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SRI VENKATESWARA UNIVERSITY: TIRUPATI – 517 502 2-Year M.Tech (CSE), I Semester Choice Based Credit System (CBCS) (With effect from the academic year 2008-09) Scheme of Instruction and Examinations
* Electives will be offered for a minimum of 12 credits (15 Instruction Hours (Approx) ), in addition to the above courses.
NOTE: For each Course: Theory Component: Sessional Marks: 40 End Semester Examination Marks: 60 Practical Component: Sessional Marks: 40 End Semester Examination Marks: 60 Seminar Sessional Marks: 100 End Semester Examination Marks: NIL Comprehensive Viva Sessional Marks: 100 End Semester Examination Marks: NIL
Duration of End Semester Examination: 3 Hours
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MCS0811 TOP SRI VENKATESWARA UNIVERSITY :: TIRUPATI M.Tech (CSE) - I SEMESTER (CBCS) (With effect from the academic year 2008 – 09) EXPERIMENTAL METHODS IN COMPUTER SCIENCE No. of Credits: 4 (Theory: 3, Practical: 1) Instruction Hours / Week: 5 Instruction Weeks / Semester: 15 (Theory: 3, Practical: 2)
UNIT I Review of probability distributions, Review of optimization techniques, Multi-objective optimization, Queuing models.
UNIT II Simulation models - Continuous, Discrete, and Discrete-continuous; Random number generation, Random variates generation, Input modeling.
UNIT III Verification and validation of simulation models, Output analysis for a single model, Comparison and evaluation of alternative system designs.
UNIT IV Variance design techniques, Simulation software, Simulation of computer systems and computer networks.
UNIT V Stochastic process, Markovian models, Design of experiments.
References: 1. Rao S S, Engineering Optimization: Theory and Practice, Revised 3rd edition, New Age International Publishers, 2007. 2. Banks J, Carson J S, Nelson B L, and Nicol D M, Discrete-event System Simulation, 4th Edition, Pearson Education, 2005. 3. Law A M, Kelton W D, Simulation Modeling and Analysis, 3rd Edition, Tata McGraw-Hill, 2003. 4. Technical papers published in reputed journals / transactions / conferences.
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MCS0812 TOP SRI VENKATESWARA UNIVERSITY :: TIRUPATI M.Tech (CSE) - I SEMESTER (CBCS) (With effect from the academic year 2008 – 09) ADVANCED TOPICS IN DATABASE MANAGEMENT SYSTEMS No. of Credits: 5 (Theory: 4, Practical: 1) Instruction Hours / Week: 6 Instruction Weeks / Semester: 15 (Theory: 4, Practical: 2)
UNIT I Physical Database Design and Tuning; Security and Authorization.
UNIT II Parallel and Distributed Databases; Object-Database Systems.
UNIT III Deductive Databases; Data Warehousing and Decision Support.
UNIT IV Data Mining; Information Retrieval and XML Data.
UNIT V Spatial Data Management; Further Reading.
Text Book: Ramakrishnan R, and Gehrke J, Database Management Systems, 3rd Edition, McGraw-Hill, 2003. (Chapters 20 to 29).
Reference Books: 1. Silberschatz A, Korth H F, and Sudarshan S, Database System Concepts, 5th edition, McGraw-Hill, 2006. 2. Date C J, An Introduction to Database Systems,8th edition, Pearson Education, 2003.
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MCS0813 TOP SRI VENKATESWARA UNIVERSITY :: TIRUPATI M.Tech (CSE) - I SEMESTER (CBCS) (With effect from the academic year 2008 – 09) ARTIFICIAL NEURAL NETWORKS No. of Credits: 4 (Theory: 3, Practical: 1) Instruction Hours / Week: 5 Instruction Weeks / Semester: 15 (Theory: 3, Practical: 2)
UNIT I Introduction - Trends in computing, Pattern and data, Pattern recognition methods. Basics of Artificial Neural Networks - Characteristics of neural networks, Historical development, Terminology, Models of neuron, Topology, Basic learning laws. Activation and Synaptic Dynamics - Activation dynamics models, Synaptic dynamics models, Learning methods, Stability and Convergence, Recall in neural networks.
UNIT II Functional Units of ANNs for Pattern Recognition Tasks - Pattern recognition problem, Basic types of ANNs, Various pattern recognition tasks performed by ANNs. Feed-forward Neural Networks - Analysis of - Pattern associative networks, Pattern classification networks, Pattern mapping networks.
UNIT III Feed-back Neural Networks - Linear auto associative FF networks, Pattern storage networks, Stochastic networks, and Simulated annealing; Boltzmann machine.
UNIT IV Competitive Learning Neural Networks - Components of a competitive learning neural network, Analysis of feedback layer for different output functions, Analysis of pattern clustering networks, Analysis of feature mapping networks. Architecture for Complex Pattern Recognition Tasks - Associative memory, Pattern mapping, Stability-Plasticity dilemma, Adaptive resonance theory, Temporal patterns, Pattern variability – Neocognitron.
UNIT V Applications of ANNs - Pattern classification – character recognition, Associative memories – content addressable memory, Information retrieval; Optimization – Linear programming problem, Traveling salesman problem, Smoothing images with discontinuities; Vector quantization, Control applications, Applications in speech, image processing and decision making.
Text Books: 1. Yegnanarayana B, Artificial Neural Networks, PHI, 2004. 2. Satish Kumar, Neural Networks: A Class Room Approach, Tata McGraw-Hill, 2004.
Reference Books:
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MCS0814 TOP SRI VENKATESWARA UNIVERSITY :: TIRUPATI M.Tech (CSE) - I SEMESTER (CBCS) (With effect from the academic year 2008 – 09) SEMINAR - I No. of Credits: 1 Instruction Weeks / Semester: 15 Instruction Hours / Week: 2
Each student must give at least two seminars, choosing topics from reputed research journals.
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MCS0815 TOP SRI VENKATESWARA UNIVERSITY :: TIRUPATI M.Tech (CSE) - I SEMESTER (CBCS) (With effect from the academic year 2008 – 09) COMPREHENSIVE VIVA - I No. of Credits: 1 Instruction Weeks / Semester: 15
The Viva is conducted at the end of the semester. Its scope is at least all subjects studied in the current semester.
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