{"id":7950,"date":"2019-12-15T03:40:14","date_gmt":"2019-12-15T03:40:14","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/neural-networks-and-ai-in-biomedical-engineering-bme-8th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-v\/"},"modified":"2019-12-15T03:40:14","modified_gmt":"2019-12-15T03:40:14","slug":"neural-networks-and-ai-in-biomedical-engineering-bme-8th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-v","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/neural-networks-and-ai-in-biomedical-engineering-bme-8th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-v\/","title":{"rendered":"Neural Networks and Ai in Biomedical Engineering BME 8th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective-V)"},"content":{"rendered":"

Neural Networks and Ai in Biomedical Engineering detail syllabus for Biomedical Engineering (BME), 2017 scheme is taken from VTU<\/a> 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.<\/p>\n

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<\/a>. For all other Professional Elective-V subjects do refer to Professional Elective-V<\/a>. The detail syllabus for neural networks and ai in biomedical engineering is as follows.<\/p>\n

Module 1 <\/h4>\n

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).\n<\/p>\n

Module 2 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Module 3 <\/h4>\n

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).\n<\/p>\n

Module 4 <\/h4>\n

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).\n<\/p>\n

Module 5 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Course Outcomes:<\/h4>\n

After studying this course, students will be able to:<\/p>\n

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

    Question paper pattern:<\/h4>\n