VIRUDHUNAGAR HINDU NADARS’ SENTHIKUMARA NADAR COLLEGE
(An Autonomous Institution Affiliated to Madurai Kamaraj University)
[Re-accredited with ‘A’ Grade by NAAC]
Virudhunagar – 626 001.
………………………………………………………………………………………………………………………………………………………………………………………………………………………..
Course Name: M.Phil
Discipline
: Computer Science (Semester)
COURSE OBJECTIVES:
To motivate the PG students of Computer Science, Computer Applications and
Information Technology in initiating research work and to expose them the current areas of
research and make them reveal the technology and trends.
ELIGIBILITY FOR ADMISSION:
Candidates who have passed PG degree in the area of Computer Science, Computer
Applications and Information Technology from the Madurai Kamaraj University or equivalent
examination of other university accepted by the syndicate as equivalent shall be permitted to join
the M.Phil degree programme in Computer Science
DURATION OF THE COURSE:
One Year
Medium of Course: English
COURSE SCHEME:
Semester
Part
I
Subject
Hour
Core 1 Research Methodology
Core 2 Data Mining
Elective Artificial Neural Networks
/ Digital Image Processing
Library
Semester
Subject
Hour
II
Dissertation & Viva Voce
30
SYLLABUS FOR EACH PAPER:
PAPER I: RESEARCH METHODOLOGY
UNIT I:
6
6
6
Marks
Int+Ext
40+60
40+60
40+60
Total
Marks
100
100
100
12
-
-
Marks
Dissertation
+ Viva
150+50
Code
M1CSC11
M1CSC12
M2CSE11/
M1CSE12
---
Total
Marks
Code
200
M1CS2PV
Subject Code: M1CSC11
18-Hrs
Research Methods: Objectives of research – types of research – approaches – research
process – selecting the problem – defining the problem – research designs – interpretations –
techniques – report writing – types of reports – computer role in research – computer
technologies and applications – computers and researchers.
UNIT II:
18-Hrs
Latex: Basics – input files, structures, command line session- layout of the document.
Typesetting text – space between words – titles, chapters and sections, cross references,
Syllabus for the academic year 2014 – 2015
M.Phil. Computer Science
998
VIRUDHUNAGAR HINDU NADARS’ SENTHIKUMARA NADAR COLLEGE
(An Autonomous Institution Affiliated to Madurai Kamaraj University)
[Re-accredited with ‘A’ Grade by NAAC]
Virudhunagar – 626 001.
………………………………………………………………………………………………………………………………………………………………………………………………………………………..
footnotes, emphasized words, environments, floating bodies – Typesetting mathematical
formulae – specialties – producing mathematical graphics – customizing latex.
UNIT III:
18-Hrs
MATLAB: Introduction – Common System Commands and Mathematical Operators –
Handling of Arrays – Handling of Matrices – Strings, Time and Date – Cell Arrays and
Structures – Programming in MATLAB, M-File Scripts - Programming in MATLAB, M-File
Functions – File I/O handling in MATLAB – Two Dimensional Plots - Graphical User Interface.
18-Hrs
UNIT IV:
SPSS: Introduction to SPSS – Basic Statistical Concepts – Descriptive Statistics – One or
Two Samples t-Tests – Analysis of Variance – Chi-Square test of Independent for Discrete data
– Correlation Analysis – Multiple Regression – Logistic Regression – Factor Analysis –
Advanced Data Handling
UNIT V:
18-Hrs
UML: A picture is worth a Thousand Lines of Code – Start at the beginning with use
cases – Diagramming features as processes – Discovering behaviors with interaction diagrams –
What are the things that describe my problem? – Showing how classes are related – Using State
Chart Diagrams – Modeling Components – Fit and Finish – Visualizing your deployment
topology
Text Books:
1. Research methodology and Techniques, C.R. Kothari, second edition, Wisva
Prakasan Publications, New Delhi, 2001.
2. MATLAB Programming, Y. Kirani Singh, B.B.Chaudhuri,PHI Learning Private
Limited, New Delhi, 2010.
3. Statistical Methods for Practice and Research, Ajai S. Gaur, Sanjaya S. Gaur,
Response Books, 2006
4. UML Demystified, Paul Kimmel, Tata McGraw-Hill publication, 2005
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PAPER II: DATA MINING
UNIT I
Subject Code: M1CSC12
18-Hrs
Introduction: What motivated Data Mining? Why is it important – So, What is Data
Mining – Data Mining-On what kind of Data – Data Mining Functionalities-What kinds of
Patterns can be Mined? – Are All of the Patterns Interesting? – Classification of Data Mining
Systems – Data Mining Task Primitives – Integration of a Data Mining Systems with a Data
Base or Data Warehouse System – Major issues in Data Mining
Data Preprocessing: Why Preprocess the Data – Descriptive Data Summarization –
Data Cleaning – Data Integration and Transformation – Data Reduction – Data Discretization
and Concept Hierarchy Generation
Syllabus for the academic year 2014 – 2015
M.Phil. Computer Science
999
VIRUDHUNAGAR HINDU NADARS’ SENTHIKUMARA NADAR COLLEGE
(An Autonomous Institution Affiliated to Madurai Kamaraj University)
[Re-accredited with ‘A’ Grade by NAAC]
Virudhunagar – 626 001.
………………………………………………………………………………………………………………………………………………………………………………………………………………………..
UNIT II
18-Hrs
Mining Frequent Patterns, Associations, and Correlations: Basic Concept and a Road
Map – Efficient and Scalable Frequent Itemset Mining Methods – Mining various kinds of
Association Rules – From Association Mining to Correlation Analysis – Constraint Based Association Mining
UNIT III
18-Hrs
Classification and Prediction: What is Classification? What is Prediction? – Issues
Regarding Classification and Prediction – Classification by Decision Tree Induction – Bayesian
Classification – Rule-Based Classification – Classification by Backpropagation – Support Vector
Machines – Associative Classification: Classification by Association Rule Analysis 344 – Lazy
Learners ( or Learning from your neighbors ) – Other Classification Method – Prediction –
Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble
Methods-Increasing the Accuracy – Model Selection
UNIT IV
18-Hrs
Cluster Analysis: What is Cluster Analysis? – Types of Data in Cluster Analysis – A
Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical Methods –
Density -Based Methods – Grid-Based Methods – Model-Based Clustering Methods – Clustering
High Dimensional Data – Constraint-Based Cluster Analysis – Outlier Analysis
UNIT V
18-Hrs
Mining Object, Spatial, Multimedia, Text, and Web Data: Multidimensional Analysis
and Descriptive Mining of Complex Data Objects – Spatial Data Mining – Multimedia Data
Mining – Text Mining – Mining the World Wide Web
Text Books:
Data Mining: Concepts and Techniques , Han. J and Kamber.M, Morgan Kaufman
Publishers, San Francisco,2009
Reference Book:
1. Introduction to Data Mining, Herbert A. Edelstin, Springa Verlay, USA. First
Edition,2003.
2. Principles of Data Mining (Adaptive Computation and Machine Tools), David j.
Hand, Heikki Mannila, Padhraic Smyth, MIT Press, USA, 2001.
3. Data Mining with Microsoft SQL Server 2000, Deidman, Claude PHI New
Delhi,2000.
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ARTIFICIAL NEURAL NETWORKS
Contact Hours per week
:6
Subject Code: M2CSE11
Contact Hours per semester: 90
UNIT I
(18 Hrs)
Artificial Neural Systems: Preliminaries - Neural Computation - History of Artificial
Neural Systems Development - Future outlook
Syllabus for the academic year 2014 – 2015
M.Phil. Computer Science
1000
VIRUDHUNAGAR HINDU NADARS’ SENTHIKUMARA NADAR COLLEGE
(An Autonomous Institution Affiliated to Madurai Kamaraj University)
[Re-accredited with ‘A’ Grade by NAAC]
Virudhunagar – 626 001.
………………………………………………………………………………………………………………………………………………………………………………………………………………………..
Fundamental concepts and models of Artificial Neural Systems: Biological Neurons and
their Artificial Models - Models of Artificial Neural Networks - Neural Processing - Learning
and Adaptation - Neural Network Learning Rules - Overview of Neural Networks
UNIT II
(18 Hrs)
Single Layer Perceptron Classifiers: Classification Models, Features and Decision
Regions - Discriminant Functions - Linear Machine and Minimum Distance Classification Nonparametric Training Concept - Training and Classification using the Discrete Perceptron:
Algorithm and Example - Single-layer Continuous perceptron Networks for Linearly Separable
Classifications - Multicategory Single-layer perceptron Networks
UNIT III
(18 Hrs)
Multilayer Feedforward Networks: Linearly Nonseparable Pattern Classification - Delta
Learning Rule for Multiperceptron layer - Generalized Delta Learning Rule - Feedforward recall
and Error Back-Propagation Training - Learning Factors - Classifying and Expert Layered
Networks - Functional Link Networks
UNIT IV
(18 Hrs)
Single-Layer Feedback Networks: Basic Concepts of Dynamic Systems - Mathematical
Foundations of Discrete- Time Hopfield Networks - Mathematical Foundations of Gradient-Type
Hopfield Networks - Transient Response of Continuous-Time Networks - relaxation Modeling in
Single-Layer Feedback Networks - Example Solutions of Optimization Problems
Associative Memories: Basic Concepts - Linear Associator - Basic Concepts of Recurrent
Autoassociative Memory
UNIT V
(18 Hrs)
Associative Memories: Bidirectional Associative Memory - Associative Memory of
Spatio-Temporal Patterns
Matching and Self-Organizing Networks: Hamming Net and MAXNET - Unsupervised
Learning of Clusters - Counterpropagation Network - Feature Mapping - Self-Organizing
Feature Maps - Cluster Discovery Network(ART1)
Text Book
Introduction to Artificial Neural System – Jacek M.Zurada, JAICO Publishing House,2006.
Reference Book
1) Neural Computing: Theory and practice – Philip D. Wasserman, Van Nostrant
Reilnhold Publication, 1989.
2) Neural Network Algorithms, Application Programming Techniques – James A.Freeman &
David, Addition Wasley Publishing Company, 1991.
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E2: DIGITAL IMAGE PROCESSING
Contact Hours per week
:6
Subject Code: M1CSE12
Contact Hours per semester: 90
Objectives:
The subject provides an introduction to basic concepts and methodologies for digital
image processing, and develops a foundation that can be used as the basis for further study and
research in this field.
Syllabus for the academic year 2014 – 2015
M.Phil. Computer Science
1001
VIRUDHUNAGAR HINDU NADARS’ SENTHIKUMARA NADAR COLLEGE
(An Autonomous Institution Affiliated to Madurai Kamaraj University)
[Re-accredited with ‘A’ Grade by NAAC]
Virudhunagar – 626 001.
………………………………………………………………………………………………………………………………………………………………………………………………………………………..
UNIT 1: Introduction
(18 Hrs)
Digital Image Processing- Simple image formation - Image Sampling and QuantizationBasic relationships between pixels - Histogram processing.
UNIT 2: Filtering, Restoration and Reconstruction
(18 Hrs)
Sampling and the Fourier transform of sampled functions: Sampling- Fourier transform
of sampled functions. Filtering in the frequency domain - Image Smoothing and Image
Sharpening using frequency domain filters – Restoration in Noise – Spatial Filtering - Image
Reconstruction from projections.
UNIT 3: Color image processing
(18 Hrs)
Color fundamentals - Color models - Pseudo color image processing - Full color image
processing - Color transformations - Smoothing and Sharpening- Image Segmentation based on
Color.
UNIT 4: Wavelets and Morphological Image Processing
(18 Hrs)
Wavelet transforms in one dimension and two dimensions - The Fast Wavelet Transform
- Erosion and Dilation - Opening and Closing - Hit or Miss transformation - Basic
Morphological algorithm - Gray Scale Morphology.
UNIT 5: Segmentation and Object Recognition
(18 Hrs)
Fundamentals - Point, Line and Edge detection – Thresholding - Region based
Segmentation - Segmentation using Morphological Watersheds - Motion in Segmentation Patterns and Pattern classes - Recognition based on decision theoretic methods.
Text book:
Rafael C.Gonzalez, Richard E.Woods, “Digital Image Processing”, Prentice Hall 3/E, 2008.
UNIT I: 1.1, 2.3.4, 2.4, 2.5, 3.3
UNIT II: 4.3: 4.3.1, 4.3.2, 4.7.3, 4.8, 4.9, 5.3, 5.11
UNIT III: 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7
UNIT IV: 7.3, 7.4, 7.5, 9.2, 9.3, 9.4, 9.5, 9.6
UNIT V: 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 12.1, 12.2
Reference:
1. Rafael C.Gonzalez, Richard E.Woods, Steven L.Eddins, “Digital Image Processing Using
MATLAB”, Prentice Hall, 2004.
2. Jayaraman S, Veerakumar T, Esakkirajan S, DIGITAL IMAGE PROCESSING,
McGrawHill, 2009.
3. Poonam Yadav, Abhishek Yadav, Digital Image Processing, University Science Press,
2010.
4. Wilhelm Burger, Mark J Burge, Digital Image Processing, Springer, 2008.
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Syllabus for the academic year 2014 – 2015
M.Phil. Computer Science
1002