Periyar university question papers

I am doing M.phil in periyar university. Please provide the old question papers of M.phil computerscience

[Raman Vij]

Hello friend as you want the question papers of M.phil computer science of Periyar University so here I am providing you the same…

Paper pattern:
M.PHIL-QUESTION PAPER PATTERN FOR Courses, I, II, III Duration: 3 Hours Max Marks: 75 Section – A 5 X 5 = 25 All questions carry equal marks. Five questions either or type and one question from each unit Section – B 5 X 10 = 50 All questions carry equal marks. Five questions either or type and one question from each unit

Syllabus:
Course 01 Research Methodology 4 Credits

UNIT I:
Basic Elements: Thesis Elements – Paper Elements – Order of Thesis and Paper Elements – Concluding Remarks – Identification of the Author and His Writing: Author’s Name and Affiliation – Joint Authorship of a Paper: Genuine Authorship and Order of Authors. Identification of Writing: Title, Keyboards, synopsis, preface and abstract – Typical Examples. Chapters and Sections: Introductory Chapters and Section – Core Chapters and Sections. Text‐Support materials: Figures and Tables – Mathematical Expressions and Equations – References – Appendixes and Annexure – Listing of Materials. Numbering of elements: Pagination – Numbering of Chapters, Sections and Subsections – Numbering of figures and Tables – Equation Numbering – Appendix Numbering – Reference Numbering.

UNIT II:
Fuzzy Sets: Introduction – Basic Definitions and terminology – Set‐theoretic operations – MF formulation and parameterization – More in fuzzy union, intersection and complement. Fuzzy rules and fuzzy reasoning: Introduction ‐ extension principle and fuzzy relations – fuzzy If‐Then rules – fuzzy reasoning. Fuzzy Inference Systems: Introduction – Mamdani fuzzy models – Sugeno fuzzy models – Tsukamoto fuzzy models – Other considerations.

UNIT III:
Introduction to Artificial Neural Networks: Introduction – Artificial neural networks – Historical development of neural networks – Biological neural networks – Comparison between the brain and the computer – Comparison between artificial neural networks – Artificial Neural Networks (ANN) terminologies. Fundamental Models of Artificial Neural Networks: Introduction – McCulloch‐Pitts neuron model – Learning rules – Hebb Net. Perceptron Networks: Introduction – Single layer perceptron – Brief introduction to multilayer perceptron networks.

UNIT IV:
Feed forward networks: Introduction – Back Propagation Network (BPN) – Radial Basis Function Network (RBFN). Self Organizing Feature Map: Introduction – Methods used for determining the Winner ‐ Kohonen Self Organizing Feature maps (SOM) – Learning Vector Quantization – Max Net – Mexican Hat – Hamming Net.

UNIT– V
Statistical Decision Making: Introduction – Bayes’s Theorem – Multiple Features – Conditionally Independent Features – Decision Boundaries – Unequal Costs of Error – Estimation of Error Rates – The Leaving – One – Out Technique – Characteristic Curves – Estimating the Composition of Populations – Problems – Clustering: Introduction – Hierarchical Clustering – Partitional Clustering ‐ Problems.

Text Books:

1.
B.N. Basu, “Technical Writing”, PHI, Pvt., Ltd., New Delhi, 2007.

(Chapters: 4, 5, 6, 7, 8)

2.
J.S.R. Jang, C.T. Sun, E. Mizutani, ‘Neuro – Fuzzy and Soft Computing A Computational Approach a Learning and Machine Intelligence’, Pearson education, 2007. (Chapters: 2, 3, 4)


3.
S.N Sivanandam, S. Sumathi, S.N.Deepa, ‘Introduction to Neural Networks using MatLab 6.0’, TMH, 2008. (Chapters: 2, 3, 4, 8, 9)


4.
Earl Gose, Richard Johnson Laugh, Steve Jost ‘Pattern Recognition and Image Analysis’, – PHI – 1997. (Chapters: 3, 5).

Reference Books:

1.
Anderson, Durston, Poole, ‘Thesis and Assignment Writing’, Wiley Eastern University Edition, 1970.
2.
Donald H. McBurney, ‘Research Methods’, Thomson Asia Pte Ltd., 2002.
3.
George J. Klir, Bo Yuan. ‘Fuzzy sets and Fuzzy Logic Theory and Application’, PHI, 1995.
4.
George J. Klir, Tina A. Folger, ‘Fuzzy sets, Uncertainty and Information’, PHI, 2007.
5.
Richard O. Duda, Peter E. Hart, David G. Stork, ‘Pattern Classification’, John Wiley & Sons Inc. 2001.
6.
Naresh K. Sinha, Madan M. Gupta, ‘Soft Computing & Intelligent Systems Theory and Applications’, Elsevier, 2000.
7.
Philip D.Wasserman, ‘Neural Computing Theory and Practice’, Anza Research Inc.
8.
Earl Cox, ‘Fuzzy modeling and genetic algorithms for data mining and exploration’, Elsevier Inc, 2005.
9.
S. Rajasekaran, G.A. Vijaya lakshmi Pai, ‘Neural Networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Applications’, PHI, 2006.
10.
N.P. Padhy, ‘Artificial Intelligence and Intelligent Systems’, Oxford University Press, 2005.
11.
Oded Maimon, Lior Rokach, ‘The Data Mining and Knowledge Discovery hand book’, Springer Science + Business Media, Inc. 2005.
12.
Alex A. Freitas, ‘Data Mining and Knowledge discovery with Evolutionary Algorithms’, Springer International Edition, 2008.
13.
János Abongyi, Balazs feil, ‘Cluster Analysis for Data Mining and system identification’, Birkhäuser Verlag AG , 2007






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i am studied in padmavani arts and science college.i want pg cs second semester old question papers.