Neural Networks and Its Applications Med Ele 7th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective III) detail syllabus for Medical Electronics (Med Ele), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (MD8002), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other med ele 7th sem syllabus for be 2017 regulation anna univ you can visit Med Ele 7th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective III subjects do refer to Professional Elective III. The detail syllabus for neural networks and its applications is as follows.
Course Objective:
The student should be made to
- Understand the basic neural network architectures and learning algorithms, for applications in pattern recognition, image processing, and computer vision.
- Explore the use of Pattern and Neural Classifiers for classification applications.
- To introduce neural computing as an alternative knowledge acquisition/representation paradigm.
Unit I
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Unit II
Supervised Network Learning Paradigms
Perceptron and backpropagation – Single Layer Perceptron, Convergence theorem, delta rule, Linear Separability, Multilayer Perceptron, Backpropagation of error, variation and extension to backpropagation. Recurrent perceptron like networks.
Unit III
Associative Network and Network Based On Competition
Associative Memory – Different types of Pattern Association, Bidirectional Associative Memory, and Hopfield Memory. Self Organizing feature maps, Linear Vector Quantization, Counter Propagation Networks,
Unit IV
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Unit V
Application of Neural Networks
ANN in Computer-Aided Diagnosis, ANN as multivariate statistical model, ANN for medical Image segmentation, ANN as a predictive model, ANN as a optimizer.
Course Outcome:
Upon successful completion of the course student should be able to
- Describe the neural network architecture and learning algorithms
- Implement Pattern and Neural Classifiers for various classification applications
Text Books:
- David Kriesel, A Brief Introduction to neural networks,
References:
- Laurene Fausett, Fundamentals of neural networks- Architectures, algorithms and applications, Prentice Hall, 1994.
- James A Freeman and David M.Skapra, Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-Wesley, 1991, Digital Version 2007.
- Simon O. Haykins, Neural Networks: A Comprehensive Foundation, 2nd Edition, Pearson 1994
- Edited by Kenji Suzuki, Artificial Neural Networks – Methodological Advances and Biomedical Applications, ISBN 978-953-307-243-2, 374 pages, Publisher: InTech, Chapters published April 11, 2011 under CC BY-NC-SA 3.0 license DOI: 10.5772/644
For detail syllabus of all other subjects of BE Med Ele, 2017 regulation do visit Med Ele 7th Sem syllabus for 2017 Regulation.
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