M Phil Periyar University

Please tell me from where I can download the fee structure of the M.Phil course of Periyar University. give me the address of it?

[Raman Vij]

Periyar University is a university which is situated in Salem, Tamil Nadu, India. It was founded by the Government of Tamil Nadu in 1997. It offers M.Phil course.

Fee structure:
Here I am uploading a file which contains the Fee structure of M.Phill. Click to download:

Fee structure of M.Phill



DURATION
One year from the commencement of the programmed comprising of two semesters.

Course 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 – Settheoretic
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.

For more details download the following attachment:

M.Phill Broucher







Address:
Periyar University
Karuppur, Tamil Nadu 636011 ‎
0427 234 5766

Map:

[MAP]https://maps.google.co.in/maps?q=Periyar+University&hl=en&ll=11.718973,78.07 7313&spn=0.006083,0.010278&sll=11.718756,78.077317 &sspn=0.0322725,0.0439466&fb=1&gl=in&cid=176066338 80467998354&t=m&z=17&iwloc=A[/MAP]