BTBME506C: Applied Neural Networks and Fuzzy Logic in Medicine Syllabus for BM 5th Sem 2019-20 DBATU (Elective-IV)

Applied Neural Networks and Fuzzy Logic in Medicine detailed syllabus scheme for Biomedical Engineering (BM), 2019-20 onwards has been taken from the DBATU official website and presented for the Bachelor of Technology students. For Subject Code, Course Title, Lecutres, Tutorials, Practice, Credits, and other information, do visit full semester subjects post given below.

For 5th Sem Scheme of Biomedical Engineering (BM), 2019-20 Onwards, do visit BM 5th Sem Scheme, 2019-20 Onwards. For the Elective-IV scheme of 5th Sem 2019-20 onwards, refer to BM 5th Sem Elective-IV Scheme 2019-20 Onwards. The detail syllabus for applied neural networks and fuzzy logic in medicine is as follows.

Applied Neural Networks and Fuzzy Logic in Medicine Syllabus for Biomedical Engineering (BM) 3rd Year 5th Sem 2019-20 DBATU

Course Objectives:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
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Course Outcomes:

1. To enable the students to acquire knowledge about the artificial intelligence techniques
2. To recognize the patterns and its application in medicine.

Unit I

Artificial Intelligence
Artificial Intelligence (AI): Introduction, definition and history, Components, Problem definition- Structures and Strategies for state space search- Depth first and breadth first search-DFS with iterative deepening- Heuristic Search- Best First Search- A* Algorithm- AND, OR Graphs, Problems

Unit II

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It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.
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Unit III

Pattern Recognition
Classes, patterns and features- Pattern similarity and PR Tasks- Pattern discrimination-Feature space metrics and Covariance matrix- Feature assessment Unsupervised clustering- Tree clustering- K-means clustering, Statistical, syntactic and descriptive approaches.

Unit IV

Classification
Linear discriminants, Bayesian classification, Bayes rule for minimum risk, minimum error rate classification, discriminant functions, and decision surfaces, Model free technique – ROC Curve, Classifier evaluation, Back propagation learning, Competitive learning.

Unit V

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.
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Text Books:

1. George F Luger, “Artificial Intelligence- Structures and Strategies for Complex Problem Solving”, 4/e, 2002, Pearson Education.
2. Duda and Hart P E, “Pattern classification and scene analysis”, John wiley
3. and sons, NY, 1973.

Reference Books:

1. Earl Gose, Richard Johnsonbaugh, and Steve Jost; “PatternRecognition and Image Analysis”, PHI Pvte. Ltd., NewDelhi-1, 1999.
2. Fu K S, “Syntactic Pattern recognition and applications”, Prentice Hall, Eaglewood cliffs, N J, 1982.
3. Rochard O, Duda and Hart P E, and David G Stork, “Pattern classification”, 2nd Edn., John Wiley and Sons Inc., 2001.
4. Carlo Combi, Yuval Shahar; “Artificial Intelligence in Medicine” – 12th Conference – Springer.

For detail syllabus of all subjects of Biomedical Engineering (BM) 5th Sem 2019-20 onwards, visit BM 5th Sem Subjects of 2019-20 Onwards.