Course

• LO1: To introduce concepts of Bio-inspired Computing and its applications
• LO2: To provide insight on Artificial Neural Networks
• LO3: To introduce Fuzzy logic and Fuzzy Systems
• LO4: To provide knowledge on optimization algorithms

• CO1: Ability to understand the principles of bio-inspired algorithms
• CO2: Ability to apply bio-inspired techniques for pattern recognition and optimization tasks
• CO3: Ability to analyse problems in medical applications using bio-inspired approaches
• CO4: Ability to evaluate performance of optimization algorithms

Neural networks – Artificial neurons – Activation functions – Learning rules – Supervised and Unsupervised Learning – Single layer and multilayer perceptron – Kohenen’s self-organizing networks – Hopfield networks. Fuzzy systems – Fuzzy sets and relations – Membership functions – Rule base reduction methods – Decision making with fuzzy information – Fuzzy classification and pattern recognition – Neuro-fuzzy systems. Introduction to genetic algorithms – Parent selection – Crossover – Mutation – Genetic Programming – Particle Swarm Optimization – Ant colony optimization – Artificial immune systems – Case Studies – Fuzzy region growing for segmentation of calcifications in mammograms – Classification of normal and ectopic beats using neural networks – Image registration using hybrid bio-inspired approaches.

1. Fausett, Laurene V. Fundamentals of neural networks: architectures, algorithms and applications, Pearson Education India, 2006.
2. Ross, Timothy J. Fuzzy logic with engineering applications. Vol. 2. New York: Wiley, 2004.
3. Goldberg, David E. Genetic algorithms, Pearson Education India, 2006.