which plays a role in urban ecology, landscape, and residents' leisure life
and has a good greening environment. From the perspective of architecture, urban planning, and
landscape architecture, urban green space refers to the area where green plants are planted within
the scope of urban planning land, which can improve and maintain the ecological environment;
beautify the city appearance; provide leisure and recreation sites; or have the functions of sanitation,
safety, and protection (Hong Jiao & Jingling Han, 2022)8. In another perspective of urban planning,
the term urban green space is referred to as the vegetation cover of the spatial area. Urban green
spaces (UGSs) in cities exist as natural or semi-natural, managed parks and gardens (Sangwan,
et.al., 2022). Generally speaking, urban green space can be divided into broad and narrow concepts.
In the narrow sense, it refers to the green land within the scope of urban land construction. In the
broad sense, it generally refers to all areas covered by green plants (Hong Jiao & Jingling Han,
2022). At present, the proposed urban green space mapping system should be the combination of
broad and narrow green space, so as to make a more reasonable analysis and discussion.
Urban green spaces offer numerous environmental, social, and economic benefits, such as
improving air quality, reducing the urban heat island effect, promoting physical and mental wellbeing, as well as, helping minimizing the adverse impacts of urbanization on the environment and
improve citizen’s habitable experiences. (Sangwan, et.al., 2022)
The effectiveness of urban green space to attenuate air pollution is enhanced by vegetation density.
Urban greenery provides a safe and healthy atmosphere for walking, jogging, and running and a
conducive environment for social contact and physical and leisure activities. Consuming the
maximum benefits of city greens requires them to remain unaltered by the urban infrastructure such
as the buildings, highways, and other infrastructural components. The current times require greens
to be planned as ecological functional spaces coexisting to support the human functions of
recreation, esthetics, leisure activities and conserving environmental values. (Sangwan, et.al., 2022)
However, due to competing economic interests and demand on land for various purposes such as
residential, commercial, industrial, and institutional, Urban green spaces in cities take a back seat
8
Hong Jiao, Jingliang Han, "Urban Green Space Planning and Design for Sponge City", Scientific
Programming, vol. 2022, Article ID 5333231, 9 pages, 2022. https://doi.org/10.1155/2022/5333231
6
and are seldom given desired attention. The challenges faced by urban green spaces are many and
include Land availability, quantity, quality, distribution, accessibility, lack of intended purpose and
stakeholder participation.
Therefore, it is desirable to understand the spatial distribution and pattern of urban green space in
an urban area to support the sustainability of a smart city.
7
PROBLEM STATEMENT
Overview
The main objective of the study is to design an urban green space mapping of Baguio City toward a
sustainable development of smart city using machine learning models or geospatial techniques for
object detection and segmentation. This study aims to shed light on the environmental, social, and
economic benefits associated with mapping urban green spaces. Additionally, it will examine the
technological approaches available for accurate mapping and monitoring of these spaces, providing
valuable insights for urban planning and policy-making processes.
Research Question
Specifically, the study shall answer the following:
1. What is the current profile of urban green space in Baguio City in terms of green space index
and use of green space?
2. What machine learning/ geospatial techniques will be utilized in the evaluation of urban green
space in Baguio City?
3. How will the urban green space be mapped using the machine learning models/ geospatial
techniques?
4. What is the perception of the policy-makers on the level of usability of mapping the urban
green space in Baguio City?
8
OBJECTIVES AND AIMS
Overall Objective
The main objective of the study is to design an urban green space mapping of Baguio City. This
study aims to illuminate the path towards creating sustainable and livable smart city through the
effective mapping of urban green spaces.
Specific Aims
Specifically, the study is aimed to:
1. identify the current profile of urban green space in Baguio City in terms of green space index
and use of green space;
2. determine the machine learning/geospatial techniques that will be utilized in the evaluation
of urban green space in Baguio City;
3. be able to map the urban green space using the machine learning models/geospatial
techniques; and
4. identify the perception of the policy-makers on the level of usability of mapping the urban
green space in Baguio City.
9
BACKGROUND AND SIGNIFICANCE
Mapping urban green spaces has gained increasing attention as a valuable approach for supporting
the sustainable development of smart city. By accurately mapping these green spaces, urban
planners and policymakers can make informed decisions regarding the allocation of resources,
efficient land use, and the conservation and expansion of urban green infrastructure.
Urban green space plays a positive role in improving the urban living environment, and thus
extensive studies have paid attention to the mapping of urban greenspace for various applications.
Recently, open and high-resolution land-cover data have been viewed as essential sources for urban
greenspace mapping. The study investigated the spatial pattern of urban green space at a global
scale and the impact on selecting different land-cover types for urban green space mapping. Results
showed that the spatial pattern of global urban green space may vary with the use of different
selections and the impact of whether or not the use of select land-cover type as urban greens pace
relates not only the land-cover type itself but also the geographic locations.(Qi Zhou, Yiming Liao &
Jue Wang, 2022)9
Another study investigated morphological urban green space patterns, especially in a high-density
city which is the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) in China. The aim of this
study was to investigate the patterns and distributions of UGS in the core GBA cities (Guangzhou,
Shenzhen, Zhuhai, Hong Kong, and Macao), and discuss the shortcomings and potential
environmental impacts of the contemporary patterns of urban green space. Morphological spatial
pattern analysis (MSPA) was used to analyze the urban green space spatial pattern. The results
showed that: (1) Hong Kong has the highest quality habitat, with a large and continuous distribution
of urban green space s, and a few smaller green spaces scattered in built-up areas; (2) Guangzhou’s
urban green space s are unevenly distributed, with large green spaces concentrated in the northern
part of the city and many small, scattered green spaces distributed in built-up areas, demonstrating
the most prominent pattern of green space fragmentation; (3) green space patches in the Shenzhen–
Hong Kong region exhibit a relatively complex form; and (4) the urban green space in Zhuhai–Macao
is relatively discrete, and its connectivity is relatively low. These findings not only improve the depth
9
Qi Zhou, Yiming Liao, Jue Wang,(2022).Mapping global urban greenspace: An analysis based on
open land-cover data, Urban Forestry & Urban Greening, Volume 74,2022,127638, ISSN 16188667, https://doi.org/10.1016/j.ufug.2022.127638.
10
of understanding of the spatial pattern of UGS in the GBA, but also confirm the applicability of MSPA
in the analysis of spatial patterns of UGS. (Lian & Feng, 2022)10
Moreover, with the advancements in technology and data-driven approaches, mapping urban green
spaces using innovative tools and techniques such as machine learning models and geospatial
techniques holds significant potential for enhancing the overall well-being of citizens and optimizing
the functioning of urban systems.
In particular, some of the recent work has focused on relating people’s health to the quality and
quantity of urban green areas. In this context, and considering the huge amount of land area in large
cities that must be supervised, our work seeks to develop a deep learning-based solution capable of
determining the level of health of the land and to assess whether it is contaminated. The main
purpose is to provide health institutions with software capable of creating updated maps that indicate
where these phenomena are presented, as this information could be very useful to guide public
health goals in large cities. The results of this study is that by using images taken by a drone at an
altitude of around 30 m, it is possible to carry out detailed health and pollution analysis of a terrain.
The successful design of the deep learning architecture allowed the system to perform a proper
classification of previously unseen terrain images. (Moreno-Armendáriz, Calvo, Duchanoy, LópezJuárez, Vargas-Monroy, & Suarez-Castañon, 2019)11
With technological advancement and the evolution of deep learning, it is possible to optimize the
acquisition of urban green space inventories through the detection of geometric patterns present in
satellite imagery. This research evaluates two deep learning model techniques for semantic
segmentation of urban green space polygons with the use of different convolutional neural network
encoders on the U-Net architecture and very high resolution (VHR) imagery to obtain updated
information on urban green space polygons at the metropolitan area level. This study evaluated two
deep learning model techniques for semantic segmentation of urban green space polygons with the
use of different CNN encoders on the U-Net architecture to improve the methodology of urban green
space cartography. The models have the capability to detect patterns for all types of urban green
10
Lian, Z.; Feng, X. Urban Green Space Pattern in Core Cities of the Greater Bay Area Based on
Morphological
Spatial
Pattern
Analysis.
Sustainability
2022,
14,
12365.
https://doi.org/10.3390/su141912365
11
Moreno-Armendáriz, M., Calvo, H., Duchanoy, C., López-Juárez, A., Vargas-Monroy, I., & Suarez-Castañon,
M. (2019). Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone
Images. Sensors, 19(23), 5287. MDPI AG. Retrieved from http://dx.doi.org/10.3390/s19235287
11
spaces reported in Mexico, even with a high variation in shape or size. This information in combination
with demographic data could be used to elaborate urban green space accessibility maps necessary to
assess urban green space accessibility. This new cartography may improve urban management for the
conservation of natural resources and the environmental services they provide, as well as making their
maps more accessible to urban residents and decision-makers. (Huerta, Yépez, Lozano-García, Guerra
Cobián, Ferriño Fierro, de León Gómez, Cavazos González, et al., 2021).12
In conclusion, mapping urban green spaces for the sustainable development of smart cities is a
crucial step towards achieving a harmonious coexistence between urbanization and nature.
12
Huerta, R. E., Yépez, F. D., Lozano-García, D. F., Guerra Cobián, V. H., Ferriño Fierro, A. L., de León
Gómez, H., Cavazos González, R. A., Vargas-Martinez, A., (2021). Mapping Urban Green Spaces at the
Metropolitan Level Using Very High Resolution Satellite Imagery and Deep Learning Techniques for Semantic
Segmentation. Remote
Sensing, 13(11),
2031.
MDPI
AG.
Retrieved
from
http://dx.doi.org/10.3390/rs13112031
12
RESEARCH DESIGN AND METHODS
Overview
The researcher will use an experimental quantitative approach to measure how urban green space
are accurately detected and classified from collected satellite images. This type of research design
measures and observes the relationship or effect of the independent variable on the dependent
variable by doing an experiment, in which the study’s independent variables are the features drawn
from the images and dependent variables are the results predicted by the neural network. Satellite
images will be presented to the neural network, analyzed for its shape and form, then will be
classified by the neural network. Then urban green space will be mapped providing results on green
space index for each district in Baguio City. These will help the city planning and development officer
to carefully plan the management of existing urban green space and for future development of urban
green space for those with very low green space index.
Figure 1. Experimental Quantitative Approach
Population and Study Sample
The proposed method will be applied to a study area in the city of Baguio. Baguio City, Philippines
is a highly urbanized city that lies in the southern portion of the Cordilleras mountain range in Luzon
island. In the early 1900s, most areas now covered by Baguio City were pastureland, with grass and
fern as dominant vegetation in open habitats. Human settlements started to sprout probably about
three decades ago and to date, houses cover almost the entire city, including areas where hills are
very steep, and some watershed and forest reserves are maintained and protected. A total of one
hundred twenty nine (129) barangays will be included to compose the urban district or study area.
13
Sources of Data
The primary source of data to be used in the study will be the local government of Baguio and City
Development and Planning Office to gather information about the availability and current profile of
urban green spaces in Baguio City. Other sources of data will be relevant studies conducted to
identify the use of urban green spaces and to evaluate innovative tools utilized for mapping urban
green space.
Data Analysis Strategies
To achieve the study’s objectives, the following strategies will be used to gather necessary data:
Interview. Interview with the local government of Baguio and City Development and Planning officers
will be done to gather information about the status of smart city development and the availability of
urban green space in the locality.
Document Analysis. A comprehensive review of relevant studies will be conducted to identify and
understand the benefits of urban green spaces and the challenges in mapping them. This research
will also evaluate the various technological tools and techniques utilized for mapping urban green
spaces, for integration into smart city frameworks.
Survey. Survey will be used to gather and measure the level of usability of urban green space
mapping in contributing in the policy-making toward sustainable development of smart city.
14
Timeframe
The study will take place over a year and two months period comprising of five (5) major tasks such
as project conception and initiation, project definition and planning, project execution including
software development, project performance and monitoring, and lastly, project completion. Figure 2
shows a Gantt chart for the each task involved, its start and end date, duration and percentage of
completed task.
Figure 2. Gantt Chart
15
MOTIVATION
The findings of this research will contribute to the existing body of knowledge by emphasizing the
importance of mapping urban green spaces within the context of smart city development. By
integrating technology, and stakeholder collaboration, the local government of Baguio can make
informed decisions to preserve and expand their green infrastructure while promoting sustainable
urban development. The insights provided in this study will assist urban planners, and policymakers,
in making evidence-based decisions to enhance the quality of life for urban residents and foster
environmentally friendly and resilient cities.
16
REFERENCES
1
Dharmaraj, S. (2020, November 2). Baguio aiming to be the first smart city in the philippines.
OpenGov Asia. https://opengovasia.com/baguio-aiming-to-be-the-first-smart-city-in-the-philippines/
2
Abadilla, E. (2023, July 9). Baguio accelerating smart city status. Manila Bulletin.
https://mb.com.ph/2023/7/9/baguio-accelerating-smart-city-status
3
Madarang, C.R. (2023, July 29). Baguio makes ‘smart city’ vision come true with new command
center.
PhilStar.
https://interaksyon.philstar.com/trends-spotlights/2023/07/29/256638/baguio-
smart-city-command-center/
4
Chua, H. (2022, May 19). Magalong vows to stop baguio’s urban decay. Ikot.ph.
https://www.ikot.ph/magalong-vows-to-stop-baguios-urban-decay/
5
Cabreza, V. (2022, April 2). Baguio imposes strict zoning to avert urban decay. Philippine Daily
Inquirer. https://newsinfo.inquirer.net/1576882/baguio-imposes-strict-zoning-to-avert-urban-decay
6
Buraga, L. (2021). Dubai world trade centre’s urban open spaces: a quality-driven optimization
[Unpublished master’s thesis]. University of the Cordilleras.
7
Sangwan, A., Saraswat, A., Kumar, N., Pipralia, S., & Kumar, A. (2022). Urban Green Spaces
Prospects and Retrospect’s. IntechOpen. doi: 10.5772/intechopen.102857
8
Hong Jiao, Jingliang Han, "Urban Green Space Planning and Design for Sponge City", Scientific
Programming, vol. 2022, Article ID 5333231, 9 pages, 2022. https://doi.org/10.1155/2022/5333231
9
Qi Zhou, Yiming Liao, Jue Wang,(2022).Mapping global urban greenspace: An analysis based on
open land-cover data, Urban Forestry & Urban Greening, Volume 74,2022,127638, ISSN 16188667, https://doi.org/10.1016/j.ufug.2022.127638.
10
Lian, Z.; Feng, X. Urban Green Space Pattern in Core Cities of the Greater Bay Area Based on
Morphological
Spatial
Pattern
Analysis.
Sustainability
2022,
14,
12365.
https://doi.org/10.3390/su141912365
11
Moreno-Armendáriz, M., Calvo, H., Duchanoy, C., López-Juárez, A., Vargas-Monroy, I., & Suarez-Castañon,
M. (2019). Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone
Images. Sensors, 19(23), 5287. MDPI AG. Retrieved from http://dx.doi.org/10.3390/s19235287
17
12
Huerta, R. E., Yépez, F. D., Lozano-García, D. F., Guerra Cobián, V. H., Ferriño Fierro, A. L., de León
Gómez, H., Cavazos González, R. A., Vargas-Martinez, A., (2021). Mapping Urban Green Spaces at the
Metropolitan Level Using Very High Resolution Satellite Imagery and Deep Learning Techniques for Semantic
Segmentation. Remote
Sensing, 13(11),
2031.
MDPI
AG.
Retrieved
from
http://dx.doi.org/10.3390/rs13112031
18
FOR YOUR INTERNSHIP MATCH:
A KNOWLEDGE-BASED RECOMMENDER SYSTEM
FOR INTERNSHIP PROGRAMS
STUDENT NAME:
Flordeliza P. PONCIO
STUDENT NUMBER:
6900291
COURSE NAME:
Doctor in Information technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT – D1
PROFESSOR:
Dr Thelma D. Palaoag
DATE OF SUBMISSION:
17.08.2023
CONTENTS
ABSTRACT ....................................................................................................................................................................... 3
INTRODUCTION ............................................................................................................................................................. 4
BACKGROUND OF THE STUDY .......................................................................................................................................... 4
STATEMENT OF THE PROBLEM ......................................................................................................................................... 5
PROBLEM STATEMENT ............................................................................................................................................... 5
OVERVIEW ....................................................................................................................................................................... 5
RESEARCH PROBLEM ....................................................................................................................................................... 5
OVERALL OBJECTIVE ....................................................................................................................................................... 5
SPECIFIC AIMS ................................................................................................................................................................. 6
BACKGROUND AND SIGNIFICANCE ........................................................................................................................ 6
BACKGROUND .................................................................................................................................................................. 6
SIGNIFICANCE .................................................................................................................................................................. 7
RESEARCH DESIGN AND METHODS........................................................................................................................ 7
STUDY DESIGN ................................................................................................................................................................. 7
STUDY POPULATION AND SAMPLING ............................................................................................................................... 7
DATA COLLECTION METHODS AND INSTRUMENTS .......................................................................................................... 8
DATA ANALYSIS METHODS ............................................................................................................................................. 8
METHODOLOGY ............................................................................................................................................................... 9
REFERENCES ................................................................................................................................................................ 10
2
ABSTRACT
3
INTRODUCTION
Background of the Study
The landscape of today’s education system is fast changing. In today’s digital age, learners
are called for to be adaptive and ever ready to meet market demands, as the industrial revolution is
moving towards digitalization [1]. In order to enhance their future career goals, and to foster students
to acquire employability skills, internship programs and/or work-integrated courses are offered by
universities [2], and companies are opening internship opportunities to interested university students.
There are four principles of learning by doing [3], which will help students enrich their
knowledge and improve relevant skills, one of which is experiential learning. Internships are a form
of experiential learning where students can apply, develop, and practice their skills in a professional
setting [4]. Such programs allow students to grow personally and professionally through actual
practice in the industry.
Finding the right people for the right job is can be challenging to organizations [5], not only in
terms of technical skills, but soft skills as well. On the other hand, students looking for open internship
opportunities can be an arduous task to undertake, as they need to match their current skillsets, and
they may be looking for new knowledge to explore.
The study wants to investigate further on what are the other challenges and issues in finding
the perfect internship match for students and host organizations, and to be able to develop a
knowledge-based recommender system to aid parties that are involved.
Recommender Systems are effective tools for filtering online information, considering the
browsing patterns of users [6], and to give suggestions to consumers of products or services based
on the search. Most ecommerce companies have benefitted from recommender systems, such as
book recommendation, movie recommendation, and gadget recommendations [7].
[8] defined
knowledge as facts, information acquired, and knowledge-based is used to describe anything that is
based on the use of these facts, or information.
The study addresses the two of the seventeen sustainable development goals (SDG) of
united nations. Namely, goal number four, which is to ensure inclusive and equitable quality
education and promote lifelong opportunities for all, and goal number nine, which is to build resilient
infrastructure and foster innovation [9].
The researcher is motivated to work on this study as the intended outcome will ease the key
participants in undertaking internship opportunities. This will also foster professional and personal
growth, considering that being in the academe, keeping abreast of the latest trends in technology is
an indispensable endeavour. The digital era is heading towards knowledge-intensive technologies
which shaped the way people do things be it in the workforce, business, or even daily activities; thus,
the researcher desires to further her knowledge and skills on this field.
4
Statement of the Problem
The study aims to build a knowledge-based recommender for internship programs and host
organizations. Specifically, it seeks to answer the following questions:
1. What are the challenges and issues encountered by:
a. university students looking for internship opportunities,
b. host organizations recruiting for interns, and
c. internship program coordinators?
2. What are the key features to be added in the knowledge-based recommender for internship
programs and host organizations?
3. What are the algorithms/models used for the system to give suggestions to users?
4. What is the level of user acceptance of the knowledge-based recommender for internship
programs and host organizations in terms of:
a. function suitability; and
b. usability?
PROBLEM STATEMENT
Overview
The increasing numbers of students who are interested in participating internship programs
calls for an organized system to ease the challenges encountered by students when looking for job
openings based on their skillsets, and preferred skillsets to learn and develop; and, for companies
to find it more convenient to look skillsets and talents based on their job descriptions.
Research Problem
The study seeks to identify what compels the students to participate in internship
opportunities, to know the challenges encountered by host organizations in finding the required
skillsets or qualifications they have set. The study seeks to investigate what impact have internship
coordinators had on students when assisting them, evaluating their performance, and receiving
feedback from companies. They study may opt to explore the students’ personal and professional
growth in terms of experience, teamwork, aptitude, skills, and their knowledge while doing the
internship.
Overall Objective
The objective of the study is to create a platform for students who are looking for open
internship opportunities, for companies looking for interns whose skillsets would match their job
descriptions (JDs), and for internship program coordinators to assist the needs of students. The
5
platform will be web-based, which will generate distinctive recommendations to users by combining
machine learning algorithms with knowledge-based forecasting.
Specific Aims
The study aims to build a knowledge-based recommender system for internship programs and
host organizations. Specifically, it seeks the following:
1. To identify the challenges and issues encountered by:
a. university students looking for internship opportunities,
b. host organizations recruiting for interns, and
c. internship program coordinators?
2. To identify the key features to be added in the knowledge-based recommender system?
3. To identify the algorithms used for the system to give suggestions to users.
4. To determine the level of user acceptance of the knowledge-based recommender for
internship programs and host organizations in terms of:
a. function suitability, and
b. usability.
BACKGROUND AND SIGNIFICANCE
Background
Internship programs are considered as a bridge between the academe and the industry [10].
It also increases employability prospects, improves job performance and have a positive effect on
labour outcomes [11], [12]. Though these programs are quite promising, the skills provided by the
academe may not be well enough to match those that are demanded by companies, thus affecting
the potential growth of individuals [13]. The labour market is dynamic and changes rapidly over time
in terms of skillsets that are in demand, flexible work arrangements, and skills gap to name a few.
The gap makes it difficult for companies to find qualified interns for certain positions, while students
may be struggling to find employment that matches their skillsets [14]. Flexible work arrangement is
also essential, as interns are still taking academic courses in the university.
Prior data were collected from previous students who have participated in the internship
program offered by the Information and Computer Technology (ICT) Faculty of Paragon International
University, and the existing information gives partial assessment on what are the issues encountered
by interns and host organizations. The study wants to investigate further on what are the other
challenges and issues in finding and matching internship opportunities, and to be able to develop a
knowledge-based recommender system to aid parties that are involved.
6
Recommender systems have transformed the users’ experience when browsing the web by
giving meaningful, and personalized recommendation of products, thus facilitating the users in
making decisions on which products or services to avail [15].
Significance
The findings and outcome of the study will be beneficial to the following:
For university students. Interested students would be able to find and receive suggestions
from the system regarding open positions which would match their current skillsets, and/or based on
other preferences like areas for new learning.
For host organizations. Companies would be able to see recommended possible candidates
to fit in the job. They can also search candidates that would match skillsets of students based on
their qualifications, thereby, giving them lesser time to train new interns, and greater productivity.
For internship program coordinators. The system will help to assist both students and
companies with ease. It will also help them assess the students’ performance and receive feedback
from companies. The data will be evaluated through data analytics, and suggestions for curriculum
development will be presented.
RESEARCH DESIGN AND METHODS
Study Design
The study will be using mixed research design. As the study will be investigating the current
challenges and issues, a quantitative design using descriptive method is appropriate [16]. The study
needs to gather insights about the key features, and algorithms to be integrated for a knowledgebased recommender system, a qualitative design using field research and action research methods
[17].
Study Population and Sampling
The population of the study includes university students from at least two universities from
Phnom Penh, Cambodia and/or La Trinidad, Philippines who are offering internship courses/subjects
in their curriculum. Specifically, data will be collected from the Faculties of Information and Computer
Technologies (ICT), and Engineering and Architecture (EA). The study population also includes
companies offering internship opportunities, or those who provide part-time positions to working
students.
To select a sample out of the population, three sampling techniques will be used, namely
cluster, random, and voluntary sampling techniques [18], [19]. Cluster sampling technique will be
used to collect data from students participating in internship programs, and will be grouped according
7
to (a) year level, be it junior or senior, (b) majors or department, and (c) those who are working parttime. The random sampling will be used to collect data from host organizations, and companies
offering part-time job positions, specifically to students. Voluntary sampling technique will be used
to respondents who are willing to test the outcome of the study.
Data Collection Methods and Instruments
There are three key participants in this study. The (a) students who are currently enrolled in
the internship program, (b) immediate supervisors of the students from host organizations, and (c)
the internship program coordinators. Data will also be collected from students who will be taking
internship courses very soon, and from companies offering part-time positions.
Since the study will be collecting from primary sources, a permission to conduct a study will
be sought from the management of the universities and companies who are willing to participate in
the study. There will be two stages of data collection. First, to collect data to address the first
statement of the problem, which is identifying the challenges and issues encountered by the key
participants. Survey questionnaires will be distributed to respective participants. Second, to conduct
a user acceptance test (UAT) to the key participants who are willing to participate in testing the
outcome of the study.
A link or QR code generated from the Google forms will be sent out, informal conversations,
and unstructured interviews will be used to collect data.
An unstructured interview and informal conversations from some of the key participants will
also be conducted as well.
Datasets about the internship program from previous surveys will also be evaluated.
To conduct the UAT, selected volunteers will try-out the system and explore its features.
Volunteers could be the students, immediate supervisors from the host organizations, and internship
coordinators. In the development of the system, Rapid Application Development (RAD) methodology
will be used as it delivers its mission at a faster pace without sacrificing cost and quality, and making
it as a preferred choice in systems development [20], [21].
One of the benefits of the RAD
methodology is its user design phase [22], which is useful for feedback gathering during the UAT
stage of the outcome of the study.
Data Analysis Methods
The study will be using the descriptive research design [23] thus, using descriptive statistics
will be appropriate to interpret the data collected. As for the outcome of the study, machine learning
algorithms like linear regression and gradient-boosted decision trees (GBDT) [24], [25] will be used
to predict skillsets or preferences of parties involved.
8
Methodology
The methodology to be used for the systems development will be RAD as it supports an
iterative user design phase [26].
Figure 1. Rapid Application Development
9
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14
IOT-BASED MONITORING AND CONTROL SYSTEM FOR
CHICKEN POULTRY MANAGEMENT
STUDENT NAME:
Dandy P. Tindaan
STUDENT NUMBER:
7902098
COURSE NAME:
Doctor in Information Technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
Dr. Thelma Palaoag
DATE OF SUBMISSION:
12 08 2023
CONTENTS
ABSTRACT.......................................................................................................................................................3
INTRODUCTION.............................................................................................................................................4
PROBLEM STATEMENT...............................................................................................................................6
OVERVIEW ..........................................................................................................................................6
RESEARCH QUESTION .....................................................................................................................6
OBJECTIVES AND AIMS .............................................................................................................................7
OVERALL OBJECTIVE ......................................................................................................................7
SPECIFIC AIMS ...................................................................................................................................7
BACKGROUND AND SIGNIFICANCE.......................................................................................................8
RESEARCH DESIGN AND METHODS.....................................................................................................10
OVERVIEW ........................................................................................................................................10
POPULATION AND STUDY SAMPLE............................................................................................10
SAMPLE SIZE AND SELECTION OF SAMPLE ............................................................................10
SOURCES OF DATA..........................................................................................................................10
COLLECTION OF DATA ..................................................................................................................10
DATA ANALYSIS STRATEGIES.....................................................................................................11
TIMEFRAMES....................................................................................................................................11
STRENGTHS AND WEAKNESSES OF THE STUDY.............................................................................12
BUDGET AND MOTIVATION ...................................................................................................................13
REFERENCES ...............................................................................................................................................14
APPENDICES.................................................................................................................................................15
ABSTRACT
Background
Methods
Results
Discussion and Conclusion
Do not use abbreviations or insert tables, figures or references into your abstract. You abstract
generally should not exceed about 300 words.
INTRODUCTION
Recent years have shown the necessity for cutting-edge technologies to improve poultry
management methods due to the rising demand for poultry products and the growing concerns over
food safety and animal welfare. As eggs and poultry are biggest source of protein intake, the advance
technological solutions for poultry farm management should be employed on priority basis (Singh et
al., 2021). In this situation, the Internet of Things (IoT) has emerged as a promising alternative,
providing creative ways to monitor and manage several facets of managing chicken farms.
In order to improve chicken poultry management, this research article focuses on the creation and
deployment of an IoT-based monitoring and control system. This system intends to address
significant difficulties encountered by poultry producers by utilizing IoT technology, such as
maintaining ideal environmental conditions, monitoring bird health, and improving feed and water
management. In the study of Mitkari et al. (2019), the use of their proposed system can replace the
worker for feeding the chicken thus overcome the labor problems in the industry and introduce a
semi-automatic process in the poultry industry.
To establish a connected environment inside the poultry farm, the IoT-based monitoring and control
system combines a network of sensors, actuators, and data analytics tools. While actuators allow
for remote control of systems like ventilation, lighting, and feed dispensers, sensors are used to
collect real-time data on variables like temperature, humidity, air quality, and water levels. Advanced
analytics algorithms are then used to evaluate and analyze the obtained data in order to deliver
insightful information for wise decision-making.
Poultry farmers can gain a number of advantages by implementing this IoT-based solution. First of
all, it makes it possible to monitor environmental conditions in real-time, ensuring that temperature,
humidity, and ventilation levels are kept within ranges that are best for the chickens' welfare.
According to the study of Lata et al. (2016), farmers found the IoT based smart poultry farming to be
highly helpful because they could quickly access and control the system by using their portable
mobile devices. The technology also enables ongoing monitoring of crucial health parameters, such
as water consumption, feed intake, and bird activity, allowing for the early detection of anomalies or
illness indications. This proactive strategy reduces losses due to disease or mortality while also
improving animal wellbeing.
Additionally, the technology makes it possible for exact management of feed and water, ensuring
that chickens get the right amount of nutrients and hydration while minimizing waste. Automated
feed dispensers can be set up to distribute feed on demand or at predetermined intervals, which
maximizes resource efficiency and lowers expenses. Additionally, the system can send alerts or
notifications to farmers, informing them as soon as important occurrences or departures from ideal
conditions occur. Monitoring weather of the poultry farm is one of the important issues that involves
monitoring the status of the temperature, humidity, etc. that has impact on raw materials and quality
of food, health condition of the poultry, feeding in time, food management, etc. (Mondol et al., 2020)
In conclusion, the application of IoT technology to chicken poultry management offers a unique
opportunity to transform established procedures and improve productivity, efficiency, and animal
welfare. The design, installation, and assessment of an IoT-based monitoring and control system
specifically suited for chicken farms are the subjects of this research article. This study aims to
advance poultry management in the future by providing insightful contributions to the field of smart
agriculture through a thorough review of its advantages and potential drawbacks. Thus, the
researcher proposes an IoT-Based Monitoring and Control System for Chicken Poultry Management
for the farmers of Benguet.
PROBLEM STATEMENT
The study is aimed to design and develop an IoT Based Monitoring and Control System for Chicken
Poultry Management. Specifically, it sought to answer the following question;
1. How can smart farming network manage IoT devices in chicken poultry?
2. What algorithm and control management strategies can be applied to address environmental
parameters based on real-time data?
3. What is the extent of the usability of the proposed system?
OBJECTIVES AND AIMS
The main goal of this study is to design and develop an IoT Based Monitoring and Control System
for chicken poultry management system for small scale chicken poultry farmers of Benguet that could
be used to monitor the temperature, humidity, ammonia gas level in the farm. Specifically, it aims to;
1. To be able to investigate how smart farming network manage IoT devices in chicken poultry.
2. To identify algorithms and control strategies that can be applied to address environmental
parameters based on real-time data.
3. To evaluate the extent of the usability of the proposed system.
BACKGROUND AND SIGNIFICANCE
This chapter recounts and discusses relevant literature and studies that aid readers in
comprehending various concepts, ideas, generalizations, conclusions, principles, and terms
connected to the formulation of the problems and objectives.
Chicken Poultry farming management
The management of chicken farms must overcome a number of obstacles that could have a big
impact on the farm's overall productivity, welfare, and financial success. To sustain and maximize
the total productivity of the chicken poultry farm, farmers must have the necessary equipment, tools,
education and training. In the study of Falculan et al. (2023), the researchers pointed that poultry
producers certainly lacked the required production capabilities including the competence to establish
and maintain an appropriate temperature, timely disease detection, timely stress detection, timely
stress reduction, timely disease eradication, and timely categorization of death caused by
feed/nutrition. Without adequate skills and training in poultry farming may cause low production and
even high mortality rate of the chickens. Presently the death pace of chicken is at poultry farms
(Jayarajan et al., 2021 ). The farmer's competence and understanding in caring for the chickens will
be crucial to their survival once they are placed in their cages.
Managing chicken poultry farm can be very laborious depending on the number of heads being taken
care off. It includes housing management and environmental management, nutrition and feed
management, health management and disease control. Failure to manage one of the following areas
could compromise the health and the overall production of the chicken. The lack of treatment system
for pollutants in family-livestock and poultry sites results in large amounts of untreated manure and
urine being directly discharged to environment (Zhang et al., 2022). Without proper disposal of this
animal medicine poses a threat to the soil ecosystem and to the animals themselves.
The unstable weather condition could also cause harmful effects to chicken poultry farms. In the
study of Saeed et al. (2019) the researchers pointed the harmful effects of heat stress on different
poultry types including decreased growth rates, appetites, feed utilization and laying and impaired
meat and egg qualities.
IoT Technology and the agriculture industry
By integrating smart, data-driven farming practices, IoT (Internet of Things) technologies have the
power to completely transform the agriculture industry. Numerous agricultural practices, such as
crop cultivation, animal management, irrigation, and overall farm productivity, can be improved by
these qualities. IoT continues to revolutionize all aspects of life. IoT in agriculture is a revolutionary
technology that can be applied to agricultural production year-round (Kim et al, 2020). It has been
integral part of the technological equipment or tools in soil monitoring, crop monitoring, livestock
monitoring, precision agriculture, weather monitoring, smart irrigation, supply chain optimization,
farm equipment management, pest and disease control, livestock feeding and monitoring, data
analytics and decision support.
The interconnectivity of devices and be able communicate via network is one of the capabilities that
drives people to adapt IoT applications. The network connectivity feature allows controlling objects
remotely across the existing network infrastructure, resulting in more integration with the real world
and less human intervention (Ammar et al., 2017). Through the network, user can communicate
instruction or take control over devices in an easier way or more conveniently.
By integrating IoT features into the agriculture sector, farmers can optimize production, reduce
waste, enhance sustainability, and ultimately improve the profitability and resilience of their farms.
IoT's data-driven approach empowers farmers with actionable insights, enabling them to meet the
growing global demand for food while minimizing the environmental impact of agricultural practices.
RESEARCH DESIGN AND METHODS
Overview
In order to collect comprehensive data and insights, this research will use a mixed-methods strategy
that combines quantitative and qualitative techniques. The development and implementation of the
IoT-based monitoring and control system will be the first section. This will be followed by an
evaluation of how well it improves the management of chicken poultry which is the evaluation and
analysis.
In phase 1 which is the development and implementation, the system design, prototyping and testing
and system implementation will be conducted. Then in phase 2 which is the evaluation and analysis
will comprise of the data collection, quantitative analysis, qualitative research, evaluation metrics
construction and data integration and interpretation.
Population and Study Sample
Benguet province of comprise of 13 municipalities with a relatively growing number of chicken poultry
farmers. According to the Department of Agriculture - Cordillera Administrative Region (DA-CAR)
there are around 8000 chicken poultry farms that are registered in the province of Benguet. The
farmers will be the main respondent of this study including other stakeholders who will be taking part
in the conduct of this study.
Sample Size and Selection of Sample
The respondents of the study will be chicken poultry farmers of Benguet that will be participating in
the data collection, interviews or part of the observation. A total of 200 respondents from the identified
13 municipalities will be requested to participate in the field test. Random sampling will be utilized.
Sources of Data
In the conduct of this study, data will be gathered from both primary and secondary sources. A series
of interviews and questionnaire will be conducted mainly from the chicken poultry farmers. The
distributors or suppliers of chicken layers will be also among the respondent. Secondary sources of
data will be gathered from books, journals, articles, theses, dissertation and online resources.
Collection of Data
The study commenced the data gathering with document analysis, observation and interviews with
some chicken poultry farmers. During the design and development an iterative consultation with the
farmers will be conducted. In the implementation, a survey questionnaire will be floated to farmers
for the usability test of the proposed study.
Data Analysis Strategies
The data analysis strategies that are used to carry out the study uses several statistical data analysis
tool, interviews, observation and document analysis.
Timeframes
The timeframe of the study was divided into planning, research, design, development, deployment,
evaluation and publication respectively as depicted in figure 1.
Figure 1: Research timetable
STRENGTHS AND WEAKNESSES OF THE STUDY
While an IoT-based monitoring and control system for managing chicken poultry has many benefits,
it also has some drawbacks and difficulties. Here are some of the strengths and weaknesses:
Strengths
The strengths of an IoT-based monitoring and control system for enhanced chicken poultry includes,
real-time monitoring, early disease detection, automation, remote access control, improved animal
welfare.
Weaknesses
The weaknesses of this study include expensive initial investment, technical complexity, power
sufficiency, connectivity issues, data security and privacy concerns, and data overload.
BUDGET AND MOTIVATION
An IoT based monitoring and control system for enhance chicken poultry management can range in
price greatly depending on a number of variables. In this study the cost would be dependent on the
following features;
For the prototype the estimated budget may cost 20, 000 pesos. The cost includes the Arduino kit
including the temperature sensor, humidity sensor, air quality sensor, water level sensor, water
quality sensor, motion sensors, remote actuators, rfid tags, IoT gateways, data logging devices and
alarm and notification devices.
The researcher's aim for the proposed study is to improve poultry management in order to increase
overall productivity, the welfare of chicken layers, and ultimately the quality of life for farmers.
Specifically, the following key motivation of the researcher including, providing real-time monitoring
and data insights, improved the efficiency and automate various tasks, remote monitoring and control
and enhance animal welfare.
REFERENCES
M. Singh, R. Kumar, D. Tandon, P. Sood and M. Sharma, "Artificial Intelligence and IoT based
Monitoring of Poultry Health: A Review," 2020 IEEE International Conference on Communication,
Networks
and
Satellite
(Comnetsat),
Batam,
Indonesia,
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pp.
50-54,
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10.1109/Comnetsat50391.2020.9328930.
Shubham Mitkari, Ashwini Pingle, Yogita Sonawane, Sandip Walunj, Anand Shirsath, IOT Based
Smart Poultry Farm, International Research Journal of Engineering and Technology (IRJET), 2019
Mar; 6(3).
Lata S. Handigolkar, M.L. Kavya and P.D. Veena. Iot Based Smart Poultry Farming using Commodity
Hardware and Software.
Bonfring International Journal of Software Engineering and Soft
Computing, 2016 October; Vol. 6, Special Issue.
J. P. Mondol, K. R. Mahmud, M. G. Kibria and A. K. Al Azad, "IoT based Smart Weather Monitoring
System for Poultry Farm," 2020 2nd International Conference on Advanced Information and
Communication
Technology
(ICAICT),
Dhaka,
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10.1109/ICAICT51780.2020.9333535.
Falculan, K. N. ., & Aungon, C. S. . (2023). Performance of Poultry Farmers in Agriculture Sector: A
Case of the Poultry Farmers in Odiongan, Romblon. Indonesian Journal of Agriculture and
Environmental Analytics, 2(1), 55–68. https://doi.org/10.55927/ijaea.v2i1.3849
P. Jayarajan, M. Annamalai, V. A. Jannifer and A. A. Prakash, "IOT Based Automated Poultry Farm
for Layer Chicken," 2021 7th International Conference on Advanced Computing and Communication
Systems
(ICACCS),
Coimbatore,
India,
2021,
pp.
733-737,
doi:
10.1109/ICACCS51430.2021.9441939.
Xiaorong Zhang, Zongqiang Gong, Graeme Allinson, Mei Xiao, Xiaojun Li, Chunyun Jia, Zijun Ni,
Environmental risks caused by livestock and poultry farms to the soils: Comparison of swine,
chicken, and cattle farms, Journal of Environmental Management, Volume 317, 2022,115320, ISSN
0301-4797, https://doi.org/10.1016/j.jenvman.2022.115320.
Kim, WS., Lee, WS. & Kim, YJ. A Review of the Applications of the Internet of Things (IoT) for
Agricultural Automation. J. Biosyst. Eng. 45, 385–400 (2020). https://doi.org/10.1007/s42853-02000078-3
Mahmoud Ammar, Giovanni Russello, Bruno Crispo, Internet of Things: A survey on the security of
IoT frameworks, Journal of Information Security and Applications, Volume 38, 2018, Pages 8-27,
ISSN 2214-2126, https://doi.org/10.1016/j.jisa.2017.11.002.
PRECISION AGRICULTURE MAPPING SYSTEM
STUDENT NAME:
ANJELA C. TOLENTINO
STUDENT NUMBER:
21-3884-861
COURSE NAME:
Doctor in Information Technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
Dr. THELMA D. PALAOAG
DATE OF SUBMISSION:
13 08 2023
CONTENTS
ABSTRACT ..................................................................................................................................................................3
INTRODUCTION ........................................................................................................................................................4
PROBLEM STATEMENT ..........................................................................................................................................5
OVERVIEW..................................................................................................................................................................5
RESEARCH QUESTION/HYPOTHESIS .............................................................................................................................5
OBJECTIVES AND AIMS ..........................................................................................................................................6
OVERALL OBJECTIVE ..................................................................................................................................................6
SPECIFIC AIMS ............................................................................................................................................................6
BACKGROUND AND SIGNIFICANCE ....................................................................................................................7
RESEARCH DESIGN AND METHODS ....................................................................................................................8
OVERVIEW..................................................................................................................................................................8
POPULATION AND STUDY SAMPLE...............................................................................................................................8
SAMPLE SIZE AND SELECTION OF SAMPLE ...................................................................................................................8
SOURCES OF DATA ......................................................................................................................................................9
COLLECTION OF DATA ................................................................................................................................................9
DATA ANALYSIS STRATEGIES .....................................................................................................................................9
ETHICS AND HUMAN SUBJECTS ISSUES ........................................................................................................................9
TIMEFRAMES ..............................................................................................................................................................9
STRENGTHS AND WEAKNESSES OF THE STUDY ........................................................................................... 11
BUDGET AND MOTIVATION ................................................................................................................................ 12
REFERENCES ........................................................................................................................................................... 13
APPENDICES ............................................................................................................................................................ 15
APPENDIX 1: QUESTIONNAIRE ...................................................................................................................................15
APPENDIX 2: CONSENT LETTER………………………………………………………………………………… 16
2
ABSTRACT
Precision is required for agricultural advancements to be sustainable. Traditional farming lacks
effective monitoring, resulting in resource waste and environmental problems. Existing mapping
systems struggle with dynamic farmlands, causing decisions to be limited. Farmland mapping is
important for agricultural management, land-use planning, and environmental monitoring. The
study will focus on the development of a system that will integrate farmland mapping and advanced
machine learning algorithms for the Local Government Unit of Science City of Munoz under the
Department of Agriculture. This study presents an innovative approach to farmland mapping based
on Convolutional Neural Networks a subset of machine learning algorithms known for its unique
ability to learn geographical characteristics from visual data. Compared to earlier methods, this
study emphasizes the elimination of repetition, resulting in more accurate and efficient mapping
results. The study's findings could aid the Department of Agriculture in effectively managing land
and enhancing support for farmers. This research contributes to the advancement of automated
farmland mapping by utilizing the capabilities of machine learning algorithms. The presented
approach not only provides an accurate and efficient means of mapping farmland but also holds
potential for broader applications in remote sensing, land-use planning, and environmental
assessment. As the field of machine learning continues to evolve, the combination of new
techniques with geographical insights promises more advance in precision, scalability, and realtime capabilities for farmland mapping.
3
INTRODUCTION
More than half of the world's population relies on rice as their primary source of energy,
making it one of the most significant crops in the world (Lu & Tan, 2021). One of the nations that
produces rice as their primary food crop and source of income for some farmers and stakeholders
is the Philippines. Most of the farmers in the Philippines choose rice for their main crop (Albarillo et
al., 2020; Casinillo, 2022). Nueva Ecija, located in the heart of the Philippines, is known as the
"Rice Granary of the Philippines." It is home to vast rice farms and is important to the nation's
agricultural industry. Rice is not only a staple food in the Philippines but also a significant source of
income for local farmers (Casinillo, 2022).
Farmland mapping is essential to the agricultural sector because it helps farmers,
stakeholders, and researchers understand and successfully manage land resources. Farmland
mapping used to be a labor - and time - intensive operation that needed lengthy field surveys and
manual data collection to estimate the rice growing area (Shuangpeng, 2019). However, recent
developments in machine learning methods have completely changed this field by providing
precise and effective solutions for farmland mapping. A subset of artificial intelligence known as
"machine learning" enables computers to learn from data and develop over time without explicit
programming. We can effectively analyze enormous amounts of satellite imagery, aerial
photography, and geospatial data to automatically identify and classify various types of agricultural
land by utilizing the power of machine learning algorithms. This technology has the potential to
revolutionize farmland mapping, unlocking new insights and efficiencies that were previously
unattainable through traditional methods (Katarya et al., 2020).
The Department of Agriculture (DA) introduce to the farmer’s the Farmers and Fisherfolk
Registration System (FFRS), it will enable all the farmers to acquire all the benefits provided by the
DA. After registration, famers are given priority to receive agricultural assistance in the form of
cash or farm inputs like seeds and fertilizers, fuel subsidy vouchers, and crop insurance. The Local
Government of Munoz under the Department of Agriculture adopt the FFRS to provide benefits to
all the farmers in Science city of Munoz, Nueva Ecija. In the said registration system, the number
one requirement of the farmers is their farmland title, this farmland will provide them an edge when
applying for incentives from the DA. Checking of farmland in the registration process are not
included in the system, by this it is possible that farmers can avail all the benefits with the same
farmland title. However, the farmers registration system only provides the DA - Munoz the list of
farmers that are already been registered, and then all the benefits that the farmer will have is
manually distributed to them. The process of checking the farmland if it is already been registered
is not available in the system. In this case, most of the farmers can register the same farmland and
received the same benefits.
4
To address these challenges, the researcher will develop a farmland mapping system with
machine learning for the Local Government Unit of Science City of Munoz, which will aid the
Department of Agriculture in securing and distributing benefits to all farmers in the province.
Remote sensing, satellite photography, and geospatial analysis are all effective methods for
mapping and monitoring rice farms of various sizes. These datasets can be analyzed using
machine learning techniques, which can provide information about land cover and crop types
(Sishodia & Ray, 2020). By employing these advanced techniques, stakeholders, agricultural
researchers, and farmers in Science City of Munoz, Nueva Ecija can make informed decisions
about the farmland of every farmers in the municipality. This will help the DA and also the farmers
to be more responsible. This knowledge helps to promote sustainable agricultural methods,
optimize resource allocation, and ensure food security for the region and the Philippines as a
whole.
PROBLEM STATEMENT
Overview
In modern agriculture and land management, precision and efficiency are essential for sustainable
and optimal resource utilization. Traditional agricultural approaches, on the other hand, frequently
lack the ability to properly monitor and manage large areas of farmland, resulting in resource
waste, poor output, and environmental issues. Furthermore, traditional land mapping approaches
frequently fail to capture the dynamic nature of farming, limiting effective decision-making
processes (Khan et al., 2021; Khanna, 2019). This study will attempt to develop a Web – Based
application that combines advanced farmland mapping technologies with machine learning
algorithms for the LGU – Science city of Munoz, Department of Agriculture (DA). It will assist in
land management as well as monitoring and tracking of farmers who have received farm benefits
from the DA.
Research Question
1.
How can machine learning techniques be applied in the Precision Agriculture Mapping
System?
2.
What are the key features of the precision agriculture mapping system?
3.
What is the level of acceptability of the proposed system in terms of the following criteria?
3.1. Functionality;
3.2. Usability;
3.3. Efficiency.
5
OBJECTIVES AND AIMS
Overall Objective
The main goal of the study is to develop an accurate and efficient machine learning-based
approach for mapping and classifying farmland areas using remotely sensed data, with the aim of
providing valuable insights and support for agricultural management and land use planning.
Specific Aims
These objective lead to the following particular project goal:
1. To effectively apply machine learning techniques within the precision agriculture mapping
system.
2. To identify the key features of the precision agriculture mapping system.
3. To evaluate the level of acceptability of the application based on the criteria of:
3.1.
Functionality;
3.2.
Usability;
3.3.
Efficiency.
6
BACKGROUND AND SIGNIFICANCE
Farmland mapping has typically relied on labor-intensive and subjective approaches,
despite its importance in agricultural management and land-use planning(Wibowo et al., 2020).
The emergence of machine learning, particularly Convolutional Neural Networks (CNNs),
represents a paradigm leap in automating and enhancing farmland mapping accuracy(Helber et
al., 2019). Remote sensing technologies, such as satellite imagery, have transformed the
extraction
of
geographical
data,
providing
a
wealth
of
information
for
land
cover
classification(Sharma et al., 2021). However, manual interpretation of this data for farmland
mapping poses limitations in terms of efficiency, consistency, and scalability. Machine learning
techniques, particularly CNNs, have displayed excellent performance in a variety of image
processing applications. CNNs are meant to imitate the ability of the human visual system to
perceive patterns, making them ideal for extracting fine spatial characteristics from satellite data.
Because of their versatility, they have found use in remote sensing, such as land cover
classification(Thorp & Drajat, 2021).
The purpose of this research is to create a Web-based system for the Local Government
Unit of Science City of Munoz, Department of Agriculture. It will track and monitor farmers'
farmland and all benefits provided to them. To Department of Agriculture employees, to have a
reliable, accurate, and complete land management of all farmers who have registered in the
Farmers and Fisherfolk Registration System (FFRS). The system can also be used by other
provincial Local Government Units to manage their farmers and agricultural land. The availability
of reliable information on their lands and benefits provided by the Department of Agriculture to
farmers in the municipality. The paper will also be a valuable resource for future researchers
interested in smart farming with machine learning.
7
RESEARCH DESIGN AND METHODS
Overview
This chapter's goal is to provide specifics on the research strategy and technique used for this
study. This section of the study will describe all the research methods, including population and
study sample, the sample size and selection of sample, sources of data, and collection of data,
data analysis strategies and the timeframe of the study. Agile Method will be used for the
development of the system, the study will also use a qualitative and quantitative research approach
and weighted arithmetic mean to answer some of the research questions of the study.
Population and Study Sample
The target respondents of this study were the employees of the Local Government Unit (LGU) of
Science City of Munoz, Nueva Ecija under the Department of Agriculture (DA). There are 37
barangays and 5,700 registered farmers under the Farmers and Fisherfolk Registry System in the
said municipality. IT Experts is also included as technical respondents of the study. The study's
respondents played a significant part because they reviewed and examined the system.
Sample Size and Selection of Sample
There are 30 employees under the Department of Agriculture (DA) assigned to facilitate and assist
in the distribution of benefits of the farmers. Random sampling will be use for the employees of the
Department of Agriculture.
Table 1. Number of respondents
Respondents
Frequency
Employees (DA office)
20
IT Experts
5
Total
25
8
Sources of Data
The Department of Agriculture will provide the data needed for the gathering procedure. Among
the primary sources of information are the list of registered farmers in their Farmers Fisherfolk
Registry System (FFRS).
Collection of Data
The data to be collected will be from the Department of Agriculture from their Farmer and
Fisherfolk Registry System (FFRS). The first step will be the communication letter addressed to the
Local Government Unit of Science City of Munoz under the Department of Agriculture. This letter's
main goal is to explain why the study is being conducted and to reassure the recipient that any
information obtained from them will be treated with absolute confidentiality in accordance with
accepted research ethics. The data collection method to be used are, observation, interviews,
survey questions that is based on the ISO 9126 model of evaluation, and analysing existing data
sets from the office of Department of Agriculture.
Data Analysis Strategies
The results from the survey forms given to respondents will be presented through the following
statistical methods. Through the evaluation procedure, the weighted mean will be used to
determine the acceptability of the system. Survey questions with the used of 5 – point Likert Scale,
where 1 – very poor and 5 – Excellent. Descriptive statistics will be used to analyze the quantitative
response.
Timeframes
This stage of the study is essential because it will serve as a guide for the researcher in
determining whether the research objectives can be completed in the allotted period. This will work
as a guide for daily tasks to effectively manage time so that the study is completed in time for the
scheduled presentation.
Table 2. Time Frame of the Study
Month
Activities
May - July
Aug
Sep-Oct
Nov-Dec
Jan-Mar
May-July
2023
2023
2023
2023
2024
2024
Preliminary Activities
1. Proposal Preparation
2. Presentation and Approval of
the proposal
9
Data Gathering Phase
Data Encoding Phase
Report Analysis Phase
Report Writing Phase
Presentation
of
the
Final
Result
Submission of Revise Copy
10
STRENGTHS AND WEAKNESSES OF THE STUDY
Through advanced data analysis and decision-making, the integration of farmland mapping and
machine learning has the potential to transform precision agriculture by improving the resources
and land use planning of the Department of Agriculture for every Local Government Unit who
wishes to use the proposed system. However, challenges may arise in the future for machine
learning algorithms model in terms of data accuracy, model complexity, and the requirement for
significant computational resources, potentially impeding the smooth adoption of this unique
approach to land management.
11
BUDGET AND MOTIVATION
1. Laptop (System Development)
2. Desktop (System Testing)
3. Internet (Wi-Fi) Subscription
4. Domain subscription
12
REFERENCES
Abraham, L., Davy, S., Zawish, M., Mhapsekar, R., Finn, J. A., & Moran, P. (2022). Ireland Using
Deep Neural Networks.
Akhter, R., & Sofi, S. A. (2021). Precision Agriculture using IoT Data Analytics and Machine
Learning. Journal of King Saud University - Computer and Information Sciences.
https://doi.org/10.1016/j.jksuci.2021.05.013
Albarillo, J., Iv, M., Fliert, E. Van De, Fielding, K., & Fielding, K. (2020). Rice farmers adapting to
drought in the Philippines Rice farmers adapting to drought in the Philippines.
https://doi.org/10.1080/14735903.2020.1807301
Algorithm, Y., Ciou, U., & Box, A. (2022). Detection of Farmland Obstacles Based on an Improved.
Bazzi, H., Baghdadi, N., Hajj, M. El, Zribi, M., Ho, D., Minh, T., Ndikumana, E., Courault, D., &
Belhouchette, H. (2019). Mapping Paddy Rice Using Sentinel-1 SAR Time Series in
Camargue , France. 1–16.
Casinillo, L. F. (2022). MODELING PROFITABILITY IN RICE FARMING UNDER PHILIPPINE
RICE TARIFFICATION LAW : AN ECONOMETRIC APPROACH. 22(3), 123–130.
Chang, L., Chen, Y., Wang, J., & Chang, Y. (2021). Rice-Field Mapping with Sentinel-1A SAR
Time-Series Data.
Fuentes, S., & Chang, J. (2022). Methodologies Used in Remote Sensing Data Analysis and. 4–6.
Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019). EuroSAT : A Novel Dataset and Deep
Learning Benchmark for Land Use and Land Cover Classification. 12(7), 2217–2226.
Hossam, M., Kamal, M., Moawad, M., Maher, M., Salah, M., Abady, Y., Hesham, A., Khattab, A., &
Architecture, A. O. S. (2018). PLANTAE : An IoT-Based Predictive Platform for Precision
Agriculture. 2018 International Japan-Africa Conference on Electronics, Communications and
Computations (JAC-ECC), 87–90.
Katarya, R., Raturi, A., Mehndiratta, A., & Thapper, A. (2020). Impact of Machine Learning
Techniques in Precision Agriculture. February, 7–8.
Khan, N., Ray, R. L., Sargani, G. R., Ihtisham, M., & Khayyam, M. (2021). Current Progress and
Future Prospects of Agriculture Technology : Gateway to Sustainable Agriculture. 1–31.
Khanna, A. (2019). Evolution of Internet of Things ( IoT ) and its signi fi cant impact in the fi eld of
Precision
Agriculture.
157(November
2018),
218–231.
https://doi.org/10.1016/j.compag.2018.12.039
Lu, J., & Tan, L. (2021). Review on Convolutional Neural Network ( CNN ) Applied to Plant Leaf
Disease Classification. 1–18.
Mique, E. L., & Palaoag, T. D. (2018). Rice Pest and Disease Detection Using Convolutional
Neural Network.
Muhammad, R., Latif, A., & He, J. (2023). Mapping Cropland Extent in Pakistan Using Machine
13
Learning Algorithms on Google Earth Engine Cloud Computing Framework.
Nishant, P. S., Venkat, P. S., Avinash, B. L., & Jabber, B. (2020). Crop Yield Prediction based on
Indian Agriculture using Machine Learning. 5–8.
Ossa, L. De, & Calera, A. (2022). Convolutional Neural Networks for Agricultural Land Use
Classification from Sentinel-2 Image Time Series.
Sharma, A., Jain, A., Chowdary, V., & Gupta, P. (2021). Machine Learning Applications for
Precision
Agriculture :
A
Comprehensive
Review.
4843–4873.
https://doi.org/10.1109/ACCESS.2020.3048415
Shuangpeng, Z. (2019). Farmland Recognition of High Resolution Multispectral Remote Sensing
Imagery using Deep Learning Semantic Segmentation Method. 0–7.
Sishodia, R. P., & Ray, R. L. (2020). Applications of Remote Sensing in Precision Agriculture : A
Review. 1–31.
Tariq, A., Yan, J., Gagnon, A. S., Khan, M. R., & Gagnon, A. S. (2022). Geo-spatial Information
Science Mapping of cropland , cropping patterns and crop types by combining optical remote
sensing images with decision tree classifier and random forest. Geo-Spatial Information
Science, 00(00), 1–19. https://doi.org/10.1080/10095020.2022.2100287
Thorp, K. R., & Drajat, D. (2021). Remote Sensing of Environment Deep machine learning with
Sentinel satellite data to map paddy rice production stages across West Java , Indonesia.
Remote
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112679.
https://doi.org/10.1016/j.rse.2021.112679
Treboux, J., & Genoud, D. (2018). Improved Machine Learning Methodology for High Precision
Agriculture. 2018 Global Internet of Things Summit (GIoTS), 1–6.
Wibowo, A., Chrismanto, A. R., Santoso, H. B., & Delima, R. (2020). The Development of Mobilebased Farmland Mapping. October. https://doi.org/10.30534/ijatcse/2020/141952020
14
APPENDICES
Appendix 1: Questionnaire
SOFTWARE EVALUATION FORM
Dear Respondent,
This survey will serve as an instrument to assess the level of acceptability of the developed
system. Your assistance in completing this form will greatly aid in the collection of the accurate and
trustworthy data necessary for the evaluation of the created system.
You can be sure that the information you provide will be handled with the highest confidentiality.
Please use the following scales to evaluate the developed system: “Synergizing Farmland Mapping
and Machine Learning: Enhancing Precision Agriculture and Land Management.”
5 – Excellent, 4 – Very Satisfactory, 3 – Satisfactory, 2 – Poor, 1 – Very Poor
Respondent’s Name:
*Type of Respondent
End User (DA - Employee)
IT Experts
Please confirm your responses by signing. Thank you very much for your time and insights.
*Signature
Characteristics
*Date
Sub-
Descriptions
5
4
3
2
1
characteristics
Suitability
The software has suitable, acceptable
and appropriate set of functions in
Functionality
accordance to its system objectives.
Accuracy
The software provides accurate results.
Security
Does the software prevent unauthorized
access?
Understandability
It is easy for the users to recognize its
logical concept and applicability.
Usability
Learnability
It is easy for the users to learn its
application.
Operability
The software is easy to operate.
Time behavior
It has acceptable response and
processing time.
Efficiency
Resource Utilization
The software utilizes resources efficiently
15
Appendix 2: Consent Letter
16
CO-MARKET: AN INTELLIGENT APP OF COCONUT AND
SARAKAT PRODUCTS
STUDENT NAME:
DANIEL T. URSULUM, JR.
STUDENT NUMBER:
16-4733-243
COURSE NAME:
Doctor in Information Technology
COLLEGE:
College of Information Technology and Computer Science
COURSE CODE:
DIT D1 – Dissertation 1
PROFESSOR:
THELMA D. PALAOAG
DATE OF SUBMISSION:
August 11, 2023
CONTENTS
ABSTRACT ..................................................................................................................................................................3
INTRODUCTION ........................................................................................................................................................4
PROBLEM STATEMENT ..........................................................................................................................................5
OVERVIEW..................................................................................................................................................................5
RESEARCH QUESTION .................................................................................................................................................5
OBJECTIVES AND AIMS ..........................................................................................................................................6
OVERALL OBJECTIVE ..................................................................................................................................................6
SPECIFIC AIMS ............................................................................................................................................................6
BACKGROUND AND SIGNIFICANCE ....................................................................................................................6
RESEARCH DESIGN AND METHODS ....................................................................................................................7
OVERVIEW..................................................................................................................................................................7
POPULATION AND STUDY SAMPLE...............................................................................................................................7
SAMPLE SIZE AND SELECTION OF SAMPLE ...................................................................................................................8
SOURCES OF DATA......................................................................................................................................................8
COLLECTION OF DATA ................................................................................................................................................8
DATA ANALYSIS STRATEGIES .....................................................................................................................................8
TIMEFRAMES ..............................................................................................................................................................8
STRENGTHS AND WEAKNESSES OF THE STUDY .............................................................................................9
STRENGTHS: ...............................................................................................................................................................9
WEAKNESSES: ............................................................................................................................................................9
BUDGET AND MOTIVATION ..................................................................................................................................9
REFERENCES ........................................................................................................................................................... 10
2
ABSTRACT
Coconut (Cocos nucifera L.) is a tropical tree that grows all over tropical islands and coastal
areas around the globe. It is a significant source of income for 30 million farmers, while 60 million
families rely on the coconut sector directly as farm laborers and indirectly through the distribution,
marketing, and processing of coconut and coconut-based products. Moreover, (Pandanus spp.) is a
plant species commonly known as the "screw pine" or "pandan” and it is locally known as “sarakat”
in Northwestern Cagayan. The sarakat plant is high valued by indigenous populations due to its
various uses. The leaves are extensively utilized in the weaving of mats, baskets, caps, and other
traditional crafts. To address the challenges of coconut farmers and sarakat women weavers, the
study “CO-MARKET: An Intelligent App of Coconut and Sarakat Products” intends to develop an
intelligent web application that can help coconut farmers and sarakat weavers in promoting and
marketing their products. The expected impact of the study includes but not limited in promoting
sustainable and profitable farming practices, production and marketing that yields to poverty
alleviation and economic growth.
The ultimate goal of this study is to develop an intelligent web app that uses machine learning
analysis to support farmers and weavers in choosing right decisions on their products regardless of
production, advertisement and marketing. The web application will be embedded with recommender
system using market basket analysis that described the historical data and predicts valuable insights.
The study's impact includes providing valuable information for improving economic growth, as well
as gathering new insights for future ventures. Finally, with this web application that uses machine
learning, farmers and weavers of Northwestern Cagayan can improve their productions,
marketability, and profitability that leads to a better life.
3
INTRODUCTION
In the Philippines, 50% of the 3.5 million coconut farmers live below the poverty line, earning
less than USD 2 per day. As there are hardly any alternative sources of income, there is a very high
poverty incidence. In short, hunger strikes and it led to a very alarming and challenging situation. In
the world, the Philippines rank 69th among the 116 countries in Global Hunger Index 2022 and
according to the Social Weather Station survey, an estimated three million families experienced
involuntary hunger at least once in the fourth quarter of 2022.
The Philippines is known to be the second largest producer of coconut products in the world,
and Sanchez Mira is considered the coconut center of Cagayan for they also assist the groups of
processors in marketing their products inside and outside the region. Moreover, weaving industry is
a practiced all throughout the nation. In the northern part of Luzon, weaving communities are mostly
concentrated in northwestern tip of Cagayan using Pandanus glaucocephalus, locally known as
Sarakat. For the municipal government of Santa. Praxedes, the maximum utilization of this wild
grown plant that is abundant in the swampy areas of this municipality has been eyed as a globally
competitive hand woven and earth-friendly product that promotes climate change awareness which
can also be an additional livelihood for local residents.
However, with these strengths of Northwestern Cagayan, the main problems of coconut
farmers and sarakat weavers is lack of market access, middlemen exploitation and lack of bargaining
power of coconut farmers and sarakat weavers. Many coconut farmers and sarakat weavers struggle
to find reliable and profitable markets for their products. Limited access to buyers who offer fair prices
can result in low income for the farmers. In some cases, farmers and weavers have to rely on
middlemen or intermediaries to sell their coconut products. These middlemen may take advantage
of the farmers' lack of market knowledge or bargaining power to offer low prices, resulting in reduced
profits for the farmers and weavers. Small-scale coconut farmers and sarakat weavers often lack
collective bargaining power. They may not have the resources or infrastructure to negotiate better
prices or access larger markets. As a result, they are more vulnerable to price fluctuations and
exploitation.
Thus, it needs scalability to run in an online advertisement with an intelligent web application
to raise marketing for the farmers and weavers’ business to grow efficiently. Hence the main
objective of the study is to develop an intelligent web app which is “CO-MARKET” that accomplishes
its social missions of reducing time waste and allowing everyone access quality coconut products
and sarakat products at an affordable price to gain more profit for economic growth, make good
decisions and gain more insights on the part of the farmers and weavers. Also, this app is where
consumers want quick, simple ways to conduct every transaction, from banking to shopping, directly
from their laptops, tablets and smart phones.
The development will focus on using machine learning analysis to advertise and market the
by-products of the farmers and weavers. The machine learning analysis can describe the historical
4
data as basis of predicting the good decisions, best product to advertise and sell, and other valuable
insights to have a sustainable profit and long-term economic stability.
Additionally, the recommender system using market basket analysis which will be embedded
to the web application can recommend the best market technique and best time for production,
allowing farmers and weavers to take early action maintaining the economic stability. The use of this
machine learning in marketing can help improve the efficiency and sustainability of coconut and
sarakat production to promote local quality products and boost the welfare of small and medium
enterprises as well as consumer welfare for sustainable development, which is an important industry
in many parts of the world.
This initiative has a societal impact through improving farmers' and weavers’ livelihoods and
encouraging sustainable agricultural techniques. Farmers and weavers may boost their yields and
revenue by optimizing harvesting of coconut and sarakat by-products, creating a more stable living
for themselves and their family. In closing, sustainable agricultural techniques that prioritize plant
health and efficient harvesting can assist the production of eco-friendly product that promotes climate
change awareness and to safeguard the environment while also promoting long-term economic
stability which can also be an additional livelihood for local residents
PROBLEM STATEMENT
Overview
The underlying target of this study is to investigate how an intelligent Co-Market Application
can be beneficial for coconut farmers and women sarakat weavers of Northwestern Cagayan in
promoting and marketing their products. In order to investigate this, a machine learning web
application will be developed in a way that it makes easy, convenient, effective and efficient for
farmers and weavers to promote and market their products, with the main goal of promoting
sustainable and profitable small scale knowledgeable entrepreneurs. This will also investigate how
clients might be benefited from cloud services to buy or acquire these products at their own
convenience.
Research Question
This study aims to answer the following research questions:
1. What techniques can be applied for integrating AI-Driven approaches into intelligent web app
for customer engagement influencing user satisfaction and brand perception?
2. How can intelligent web apps be applied to optimize content presentation and lay-out based
on user preferences?
3. What is the extent of usability of an intelligent app for coconut and sarakat products?
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OBJECTIVES AND AIMS
Overall Objective
Generally, this study aims to design and develop an intelligent app of coconut and sarakat
products towards economic growth of coconut farmers and sarakat weavers of Northwestern
Cagayan.
Specific Aims
Specifically, the study aims to achieve the following objectives:
1. identify the different techniques that can be applied for integrating AI-Driven approaches into
intelligent web app for customer engagement influencing user satisfaction and brand
perception.
2. investigate the application of intelligent web apps to optimize content presentation and lay-out
based on user preferences.
3. assess the extent of usability of an intelligent app for coconut and sarakat products.
BACKGROUND AND SIGNIFICANCE
One of the Philippines' key economic sectors is the coconut industry. In the Philippines,
coconut products constitute the largest agricultural export. Due of coconut's success as a key source
of revenue in the economy, it is seen as a predictor of the nation's overall economic activity. However,
in the study of (Moreno et al, 2020), they discovered in their systematic review that the coconut
farmer is the one who produces and processes the nuts. There are two categories of traders: (1)
barrio/community traders and (2) municipality/town traders. The farmer is reliant on the price set by
the traders. They are not given the attention they require to grow and empower themselves as
entrepreneurs in the coconut sector. They continue to lag behind all other supply chain participants.
Price and arrival data information strengthens farmers' negotiating power and increases
competitiveness among dealers. When pricing information is available, the farmer is better equipped
to choose between other neighbouring markets to dispose of the produce and earn good rates for
their items. Farmers can utilize the information to make marketing timing decisions. In their work,
Paul RK et al. (2022) shown that machine learning might be of significant use in anticipating farmers'
output. In the present inquiry, an attempt has been made to examine efficient ML algorithms e.g.
Forecasting the wholesale price of Brinjal in seventeen main marketplaces of Odisha, India using
the Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF),
and Gradient Boosting Machine (GBM).
The research of (Liakos et al., 2018) demonstrates the potential benefits of machine learning
technology in agriculture. They utilized extensive reviews and various articles to demonstrate how
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machine learning will benefit agriculture. The authors discovered that applying machine learning to
sensor data is causing farm management systems to evolve into real-time artificial intelligenceenabled algorithms. These programs can provide helpful tips and insights to help farmers make
decisions and take action.
Also, in the article of (Aldino et al., 2021), they concluded that companies can take advantage
of sales increase by using data mining approach in order to find out consumer buying patterns that
leads to better decision making. In this paper, they implement association rules mining or often
referred to as Market Basket Analysis for transaction data processing using RapidMiner by
comparing FP-Growth and Apriori algorithm.
The paper of (Castelo-Branco et al., 2020) presented some common applications of business
intelligence, as well as successful examples of data mining applied to retail sales. They created a
body of knowledge, so that a web application can use tools and techniques associated with data
mining in retail sales like market basket analysis, association rules, cross-selling and up-selling.
Moreover, (Tareq et al., 2020) study proposed a model of dynamic recommendation system (DRS)
for online market. Their technique provides an intelligent solution model to overcome the problems
of customers’ rating and feedback by integrating market basket analysis, frequent item mining,
bestselling items and customer personalization.
RESEARCH DESIGN AND METHODS
Overview
To address the significant aims of this study, mixed-method approach will be employed. The
first part is to conduct semi-structured interviews to the coconut farmers and women sarakat weavers
together with consumers of coconut and sarakat products to provide an in-depth analysis enabling
thorough understanding of the best practices and will be used as basis in designing and developing
the proposed intelligent web application. In connection to this, to determine the requirements needed
for the design and development of the web app, the researcher will conduct problem identification,
fitting best solution and analysis of the selected solution.
Moreover, the researcher will utilize Feature-Driven Development (FDD) in the realization of
the design and development of the web app. In FDD, each feature is useful and important to the
client and results in something tangible to showcase. It is classified as lightweight or Agile method
of developing software.
Population and Study Sample
The population for this study is a group of coconut farmers and women sarakat weavers of
Northwestern Cagayan. The study sample will include a subset of this population, which will be
selected based on the research objectives and the sampling strategy
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Sample Size and Selection of Sample
The study will use a purposive sampling strategy to select coconut farmers and women
sarakat weavers who can share best practices and problems in promoting and marketing products.
This will help to ensure that the study samples are representative of the population. The sample size
for this study is dependent on the sampling strategy and the research objectives.
Sources of Data
The sources of data are from coconut farmers and women sarakat weavers of Northwestern
Cagayan and consumers of coconut and sarakat products. Additionally, data will be categorized into
primary and secondary data. Primary data includes surveys, interviews and actual observation on
the field. While secondary data includes a systematic review of published articles, government and
industry reports that could help in the realization of this study.
Collection of Data
Data collection both primary and secondary data will be done in this study. It is focused on
quantitative and qualitative data collected through surveys, interviews and direct observation in the
field with coconut farmers and women sarakat weavers, published articles in promoting and
marketing coconut products and sarakat products.
Data Analysis Strategies
The collected data will be analysed using qualitative data analysis techniques such as
narrative analysis. This analysis will be used to interpret research participants’ sentiments like
testimonials, case studies, interviews, and other text or visual data that will help to identify the needed
requirements or features of the web app.
Timeframes
The duration of this study is 1 year that will commence on August 2023 – July 2024
Activities
Quarter 1
Quarter 2
Quarter 3
Quarter 4
Assessment of the current processes
Identify best fitted solution
Design and development of the Web App
Testing and Deployment
Results Analysis and Evaluation
Final Deployment and Maintenance
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STRENGTHS AND WEAKNESSES OF THE STUDY
Strengths:
The study has big contribution to the coconut farmers and women sarakat weavers to gain
for income for economic sustainability by providing them an intelligent web application for promoting
and marketing their products.
The web application can also have a major social impact by helping farmers and weavers in
promoting sustainable and environmentally friendly agricultural practices. Coconut and Sarakat are
major source of renewable means producing by-products that does not require the use of harmful
pesticides or chemicals. Promoting and marketing the by-products of these using an intelligent web
app that uses market basket analysis and recommender system could improve the profit of the
farmers and weavers.
Using machine learning technique and artificial intelligence in promoting and marketing
agricultural products will lead to further advancements and innovations. With this, it can create more
job opportunities which contribute to overall economic improvement which is the main goal of this
undertaking.
Weaknesses:
The study is focused only to coconut and sarakat products which could lead to community bias.
The study will require cost and resource requirements which tends to limit the adoption and
acceptance in low-resource settings.
Customer data collection and analysis using machine learning analysis for purposes of marketing
creates privacy issues. If the app fails to appropriately secure user data or comply with privacy
requirements, it may result in legal and ethical concerns, harming the farmers’ and weavers’
reputation.
The output of this study, like any other software program, are prone to technical difficulties, bugs,
and malfunctions. These technological constraints can cause chaos on the app's functionality,
damage user experience, and weaken its overall efficacy in marketing activities.
BUDGET AND MOTIVATION
BUDGET
Line Item
BUDGET
1. Materials and Supplies
Subscription, Integration Cost, Analytics and 35,000
Tracking, Miscellaneous Expenses
2. Infrastructure Cost:
a. Web Hosting
15,000
b. Domain and SSL Certificates
TOTAL
50,000
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MOTIVATION
This initiative has a societal impact by improving farmers' and weavers’ livelihoods and
encouraging sustainable agricultural techniques with combine effort of alleviating the effects of global
warming by improving the productivity of coconut tree and sarakat plant.
REFERENCES
Castelo-Branco, F., Reis, J. L., Vieira, J. C., & Cayolla, R. (2020). Business Intelligence and Data
Mining to Support Sales in Retail BT - Marketing and Smart Technologies (Á. Rocha, J. L. Reis,
M. K. Peter, & Z. Bogdanović (eds.); pp. 406–419). Springer Singapore.
Tareq, S. U., Noor, M. H., & Bepery, C. (2020). Framework of dynamic recommendation system for
e-shopping. International Journal of Information Technology, 12(1), 135–140.
https://doi.org/10.1007/s41870-019-00388-6
Paul, R. K., Yeasin, M., Kumar, P., Kumar, P., Balasubramanian, M., Roy, H. S., Paul, A. K., &
Gupta, A. (2022). Machine learning techniques for forecasting agricultural prices: A case of
brinjal
in
Odisha,
India.
PLOS
ONE,
17(7),
e0270553.
https://doi.org/10.1371/journal.pone.0270553
Aldino, A. A., Pratiwi, E. D., Setiawansyah, Sintaro, S., & Putra, A. D. (2021). Comparison Of
Market Basket Analysis To Determine Consumer Purchasing Patterns Using Fp-Growth And
Apriori Algorithm. 2021 International Conference on Computer Science, Information
Technology,
and
Electrical
Engineering
(ICOMITEE),
29–34.
https://doi.org/10.1109/ICOMITEE53461.2021.9650317
Moreno, M. L., Kuwornu, J. K. M., & Szabo, S. (2020). Overview and Constraints of the Coconut
Supply Chain in the Philippines. International Journal of Fruit Science, 20(sup2), S524–S541.
https://doi.org/10.1080/15538362.2020.1746727
Castillo, M., & Ani, P. (2019). The Philippine Coconut Industry: Status, Policies and Strategic
Directions for Development | FFTC Agricultural Policy Platform (FFTC-AP). In FFTC
Agricultural Policy Platform (Vol. 2017, pp. 1–13).
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