References:4. H. T. Davis, Introduction to Nonlinear Differential <strong>and</strong> Integral Equations, Dove5. Harry Hotchstadst, Integral Equations, John Wiley.6. A S Gupta, Calculus <strong>of</strong> Variations.7. C Fox, An Introduction to the Calculus <strong>of</strong> Variations, Dover.8.(xvi) S<strong>of</strong>t Computing (MA6007):3-1-0: 4 Credits Prerequisite: NilIntroduction to S<strong>of</strong>t Computing, Components <strong>of</strong> S<strong>of</strong>t Computing, Importance <strong>of</strong> S<strong>of</strong>t Computing,Fuzzy Set Theory - Definition, Different types <strong>of</strong> fuzzy set Membership Functions, Fuzzy Settheoretic operations, Fuzzy Rules <strong>and</strong> Fuzzy Reasoning, Fuzzy Inference Systems, GA, SimulatedAnnealing, Particle swarm optimization, Neural Networks- Supervised Learning, UnsupervisedLearning, Hybrid Systems - Neuro Fuzzy Modeling, Fuzzy c-means, Applications in ImageProcessing, Neuro-fuzzy control, Data Mining.Implementation <strong>of</strong> the problems using MATLAB.Texts:1. Neuro Fuzzy <strong>and</strong> S<strong>of</strong>t Computing – J.S.R.Jang, C.T.Sun <strong>and</strong> E. Mizutani , Amazon2. An Introduction to Neural Networks - HaykinReferences:3. Fuzzy Sets <strong>and</strong> Fuzzy Logic - Klir <strong>and</strong> Yuan4. Genetic Algorithms – Goldberg.5. Particle Swarm Intelligence: J. Kennedy, R.C. Eberhart <strong>and</strong> Y. Shi, Morgan Kaufman Publisher6. Particle swaram Optimization: Maurice Clerce, ISTE7. Particle swarm Optimization: Andrea E. Olsson(xvii) Neural Network (MA6008):3-1-0: 4 Credits Prerequisite: NilBiological <strong>and</strong> Artificial Neuron, Perceptron model, Adaline model, Different types <strong>of</strong> Activationfunctions, Learning Techniques: Supervised <strong>and</strong> Unsupervised, Multilayered feed forward Networks,Back propagation algorithm <strong>and</strong> its improvements, Applications <strong>of</strong> Back propagation algorithm tostatistical pattern recognition, classification <strong>and</strong> regression problems, Advantages <strong>of</strong> Neural Networksover statistical classification techniques, Performance Surfaces <strong>and</strong> Optimum Points, SteepestDescent ,Stable learning Rates, Minimizing Along a line ,Newton’s method <strong>and</strong> conjugate method.Recurrent networks, Radial Basis Function Networks as an interpolation model, Time delay neuralnetworks for forecasting problems, Probabilistic Neural Networks, Kohonen’s self organizing map,Self organizing maps with quadratic functions <strong>and</strong> its applications medical imaging, AdaptiveResonance, Theory model, Applications <strong>of</strong> Art model for knowledge acquisition, Extensive sessionsin MATLAB for solving statistical pattern recognition, classification, regression <strong>and</strong> predictionproblems using different kinds <strong>of</strong> Neural Network modelsTexts:1. Neural Networks Design: Martin T. Hagan, Howard B. Demuth <strong>and</strong> Mark Beale, Cengage LearningIndia Pvt.Ltd2. Neural Networks: Simon S. Haykin, Macmillan
References:3. Neural Networks,Fuzzy logic <strong>and</strong> Genetic Algorithm: Rajasekaran <strong>and</strong> G.A. Vijayalaxmi Pai, PrenticeHall <strong>of</strong> India4. Neural Networks: Algorithms, Applications, And Programming Techniques, Freeman,PearsonEducation India.5. Neural Networks: Stish Kumar, Tata Mc-GraHill Education(xviii) Queueing Theory in Computer Science (MA6009):3-1-0: 4 Credits Prerequisite: Probability <strong>and</strong> StatisticsProbability <strong>and</strong> r<strong>and</strong>om variable, discrete <strong>and</strong> continuous univariate <strong>and</strong> multivariate distributions,moments, law <strong>of</strong> large numbers <strong>and</strong> central limit theorem (without pro<strong>of</strong>). Poisson process, birth <strong>and</strong>death process, infinite <strong>and</strong> finite queueing models M/M/1, M/M/C, M/G/1, M/M/1/N, M/E/1, E/M/1,M/G/1/N, GI/M/1, <strong>and</strong> more complex non-Markovianqueueing models, such as GI/G/1 queues,Multiserver Queues: M/M/c, M/G/c, GI/M/c modles, Erlan’sg loss system, Queues with finitepopulations: M/M/1/N/K, M/G/1/N/K etc. models <strong>and</strong> Engset formula, Concept bulk queues:M [X] /M/1, M/M [Y] /1, M/M (a,b) /1, M [X] /G/1, GI [X] /M/1, M/G (a,b) /1, GI/M (a,b) /1 etc. queueing models.Priority queueing models, Vacation queueing models,Network <strong>of</strong> queues, finite processor sharingmodels, central server model <strong>of</strong> multiprogramming, performance evaluation <strong>of</strong> systems usingqueueing models. Concepts <strong>of</strong> bottleneck <strong>and</strong> system saturation point. Introduction to discrete timequeues <strong>and</strong> its applications.Texts/ References:1. Donald Gross <strong>and</strong> Carl M. Harris, Fundamentals <strong>of</strong> Queueing Theory, Wiley-India, 1998.2. Leonard Kleinrock, Queueing Systems. Volume 1 : Theory, Wiley-Interscience, 1975.3. Leonard Kleinrock, Computer Applications, Volume 2, Queueing Systems, Wiley-Interscience. 1975.(xix) Queueing, Inventory <strong>and</strong> Reliability (MA6010):3-1-0: 4 Credits Prerequisite: Probability <strong>and</strong> StatisticsMarkov chains, Poisson process, Birth-Death process, Simple Markovianqueueing models: M/M/1,M/M/1/N, M/M/C, transient behaviour <strong>of</strong> M/M/1 <strong>and</strong> M/M/1/N queue. Models with general arrival orservice: M/G/1, GI/M/1. Concept bulk queues, Some Markovian <strong>and</strong> no-Markovianbulk arrival orbulk service queueing models: M [X] /M/1, M/M [Y] /1, M/M (a,b) /1, M [X] /G/1, GI [X] /M/1, M/G (a,b) /1,GI/M (a,b) /1 etc. models.Network <strong>of</strong> Markovian queues.General concept <strong>of</strong> discrete time queues, system reliability, Reliability<strong>of</strong> Systems <strong>of</strong> Independent Components, Markovian models in reliability theory, Life testing using theexponential <strong>and</strong> Weibull models.Expected System Lifetime, Bounds on the expected system lifetime,Systems with RepairElementary inventory models, Deterministic <strong>and</strong> Stochastic models, Inventory models with pricebreaks, Multi-item Economic-order-quantity (EOQ) model with storage limitation, Single- <strong>and</strong> multiperiodprobabilistic inventory models, Concept <strong>of</strong> just-in-time inventory.Texts/ References:1. Donald Gross <strong>and</strong> Carl M. Harris, Fundamentals <strong>of</strong> QueueingTheory ,Wiley-Interscience.2. Sheldon M. Ross, Introduction to Probability Models, Academic Press , Tenth Edition, 2009.3. Hamdy A. Taha, Operations Research: An Introduction, 9 th edition, Pearson/Prentice Hall, 2010.
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Groundwater contamination, sources
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Books:1. A Handbook of Silicate Roc