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Partitional clustering, k- means algorithm - Validity of clustering solutions.

UNIT III FEATURE EXTRACTION AND STRUCTURAL PATTERN RECOGNITION 9

KL Transforms - feature selection through functional approximation - Binary selection Elements of
formal grammars, syntactic description, stochastic grammars, Structural representation.

UNIT IV FUZZY SYSTEMS 9

Fuzzy sets and fuzzy reasoning- fuzzy matrices-fuzzy functions-decomposition – Fuzzy inference
systems Mamdani and Sugeno model, Fuzzy clustering- fuzzy c- means algorithmfuzzy control method-
fuzzy decision making.

UNIT V RECENT ADVANCES AND APPLICATIONS 9

Principle of neuro fuzzy techniques, Application of PR in image segmentation – CAD system in Breast
cancer detection, ECG signal analysis, Fingerprint identification - Cell cytology classification

TOTAL PERIODS =45

OUTCOMES:

At the end of the course, learners will be able to:

 Develop an idea about the fundamentals of Pattern recognition.
 Demonstrate on unsupervised classification
 Describe feature extraction and structural pattern recognition.
 Acquire the knowledge of fuzzy systems & its applications.
 Carry out recent advancements in life science & technology using Fuzzy techniques

REFERENCES:
1. Duda R.O., and Hart P.G.,Pattern Classification and scene analysis, JohnWiley, New York,
1973.
2. Earl Gose, Richard Johnsonbaugh, Steve Jost, Pattern Recognition and Image analysis, Prentice
Hall of India, New Delhi - 2007.
3. Robert J. Schalkoff , Pattern recognition: Statistical, Structural and Neural approaches,John
Wiley and Sonslnc, New York, 1992.
4. Morton Nadier and Eric Smith P., Pattern Recognition Engineering, John Wiley and sons, New
York, 1993.
5. Andrew Webb, Statistical Pattern Recognition, Arnold publishers, London,1999.
6. Donna L. Hudson, Maunee E. Cohan, Neural Networks & Artificial Intelligence for Biomedical
Engineering, Prentice Hall of India, New Delhi - 2001.
7. Timothy Ross, Fuzzy Logic with Engineering applications,2nd Edition John Wiley and
sons,West Sussex,2004.

Curriculum and Syllabus | Open Electives | R 2017 | REC Page 67