Neural Networks and Ai in Biomedical Engineering BME 8th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective-V)

Neural Networks and Ai in Biomedical Engineering detail syllabus for Biomedical Engineering (BME), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17BM834), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.

For all other bme 8th sem syllabus for be 2017 scheme vtu you can visit BME 8th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective-V subjects do refer to Professional Elective-V. The detail syllabus for neural networks and ai in biomedical engineering is as follows.

Module 1

Overview: Early Biomedical Systems, Medical and Biological Data.(Text 1: O.1, O.2) Neural Network: Introduction, Human Brain, Benefits of Neural Networks, Models of a Neuron, Neural Networks viewed as Directed Graph, Feedback. (Text 2: 1.1, 1.2, 1.3, 1.4, 1.5).

Module 2

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Module 3

Supervised Learning: Decision Surfaces, Two Category Separation, Linearly Separable Sets, Non-Linearly Separable Sets, Multiple Category Classification Problems, Relationship to Neural Networks Models, Comparison of Methods, Applications. (Text 1: Chapter 4) Unsupervised Learning: Clustering, Kohonen Networks and Competitive Learning, Hebbian Learning, Adaptive Resonance Theory, Applications. (Text 1: Chapter 5) Design Issues: Introduction, Input Data Types, (Text 1: 6.1, 6.2).

Module 4

Foundations of Computer-Assisted Decision Making: Motivation, Data Bases and Medical Records, Mathematical Modeling and Simulation, Pattern Recognition, Bayesian Analysis, Decision Theory, Symbolic Reasoning Techniques. (Text 1: Chapter 9) Knowledge Representation: Production Rules, Frames, Data Bases, Predicate Calculus and Semantic Nets, Temporal Data Representation. (Text 1: Chapter 10).

Module 5

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Course Outcomes:

After studying this course, students will be able to:

1. Describe the classes of neural networks and their models.
2. Explain the approaches to the development of neural network models.
3. Employ the learning techniques to classify the data.
4. Discuss general types of knowledge representations that are useful in decision-support systems.
5. Explain the reasoning methodologies utilized in knowledge based systems.

Question paper pattern:

• The question paper will have TEN questions.
• Each full question carry 16 marks.
• There will be TWO full questions (with maximum of THREE sub questions) from each module.
• Each full question will have questions covering all the topics under a module.
• The students will have to answer FIVE full questions, selecting ONE full question from each module.

Text Books:

1. Neural Networks and Artificial Intelligence for Biomedical Engineering, Donna L. Hudson, Maurice E. Cohen, IEEE Press, 2000.
2. Neural Networks: A Comprehensive Foundation, Simon Haykin, 2nd Edition, Prentice Hall International.

Reference Books:

1. Artificial Neural Networks, Robert J. Schalkoff, Tata McGraw Hill, 1997.
2. Introduction Artificial Neural System, Jacek M. Zurada, Jaico Publication House, 2004.
3. Neural Networks: A Classroom Approach, Sathish Kumar, Tata McGraw Hill, 2004.
4. Artificial Intelligence: A Modern Approach, Stuart Russell, Peter Norvig, 2nd Edition, Pearson Education, 2013.

For detail syllabus of all other subjects of BE Bme, 2017 regulation do visit Bme 8th Sem syllabus for 2017 Regulation.

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