Department Of INFORMATION TECHNOLOGY R.V.R. &J.C. .COLLEGE OF .
Department of INFORMATION TECHNOLOGYR.V.R. &J.C. COLLEGE OF ENGINEERING (A) :: GUNTUR – 522019.PROPOSED SCHEME OF EXAMINATION AND INSTRUCTION FOR M.TECH(CST) w.e.f 2021-22I/II M.TECH (COMPUTER SCIENCE & TECHNOLOGY) :: FIRST SEMESTERSl.No12345678Hours/WeekCode No & SubjectCT511 – Advanced DataStructures and AlgorithmsCT512– Advanced DatabaseManagement SystemsCT513– AdvancedOperating SystemsElective – IElective – IIElective – IIICT551– AdvancedProgramming LabCT552– Advanced DatabaseManagement Systems LabTOTALEvaluation of MarksCreditsInternalExternalTheory 100100100100--3240--6010024628320360120800100I/II M.TECH (COMPUTER SCIENCE & TECHNOLOGY):: SECOND SEMESTERHours/WeekSl.No123456789Code No & SubjectCT521 – Number Theoryand CryptographyCT522 –TCP/IPCT523 –Machine LearningElective-IVElective-VElective- VICT MC01– ResearchMethodology and IPRCT561 –Machine LearningLabCT562 – Industry RelatedLabTOTALEvaluation of 0010040--60100240--6010028420360120900
II/II M.TECH (COMPUTER SCIENCE & TECHNOLOGY) :: FIRST SEMESTERHours/WeekSl. NoCode No & Subject1LecturePracticalCT 651 – MOOCSCT 652 – SummerInternship------CT 653 – Project Phase - I23Evaluation of 0--100----4100--100----8200--200II/II M.TECH (COMPUTER SCIENCE & TECHNOLOGY) :: SECOND SEMESTERHours/WeekSl.NoCode No & Subject1CT 661 – Project Phase - IILecturePractical----Evaluation of MarksCredits10TOTAL MARKS: 2000Proposed Electives:1. CT5712. CT5723. CT5734. CT5745. CT5756. CT5767. CT5778. CT5789. CT57910. CT58011. CT58112. CT58213. CT58314. CT58415. CT58516. CT58617. CT58718. CT58819. CT58920. CT59021. CT59122. CT59223. CT59324. CT594– Automata and Compiler Design– Advanced Computer Architecture– Advanced Web Technologies– Advanced Software Engineering– Artificial Intelligence– Digital Image Processing– Block Chain Technology- Quantum Computing and Information Theory– Information Security– AR & VR– Wireless Networks– Deep Learning– Big Data Analytics– Cloud Computing– Internet of Things– Mobile Computing– Agile Software Development– Data Engineering– Evolutionary Computation– Parallel Algorithms– Fuzzy Set Theory and Applications– Natural Language Processing– Software Architecture– Semantic WebInternalExternalTotal4060100TOTAL: 74 Credits
RVR & JC COLLEGE OF ENGINEERING:: GUNTURM.Tech(Computer Science & Technology)Syllabus w.e.f. 2021-22CT 511 – Advanced Data Structures and AlgorithmsLecture: 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 4Course Learning Objectives: At the end of the Course Students will understand1. Fundamentals of analysis of algorithm at depth.2. Study of advanced data structures and its uses.3. Analysis of problems from different domains.Course Learning Outcomes: After successful completion of this course, student will be able to1. Identify and use suitable data structures for given problem from different domains.2. Appreciate the role of Graph algorithms in solving variety of problems.3. Appreciate the role of Optimization by using linear programming.4. Analyze the various algorithms from different domains.5. Understand the importance of advanced algorithms and techniques.UNIT – I:[9 Periods]Introduction, Asymptotic notations Big O, Big Θ, Big Ω, ο, ω notations, Proofs of master theorem,applying theorem to solve problems.UNIT – II:[9 Periods]Advanced Data Structures Red-Black Trees: properties of red-black trees, Insertions, Deletions B-Treesand its operations Binomial Heaps: Binomial trees and binomial heaps, Operation on Binomial heaps.UNIT - III :[9 Periods]Dynamic Programming matrix chain multiplication, cutting rod problem and its analysis GraphAlgorithms Bellman ford algorithm, Dijkstra algorithm, Johnson’s All pair shortest path algorithm forsparse graphs.UNIT – IV:[9 Periods]Maximum Flow, Flow networks, the ford Fulkerson method, max bipartite matching, push RelabelAlgorithm, the relabel to front algorithmUNIT – V :[9 Periods]Linear Programming Standard and slack forms, Formulating problems as linear programs, simplexalgorithm, Duality, Initial basic feasible solution.
Text Books:1. T.H. Coreman , C.E. Leiserson, R.L. Rivest, and C. Stein, “Introduction to algorithms”,2nd edition, PHIpublication 2005.2. Ellis Horowitz, SartajSahni , S. Rajsekaran. “Fundamentals of computer algorithms” University Press.References:1. Robert Sedgewick Philippe Flajolet, “An Introduction to the Analysis of Algorithms”, First Edition,McGraw Hill, 1995.2. G.A.V. Pai, “Data Structures and Algorithms”, TMH, 2009.
CT 512 – Advanced Database Management SystemsLecture : 4 Periods/WeekInternal: 40 MarksPractical: -External: 60 MarksCredits: 4Course Learning Objectives: At the end of the Course Students will understand1. Fundamental Concepts of Standard and Advanced Databases.2. Active databases, Knowledge Based Systems(KBSs), Deductive Databases, and Relation Shipbetween KBSs and DBMSs.3. Advance solutions for KBSs, Temporal Databases, Multimedia Databases, Ontology and DataMining.Course Learning Outcomes: After successful completion of this course, student will be able to1. Use Data Models and understand the Database Context.2. Know the importance of Active Database systems and it role in the context of KnowledgeBased systems.3. Apply Deductive databases and its coupling to knowledge based systems.4. Acquire the knowledge of Temporal Databases used in the latest technologies.5. Know the Internet retrieval and Indexing mechanisms.UNIT – I:[9 Periods]Relational Databases: Integrity Constraints, Functional Dependency, Multi-valued Dependency;Query Processing and Optimization: Evaluation of Relational Operations, Transformation ofRelational Expressions, Indexing and Query Optimization, Data access from disk, Index based access,Sort and Join Processing, Physical plan selection, Limitations of Relational Data Model;UNIT— II:[9 Periods]Parallel and Distributed Databases: Distributed Data Storage, Fragmentation & Replication, Locationand Fragment Transparency, Distributed Query Processing and Optimization, Distributed Transaction,Modeling and Concurrency Control, Distributed Deadlock, Commit ProtocolsUNIT – III:[9 Periods]Advanced Transaction Processing: Nested and Multilevel Transactions, Compensating Transactionsand Saga, Long Duration Transactions, Weak Levels of Consistency, Transaction Work Flows,Transaction Processing Monitors;UNIT – IV:[9 Periods]Objected Oriented and Object Relational Databases: Modeling Complex Data Semantics,Specialization, Generalization, Aggregation and Association, Objects, Object Identity, Equality andObject Reference, Architecture of Object Oriented and Object Relational Databases
Unit – V:[9 Periods]NoSQL databases: Cassandra, MongoDB, etc.,References:1. Avi Silberschatz, Henry F. Korth & S. Sudarshan, “Database System Concepts”, Tata McGraw-Hill.2. W. Kim, “Introduction to Object Oriented Databases”, MIT Press.3. J. D. Ullman, “Principles of Database and Knowledge Base Systems”, Computer Science Press
CT 513 – Advanced Operating SystemsLecture : 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 4Course Learning Objectives: At the end of the Course Students will understand1. Fundamentals of Scheduling and Inter process Communication.2. Concepts of Files, File systems, Devices and Device Drivers.3. Concepts of Resource management and Security.Course Learning Outcomes: After successful completion of this course, student will be able to1. Analyze, design and implement different scheduling algorithms.2. Design and implement inter process communication mechanisms and synchronizationproblems in Uni-processor and multiprocessor systems.3. Use files and files systems in UNIX environment.4. Understand and use the concepts of devices and device drivers.5. Understand Resource Management and Security Issues for protection.UNIT – I:[9 Periods]PROCESSES AND SCHEDULING: Process States and System Call Interface; Life Cycle of a Process;Process Dynamics; Scheduler; working and implementation; Linux Process States and System Calls;Process Groups, Sessions, Foreground and Background Processes.UNIT – II:[9 Periods]INTERPROCESS COMMUNICATION AND SYNCHRONISATION: Signals, Pipes and Named Pipes (FIFOs);Threads and pthread library; Mutexes and Condition Variables; Semaphores; Producer-ConsumerProblem and Solutions using mutexes, condition variables and semaphores.UNIT – III:[9 Periods]FILES AND FILE SYSTEMS: File and File Meta-data; File Naming Systems; File System Operations; FileSystem Implementation; File System Structures; Booting an OS; File System Optimization.UNIT – IV:[9 Periods]DEVICES AND DEVICE DRIVERS: Devices and Types of Devices; Terminal, Disk, SCSI, Tape and CDdevices; Unification of Files and Devices; Device Drivers; Concepts and Implementation Details.UNIT – V:[9 Periods]RESOURCE MANAGEMENT AND SECURITY: Resource Management Issues; Types of Resources;Integrated Resource Scheduling; Queuing Models of Scheduling; Protection of Resources – hardware,software, and attacks; Security Policies.
TEXT BOOKS:1. Charles Crowley, Operating Systems: A Design-Oriented Approach,Tata McGraw-Hill (2001 or later)2. Richard Stevens, Stephen Rago, Advanced Programming in the Unix Environment, Addison-Wesley(2013).REFERENCES:1. Maekawa, M. and Arthur E. Oldehoeft and Oldehoeft, R.R. Operating Systems: Advanced Concepts,Benjamin Cummings (1987)2. David A.Rusling. The Linux Kernel
CT 521 – Number Theory and CryptographyLecture: 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 4Course Learning Objectives: At the end of the Course Students will understand1. The fundamentals in number theory, finite fields and quadratic residues.2. The public key and elliptic curve cryptography.3. The primality and factoring principles and methods.Course Learning Outcomes: After successful completion of this course, student will be able to1. Know the concepts of the number theory and the quadratic residues.2. Know the enciphering matrices and the simple cryptosystems.3. Familiar with public key cryptography algorithms.4. Apply the concepts of primality and factoring in cryptosystems.5. Familiar with elliptic curve cryptography algorithms.UNIT I:[9 Periods]Some Topics in Elementary Number Theory: Time estimates for doing arithmetic, Divisibility and theEuclidean algorithm, Congruence, Some applications to factoring.Finite Fields and Quadratic Residues: Finite fields, Quadratic residues and reciprocityUNIT II:Cryptography: Some simple cryptosystems, Enciphering matrices.[9 Periods]UNIT III:[9 Periods]Public Key: The idea of public key cryptography, RSA, Discrete log, Knapsack, Zero-knowledge protocolsand oblivious transfer.UNIT IV:[9 Periods]Primality and Factoring: Pseudo primes, the rho method, Fermat factorization and factor base, thecontinued fraction method, the quadratic sieve method.UNIT V:[9 Periods]Elliptic Curves: Basic facts, Elliptic curve cryptosystems, Elliptic curve primality test, Elliptic curvefactorization.TEXT BOOK:1. Neal Koblitz, “A Course in Number Theory and Cryptography”, Second Edition, SpringerVerlag.Reference Books:2. M.R. Schroeder, “Number Theory in Science and Communication”, Springer; 4th ed. Edition.3. Douglas R. Stinson, “Cryptography: Theory and Practice”, Third Edition, Chapman and Hall/CRC.
CT 522 – TCP/IPLecture: 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 4Course Learning Objectives: At the end of the Course Students will understand1. To understand the basic concepts of TCP/IP Architecture.2. To understand about various Routing Network Transport Layer Protocols.3. To understand the functionalities of various application layer protocols.Course Learning Outcomes: After successful completion of this course, student will be able to1. An ability to understand the basic concepts of data communication and responsibility of eachlayers of reference model2. Familiarize with network layer.3. Familiarize with transport layer protocols and its applications.4. An Ability to understand the client-server model of interaction.5. An ability to understand the concept of client server technology and remote login protocolsUNIT I :[9 Periods]Introduction and Overview, Review Of Underlying Network Technologies, Internetworking Conceptand Architectural Model , Classful Internet Addresses .UNIT II:[9 Periods]Mapping internet Addresses To Physical Addresses (ARP) , Determining An Internet Address At Startup(RARP) , Internet Protocol: Connectionless Datagram Delivery , Routing IP Datagrams , Error AndControl Messages (ICMP) .UNIT III:[9 Periods]User Datagram Protocol (UDP) , Reliable Stream Transport Service (TCP) , Routing: Cores, Peers, AndAlgorithms .UNIT IV:[9 Periods]Client-Server Model Of Interaction, The Socket Interface, TCP/IP over ATM Networks, Mobile IP, DHCPUNIT V:[9 Periods]Applications: Voice And Video Over IP (RTP), Internet Management (SNMP)Text Book:1.Internetworking with TCP/IP, Volume-1, 4/e (Principles, Protocols & Architectures) - DouglasE.Comer, PHI.
References:1.Internetworking with TCP/IP, Volume-)!, 3/e (Design, Implementation & Internals) - Douglas EComer, David L.Stevens., PHI2.Internetworking with TCP/IP, Volume-ill, 2/e (Client-Server Programming & Applications) - DouglasE.Comer, David L.Stevens. PHI
CT 523 – Machine LearningLecture: 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 4Course Learning Objectives: At the end of the Course Students will understand1. and know what the goals and objectives of machine learning are how to use machine learningto build real-world systems2. and have knowledge of popular classification and prediction techniques and learn how to buildsystems that explore unknown and changing environments3. and get some exposure to machine learning theory, in particular how learn models that exhibithigh accuracies.Course Learning Outcomes: After successful completion of this course, student will be able to1. know what the goals and objectives of machine learning are2. have a basic understanding on how to use machine learning to build real-world systems3. have sound knowledge of popular classification and prediction techniques, such as decision trees,support vector machines, nearest-neighbor approaches.4. learn how to build systems that explore unknown and changing environments5. have some exposure to more advanced topics, such as ensemble approaches, kernel methods,unsupervised learning, feature selection and generation.Unit-I:[9 Periods]Introduction to Machine Learning. Supervised Learning, Bayesian Decision Theory and Naïve BayesianApproaches, Parametric Model Estimation.Unit-II:[9 Periods]Dimensionality Reduction Centering on PCA, Clustering1: Mixture Models, K-Means and EM, NonParametric Methods Centering on kNN and Density Estimation.Unit-III:[9 Periods]Clustering2: Density-based Approaches, Decision and Regression Trees, Comparing Classifiers,Ensembles: Combining Multiple LearnersUnit-IVSupport Vector Machines, More on Kernel Methods,[9 Periods]Unit-V:[9 Periods]Belief Networks, Reinforcement Learning, Neural Networks, Computational Learning Theorooks
Text Books:1. Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2010.References:1. Tom Mitchell, “Machine Learning”, Mc Graw Hill publications, 1997.2. Christopher. M.Bishop, “Pattern Recognition and Machine Learning”, Springer publications, October, 2007.3. Ethem Alpaydin, “Introduction to Machine Learning”, 2nd Edition, MIT Publisher, 2010.
CT 524 – Research Methodology and IPRUNIT-IOverview of Research Methodology: Mathematical tool for analysis, Types of Research, Researchprocess,Review of basic statistical measures: measures of central tendency, measures of variation,measure of skewness.UNIT-IIProbability distributions: Introduction, approaches to probability, probability distributions.Sampling methods and distributions: sampling distribution of mean when normal populationvariance is unknown, sampling distributions of variance, confidence interval estimation,determination of sample size.UNIT-IIITest of Hypothesis: test of hypothesis concerning mean, Test of Hypothesis concerning variances,Chi-square test for checking independence of categorized data, goodness of fit test.UNIT-IVBasic multivariate analysis: Correlation analysis.Design and analysis of experiments: introduction, analysis of variance, completely randomizeddesign, Lattin square design, duncann’s multiple range test.UNIT-VAdvanced multivariate analysis: Discriminant analysis, Factor analysis, terminologies of factoranalysis, methods of factor analysis, cluster analysis.Simulation: Need for simulation, types of simulation, simulation languages.Prescribed Book: R.Panneerselvan, Research methodology,PHIReference Book: C.R.Kothari Research Methodology, Methods and Techniques,Viswaprakasan
CT 571 – Automata and Compiler DesignLecture : 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 4Course Learning Objectives: At the end of the Course Students will understand1. Know the abstract machines and its parsing techniques.2. Concepts of parsing techniques and language semantics and context sensitive features.3. Know Runtime storage and code optimization and concepts of code generation.Course Learning Outcomes: After successful completion of this course, student will be able to1. Understand the concept of abstract machines and their power to recognize the languages.2. Attains the knowledge of semantics of the language.3. Know grammars relationship among them with the help of chomsky hierarchy.4. Understand the design of a compiler given features of the languages.5. Implement practical aspects of automata theory.UNIT - I:[9 Periods]FORMAL LANGUAGE AND REGULAR EXPRESSIONS: Languages, Definition Languages regularexpressions, Finite Automata – DFA, NFA. Conversion of regular expression to NFA, NFA to DFA.Applications of Finite Automata to lexical analysis, lex tools.CONTEXT FREE GRAMMARS AND PARSING: Context free grammars, derivation, parse trees, ambiguityLL(K) grammars and LL(1) parsing.UNIT - II:[9 Periods]Bottom up parsing handle pruning LR Grammar Parsing, LALR parsing, parsing ambiguousgrammars, YACC programming specification.SEMANTICS : Syntax directed translation, S-attributed and L-attributed grammars, Intermediate code –abstract syntax tree, translation of simple statements and control flow statements.UNIT - III:[9 Periods]CONTEXT SENSITIVE FEATURES: Chomsky hierarchy of languages and recognizers. Type checking,type conversions, equivalence of type expressions, overloading of functions and operations.
UNIT – IV:[9 Periods]RUN TIME STORAGE: Storage organization, storage allocation strategies scope access to how localnames, parameters, language facilities for dynamics storage allocation.CODE OPTIMIZATION: Principal sources of optimization, optimization of basic blocks, peepholeoptimization, flow graphs, Data flow analysis of flow graphs.UNIT - V:[9 Periods]CODE GENERATION: Machine dependent code generation, object code forms, generic codegeneration algorithm, Register allocation and assignment. Using DAG representation of Block.TEXT BOOKS:1. Introduction to Theory of computation, Sipser, 2nd Edition, Thomson.2. Compilers Principles, Techniques and Tools Aho, Ullman, Ravisethi, Pearson Education.REFERENCES:1. Modern Compiler Construction in C , Andrew W.Appel Cambridge University Press.2. Compiler Construction, LOUDEN, Thomson.3. Elements of Compiler Design, A. Meduna, Auerbach Publications, Taylor and Francis Group.4. Principles of Compiler Design, V. Raghavan, TMH.5. Engineering a Compiler, K. D. Cooper, L. Torczon, ELSEVIER.6. Introduction to Formal Languages and Automata Theory and Computation - Kamala Krithivasan andRama R, Pearson.7. Modern Compiler Design, D. Grune and others, Wiley-India.8. A Text book on Automata Theory, S. F. B. Nasir, P. K. Srimani, Cambridge Univ. Press.9. Automata and Language, A. Meduna, Springer.
CT 572 – Advanced Computer ArchitectureLecture : 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 4Course Learning Objectives: At the end of the Course Students will understand1. Advanced computer and high performance computing architectures.2. Concepts of system interconnection architectures and performance measures.3. Performance issues related to pipelined processors and programming concepts for parallelcomputers.Course Learning Outcomes: After successful completion of this course, student will be able to1. Familiarize with the concepts of parallel computer models.2. Familiarize with interconnection architectures and performance measures for multiprocessorsand multi computers.3. Analyze design structures, instruction level parallelism and dynamic level parallelism withpipelined processors.4. Familiarize with scalable, multithreaded and dataflow architectures.5. Know the programming models and code optimization techniques for parallel computers.UNIT – I:[12 Periods]Parallel Computer Models: The state of computing, Classification of parallel computers,Multiprocessors and Multicomputers, Multivector and SIMD computers.Program and network properties: Conditions of parallelism, Data and resource Dependences,Hardware and Software parallelism, Program partitioning and scheduling, Grain Size and latency,Program flow mechanisms, Control flow versus data flow, Data flow Architecture, Demand drivenmechanisms, Comparisons of flow mechanisms.UNIT – II:[12 Periods]System Interconnect Architectures: Network properties and routing, Static interconnectionNetworks, Dynamic interconnection Networks, Multiprocessor system Interconnects, Hierarchicalbus systems, Crossbar switch and multi-port memory, Multistage and combining network.Principles of Scalable Performance: Performance Metrics and Measures, Parallel ProcessingApplications, Speedup Performance Laws - Amdahl’s law for fixed load, Gustafson’s law for scaledproblems, Memory Bounded Speedup Model.UNIT-III:[12 Periods]Pipelining: Linear pipeline processor, nonlinear pipeline processor, Instruction pipeline Design,Mechanisms for instruction pipelining, Dynamic instruction scheduling, Branch Handling techniques,branch prediction.
Pipelining: Arithmetic Pipeline Design, Computer Arithmetic principles, Static Arithmetic pipeline,Multifunctional arithmetic pipelines.UNIT –IV:[12 Periods]MULTI Processors: Multiprocessor System Interconnect, Cache Coherence and SynchronizationMechanisms, Message-passing Mechanism.Scalable, Multi-Threaded and Dataflow Architectures: Latency-Hiding Techniques, Principles ofMultithreading, Scalable and Multithreaded Architectures.UNIT-V:[12 Periods]Parallel Models, Languages and Compilers: Parallel Programming Models, Parallel Languages andCompilers, Dependence analysis of Data Arrays.Parallel Models, Languages and Compilers: code optimization and Scheduling, Loop parallelizationand pipelining.Text Book:1. Kai Hwang, "Advanced Computer Architecture", TMH.Reference Books:1. D.A. Patterson and J.L.Hennessey, "Computer organization and Design", Morgan Kaufmann, 2ndEdition.2. V.Rajaram & C.S.R.Murthy, "Parallel Computer", PHI.3. Barry Wilkinson and Michael Allen, “Parallel Programming” Pearson Education.
CT 573 – Advanced Web TechnologiesLecture : 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 4Course Learning Objectives: At the end of the course the students will understand1. The basic concepts to develop dynamic complex web applications.2. The basic concepts of XML, Web servers, Ruby script and PHP.3. Java Server side technologies and Semantic Web Concepts.Course Learning Outcomes: At the end of the course the students will be able to1. Familiar with Advanced Web technologies.2. Design dynamic Web documents using client side scripting.3. Develop XML applications and web documents with ruby script & PHP.4. Write java server side programs.5. Familiar with Semantic Web technologies.UNIT –I:[9 Periods]Introduction: XHTML, Cascading Style Sheets (CSS), JavaScript: Introduction to Scripting, ControlStatements, Functions, Arrays, ObjectsUNIT –II:[9 Periods]Dynamic HTML: Object Model and Collections, Dynamic HTML: Event Model, XML: Introduction,DTD, Schema, XSLUNIT –III:[12 Periods]Web Servers: (IIS and Apache), Ruby on Rails, PHP: Introduction, Using Variables and Operators,Controlling Program Flow,Working with Arrays, Using Functions and ClassesUNIT –IV:[11 Periods]SERVLETS : Overview, Servlet Implementaion, Servlet Configaration, Servlet Lifecycle, Servlet request,Servlet response, Session Tracking, Cookies.AJAX-ENABLED RICH INTERNET APPLICATIONS : Introduction, Traditional Web Applications vs AjaxApplication, XML Http Request Object, Creating AjaX Application.UNIT –V:JSP: JSP Directives, Scripting Elements, Standard Actions, Implicit Objects, Scope.SEMANTIC WEB: Introduction, A Layered Approach, RDF, OWL.[9 Periods]
TEXT BOOKS:1. Harvey M. Deitel and Paul J. Deitel, “Internet & World Wide Web How to Program”, 4/e,Pearson Education.2. Antoniou Grigoris , Groth Paul, Harmelen Frank Van, Hoekstra Rinke, “A Semantic WebPrimer” , 3 ed , PHI publications.REFERENCES:1. Vikram Vaswani, “PHP: A Beginner’s Guide”, McGraw-Hill.2. Subrahmanyam Allamraju et.al, "Professional Java Server Programming", APress.3. Jim Keogh, “The complete Reference J2EE”, Tata McGraw Hill.4. Tom NerinoDoli smith, “JavaScript & AJAX for the web”, Pearson Education, 2007.5. Joshua Elchorn, “Understanding AJAX”, Prentice Hall 2006.6. Karin K Brietman, Marco Antonio Casanova, Walter Truszkowski, “Semantic Web – Concepts”,Technologies and Applications, Springer 2007.
CT 574 – Advanced Software EngineeringLecture : 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 4Course Learning Objectives: At the end of the Course Students will understand1. Cover concepts of process models and design techniques.2. Learn the verification, validation and estimation factors required for software development.3. Know the advanced topics and tools required for software engineering.Course Learning Outcomes: After successful completion of this course, student will be able to1. Use Process Models and different Modeling Methods.2. Apply the concepts of Good Program Design and Models used in various developmentplatforms.3. Apply verification and validation and software software project management.4. Use different cost estimation factors required for software development.5. Advanced Topics and tools required for software engineering.UNIT-I:[9Periods]INTRODUCTION: Notion of Software as a Product – characteristics of a good Software Product.Engineering aspects of Software production – necessity of automation. Job responsibilities ofProgrammers and Software Engineers as Software developers.PROCESS MODELS AND PROGRAM DESIGN TECHNIQUES: Software Development Process Models –Code & Fix model, Waterfall model, Incremental model, Rapid Prototyping model, Spiral (Evolutionary)model.UNIT – II:[9Periods]GOOD PROGRAM DESIGN TECHNIQUES: Structured Programming, Coupling and Cohesion, Abstractionand Information Hiding, Automated Programming, Defensive Programming, Redundant Programming,Aesthetics.Software Modeling Tools –Data flow Diagrams, UML and XML. Jackson System Development.UNIT – III:[9 Periods]VERIFICATION AND VALIDATION: Testing of Software Products – Black-Box Testing and White-BoxTesting, Static Analysis, Symbolic Execution and Control Flow Graphs –Cyclomatic Complexity.Introduction to testing of Real-time Software Systems.SOFTWARE PROJECT MANAGEMENT: Management Functions and Processes, Project Planning andControl, Organization and Intra-team Communication, Risk Management.
UNIT – IV:[9 Periods]SOFTWARE COST ESTIMATION: Underlying factors of critical concern. Metrics for estimating costs ofsoftware products – Function Points. Techniques for software cost estimation –Expert judgment,Delphi cost estimation, Work break-down structure and Process break down structure, COCOMO andCOCOMO-II.UNIT –V:[9 Periods]ADVANCED TOPICS: Formal Methods in Software Engineering – Z notation, Hoare‟snotation.Formalization of Functional Specifications – SPEC. Support environment forDevelopment of SoftwareProducts. Representative Tools for Editors, Linkers, Interpreters,Code Generators, Debuggers.TOOLS FOR DECISION SUPPORT AND SYNTHESIS: Configuration control and Engineering Databases,Project Management.Text Books:1. Fundamentals of Software Engineering – Carlo Ghezziet, Mehdi Jazayeri, Dino Mndrioli2. Software Engineering – Design, Reliability Management – Pressman.Reference Books:1. Web Engineering, The Discipline of Systematic Development of Web Applications, edited byGertiKappel, Birgit Proll, Siegfried Reich, Werner Rretschitzegger, John Wiley & Sons, Ltd.2. Software Engineering, Theory and Practice, Shari Lawrence Pfleeger, 2nd edition, PearsonEducation.3. Software Engineering, Ian Sommerville, 9th edition, Always Learning, Pearson Education.4. Fundamentals of Software Engineering, 4th edition, Rajib Mall, PHI.5. Software Engineering with Abstraction, Berzins and Luqi
CT 575 – Artificial IntelligenceLecture : 4 Periods/WeekPractical: --Internal: 40 MarksExternal: 60 MarksCredits: 3Course Learning Objectives: At the end of the Course Students will understand1. Study the concepts of Artificial Intelligence.2. Learn the methods of solving problems using Artificial Intelligence.3. Introduce the concepts of Expert Systems and machine learning.Course Learning Outcomes: At the end of the course, the student should be able to:1. Identify problems that are amenable to solution by AI methods.2. Identify appropriate AI methods to solve a given problem.3. Formalize a given problem in the language/framework of different AI met
Practical: -- External: 60 Marks . Credits: 4 . Course Learning Objectives: At the end of the Course Students will understand . 1. Fundamental Concepts of Standard and Advanced Databases. 2. Active databases, Knowledge Based Systems(KBSs), Deductive Databases, and Relation Ship between KBSs and DBMSs. 3.
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the Information Technology Department, Government of Tamil Nadu immediately. 3. Entities and Responsibilities 3.1. The Information Technology Department, Government of Tamil Nadu is the Nodal Department for IT Security of Tamil Nadu. The Information Technology Department will have the following roles with respect to Cyber Security :
California Department of Technology 2 SIMM Section 180 Statement of Work Guidelines August 2016 SOW GUIDELINES . Introduction 1.0 Background . The California Department of Technology (CDT) has adopted the Project Approval Lifecycle (PAL) to improve the quality, value and likelihood of success for information technology (IT)
U.S. Department of Labor Information Technology Competency Model . At the direction of the United States Department of Labor, the Employment & Training Administration has produced a comprehensive competency model for the Information Technology industry. The Information Technology Competency Model identifies the knowledge,
Information Technology Careers: Past, Present, and Future Precisely when the information technology age started is a matter of conjecture. According to one source, it started when the first abacus was created nearly 2000 years ago (19). Many professors of introductory information technology courses place the beginning in 1890 when
This Guideline presents a methodology for Information Technology (IT) security audits suitable for supporting the requirements of the Commonwealth of Virginia (COV) Information Technology Security Policy (ITRM Policy SEC500-02), the Information Technology Security Standard (ITRM Standard SEC501-01), and the Information Technology Security Audit
6)Department of Food Engineering and Technology 17. M.Tech in Food Engineering and Technology 18. B.Tech in Food Engineering and Technology 19. Ph.D in Food Engineering and Technology 7) Department of Energy 20. M.Tech in Energy Technology 21. Ph.D in Energy 8 ) Department of Applied Sciences ( new) ( Programmes are not offered )
Technology. These include the Madhya Pradesh Agency for Promotion of Information Technology (MAPIT), a society under the Information Technology department of GoMP, and the Madhya Pradesh State Electronics Development Corporation (MPSEDC), a government company under the Information Technology department.
