IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 13, No. 2, June 2024, pp. 2226∼2235
ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i2.pp2226-2235
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The rise of artificial intelligence: a concise review
Chinimilli Venkata Rama Padmaja1 , Sadasivuni Lakshmi Narayana2 , Gouthami Latha Anga2 ,
Priyanka Kumari Bhansali2
1 Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, India
2 Department of Computer Science and Systems Engineering, Andhra University, Visakhapatnam, India
Article Info
ABSTRACT
Article history:
Artificial intelligence (AI) has emerged as a transformative force with farreaching implications across various domains. This research review provides
a concise analysis of the rise of AI, examining its evolution, applications, ethical considerations, and future prospects. The study traces the historical development of AI, highlighting key milestones and technological advancements that
have propelled its growth. It explores the wide-ranging applications of AI in sectors such as healthcare, finance, transportation, manufacturing, human resource
management and entertainment, showcasing its impact on efficiency, decisionmaking, and user experiences. Ethical considerations surrounding AI, including
bias, privacy, and societal implications, are thoroughly discussed. The transformative potential of AI in shaping society is expolred, with insights into its effects
on employment, education, governance, and societal challenges. Looking ahead,
the review identifies emerging technologies and discusses challenges related to
data privacy, security, and transparency. The research review concludes by emphasizing the importance of responsible and ethical development of AI, while
underscoring the need for continued research and collaboration to fully harness
its potential. This review serves as a valuable resource for researchers, and practitioners seeking a holistic understanding of the rise of AI and its implications.
Received Aug 8, 2023
Revised Oct 27, 2023
Accepted Dec 14, 2023
Keywords:
Applications
Artificial intelligence
Ethical considerations
Evolution
Social challenges
This is an open access article under the CC BY-SA license.
Corresponding Author:
Chinimilli Venkata Rama Padmaja
Department of Computer Science and Engineering, Institute of Aeronautical Engineering
Dundigal, Hyderabad, India
Email: cvrpadmaja@gmail.com
1.
INTRODUCTION
Artificial intelligence (AI) has experienced a remarkable rise in recent years, transforming numerous
industries and revolutionizing the way we live, work, and interact. It has witnessed significant advancements,
driven by breakthroughs in machine learning, deep learning, and natural language processing. These developments have led to the emergence of powerful AI models and algorithms that have surpassed human performance
in various tasks like image recognition, language translation, and playing complex games [1], [2]. As a result,
AI has become a transformative force across a wide range of industries, including healthcare [3], finance [4],
transportation [5], manufacturing, and entertainment [6]
The rise of AI can be traced back to the early days of computer science and the ambitious goal of
creating machines that can simulate human intelligence. The Dartmouth Workshop in 1956 marked a significant
milestone, where the term ”AI” was coined, and researchers gathered to explore the possibilities of machine
intelligence [7]. Since then, AI has undergone several waves of progress, from the rule-based expert systems
of the 1980s to the resurgence of AI through the advancements in neural networks and deep learning [8].
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The applications of AI are vast and varied. In healthcare, AI has been employed for medical imaging analysis, disease diagnosis, drug discovery, and personalized treatment recommendations [3], [9]. In the
financial sector, AI enables fraud detection, algorithmic trading, credit scoring, and risk assessment [4], [10].
AI-powered autonomous vehicles and intelligent transportation systems are revolutionizing the way we travel
and commute [5]. Moreover, AI is transforming manufacturing processes by optimizing production lines, predictive maintenance, and quality control [11]. In the entertainment industry, AI is enhancing user experiences
through personalized recommendations, content generation, and immersive technologies [6].
However, the rise of AI also brings forth important ethical considerations. As AI systems make decisions and predictions that impact human lives, concerns related to bias, transparency, privacy, and accountability have become crucial [12], [13]. Issues of algorithmic bias and the potential for AI to reinforce existing
societal inequalities have garnered significant attention [14]. Additionally, the responsible and ethical use of
AI, including the growing importance of explainable artificial intelligence (XAI) [15], in areas such as data
privacy, security, and the impact on employment and society require careful consideration [16], [17].
Looking ahead, the future of AI holds immense potential. Emerging technologies such as XAI, federated learning, and quantum computing present new opportunities and challenges in the field [15], [18]-[20].
Ethical frameworks, guidelines, and policies are being developed to guide the responsible development and
deployment of AI systems [13], [21], [22]. The ongoing research and interdisciplinary collaboration in AI are
critical to unlocking its full potential and ensuring its beneficial and ethical integration into society.
The rise of AI has brought forth significant advancements and transformative possibilities across various industries as a result. This review aims to provide a brief understanding of the current state and potential of
AI. By exploring its evolution, applications, ethical considerations, and future prospects, this article serves as a
foundation for further research and discussions on harnessing the power of AI for the betterment of society.
2.
EVOLUTION OF ARTIFICIAL INTELLIGENCE
The evolution of AI illustrated in Figure 1, is a testament to the relentless pursuit of knowledge and innovation over several decades. During this remarkable journey, AI has undergone transformative advancements
and paradigm shifts that have reshaped the very fabric of the field. This section serves as a spotlight, illuminating the pivotal contributions and significant milestones that have played instrumental roles in shaping AI’s
current state. By delving into these key developments, we gain insight into the intricate tapestry of progress,
highlighting the ingenuity and dedication of researchers who have propelled AI from theoretical concepts to
practical applications, fundamentally altering the way we perceive and interact with intelligent systems.
Figure 1. Evolution of AI
− Early foundations: the concept of AI was formally introduced at the Dartmouth Workshop in 1956,
where John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon coined the term ”AI”
[7]. This workshop laid the foundation for AI research and brought together experts to explore the
possibilities of machine intelligence.
− Symbolic reasoning and expert systems: in the 1970s and 1980s, AI research focused on symbolic reasoning and the development of expert systems. Researchers such as Allen Newell and Herbert A. Simon
made significant contributions to symbolic AI and problem-solving methods [23]. Expert systems, like
MYCIN for medical diagnosis [24], demonstrated the potential of rule-based systems and symbolic reasoning.
− Neural networks and connectionism: the resurgence of AI in the 1980s and 1990s witnessed a shift
towards neural networks and connectionism. The development of backpropagation, a learning algorithm
for training neural networks [25], fueled advancements in machine learning. The pioneering work of
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Geoffrey Hinton and colleagues on deep learning architectures [26] laid the groundwork for the deep
neural networks that power modern AI systems.
− Big data and machine learning: the advent of big data in the early 21st century, facilitated by the internet
and digital technologies, along with the availability of extensive labeled data, acted as a catalyst for AI
advancements. Machine learning algorithms, particularly those based on statistical methods and neural
networks, demonstrated exceptional abilities in pattern recognition, natural language processing, and
computer vision [8], [27]. The subsequent rise of deep learning, driven by increased computational
power and enhanced algorithms, has resulted in significant breakthroughs across various AI tasks. These
advancements have led to the predominance of intelligent systems that heavily rely on machine learning
and deep learning [28], automating analytical model construction from domain-specific training data,
surpassing traditional approaches, and presenting challenges in fields like electronic markets and humanmachine interaction within the context of AI servitization.
− Reinforcement learning and generative models: reinforcement learning, a branch of machine learning,
has made significant strides in recent years. The success of reinforcement learning algorithms, such as
AlphaGo [1], in defeating human champions in complex games has demonstrated the potential of AI
in strategic decision-making. Generative models, such as generative adversarial networks (GANs) [29],
have pushed the boundaries of AI in generating realistic content, including images, text, and music.
− Integration of AI with other technologies: the evolution of AI has been intertwined with advancements
in other domains. Integration of AI with big data analytics, cloud computing, and internet of things
(IoT) technologies has opened new avenues for AI applications [30]. Additionally, the development of
AI-specific hardware, such as graphics processing units (GPUs) and tensor processing units (TPUs), has
accelerated AI training and inference tasks [31].
− Emerging trends: recent trends in AI research include the exploration of XAI [32], [33], which aims to
provide transparency and interpretability to AI models, as well as the advancement of AI in healthcare,
robotics, autonomous systems, and natural language understanding [34], [35]. Continued efforts are
being made to develop AI systems that are not only intelligent but also ethical, fair, and aligned with
human values.
The progression of AI has been the result of a collective endeavor, with researchers, engineers, and the
accessibility of computational resources playing integral roles. Every era in AI’s development has leveraged
the accomplishments of its predecessors, culminating in the contemporary landscape where AI stands as a
transformative force across diverse domains. This cumulative effort has not only propelled AI from its nascent
stages but has also established it as a pivotal technology that continues to shape and redefine the way we engage
with intelligent systems today.
3.
ARTIFICIAL INTELLIGENCE APPLICATIONS IN VARIOUS SECTORS
AI has become a pervasive force, fundamentally altering the landscape of various sectors. Its versatile
applications have revolutionized traditional approaches, impacting how tasks are executed and decisions are
reached. In the subsequent sections, we delve into the distinct realms where AI has left an indelible mark,
exploring its transformative influence on critical domains, including healthcare, finance, transportation, manufacturing, human resource management (HRM), and entertainment, as shown in Figure 2. Each sector’s unique
utilization of AI technologies reflects the remarkable adaptability and potential of AI across multifaceted industries.
− Healthcare: AI has made significant contributions to the healthcare sector [36], enabling improved diagnostics, personalized treatment, and efficient healthcare delivery. The potential of AI in dermatology is
demonstrated by developing a deep learning model for skin cancer classification [3]. AI algorithms are
utilized to predict patient outcomes and guide clinical decision-making [9]. AI has also been employed
for drug discovery [37] and medical image analysis [34], enhancing accuracy and efficiency in disease
detection and diagnosis.
− Finance: AI has revolutionized the finance industry [38], enabling fraud detection, algorithmic trading, risk assessment, and personalized financial services. The use of AI is explored in fraud detection,
leveraging machine learning algorithms to identify fraudulent transactions [39]. The potential of deep
learning in finance is demonstrated by applying convolutional neural networks to stock market prediction
[4]. AI-powered chatbots and virtual assistants have also been utilized to deliver personalized financial
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advice and services.
− Transportation: AI is transforming the transportation sector through advancements in autonomous vehicles, intelligent traffic management, and predictive maintenance. An end-to-end deep learning model
was developed for self-driving cars, representing the power of AI in autonomous transportation [5]. AI
algorithms are also employed for traffic prediction and optimization, reducing congestion and improving travel efficiency. Additionally, AI plays a crucial role in predictive maintenance of transportation
infrastructure, enabling proactive maintenance and minimizing downtime.
− Manufacturing: AI technologies are reshaping manufacturing processes, leading to improved efficiency,
quality control, and predictive maintenance. AI techniques were utilized for optimizing production lines,
reducing costs, and improving product quality [40]. AI-driven robotics and automation are being employed for tasks such as assembly, picking, and packaging, enhancing productivity and worker safety.
Predictive maintenance models based on AI algorithms help identify potential equipment failures and
optimize maintenance schedules.
− HRM: AI’s substantial impact on HRM transcends its role in automating and enhancing HR functions,
encompassing processes like recruitment optimization, streamlined on boarding, and performance monitoring. In addition to these capabilities, AI provides predictive analytics to support data-driven decisionmaking and employs chatbots to address routine inquiries. However, this importance resonates with the
study’s core theme, which highlights HR personnel’s tendency towards unrealistic optimism regarding
AI’s influence [41]. This optimism primarily centers on expected benefits like cost reductions and efficiency gains, coupled with varying perspectives on AI’s impact on their own organizations versus others.
AI undoubtedly holds substantial promise for HR departments, but the study underscores the need for
HR professionals to maintain balanced expectations given AI’s multifaceted implications for HRM.
− Entertainment: AI is revolutionizing the entertainment industry, enhancing user experiences and content
generation. The ability of AI is articulated in game playing, developing AI agents capable of achieving
grandmaster-level performance in the game of StarCraft II [6]. AI-powered recommendation systems
analyze user preferences and behavior to provide personalized content recommendations in streaming
services and online platforms. Moreover, AI-driven content generation algorithms are being used to
create realistic visuals, music, and virtual characters.
Figure 2. AI Applications in various sectors
The applications of AI presented here merely scratch the surface of its expansive impact across sectors.
Beyond these examples, AI’s innovative prowess continues to drive transformation and disruption in various
industries. Its capabilities have ushered in a new era of enhanced efficiency, intelligent decision-making, and
unparalleled user experiences, fundamentally altering the way businesses operate and individuals engage with
technology. As AI algorithms evolve and adapt, the potential for further innovations and groundbreaking
solutions across diverse fields remains limitless, promising a future where intelligent technologies continue to
shape the world we live in.
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4.
ETHICAL CONSIDERATIONS IN ARTIFICIAL INTELLIGENCE
The rapid progression and extensive integration of AI into various aspects of our lives have given
rise to critical ethical concerns as illustrated in Figure 3, necessitating our careful consideration. This section
serves as a gateway to an exploration of these challenges, delving into complex issues such as algorithmic bias,
privacy implications, questions of accountability, and the broader societal impact of AI technologies. These
ethical dilemmas underscore the need for an understanding of the ethical landscape surrounding AI, laying the
foundation for a deeper examination into the multifaceted ethical challenges that arise in the wake of AI’s rapid
advancement.
Figure 3. Ethical considerations in AI
− Algorithmic bias: often resulting from biased training data or flawed algorithms, entails potential discrimination and unfairness in AI systems’ decision-making. Notably, gender and racial biases have been
observed in facial recognition algorithms, leading to higher error rates for women and individuals with
darker skin tones [42]. To address such biases, it is essential to implement careful data selection, utilize
diverse and inclusive training datasets, and continuously monitor and evaluate AI systems. Grounding
algorithmic accountability in social decision-making within the democratic principle of ’public reason’
[43] can provide justifications for automated system outputs, facilitating a more precise assessment of
accountability efforts in this context.
− Privacy and data protection: AI relies on vast amounts of data, often personal and sensitive, leading to
concerns about privacy and data protection. The challenges were highlighted posed by data technologies to group privacy, emphasizing the need to protect individuals’ privacy in the context of AI [44].
Safeguarding personal data, obtaining informed consent, and implementing robust security measures are
essential to ensure privacy and comply with data protection regulations.
− Transparency and explainability: AI models often operate as ”black boxes,” making it challenging to understand the underlying decision-making processes. Lack of transparency and explainability can hinder
trust and accountability. The importance of XAI was emphasized, which aims to provide interpretable
explanations for AI system decisions [45]. XAI techniques [33] enable users to understand the reasoning
behind AI outputs, fostering trust and allowing for the identification and mitigation of potential biases or
errors.
− Accountability and responsibility: determining accountability and responsibility in AI systems is complex, especially when decisions are made autonomously. An ethical framework for AI was proposed, that
emphasize the need for shared responsibility among developers, deployers, and users of AI systems [13].
Establishing clear lines of accountability, legal frameworks, and ethical guidelines is crucial to address
issues of liability and ensure responsible AI development and deployment.
− Social and economic impact: the impact of AI extends beyond technical considerations, raising social
and economic concerns. AI has the potential to disrupt industries, impacting employment and exacerbating economic inequalities. Ethical considerations involve ensuring fairness, inclusivity, and equitable
distribution of the benefits and risks associated with AI. The importance of addressing societal challenges
posed by AI were highlighted [13], such as the impact on employment, education, and social justice.
− Human-AI collaboration: the ethical integration of AI involves understanding the role of AI systems in
collaboration with humans. Preserving human autonomy, decision-making, and values in AI applications
is important. The need for human oversight, human-AI collaboration frameworks, and mechanisms for
human intervention were emphasized to ensure responsible and beneficial outcomes [13]. The goal is to
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harness the strengths of AI while respecting human rights, dignity, and values.
The complex nature of addressing ethical considerations within the realm of AI necessitates a collaborative approach that spans across multiple disciplines. Such an approach entails active involvement from
experts not only in AI but also in ethics, law, and social sciences. By bringing together these diverse fields,
we can harness their collective insights to effectively navigate the intricate ethical landscape that AI presents.
The establishment and strict adherence to ethical frameworks, guidelines, and regulatory frameworks are of
paramount importance. These measures serve as foundational pillars in steering the responsible design, development, and deployment of AI systems, ensuring that they contribute positively to society while mitigating
potential harm.
5.
ARTIFICIAL INTELLIGENCE AND SOCIETY
The widespread integration of AI is ushering in a transformative era with far-reaching consequences
for society. In this segment, we delve into an exploration of the profound influence exerted by AI on multiple
aspects of our daily existence, as highlighted in Figure 4. We will delve into the repercussions on employment,
where AI-driven automation and augmentation are reshaping job markets. Additionally, we’ll explore the
evolving landscape of education, governance, and the array of social challenges that arise in the wake of AI’s
pervasive influence. This introduction sets the stage for a detailed exploration of the multifaceted dynamics
between AI and society, unraveling the opportunities and challenges that this technological revolution brings
forth.
Figure 4. AI and society
− Employment: the adoption of AI technologies raises concerns about job displacement and the future
of work. The potential impact of AI on employment, particularly changes in the number of employees
and the share of tasks taken by AI [41], highlights the need for reskilling and adapting to new job
roles [17]. While some jobs may be automated, new opportunities may emerge as AI augments human
capabilities. Managing this transition and ensuring inclusive economic growth are critical considerations
in the context of AI and employment.
− Education: AI has the potential to transform education by personalizing learning experiences and improving educational outcomes. The importance of integrating AI into educational systems provides adaptive
and tailored instruction [46]. AI-powered tools can assist teachers, automate administrative tasks, and
facilitate personalized learning pathways for students. However, ethical and privacy concerns related
to student data and algorithmic decision-making in education need to be addressed. An objective measurement instrument [47] for AI literacy has been developed, consisting of 16 items that differentiate
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between AI-literate and control groups. This tool is valuable for assessing AI literacy in organizations
and contributes to IS education research.
− Governance: AI presents challenges and opportunities for governance and policy-making. The role
of AI was unfolded in improving public service delivery, enhancing decision-making processes, and
enabling predictive governance [48]. However, issues related to accountability, transparency, and bias
in AI algorithms need to be addressed to ensure that AI systems serve the public interest. Developing
regulatory frameworks, ethical guidelines, and standards for AI governance are crucial.
− Social challenges: the adoption of AI raises broader societal challenges that need careful consideration.
Researchers identified the potential for AI systems to perpetuate existing biases and discrimination, impacting marginalized communities [49]. They emphasize the importance of addressing issues such as
data bias, algorithmic fairness, and inclusivity in AI design and deployment. Social challenges also
include concerns about the impact of AI on privacy, human autonomy, and social interactions.
The integration of AI into society raises ethical and legal implications that require attention . Ethical
considerations include ensuring fairness, accountability, transparency, and the responsible development and use
of AI technologies [50]. Legal frameworks need to address issues such as privacy protection, algorithmic accountability, and liability in the context of AI. Balancing innovation with ethical and legal safeguards is crucial
for the responsible advancement of AI in society. Efforts are underway to address these societal implications.
Collaboration between policymakers, researchers, and stakeholders from different domains is essential to shape
the ethical, legal, and societal frameworks surrounding AI and ensure that AI technologies contribute positively
to society.
6.
FUTURE DIRECTIONS AND CHALLENGES IN ARTIFICIAL INTELLIGENCE
The dynamic field of AI stands at the forefront of technological progress, offering a tantalizing array
of opportunities and, in equal measure, formidable challenges. In this section, we initiate a forward-looking
journey, casting a discerning eye on the future horizons and the pivotal hurdles that await AI research and
development. As AI continues its relentless march, we delve into the imminent directions poised to shape its
trajectory, while also acknowledging the critical challenges that demand innovative solutions to navigate the
uncharted waters of this evolving field. This segment initiates an exploration of the evolving landscape of AI,
where its limitless potential meets the intricacies of tomorrow’s challenges.
− XAI: XAI [51] aims to enhance transparency and interpretability of AI systems, enabling users to understand how decisions are made. A comprehensive survey of XAI methods [33] was provided, including
rule-based approaches, post-hoc explanations, and model-specific techniques [32]. Developing robust
and reliable XAI methods is essential for building trust, ensuring accountability, and addressing concerns related to AI decision-making processes.
− Federated learning: federated Learning enables collaborative model training across distributed devices or
data sources while preserving data privacy. A framework for federated learning was proposed, allowing
models to be trained on decentralized data without the need for data sharing [19]. Federated Learning
offers promising avenues for advancing AI in privacy-sensitive domains, such as healthcare and finance,
while respecting data protection regulations.
− Quantum computing and AI: quantum computing has the potential to revolutionize AI by enabling faster
computation and solving complex optimization problems. A quantum computer capable of solving a
specific problem beyond the reach of classical computers was presented [20]. While quantum computing is still in its early stages, exploring its integration with AI holds promise for tackling complex AI
challenges and accelerating AI research and applications.
− Ethical and responsible AI: ensuring ethical and responsible AI development and deployment remains a
critical challenge. This includes addressing issues of algorithmic bias, privacy protection, transparency,
and accountability. Ethical frameworks and guidelines [13], play a vital role in guiding AI research
and practice. Striking the right balance between innovation and responsible AI is essential to mitigate
potential risks and maximize the societal benefits of AI.
− AI in complex domains: advancing AI in complex domains, such as robotics, and natural language understanding, presents unique challenges. A survey of natural language understanding techniques was
provided [31], highlighting the ongoing research efforts to improve language understanding, dialogue
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systems, and machine translation. Addressing the complexities of real-world scenarios, such as understanding context, ambiguity, and domain-specific knowledge, is crucial for AI systems to achieve higher
levels of performance and applicability.
− Data privacy and security: as AI relies on large-scale data collection and processing, ensuring data privacy and security remains a significant challenge. Safeguarding sensitive information, protecting against
data breaches, and complying with data protection regulations are paramount. Advances in privacypreserving techniques, such as differential privacy, secure multiparty computation, and homomorphic
encryption, are vital for maintaining privacy while enabling meaningful AI research and applications.
Addressing the future directions and challenges in the field of AI necessitates an interdisciplinary
collaboration that brings together researchers, policymakers, and stakeholders from various domains, including AI, ethics, law, and application-specific fields. This collaborative effort is essential because AI’s impact
spans a wide spectrum, touching upon diverse aspects of society. To effectively navigate these challenges, it
requires a commitment to ongoing research to stay ahead of AI’s evolving landscape. Additionally, fostering
open dialogue and proactive initiatives is crucial to develop responsible, inclusive, and beneficial AI technologies that align with ethical principles and prioritize societal well-being. This collective approach ensures that
AI continues to advance for the betterment of humanity while addressing and mitigating potential risks and
challenges.
7.
CONCLUSION
In conclusion, the journey through the landscape of AI from its evolution to its applications, ethical
considerations, and future outlook underscores its profound impact on society. AI’s rapid progress has revolutionized sectors and enhanced various aspects of our lives, yet it has also presented pressing ethical concerns.
Striking the balance between innovation and ethical responsibility is paramount for ensuring AI’s positive societal impact. The future of AI holds exciting possibilities but also challenges that require diligent attention,
from responsible development to data privacy and societal implications. Interdisciplinary collaboration among
researchers, policymakers, and stakeholders, coupled with ethical frameworks and guidelines, will be instrumental in shaping AI’s trajectory. As AI continues to evolve, the collective commitment to harnessing its
potential for the benefit of humanity, guided by fairness, transparency, accountability, and inclusivity, will lead
towards a future marked by a more equitable and sustainable society.
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BIOGRAPHIES OF AUTHORS
Chinimilli Venkata Rama Padmaja
received Ph.D. in Computer Science and Engineering from the Jawaharlal Nehru Technological University, Kakinada, India in 2020. She also received
M.Sc. (Computer Science) and M.Tech. (Computer Science & Systems Engineering) from Andhra
University in 2001 and 2008, respectively. She is an associate professor in Computer Science and
Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India. Her research interests are data mining, machine learning, artificial intelligence, and natural language processing. She
has published one design patent and 10 papers in international journals and conferences. She is a
IETE member. She can be contacted at email: cvrpadmaja@gmail.com.
Sadasivuni Lakshmi Narayana
holds a Doctor of Computer Science and Systems Engineering degree from Andhra University, India in 2005. He also received his M.Sc. (Applied Mathematics) and M.Tech. (Computer Science and Technology) from Andhara University, India in 1991
and 1997, respectively. He is currently a professor at Department of Computer Science and Systems
Engineering, Andhra University, Visakhapatnam, India. His research area includes machine learning,
data mining, and artificial intelligence. He has one granted patent and published over 40 papers in
international journals and conferences. He was a Fellow APAS, IE (India), IETE and member of
ACM, ISTE, CSI, and AGU. He can be contacted at email: sln@ieee.org.
Gouthami Latha Anga
an accomplished administrator with over 19 years of teaching
experience, currently serves as a Professor in the Department of Computer Science and Systems
Engineering at Andhra University College of Engineering, Visakhapatnam. Her areas of interest
include data mining, image processing, artificial intelligence, and machine learning. She has authored
over 17 international journals, 2 international conferences, 3 patent publications, and 1 design patent.
She has been honored with the “Jyesta Aacharya” & “Uttama Adyapika” Awards in 2022 & 2021,
respectively, from Bharat Education Excellence Awards. Additionally, she has published 1 book &
2 book chapters. She is an active member of various professional bodies such as CSI, IAENG, and
IEEE. She can be contacted at email: dr.gautamilatha@andhrauniversity.edu.in.
Dr. Priyanka Kumari Bhansali
currently working as an assistant professor in the Department of Computer Science and Systems Engineering, Andhra University College Of Engineering,
Visakhapatnam. She received Ph.D. degree in ‘IoT, cloud computing, and fog computing‘ from
PACIFIC University, Udaipur in the year 2022. She is having 12 years of teaching experience at
university level. She is a member of Indian Institute Of Engineers. Her research interests include
internet of things, fog computing, cloud computing, machine learning, and data analytics. She can be
contacted at email: dr.priyankabhansali@andhrauniversity.edu.in.
The rise of artificial intelligence: a concise review (Chinimilli Venkata Rama Padmaja)