Sathyabama LMS

COURSE OBJECTIVES:

Ø To understand the concept of Internet of Things.

Ø To identify the various elements of an IoT System.

Ø To understand the various means of communication from Node / Gateway to Cloud Platforms.

Ø To understand Cloud Computing & its relevance in IoT.

Ø To identify types of data analytics and data visualization tools.

Ø To make students aware of security concerns and challenges while implementing IoT solutions.

COURSE OBJECTIVES

Ø   To understand and analyze some fundamental data structures, such as binary search trees, disjoint sets, and

self-adjusting lists.

Ø   To understand the implementation and complexity analysis of fundamental algorithms such as RSA, primality

testing, max flow, discrete Fourier transform.

Ø   To know about algorithmic issues in a variety of areas, including linear programming and game-theory.

Ø   To understand and implement linear and non linear data structures in real time.

Ø   To analyze the design of algorithms using various performance metrics.

SUGGESTED LIST OF EXPERIMENTS

1. Polynomial Differentiation.

2. Printing the node details level wise.

3. Searching the given element from N*N matrix using Binary search.

4. Knapsack Problem using Greedy Method.

5. Traveling salesman Problem.

6. Binary Tree Traversal.

7. Implementing RED BLACK Trees.

8. Minimum Spanning Tree using KRUSKAL’S Algorithm.

9. Minimum Spanning Tree using FLOYD – WARSHALL Algorithm.

10. Implementing Splay trees.

11. Implementing quad trees.

COURSE OBJECTIVES 

• To explore the aspects of classic and public key cryptography. 
• To acquire knowledge on standard algorithms used to provide confidentiality, integrity and authenticity.
• To become aware of system security components.
• To know the technological aspects of security. ÿ To explore the vulnerabilities in any computing system.

                                   SCSB1611 COMPUTATIONAL INTELLIGENCE

COURSE OBJECTIVES  To study the basic principles of fuzzy logic and fuzzy operators.  To understand the concept of fuzzy logic controller and its applications.  To comprehend the concepts of swarm intelligence algorithms.  To study and analyse various methodologies for training multi-layer network.  To acquire knowledge about SOM and special networks.  To illustrate the concepts of Genetic Algorithms& Evolutionary strategies. UNIT 1 FUZZY LOGIC INTELLIGENCE 9 Hrs. Classical set- operations and properties -Fuzzy Set-operations and properties-problems, Classical Relations-Operations and Properties, Fuzzy Relations-Operations and Properties -Compositions-Maxmin, Max-Product-Problems, Membership function- features of membership functions-types, α cuts, Linguistic Hedges. UNIT 2 FUZZY LOGIC CONTROL SYSTEM 9 Hrs. FLCS- Fuzzy logic control system-Need for FLCS-Assumptions in FLC design. Fuzzification – Defuzzification. Fuzzy decision making, Fuzzy Rule Based System- Knowledge Base System. Mamdani and sugeno FLC architectures, Introduction to ANFIS- Architecture. Fuzzy cognitive maps. Applications - speed control of induction motor, automatic train control. UNIT 3 SWARM INTELLIGENC 9 Hrs. Introduction – Particle swarm optimization algorithm – Bat algorithm and its variants – Artificial Fish swarm optimization algorithm – Cockoo search algorithm and its variants – Firefly algorithm and its variants – Flower pollination algorithm – Artificial Bee colony optimization algorithm – real world applications of swarm intelligence algorithms. UNIT 4 MULTILAYER AND ADAPTIVE ARCHITECTURES 9 Hrs. BPN-Algorithm, Application, CPN-Training, Applications, Mexican Hat, Kohonan SOM, vector quantization, - Associate memory - Bidirectional Associative Memory (BAM) - Architecture – Hopfield – Discrete & Continuous types, Algorithm- Energy function, Adaptive Resonance Theory - ART1, ART2- training. Probabilistic neural network, Applications - Fault diagnosis, Motion control in robo