Neural Networks and Ai in Biomedical Engineering Med Elec 8th Sem Syllabus for VTU BE 2017 Scheme

Neural Networks and Ai in Biomedical Engineering detail syllabus for Medical Electronics (Med Elec), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17ML82X), 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 med elec 8th sem syllabus for be 2017 scheme vtu you can visit Med Elec 8th Sem syllabus for BE 2017 Scheme VTU Subjects. The detail syllabus for neural networks and ai in biomedical engineering is as follows.

Module 1

ARTIFICIAL NEURAL NETWORK: What is an artificial neural network, Benefits, model of a neuron, Types of activation function, neural networks viewed as directed graphs, architectural graph of a neuron with feedback, Network Architectures, Artificial intelligence and Neural Networks. Learning Processes: Learning in context to neural Networks, learning paradigms, supervised & unsupervised learning, Five basic learning rules- Error correction Learning, Memory based learning.

Module 2

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Module 3

Supervised Learning- Decision Surfaces, Two-Category Separation, Linearly Separable Sets, Nonlinearly Separable Sets Unsupervised Learning- Clustering, Kohonen Networks and Competitive Learning, Hebbian Learning, Biomedical Applications, Diagnosis of CAD as a Clustering Problem, Other Biomedical Applications.

Module 4

Artificial Intelligence- Foundations of Computer-Assisted Decision Making Mathematical Modeling and Simulation, Pattern Recognition, Bayesian Analysis, Decision Theory, Symbolic Reasoning Techniques. Knowledge Representation- Production Rules- Introduction, Frames, Databases, Knowledge Acquisition- Introduction,Learned Knowledge, Meta-Knowledge.

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 carries 16 marks
• There will be TWO full questions (with maximum of THREE sub questions) from each module.
• Each full question will have sub 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. An Introduction To Neural Networks-James A. Anderson 2e, PHI, 1995
2. Neural Networks- Simon Haykin Pearson Education/PHI, 2001.
3. Neural Networks by Satish Kumar, Tata McGraw-Hill 2009
4. Neural Networks and Artificial Intelligence For Biomedical Engineering, Donna L. Hudson, Maurice E. Cohen, IEEE Press Series in Biomedical Engineering.

Reference Books:

1. Introduction To Artificial Neural Systems- Jacck M Zurada, Jaico publishing
2. Artificial Neural Networks- B Yegnanarayana, PHI, 2001
3. Fundamentals of Artificial Neural Networks- Mohammad Hassan, PHI, 1999
4. Neural network design- Martin T.Hagan, Cengage Learning.

For detail syllabus of all other subjects of BE Med Elec, 2017 scheme do visit Med Elec 8th Sem syllabus for 2017 scheme.

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