Computer Syllabus sem 7

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    Program StructureB.E. Computer Engineering

    Fourth Year (Computer) ( Semester VII)

    ( REV 2012)

    Course Code Course Name Teaching Scheme

    (Contact Hours)

    Credits Assigned

    Theory Pract Tut Theory TW/

    Pract

    Tut Total

    CPC701 Digital Signal Processing 4 2 - 4 1 - 5

    CPC702 Cryptography and System Security 4 2 - 4 1 - 5

    CPC703 Artificial Intelligence 4 2 - 4 1 - 5

    CPE7042X Elective-II 4 2 - 4 1 - 5

    CPP701 Project I - - - - 3 - 3

    CPL701 Network Threats and Attacks Laboratory- 4 - - 2 - 2

    Total 16 12 - 16 09 - 25

    Course Code Course Name Examination Scheme

    Internal Assesment

    Internal Assesment End Sem

    Exam

    Exam

    Duration

    ( in Hrs)

    TW

    oral

    Tot

    Test 1 Test 2 Avg

    CPC701 Digital Signal Processing20 20 20 80 03 25 - 125

    CPC702 Cryptography and System Security20 20 20 80 03 25 25

    (prac

    150

    CPC703 Artificial Intelligence20 20 20 80 03 25 25 150

    CPE7042X Elective-II 20 20 20 80 03 25 25 150

    CPP701 Project I - - - - - 50 50 100

    CPL701 Network Threats and Attacks

    Laboratory- - - - - 25 50 50

    Total - - 80 320 - 200 175 775

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    Program Structure for B.E. Computer Engineering

    Second Year (Computer) ( Semester VIII)

    (REV 201)

    Course Code Course Name Teaching Scheme

    (Contact Hours)

    Credits Assigned

    Theory Pract Tut Theory TW/

    Pract

    Tut Total

    CPC801 Data Warehouse and Mining 4 2 - 4 1 - 5

    CPC802 Human Machine Interaction 4 2 - 4 1 - 5

    CPC803 Parallel and distributed Systems 4 2 - 4 1 - 5

    CPE803X Elective-III 4 2 - 4 1 - 5

    CPP802 Project II - - - - 6 - 6

    CPL801 Cloud Computing Laboratory- 2 - - 1 - 1

    Total 16 10 - 16 11 - 27

    Course Code Course Name Examination Scheme

    Internal Assesment

    Internal Assesment End Sem

    Exam

    Exam

    Duration

    ( in Hrs)

    TW

    oral

    Tot

    Test 1 Test 2 Avg

    CPC801 Data Warehouse and Mining 20 20 20 80 03 25 25 150

    CPC802 Human Machine Interaction 20 20 20 80 03 25 25 150

    CPC803 Parallel and distributed Systems 20 20 20 80 03 25 25 150

    CPE803X Elective-III20 20 20 80 03 25 25 150

    CPP802 Project II - - - - - 50 50 100

    CPL801 Cloud Computing Laboratory - - - - - 25 - -

    Total 80 320 175 150 725

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    Elective I Sem 6

    CPE6011 Operation Research

    CPE6012 Project Management

    CPE6013 Foreigh Language German

    CPE6014 Foreigh Language French

    Elective II Sem 7

    System Group CPE7021Advance Algorithms

    CPE7022 Computer Simulation and Modeling

    Electronics Group CPE7023 Image Processing

    Software Group CPE7024 Software Architecture

    CPE7025 Soft Computing

    DB Group CPE7026 ERP and Supply Chain Management

    Elective III - Sem 8

    Electronics Group CPE8031 Machine Learning

    Digital Group CPE8032 Embedded Systems

    Network Group CPE8033 Adhoc wireless networks

    CPE8034 Digital Forensic

    DB Group CPE8035 Big data Analytics

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    Course Code Course/Subject Name Credits

    CPC701 Digital Signal Processing 5

    Objectives:1. To learn the fundamental concepts of Digital Signal Processing.

    2. To explore the properties of DT in mathematical problem sol!ing.". To illustrate T calculations mathematicall# and de!elop T based DSP algorithms.$. To introduce DSP processor for real time signal processing application

    Outcomes: %earner &ill be able to'1. To understand the concept of DT Signal and perform signal manipulation2. To perform anal#sis of DT s#stem in time domain3. To de!elop T flo&(graph and ast DSP )lgorithms.4. To design DSP s#stem for *eal Time Signal Processing.

    Module Detailed Contents Hrs.

    1 Discrete !ime Signal1.1 +ntroduction to Digital Signal Processing, Discrete Time Signals,

    Sampling and *econstruction, Standard DT Signals, Concept of Digitalre-uenc#, *epresentation of DT signal using Standard DT Signals,Signal anipulationsshifting, addition, subtraction, multiplication0,Classification of Signals, %inear Con!olution formulation&ithoutmathematical proof0, Circular Con!olution formulation&ithoutmathematical proof0, atrix *epresentation of Circular Con!olution,%inear b# Circular Con!olution. )uto and Cross Correlation formulae!aluation,

    12

    2 Discrete !ime S"stem2.1 +ntroduction to Discrete Time S#stem, Classification of DT S#stems

    %inear/Non %inear, Causal/Non Causal, Time +n!ariant/Time ariantS#stems, Stable/ nstable0, 3+34 Time Domain Stabilit# Criteria. %T+s#stem, Concept of +mpulse *esponse and Step *esponse.

    2.2 Concept of ++* S#stem and +* S#stem, 4utput of ++* and +* DTs#stem using Time Domain %inear Con!olution formula ethod.

    56

    3 Discrete #ourier !rans$orm".1 +ntroduction to DTT, DT, *elation bet&een DT and DTT, Properties

    of DT &ithout mathematical proof Scaling and %inearit#, Periodicit#,

    Time Shift and re-uenc# Shift, Time *e!ersal, Con!olution Propert#and Parse!als7 8nerg# Theorem0. DT computation using DT properties.

    ".2 Transfer function of DT S#stem in fre-uenc# domain using DT. %inearand Circular Con!olution using DT. *esponse of +* s#stem calculationin fre-uenc# domain using DT.

    56

    4 #ast #ourier !rans$orm$.1 *adix(2 D+T(T algorithm, D+T(T lo&graph for N9$, : ; 6, +n!erse

    5:

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    T algorithm. Spectral )nal#sis using T, Comparison of complex andreal, multiplication and additions of DT and T.

    % DSP &lgorit'ms

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    1. %ength of first Signal % and signal !alues.2. %ength of second Signal and signal !alues.

    Problem De$inition:

    1. ind auto correlation of input signal. @hat is the significance of !alue of output signal !alue

    at n95A.2. ind auto correlation of dela#ed input signal.". ind cross correlation of input signal and dela#ed input signal,$. ind cross correlation of input signal and scaled dela#ed input signal.

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    4. !o )er$orm Discrete #ourier !rans$orm

    &im:

    The aim of this experiment is to stud# magnitude spectrum of the DT signal.

    Objective:

    1. De!elop a function to perform DT of N point signal2. Calculate DT of a DT signal and Plot spectrum of the signal.". Conclude the effect of Fero padding on magnitude spectrum.$. Calculate the number of real multiplications and real additions re-uired to find DT.

    n)ut S)eci$ications:

    1. %ength of Signal N2. Signal !alues

    Problem De$inition:1. Tae an# four(point se-uence xnE.

    ind DT =E.

    Compute number of real multiplications and real additions re-uired to find =E.

    Plot agnitude Spectrum of the signal.

    2. )ppend the input se-uence b# four Feros. ind DT and plot magnitude spectrum. *epeat thesame b# appending the se-uence b# eight Feros. 4bser!e and compare the magnitude spectrum. Bi!e#our conclusion.

    %. !o )er$orm #ast #ourier !rans$orm

    &im:To implement computationall# fast algorithms.

    Objective:

    1. De!elop a program to perform T of N point signal.2. Calculate T of a gi!en DT signal and !erif# the results using mathematical formulation.". +llustrate the computational efficienc# of T.

    n)ut S)eci$ications:

    %ength of Signal N

    Signal !alues

    Problem De$inition:Tae an# eight(point se-uence xnE.

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    ind T =E.

    @rite number of real multiplications and real additions in!ol!ed in finding =E.

    (. #iltering o$ long Data Se/uence

    &im:To perform filtering of %ong Data Se-uence using 4!erlap )dd ethod and 4!erlap Sa!e ethod.

    Objective:

    De!elop a function to implement ast 4!erlap )dd and ast 4!erlap Sa!e )lgorithm using T.

    n)ut S)eci$ications:

    1. %ength of long data se-uence and signal !alues.2. %ength of impulse response and coefficient !alues of hnE.

    Problem De$inition:

    ind the output of a Discrete Time s#stem using ast 4!erlap )dd ethod 4* ast 4!erlap Sa!eethod.

    0. -eal !ime Signal Processing&im:

    To perform real time signal processing using TS"25 Processor.

    Objective:

    Stud# real time signal processing.

    n)ut S)eci$ications:

    1. *eal Time Speech Signal

    Problem De$inition:

    10 Capture the real time audio signal.

    20 ilter it b# con!ol!ing input signal &ith the impulse response of +* filter using

    ast 4!erlap )dd filtering )lgorithm 4* ast 4!erlao Sa!e iltering )lgorithm.

    "0 4bser!e the -ualit# of output signal.

    . &))lication o$ Digital Signal Processing

    &im:

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    To implement an# Signal Processing operation on one dimensional signal.

    Objective:

    To de!elop application of signal processing.

    n)ut S)eci$ications:

    4ne dimensional signal.

    -ules? 1. Number of students in one Broup ? min ( 2 max ("

    2. Decide one DSP application of #our choice. Collect the information related to the

    application from the published granted patents. Do&nload the related published

    papers from the standard refereed journals and conferences.

    ". De!elop a bloc diagram of the proposed s#stem and flo&chart of proposed s#stem

    algorithm, implement it using Scilab/C, C>> language and obtain the appropriate

    results.

    $. Prepare the three to four pages report on the mini project in +888 paper format

    *eport should include )bstract, +ntroduction, *elated Theor#, Proposed S#stem

    Design/)lgorithm, 8xperimentation ; *esult )nal#sis, Conclusion, and *eferences.

    %.

    !erm or:

    Term &or shall consist of minimum assignments and course project.

    Gournal must include at least 1 assignment on each module and t&o -uiF.

    The final certification and acceptance of term &or ensures that satisfactor# performance

    of laborator# &or and minimum passing mars in term &or.

    The distribution of mars for term &or shall be as follo&s?

    %aborator# &or experiments0? ''''..'''.. 1

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    2. 8mmanuel C. +feachor, 3arrie @. Ger!is, KDigital Signal Processing? ) Practical )pproachL,Pearson 8ducation +S3N 5(251(

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    Course Code Course/Subject Name Credits

    CPC702 Cr")togra)'" and S"stem Securit" 5

    Objectives:1. To pro!ide students &ith contemporar# no&ledge in Cr#ptograph# and Securit#.2. To understand ho& cr#pto can be used as an effecti!e tools in pro!iding assuranceconcerning pri!ac# and integrit# of information.". To pro!ide sills to design securit# protocols for recogniFe securit# problems.

    Outcomes: %earner &ill be able to'1. nderstand the principles and practices of cr#ptographic techni-ues.2. nderstand a !ariet# of generic securit# threats and !ulnerabilities, and identif# ;anal#Fe particular securit# problems for gi!en application.". )ppreciate the application of securit# techni-ues and technologies in sol!ing real(life securit# problems in practical s#stems.$. )ppl# appropriate securit# techni-ues to sol!e securit# problem

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    0 0.1 Program Securit"Secure programs, Nonmalicious Program 8rrors, alicious Soft&are OT#pes, iruses, irus Countermeasures, @orms, Targeted aliciousCode, Controls against Program Threats.0.2 O)erating S"stem Securit"

    emor# and )ddress protection, ile Protection echanism, ser)uthentication.0.3 Database Securit"Securit# *e-uirement, *eliabilit# and +ntegrit#, Sensiti!e data, +nference,ultile!el Databases0.4 DS and #ire9alls+ntruders, +ntrusion Detection, Pass&ord anagement, ire&alls(Characteristics, T#pes of ire&alls, Placement of ire&alls, ire&allConfiguration, Trusted s#stems.

    56

    .1 P Securit"

    4!er!ie&, )rchitecture, )uthentication Meader, 8ncapsulating Securit#Pa#load, Combining securit# )ssociations, +nternet e# 8xchange, @ebSecurit#? @eb Securit# Considerations, Secure Socets %a#er andTransport %a#er Securit#, 8lectronic Pa#ment..2 on;cr")togra)'ic )rotocol

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    ". +PSec$. Spoofing

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    Course Code Course/Subject Name Credits

    CPC703 &rti$icial ntelligence 5

    Objectives:

    1. To conceptualiFe the basic ideas and techni-ues underl#ing the design of intelligents#stems.

    2. To mae students understand and 8xplore the mechanism of mind that enable intelligentthought and action.

    ". To mae students understand ad!anced representation formalism and search techni-ues.$. To mae students understand ho& to deal &ith uncertain and incomplete information.

    Outcomes: %earner &ill be able to1. )bilit# to de!elop a basic understanding of )+ building blocs presented in intelligent

    agents.2. )bilit# to choose an appropriate problem sol!ing method and no&ledge representation

    techni-ue.". )bilit# to anal#Fe the strength and &eanesses of )+ approaches to no&ledgeO intensi!e

    problem sol!ing.$. )bilit# to design models for reasoning &ith uncertaint# as &ell as the use of unreliable

    information.

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    Benetic algorithms.".$ )d!ersarial Search? Bames, 4ptimal strategies, The

    minimax algorithm , )lpha(3eta Pruning.

    4 8no9ledge and -easoning

    $.1 no&ledge based )gents, The @umpus @orld, ThePropositional logic, irst 4rder %ogic? S#ntax and Semantic,+nference in 4%, or&ard chaining, bac&ard Chaining.

    $.2 no&ledge 8ngineering in irst(4rder %ogic, nification,*esolution, +ntroduction to logic programming P*4%4B0.

    $." ?ncertain 8no9ledge and -easoning: ncertaint#, *epresenting no&ledge in an uncertain

    domain,The semantics of belief net&or, +nference in beliefnet&or.

    12

    % Planning and *earning

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    1. 4ne case stud# on N%P/8xpert s#stem based papers published in +888/)C/Springer or an#prominent journal.

    2. Program on uninformed and informed search methods.

    ". Program on %ocal Search )lgorithm.

    $. Program on 4ptimiFation problem.

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    Course Code Course/Subject Name Credits

    CPE7021 &dvanced &lgorit'ms 5

    Objectives:

    1. To teach fundamentals of anal#sis of algorithm at depth2. To pro!ide in depth stud# of ad!anced data structures and its uses". To teach anal#sis of problems from different domains

    Outcomes: %earner &ill be able to'1. +dentif# and use suitable data structures for gi!en problem from different domains2. )ppreciate the role of Braph algorithms in sol!ing !ariet# of problems". )ppreciate the role of 4ptimiFation b# using linear programing$. )nal#Fe the !arious algorithms from different domains

    Module Detailed Contents Hrs

    1 ntroduction

    1.1 Asymptotic notations Big O, Big ,Big , ,

    notations ,Proofs of master theorem, applyingtheorem to solve problems

    5"

    2 &dvanced Data Structures

    2.1 *ed(3lac Trees? properties of red(blac trees , +nsertions ,Deletions

    2.2 3(Trees and its operations2." 3inomial Meaps? 3inomial trees and binomial heaps, 4peration

    on 3inomial heaps

    5J

    3 D"namic Programing".1 matrix chain multiplication, cutting rod problem and its anal#sis

    5:

    4 Bra)' algorit'ms$.1 3ellman ford algorithm, Dijstra algorithm, Gohnson7s )ll pair

    shortest path algorithm for sparse graphs

    5:

    % Ma,imum #lo9

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    !erm or:

    Term &or should consist of at least : experiments, 2 assignments based on abo!e theor#s#llabus.

    The final certification and acceptance of term &or ensures that satisfactor# performance oflaborator# &or and minimum passing mars in term &or.

    The distribution of mars for term &or shall be as follo&s?

    %aborator# &or experiments0? ''''..'''.. 1

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    Course Code Course/Subject Name Credits

    CPE7023 mage Processing 5

    Objectives:

    1. To learn the fundamental concepts of Digital +mage Processing and ideo Processing .2. To understand basic image enhancement and segmentation techni-ues.". To illustrate +mage Transform calculations mathematicall# and de!elop fast transform

    algorithm$. To learn +mage Compression and Decompression Techni-ues

    Outcomes: %earner &ill be able to'1. nderstand the concept of Digital +mage and ideo +mage.2. 8xplain image enhancement and Segmentation techni-ue.". De!elop fast image transform flo&graph

    $. Sol!e +mage compression and decompression techni-ues

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    ". +mage Compression and De(compression )n# t&o techni-ues E10 )rithmetic Coding and Decoding20 Muffman Coding and Decoding"0 +BS uantiFation/ ector uantiFation based Compression and De(compression$0 Transform based +mage Compression and De(compression T/ MT/DCT/ D@TE

    $. 3inar# +mage Processing )n# t&o techni-ues E10 4pening follo&ed b# Closing20 Mit or iss Transform"0 Thinning/Thicening/ *egion illling / 3oundar# 8xtraction$0 Connected Component )lgorithm

    !e,t 7oos :

    1. *afel C. BonFaleF and *ichard 8. @oods, HDigital +mage Processing7, Pearson 8ducation )sia,

    Third 8dition, 255J,

    2. S. Ga#araman, 8.8sairajan and T.eerumar, KDigital +mage ProcessingL TatacBra& Mill

    8ducation Pri!ate %td, 255J,

    ". )nil . Gain, Kundamentals and Digital +mage ProcessingL, Prentice Mall of +ndia Pri!ate %td,

    Third 8dition

    $. S. Sridhar, KDigital +mage ProcessingL, 4xford ni!ersit# Press, Second 8dition, 2512.

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    Course Code Course/Subject Name Credits

    CPE7024 So$t9are &rc'itecture 5

    Outcomes:

    Soft&are architecture is foundational to the de!elopment of large, practical soft&are(intensi!eapplications.)fter successful completion of this course learner &ill be able to?

    isualiFe the architectural concepts in de!elopment of large, practical soft&are(

    intensi!e applications.

    *ather than focusing on one method, notation, tool, or process, this ne&

    course &idel# sur!e#s soft&are architecture techni-ues, enabling us to choose theright tool for thejob at hand.

    Module Detailed Contents Hrs.

    1 7asic Conce)ts

    1.1 Concepts of Soft&are )rchitecture1.2 odels.1." Processes.1.$ Staeholders

    5"

    2 Designing &rc'itectures

    2.1 The Design Process.

    2.2 )rchitectural Conception.

    2." *efined 8xperience in )ction? St#les and )rchitectural Patterns.2.$ )rchitectural Conception in )bsence of 8xperience.

    52

    3 Connectors

    ".1 Connectors in )ction? ) oti!ating 8xample.

    ".2 Connector oundations.

    "." Connector *oles.

    ".$ Connector T#pes and Their ariation Dimensions.".< 8xample Connectors.

    5:

    4 Modeling

    $.1 odeling Concepts.

    $.2 )mbiguit#, )ccurac#, and Precision.

    $." Complex odeling? ixed Content and ultiple ie&s.$.$ 8!aluating odeling Techni-ues.

    $.< Specific odeling Techni-ues.

    5$

    % &nal"sis

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    !o)ics #or +,)eriment:

    1. odeling using x)D%

    2. )nal#sis ( Case stud#

    ". isualiFation using x)D% 2.5

    $. +ntegrate soft&are components using a middle&are

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    Course Code Course/Subject Name Credits

    CPE7025 So$t Com)uting 5

    Objectives:1. To ConceptualiFe the &oring of human brain using )NN.2. To become familiar &ith neural net&ors that can learn from a!ailable examples and

    generaliFe to form appropriate rules for inference s#stems.". To introduce the ideas of fuFF# sets, fuFF# logic and use of heuristics based on human

    experience.$. To pro!ide the mathematical bacground for carr#ing out the optimiFation and

    familiariFing genetic algorithm for seeing global optimum in self(learning situation.

    Outcomes: %earner &ill be able to'1. )bilit# to anal#Fe and appreciate the applications &hich can use fuFF# logic.2. )bilit# to design inference s#stems.". )bilit# to understand the difference bet&een learning and programming and explore

    practical applications of Neural Net&ors NN0.$. )bilit# to appreciate the importance of optimiFations and its use in computer engineering

    fields and other domains.

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    3 #u" Set !'eor"

    ".1 Classical Sets and uFF# Sets, Classical *elations and uFF#*elations, Properties of membership function, uFF#extension principle, uFF# S#stems( fuFFification,defuFFification and fuFF# controllers.

    14

    4 H"brid s"stem

    $.1 +ntroduction to M#brid S#stems, )dapti!e Neuro uFF#+nference S#stem)N+S0.

    4

    % ntroduction to O)timiation !ec'ni/ues%.1 Derivative based o)timiation; Steepest Descent, Ne&ton

    method.%.2 Derivative $ree o)timiation; +ntroduction to 8!olutionar#

    Concepts.

    (

    ( Benetic &lgorit'ms and its a))lications:

    :.1 +nheritance 4perators, Cross o!er t#pes, in!ersion and

    Deletion, utation 4perator, 3it(&ise 4perators,Con!ergence of B), )pplications of B).

    (

    !erm or:

    The distribution of mars for term &or shall be as follo&s?

    %aborator# &or experiments/case studies0? ''''.. 1

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    ". To implement uFF# *elations.

    $. To implement uFF# Controllers.

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    :.1 +ntroduction to SC, Beneric T#pes of suppl# chain, ajorDri!ers of Suppl# chain, Strategic decisions in SC, 3usinessStrateg#, C* strateg#, S* strateg#, SC4* model.

    0 n$ormation !ec'nolog" in SCM

    I.1 T#pes of +T Solutions lie 8lectronic Data +nter change 8D+0,

    +ntranet/ 8xtranet, Data ining/ Data @arehousing and Dataarts, 8(Commerce, 8( Procurement, 3ar coding, *+D, *code.

    5:

    Mat'ematical modelling $or SCM

    6.1 +ntroduction, Considerations in modelling SC s#stems,Structuring the logistics chain, o!er!ie& of models? models ontransportation problem, assignment problem, !ehicle routingproblem, odel for !endor anal#sis, ae !ersus bu# model.

    5:

    &gile Su))l" C'ain

    J.1 +ntroduction, Characteristics of )gile Suppl# Chain, )chie!ing)gilit# in Suppl# Chain.

    52

    1 Cases o$ Su))l" C'ain15.1 Cases of Suppl# Chain lie, Ne&s Paper Suppl# Chain, 3oo

    Publishing, umbai Dabba&ala, Disaster management, 4rganicood, ast ood.

    52

    !erm or:

    The distribution of mars for term &or shall be as follo&s?

    ini project?''''''''''''.'''' 250 ars.

    )ttendance ''''''''''...'''''. 5

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    !e,t 7oos:

    1. 8nterprise *esource Planning ? concepts ; practices, b# .. Barg ; N..enatarishnan U PM+.

    2. Suppl# Chain anagement Theories ; Practices? *. P. ohant#, S. B. Deshmuh, (

    Dreamtech Press.

    ". 8*P Dem#stified? ++ 8dition, b# )lexis %eon,cBra& Mill .

    $. 8nterprise &ide resource planning? Theor# ; practice? b# *ahul )ltear,PM+.

    -e$erence 7oos:

    1. 8*P to 82

    8*P? ) Case stud# approach, b# Sandeep Desai, )bhishe Sri!asta!a, PM+.

    2. anagerial +ssues of 8*P s#stem, b# Da!id 4lson, cBra& Mill.

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    Course Code Course/Subject Name Credits

    CPE7022 Com)uter Simulation and Modeling 5

    Course Objectives:

    This course presents an introduction to discrete e!ent simulation s#stems.

    8mphasis of the course &ill be on modeling and the use of simulation

    languages/soft&are to sol!e real &orld problems in the manufacturing as &ell as

    ser!ices sectors. The course discusses the modeling techni-ues of entities,

    -ueues, resources and entit# transfers in discrete e!ent en!ironment. The course

    &ill teach the students the necessar# sills to formulate and build !alid models,

    implement the model, perform simulation anal#sis of the s#stem and anal#Fe

    results properl#. The Ktheor#L of simulation in!ol!es probabilit# and statistics,thus a good bacground in probabilit# and statistics is a re-uired prere-uisite

    Course Outcomes:

    1. )ppl# simulation concepts to achie!e in business, science, engineering, industr# and

    ser!ices goals

    2. Demonstrate formulation and modeling sills.

    3. Perform a simulation using spreadsheets as &ell as simulation language/pacage

    4. Benerate pseudorandom numbers using the %inear Congruential ethod

    5. 8!aluate the -ualit# of a pseudorandom number generator using statistical tests

    6. )nal#Fe and fit the collected data to different distributions

    Module Detailed Contents Hours

    Com)uter Simulation and Modeling

    1 Introduction to Simulation.Simulation Examples.General Principles

    1orld Examples @ can %e in t)e (ield o( %usiness/ transportation/ medical/

    computin"/ manu(acturin" and material )andlin" Presentation to %e ta9en.

    Practical=Oral e,amination:

    Oral e,amination 9ill be based on t'e above s"llabus.

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    Course Code Course/Subject Name Credits

    CP

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    4 4.1 !itle:Detect )*P spoofing using open source tool )*P@)TCM.

    Objective: 4bjecti!e of the module to find )*P spoofing using open source.

    Sco)e? +p spoofing using arp pacaging tool

    !ec'nolog": Net&oring

    % %.1 !itle:se the Nessus tool to scan the net&or for !ulnerabilities.

    Objective: 4bjecti!e of the module is scan s#stem and net&or anal#sis.

    Sco)e: +t used for s#stem anal#sis, securit# and process anal#sis

    !ec'nolog": Net&oring

    ( (.1 !itle:+mplement a code to simulate buffer o!erflo& attac.

    Objective: 4bjecti!e of the module+s to chec buffer o!erflo& in an NS2 en!ironment

    Sco)e: +t uses to anal#se memor# o!erflo& attac

    !ec'nolog"? Net&oring

    0 0.1 !itle:Set up +PS8C under %+N=

    Objective: 4bjecti!e of the module for implementing securit# !ulnerabilities

    Sco)e: to stud# different ipsec tools.

    !ec'nolog"? Net&oring .1 !itle:+nstall +DS e.g. SN4*T0 and stud# the logs.

    Objective: Simulate intrusion detection s#stem using tools such as snort

    Sco)e: +t is used for intrusion detection s#stem !ulnerabilit# scans

    !ec'nolog": Net&oring

    .1 !itle:se of iptables in linux to create fire&alls.

    Objective: To stud# ho& to create and destro# fire&all securit# parameters.

    Sco)e: s#stem securit# and net&or securit#

    !ec'nolog"? Net&oring

    1 1.1 !itle:ini project

    Objective: To implement Net&oring concepts

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    Sco)e: To understand Net&or ; s#stem tools

    !ec'nolog": Net&oring

    !erm or:

    The distribution of mars for term &or shall be as follo&s?

    %ab )ssignments?........................................................ 150

    ini project?''''''''''''.'''' 150 ars.

    )ttendance ''''''''''...'''''. 5

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    Course Code Course/Subject Name Credits

    CPC801 Data Warehousing and Mining 5

    Objectives:1. To study the methodology of engineering legacy databases for data warehousing and data

    mining to derive business rules for decision support systems.2. To analye the data! identify the problems! and choose the relevant models and

    algorithms to apply.

    Outcomes: "earner will be able to#

    1. $nable students to understand and implement classical algorithms in data mining

    and data warehousing% students will be able to assess the strengths and wea&nesses of the

    algorithms! identify the application area of algorithms! and apply them.

    2. Students would learn data mining techni'ues as well as methods in integrating

    and interpreting the data sets and improving effectiveness! efficiency and 'uality for data

    analysis.

    Module Detailed Contents Hrs.

    01 Introduction to Data Warehousing1.1 The Need for (ata )arehousing% *ncreasing (emand for Strategic

    *nformation% *nability of +ast (ecision Support System% ,perational -/s(ecisional Support System% (ata )arehouse (efined% enefits of (ata)arehousing %eatures of a (ata )arehouse% The *nformation low0echanism% ole of 0etadata% Classification of 0etadata% (ata )arehouse

    rchitecture% (ifferent Types of rchitecture% (ata )arehouse and (ata0arts% (ata )arehousing (esign Strategies.

    34

    02 Dimensional Modeling

    2.1 (ata )arehouse 0odeling -s ,perational (atabase 0odeling% (imensional0odel -s $ 0odel% eatures of a 5ood (imensional 0odel% The StarSchema% 6ow (oes a 7uery $8ecute9 The Snowfla&e Schema% act Tablesand (imension Tables% The actless act Table% :pdates To (imensionTables; Slowly Changing (imensions! Type 1 Changes! Type 2 Changes!Type < Changes! "arge (imension Tables! apidly Changing or "argeSlowly Changing (imensions! =un& (imensions! >eys in the (ata)arehouse Schema! +rimary >eys! Surrogate >eys ? oreign >eys%

    ggregate Tables% act Constellation Schema or amilies of Star.

    3@

    0 !"# $rocess

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    4.1 Need for ,nline nalytical +rocessing% ,"T+ -/s ,"+% ,"+ and0ultidimensional nalysis% 6ypercubes% ,"+ ,perations in0ultidimensional (ata 0odel% ,"+ 0odels; 0,"+! ,"+! 6,"+!(,"+%

    0* Introduction to data mining

    A.1 )hat is (ata 0ining% >nowledge (iscovery in (atabase B>((! )hat canbe (ata to be 0ined! elated Concept to (ata 0ining! (ata 0iningTechni'ue! pplication and *ssues in (ata 0ining

    32

    0+ Data !,-loration

    @.1 Types of ttributes% Statistical (escription of (ata% (ata -isualiation%0easuring similarity and dissimilarity.

    32

    0 Data $re-rocessingD.1 )hy +reprocessing9 (ata Cleaning% (ata *ntegration% (ata eduction;

    ttribute subset selection! 6istograms! Clustering and Sampling% (ataTransformation ? (ata (iscretiation; Normaliation! inning! 6istogramnalysis and Concept hierarchy generation.

    34

    0/ ClassiicationE.1 asic Concepts% Classiication methods:

    1. (ecision Tree *nduction; ttribute Selection 0easures! Treepruning.2. ayesian Classification; NaFve ayesG Classifier.

    /.2 $rediction; Structure of regression models% Simple linear regression!0ultiple linear regression./. Model !valuation election: &ccuracy and $rror measures! 6oldout!andom Sampling! Cross -alidation! ootstrap% Comparing Classifierperformance using ,C Curves.

    /.% Combining Classiiers; agging! oosting! andom orests.

    3@

    03 Clustering

    H.1 )hat is clustering9 Types of data! +artitioning 0ethods B>I0eans! >I0edoids 6ierarchical 0ethodsBgglomerative ! (ivisive! *C6!(ensityIased 0ethods B (SCN! ,+T*CS

    3@

    10 Mining 4re5uent $attern and &ssociation 6ule

    13.1 0ar&et as&et nalysis! re'uent *temsets! Closed *temsets! andssociation ules% re'uent +attern 0ining! $fficient and Scalable re'uent*temset 0ining 0ethods! The priori lgorithm for finding re'uent*temsets :sing Candidate 5eneration! 5enerating ssociation ules fromre'uent *temsets! *mproving the $fficiency of priori! pattern growth

    approach for mining re'uent *temsets% 0ining re'uent itemsets usingvertical data formats% 0ining closed and ma8imal patterns% *ntroduction to0ining 0ultilevel ssociation ules and 0ultidimensional ssociationules% rom ssociation 0ining to Correlation nalysis! +attern $valuation0easures% *ntroduction to ConstraintIased ssociation 0ining.

    3E

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    "erm Wor7:

    Term wor& should consist of at least of the following;

    1. ,ne case study given to a group of < /4 studentsof a data mart/ data warehouse.

    a. )rite (etail Statement +roblem and creation of dimensional modeling

    Bcreation star and snowfla&e schema

    b. *mplementation of all dimension table and fact table

    c. *mplementation of ,"+ operations.

    2. *mplementation of classifier li&e (ecision tree! NaFve ayes! andom orest

    using any languages li&e =ava

    to implement li&e (ecision tree! NaFve ayes! andom orest

    4. *mplementation of clustering algorithm li&e >Imeans! >I0edoids!

    gglomerative! (ivisive using languages any li&e =ava! CJ ! etc.

    A. :se )$> to implement the following Clustering lgorithms K >Imeans!

    gglomerative! (ivisive.

    @. *mplementation ssociation 0ining li&e priori! +0 using languages li&e =ava!

    CJ! etc.

    D. :se )$> to implement ssociation 0ining li&e priori! +0.

    E. :se tool to implement Clustering/ssociation ule/ Classification lgorithms.

    H. (etailed study of any one * tool li&e ,racle *! S+SS! Clementine! and L"0iner etc.

    Bpaper ssignment

    Internal &ssessment:

    *nternal ssessment consists of two tests. Test 1! an *nstitution level central test! isfor 23 mar&s and is to be based on a minimum of 43M of the syllabus. Test 2 is

    also for 23 mar&s and is to be based on the remaining syllabus. Test 2 may be

    either a class test or assignment on live problems or course project

    $ractical8Oral e,amination:n oral e8am will be held based on the above syllabus

    "e,t 9oo7s:

    1 6an! >amber! (ata 0ining Concepts and Techni'ues! 0organ >aufmann

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    2 +aulraj +onniah! O(ata )arehousing; undamentals for *T +rofessionalsP! )iley *ndia

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    Course Code Course/Subject Name Credits

    CPC802 Human Machine Interaction 5

    Objectives:1. To stress the importance of a good interface design.2. To understand the importance of human psychology in designing good interfaces.

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    "erm Wor7:

    The distribution of mar&s for term wor& shall be as follows;

    "aboratory wor& Be8periments/case studies; ####.. B1A 0ar&s.

    ssignment;#.#.############### B3A 0ar&s.

    ttendance ###############. B3A 0ar&s

    "O":

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    2. :nderstand the trouble of interacting with machines I edesign interfaces of

    home appliances li&e microwave oven! landIline phone! fully automatic washing

    machine.

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    11. ny other new relevant topics covering the above syllabus.

    "e,t 9oo7s:1. lan (i8! =. $. inlay! 5. (. bowd! . eale O6uman Computer *nteractionP!

    +rentice 6all.2. )ilbert ,. 5alit! OThe $ssential 5uide to :ser *nterface (esignP! )iley publication.

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    Course Code Course/Subject Name Credits

    CPC803 $arallel and Distributed 'stems 5

    Objectives:

    1. To provide students with contemporary &nowledge in parallel and distributed systems

    2. To e'uip students with s&ills to analye and design parallel and distributed applications.

    3. To provide master s&ills to measure the performance of parallel and distributed

    algorithmsOutcomes: "earner will be able to#

    1. pply the principles and concept in analying and designing the parallel and distributedsystem

    2. eason about ways to parallelie problems.

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    D.1 Cloc& Synchroniation! "ogical Cloc&s! $lection lgorithms! 0utual$8clusion! (istributed 0utual $8clusionIClassification of mutual$8clusion lgorithm! e'uirements of 0utual $8clusion lgorithms!+erformance measure! Non To&en based lgorithms; "amport lgorithm!icartKgrawalaGs lgorithm! 0ae&awaGs lgorithm

    D.2 To&en ased lgorithms; Suu&iI>asamiGs roardcast lgorithms!SinghalGs 6eurastic lgorithm! aymondGs Tree based lgorithm!Comparative +erformance nalysis.

    0/ Consistenc' and 6e-licationE.1 *ntroduction! (ataICentric and ClientICentric Consistency 0odels!

    eplica 0anagement.Distributed 4ile 'stems

    E.2 *ntroduction! good features of (S! ile models! ile ccessing models!ileICaching Schemes! ile eplication! Networ& ile SystemBNS!ndrew ile SystemBS! 6adoop (istributed ile System and 0apeduce.

    3@

    "erm Wor7:

    Term wor& should consist of at least 13 e8periments! 2 assignments based on above theorysyllabus.

    The final certification and acceptance of term wor& ensures that satisfactory performance oflaboratory wor& and minimum passing mar&s in term wor&.

    The distribution of mar&s for term wor& shall be as follows;

    "aboratory wor& Be8periments; #########.. B1A 0ar&s.

    ssignments; ################# B3A 0ar&s.

    ttendance ###############. B3A 0ar&s

    "O":

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    Syllabus for Practical

    Suggested topics for e8periment but not limited to;

    1. "oad alancing lgorithm.

    2. Scalability in (istributed $nvironment

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    Course Code Course/Subject Name Credits

    CPE8031 !lectiveAIII Machine #earning 5

    Objectives:

    1. To introduce students to the basic concepts and techni'ues of 0achine "earning.2. To become familiar with regression methods! classification methods! clustering methods.

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    lgorithm! Supervised learning after clustering! adial asis functions.

    0/ 6einorcement #earning

    E.1 *ntroduction! $lements of einforcement "earning! 0odel based learning!Temporal (ifference "earning! 5eneraliation! +artially ,bservableStates.

    3@

    "erm Wor7:

    The distribution of mar&s for term wor& shall be as follows;

    "aboratory wor& Be8periments; ###..#####... B1A 0ar&s.

    ssignments;###.############# B3A 0ar&s.

    ttendance ###############. B3A 0ar&s

    "O":

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    "e,t 9oo7s:1. +eter 6arrington O0achine "earning *n ctionP! (reamTech +ress2. $them lpaydRn! O*ntroduction to 0achine "earningP! 0*T +ress

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    Course Code Course/Subject Name Credits

    CPE8032 !lectiveAIII !mbedded 'stems 5

    Objectives:

    1. (evelop! among students! an understanding of the technologies behind theembedded computing systems% and to differentiate between such technologies.2. 0a&e aware of the capabilities and limitations of the various hardware or softwarecomponents.

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    model B6CS0! programIstate machine model B+S0! concurrentprocess model. :nified 0odeling "anguage B:0"! applications of :0"in embedded systems. *+Icores! design process model. 6ardware softwarecoIdesign! embedded product development life cycle management.

    0% High $erormance 2Abit 6IC &rchitecture

    4.1 0 processor family! 0 architecture! instruction set! addressingmodes! operating modes! interrupt structure! and internal peripherals.0 coprocessors! 0 Corte8I0

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    #ist o !,-eriments:

    "o-icA1: "roubleshooting "ools &n' One

    *nICircuit $mulator B*C$ and *nICircuit (ebugger B*C(! "ogic nalyer! Spectrumnalyer! +attern generator and (igital Storage ,scilloscope.

    "o-ic A2: &6M $rocessors Interaces &n' 4our

    "$(s and >eyboard *nterface! "C( *nterface! Counting e8ternal events with on chipcounters! eal Time Cloc& BTC! +ulse )idth 0odulation B+)0! elay and uerControl for alarm events! Stepper 0otor Control ! ,n chip (C/(C S+* / *2C / :T*nterface! luetooth/igIbee interface.

    "o-icA: 6ealAtime ignal $rocessing &6MAD$ &n' "=o

    ealItime physical model simulation! Correlation! convolution! (T! * or ** design! ealI

    time (S and 5:* using +C and 0! (esign with +rogrammable "ogic (evicesBC+"(/+5.

    "o-icA%: Device Driver Develo-ment &n' One

    (rivers for CN! (rivers for :S! (rivers for $thernet! S-5! (rivers for 5raphics TT"C(.

    "o-icA*: 6eal "ime O-erating 'stem (6"O) &n' "=o

    T"inu8 ! 0icroC/,S**! -8)or&s! )*N C$! 7NL! +alm ,S! Symbian ,S! ndroid,S or e'uivalent ,S.

    "e,t 9oo7s:

    1. $mbedded Systems an *ntegrated pproach K "yla (as! +earson2. Computers as Components K 0arilyn )olf! Third $dition $lsevieramal K Tata0c5raw 6ill2. $mbedded "inu8 K 6ollabaugh! +earson $ducation

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    Course Code Course/Subject Name Credits

    CPE8033 !lectiveAIII &dhoc Wireless ?et=or7s 5

    Objectives:

    1. To *dentify the major issues associated with adIhoc networ&s2. To identify the re'uirements for protocols for wireless adIhoc networ&s ascompared to the protocols e8isting for wired networ&.

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    Networ&s! Classification of transport layer solutions! TC+ over d hocwireless Networ&s! ,ther transport layer protocols for d hoc wirelessNetwor&s.

    0* ecurit'

    A.1 Security; Security in wireless d hoc wireless Networ&s! Networ&

    security re'uirements! *ssues ? challenges in security provisioning!Networ& security attac&s! >ey management! Secure routing in d hocwireless Networ&s.

    3D

    0+ Eo

    @.1 7uality of service in d hoc wireless Networ&s; *ntroduction! *ssues andchallenges in providing 7oS in d hoc wireless Networ&s! Classificationof 7oS solutions! 0C layer solutions! networ& layer solutions.

    3D

    "erm Wor7:

    Term work should consist of at least 12 experiments.

    Journal must include at least 2 assignments.

    The final certification and acceptance of term work indicates that performance in

    laboratory work is satisfactory and minimum passing marks may be given in term work.

    The distribution of mar&s for term wor& shall be as follows;

    "aboratory wor& Be8periments; ####..###.. B1A 0ar&s.

    ssignment;####.############ B3A 0ar&s.

    ttendance ###############. B3A 0ar&s

    "O":

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    b. Calculate the time to receive reply from the receiver using NS2.c. 5enerate graphs which show the transmission time for pac&et.

    4. *mplement wireless networ&. Capture data frame and identify fields using NS2.A. Configure )ireless ccess +oint B)+ and build different networ&s.@. *mplement 0obile device as a wireless access point.

    D. Communicate between two different networ&s which has followingspecifications;a. ,ne networ& has Class networ& with OTora protocolPb. Second has Class networ& O,(- protocolP

    Practical exam will be based on the above syllabus.

    "e,t 9oo7s:1. Siva am 0urthy and .S.0anoj! Od hoc )ireless Networ&s rchitectures and protocolsP!2nd edition! +earson $ducation! 233D2. Charles $. +er&ins! Odhoc Networ&ingP! ddison K )esley! 2333. Toh!Pdhoc 0obile )ireless Networ&sP! +earson $ducation! 2332

    6e0erence 9oo7s:1. 0atthew 5ast! OE32.11 )ireless Networ&s; The (efinitive 5uideP! 2nd $dition! ,Ueilly0edia! pril 233A.

    2. Stefano asagni! 0arco Conti! Silvia 5iordan and *van Stojmenovic! O0obile dhoc

    Networ&ingP! )ileyI*$$$ +ress! 2334.

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    Course Code Course/Subject Name Credits

    CPE8034 !lectiveAIII Digital 4orensics 5

    Objectives:

    1. To focus on the procedures for identification! preservation! and e8traction of electronicevidence! auditing and investigation of networ& and host system intrusions! analysis anddocumentation of information gathered! and preparation of e8pert testimonial evidence.

    2. To provide hands on e8perience on various forensic tools and resources for systemadministrators and information system security officers.

    Module Detailed Contents Hrs.

    01 Introduction:1.1 *ntroduction of Cybercrime; Types! The *nternet spawns crime! )orms

    versus viruses! ComputersU roles in crimes! *ntroduction to digitalforensics! *ntroduction to *ncident I *ncident esponse 0ethodology KSteps I ctivities in *nitial esponse! +hase after detection of an incident.

    3H

    02 Initial 6es-onse and 0orensic du-lication2.1 *nitial esponse ? -olatile (ata Collection from )indows system I

    *nitial esponse ? -olatile (ata Collection from :ni8 system I orensic(uplication; orensic duplication; orensic (uplicates as dmissible$vidence! orensic (uplication Tool e'uirements! Creating a orensic.

    2.2 (uplicate/7ualified orensic (uplicate of a 6ard (rive.

    3E

    0 $reserving and 6ecovering Digital !vidence

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    "erm Wor7:

    Term work should consist of at least 12 experiments.

    Journal must include at least 2 assignments.

    The final certification and acceptance of term work indicates that performance inlaboratory work is satisfactory and minimum passing marks may be given in termwork.

    The distribution of mar&s for term wor& shall be as follows;

    "aboratory wor& Be8periments; #########.. B1A 0ar&s.

    ssignment; ################# B3A 0ar&s.

    ttendance ###############. B3A 0ar&s

    "O":

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    Course Code Course/Subject Name Credits

    CPE8035 !lective III A 9ig Data &nal'tics 5

    Objectives:

    1. To providean overview of an e8citing growing field of big data analytics.2. To introduce the tools re'uired to manage and analye big data li&e 6adoop! NoS'l 0apI

    educe.eyIvalue stores! 5raph stores!

    Column family Bigtable stores! (ocument stores! -ariations of NoS7"

    architectural patterns%

    ey! The educe Tas&s!

    3@

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    Combiners! (etails of 0apeduce $8ecution! Coping )ith Node ailures.

    %. &lgorithms sing Ma-6educe; 0atri8I-ector 0ultiplication by 0apeduce !

    elationalIlgebra ,perations! Computing Selections by 0apeduce!

    Computing +rojections by 0apeduce! :nion! *ntersection! and (ifference by

    0apeduce! Computing Natural =oin by 0apeduce! 5rouping and

    ggregation by 0apeduce! 0atri8 0ultiplication! 0atri8 0ultiplication with,ne 0apeduce Step.

    0* 4inding imilar Items

    A.1 pplications of NearINeighbor Search! =accard Similarity of Sets!Similarity of (ocuments! Collaborative iltering as a SimilarISets+roblem .

    A.2Distance Measures: (efinition of a (istance 0easure! $uclidean(istances! =accard (istance! Cosine (istance! $dit (istance! 6amming(istance.

    3ompoer

    02

    0% "itle: Study and installation of Storage as Service. 02

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    Conce-t: Storage as Service BSaaS

    Objective: is that! students must be able to understand the concept ofSaaS ! and how it is implemented using ownCloud which givesuniversal access to files through a web interface.

    2co-e: is to installation and understanding features of ownCloud asSaaS.

    "echnolog':ownCloud

    0* "itle: *mplementation of identity management.

    Conce-t: *dentity 0anagement in cloud

    Objective: this lab gives an introduction about identity management incloud and simulate it by using ,penStac&

    2co-e: installing and using identity management feature of ,penStac&

    "echnolog': ,penStac&

    02

    0+ "itle: )rite a program for web feed.

    Conce-t: )eb feed and SS

    Objective: this lab is to understand the concept of form and controlvalidation

    2co-e: )rite a program for web feed

    "echnolog':+6+! 6T0"

    02

    0 "itle: Study and implementation ofSingleISingI,n.

    Conce-t: Single Sing ,n BSS,!open*(

    Objective: is to understand the concept of access control in cloud andsingle sing on BSS,! :se SS, and advantages of it! and also studentsshould able to implementation of it.

    2co-e: installing and using =,SS,

    "echnolog':=,SS,

    02

    0/ "itle: Securing Servers in Cloud.

    Conce-t: Cloud Security

    Objective: is to understand how to secure web server! how to securedata directory and introduction to encryption for own cloud.

    02

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    2co-e: *nstalling and using security feature of ownCloud

    "echnolog':ownCloud

    03 "itle: :ser 0anagement in Cloud.

    Conce-t: dministrative features of Cloud 0anagenet !:ser0anagement

    Objective: is to understand how to create! manage user and group ofusers accounts.

    2co-e: *nstalling and using dministrative features of ownCloud

    "echnolog':ownCloud

    02

    10 "itle: Case study on maon $C2.

    Conce-t: maon $C2

    Objective: in this module students will learn about maon $C2.maon $lastic Compute Cloud is a central part of maon.comUscloud computing platform! maon )eb Services. $C2 allows users torent virtual computers on which to run their own computer applications

    01

    11 "itle: Case study on 0icrosoft aure.

    Conce-t: 0icrosoft ure

    Objective: students will learn about 0icrosoft ure is a cloudcomputing platform and infrastructure! created by 0icrosoft! forbuilding! deploying and managing applications and services through aglobal networ& of 0icrosoftImanaged datacenters. 6ow it wor&!different services provided by it.

    "echnolog': 0icrosoft aure

    01

    12 "itle: 0ini project.

    Conce-t: using different features of cloud computing creating owncloud for institute! organiation etc.

    Objective: is student must be able to create own cloud using differentfeatures which are learned in previous practices.

    2co-e: creating a cloud li&e social site for institute.

    "echnolog':any open system used for cloud

    0*

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    "erm Wor7:

    Term work should consist of at least 6 experiments and a mini project.

    Journal must include at least 2 assignments.

    The final certification and acceptance of term work indicates that performance in

    laboratory work is satisfactory and minimum passing marks may be given in termwork.The distribution of mar&s for term wor& shall be as follows;

    "aboratory wor& Be8periments; #########.. B1A 0ar&s.

    0ini project presentation; ############# B3A 0ar&s.

    ttendance ###############. B3A 0ar&s

    "O":

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    Course Code Course/Subject Name Credits

    CP701 / CP802 Project I/ II 3 / 6

    Guidelines for Project

    o Students should do literature survey/visit industry/analyze current trends and identify theproblem for Project and finalize in consultation with Guide/Supervisor. Students should usemultiple literatures and understand the problem.o Students should attempt solution to the problem by experimental/simulation methods.

    o he solution to be validated with proper justification and report to be compiled in standard

    format.Guidelines for Assessment of Project I

    o Project ! should be assessed based on followin" points

    #uality of problem selected

    Clarity of Problem definition and $easibility of problem solution %elevance to the specialization

    Clarity of objective and scope &readth and depth of literature survey

    o Project ! should be assessed throu"h a presentation by the student project "roup to a panel of

    !nternal examiners appointed by the 'ead of the (epartment/!nstitute of respective Pro"ramme.Guidelines for Assessment of Project II

    o Project !! should be assessed based on followin" points

    #uality of problem selected

    Clarity of Problem definition and $easibility of problem solution

    %elevance to the specialization / !ndustrial trends

    Clarity of objective and scope

    #uality of wor) attempted *alidation of results #uality of +ritten and ,ral Presentation

    o %eport should be prepared as per the "uidelines issued by the -niversity of umbai.

    o Project !! should be assessed throu"h a presentation by the student project "roup to a panel of

    !nternal and xternal xaminers approved by the -niversity of umbaio Students should be motivated to publish a paper based on the wor) in Conferences/students

    competitions