Download JNTU Kakinada (Jawaharlal Nehru Technological University, Kakinada) M.Tech (Master of Technology) R19 CSE M.Tech Computer Science and Engineering Course Structure And Syllabus
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
COURSE STRUCTURE & SYLLABUS M.Tech CSE for
COMPUTER SCIENCE & ENGINEERING PROGRAMME
(Applicable for batches admitted from 2019-2020)
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA
I-SEMESTER
S.N
Cate
o Course Code
Courses
gory L T P C
Program Core-1
1
MTCSE1101 Mathematical Foundations of Computer Science PC 3 0 0 3
Program Core-2
2
MTCSE1102 Advanced Data Structures & Algorithms
PC 3 0 0 3
Program Elective-1
1.
3
MTCSE1103
Big Data Analytics
PE 3 0 0 3
2. Digital Image Processing
3. Advanced Operating Systems
Program Elective-2
1.
4
MTCSE1104
Advanced Computer Networks
2.
PE 3 0 0 3
Internet of Things
3. Object Oriented Software Engineering
5
MTCSE1105 Research Methodology and IPR
CC
0 2
6
MTCSE1106 Laboratory-1
LB 0 0 4 2
Advanced Data Structures & Algorithms Lab
7
MTCSE1107 Laboartory-2
LB 0 0 4 2
Advanced Computing Lab
8
MTCSE1108 Audit Course-1*
AC 2 0 0 0
Total Credits
18
*Student has to choose any one audit course listed below.
II SEMESTER
Course
Cate
S.No
Code
Courses
Gory L T P C
Program Core-3
1
MTCSE1201
PC 3 0 0 3
Machine learning
Program Core-4
2
MTCSE1202
PC 3 0 0 3
MEAN Stack Technologies
Program Elective-3
1.
3
MTCSE1203
Advanced Databases and Mining
PE 3 0 0 3
2. Ad Hoc & Sensor Networks
3. Soft Computing
Program Elective-4
1.
4
MTCSE1204
Cloud Computing
PE 3 0 0 3
2. Principles of computer security
3. High Performance Computing
Laboratory-3
5
MTCSE1205
LB 0 0 4 2
Machine Learning with python lab
Laboartory-4
6
MTCSE1206
LB 0 0 4 2
MEAN Stack Technologies Lab
7
MTCSE1207 Mini Project with Seminar
MP 2 0 0 2
8
MTCSE1208 Audit Course-2 *
AC 2 0 0 0
Total Credits
18
*Student has to choose any one audit course listed below.
Audit Course 1 & 2:
1. English for Research Paper
5. Constitution of India
Writing
6. Pedagogy Studies
2. Disaster Management
7. Stress Management by Yoga
3. Sanskrit for Technical Knowledge
8. Personality Development through
4. Value Education
Life Enlightenment Skills
III-SEMESTER
Course
Cate
S.No
Courses
L T P C
Code
gory
Program Elective-5
PE
1. Deep Learning
2. Social Network Analysis
1
MTCSE2101 3. MOOCs-1 (NPTEL/SWAYAM) 12
3 0 0 3
Week Program related to the
programme which is not listed in the
course structure
Open Elective
OE
1. MOOCs-2
(NPTEL/SWAYAM)-Any
12 Week Course on Engineering/
2
MTCSE2102
Management/ Mathematics offered
3 0 0 3
by other than parent department
2. Course offered by other departments
in the college
Dissertation-I/ Industrial
PJ
3
MTCSE2103 Project #
0 0 20 10
Total Credits
16
#Students going for Industrial Project/Thesis will complete these courses through
MOOCs
M. Tech. (CSE) IV SEMESTER
Course
Cate
S.No
Courses
L T P C
Code
gory
1
MTCSE2201 Dissertation-II
PJ 0 0 32 16
Total Credits
16
Open Electives offered by the Department of CSE
1. Python Programming
2. Principles of Cyber Security
3. Internet of Things
4. Machine Learning
5. Digital forensics
6. Next Generation Databases
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I Year - I Semester
3
0
0
3
Mathematical Foundations of Computer Science (MTCSE1101)
Course Objectives: This course is aimed at enabling the students to
? To understand the mathematical fundamentals that is prerequisites for variety of courses like Data
mining, Network protocols, analysis of Web traffic, Computer security, Software engineering,
Computer architecture, operating systems, distributed systems bioinformatics, Machine learning.
? To develop the understanding of the mathematical and logical basis to many modern techniques in
computer science technology like machine learning, programming language design, and concurrency.
? To study various sampling and classification problems.
Course Outcomes:
After the completion of the course, student will be able to
? To apply the basic rules and theorems of probability theory such as Baye's Theorem, to determine
probabilities that help to solve engineering problems and to determine the expectation and variance of
a random variable from its distribution.
? Able to perform and analyze of sampling, means, proportions, variances and estimates the maximum
likelihood based on population parameters.
? To learn how to formulate and test hypotheses about sample means, variances and proportions and to
draw conclusions based on the results of statistical tests.
? Design various ciphers using number theory.
? Apply graph theory for real time problems like network routing problem.
UNIT I: Basic Probability and Random Variables: Random Experiments, Sample Spaces Events, the
Concept of Probability the Axioms of Probability, Some Important Theorems on Probability Assignment
of Probabilities, Conditional Probability Theorems on Conditional Probability, Independent Events,
Bayes Theorem or Rule. Random Variables, Discrete Probability Distributions, Distribution Functions for
Random Variables, Distribution Functions for Discrete Random Variables, Continuous Random
Variables
UNIT II: Sampling and Estimation Theory: Population and Sample, Statistical Inference Sampling
With and Without Replacement Random Samples, Random Numbers Population Parameters Sample
Statistics Sampling Distributions, Frequency Distributions, Relative Frequency Distributions,
Computation of Mean, Variance, and Moments for Grouped Data. Unbiased Estimates and Efficient
Estimates Point Estimates and Interval Estimates. Reliability Confidence Interval Estimates of Population
Parameters, Maximum Likelihood Estimates
UNIT III: Tests of Hypothesis and Significance: Statistical Decisions Statistical Hypotheses. Null
Hypotheses Tests of Hypotheses and Significance Type I and Type II Errors Level of Significance Tests
Involving the Normal Distribution One-Tailed and Two-Tailed Tests P Value Special Tests of
Significance for Large Samples Special Tests of Significance for Small Samples Relationship between
Estimation Theory and Hypothesis Testing Operating Characteristic Curves. Power of a Test Quality
Control Charts Fitting Theoretical Distributions to Sample Frequency Distributions, The Chi-Square Test
for Goodness of Fit Contingency Tables Yates' Correction for Continuity Coefficient of Contingency.
UNIT IV: Algebraic Structures and Number Theory: Algebraic Systems, Examples, General
Properties, Semi Groups and Monoids, Homomorphism of Semi Groups and Monoids, Group, Subgroup,
Abelian Group, Homomorphism, Isomorphism. Properties of Integers, Division Theorem, The Greatest
Common Divisor, Euclidean Algorithm, Least Common Multiple, Testing for Prime Numbers, The
Fundamental Theorem of Arithmetic, Modular Arithmetic (Fermat's Theorem and Euler's Theorem)
UNIT V: Graph Theory: Basic Concepts of Graphs, Sub graphs, Matrix Representation of Graphs:
Adjacency Matrices, Incidence Matrices, Isomorphic Graphs, Paths and Circuits, Eulerian and
Hamiltonian Graphs, Multigraphs, Planar Graphs, Euler's Formula, Graph Colouring and Covering,
Chromatic Number, Spanning Trees, Algorithms for Spanning Trees (Problems Only and Theorems
without Proofs).
Text Books:
1. Foundation Mathematics for Computer Science, John Vince, Springer.
2. Probability & Statistics, 3rd Edition, Murray R. Spiegel, John J. Schiller and R. Alu Srinivasan,
Schaum's Outline Series, Tata McGraw-Hill Publishers
3. Probability and Statistics with Reliability, K. Trivedi, Wiley.
4. Discrete Mathematics and its Applications with Combinatorics and Graph Theory, 7th Edition, H.
Rosen, Tata McGraw Hill.
Reference Books:
1. Probability and Computing: Randomized Algorithms and Probabilistic Analysis, M. Mitzenmacher
and E. Upfal.
2. Applied Combinatorics, Alan Tucker, Wiley.
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I Year - I Semester
3
0
0
3
Advanced Data Structures & Algorithms (MTCSE1102)
Course Objectives: From the course the student will learn
? Single Linked, Double Linked Lists, Stacks, Queues, Searching and Sorting techniques, Trees, Binary
trees, representation, traversal, Graphs- storage, traversal.
? Dictionaries, ADT for List, Stack, Queue, Hash table representation, Hash functions, Priority queues,
Priority queues using heaps, Search trees.
? AVL trees, operations of AVL trees, Red- Black trees, Splay trees, comparison of search trees.
Course Outcomes:
After the completion of the course, student will be able to
? Ability to write and analyze algorithms for algorithm correctness and efficiency
? Master a variety of advanced abstract data type (ADT) and data structures and their
Implementation
? Demonstrate various searching, sorting and hash techniques and be able to apply and solve
problems of real life
? Design and implement variety of data structures including linked lists, binary trees, heaps, graphs
and search trees
? Ability to compare various search trees and find solutions for IT related problems
UNIT I: Introduction to Data Structures, Singly Linked Lists, Doubly Linked Lists, Circular Lists-
Algorithms. Stacks and Queues: Algorithm Implementation using Linked Lists.
UNIT II: Searching-Linear and Binary, Search Methods, Sorting-Bubble Sort, Selection Sort, Insertion
Sort, Quick Sort, Merge Sort. Trees- Binary trees, Properties, Representation and Traversals (DFT, BFT),
Expression Trees (Infix, prefix, postfix). Graphs-Basic Concepts, Storage structures and Traversals.
UNIT III: Dictionaries, ADT, The List ADT, Stack ADT, Queue ADT, Hash Table Representation,
Hash Functions, Collision Resolution-Separate Chaining, Open Addressing-Linear Probing, Double
Hashing.
UNIT IV: Priority queues- Definition, ADT, Realizing a Priority Queue Using Heaps, Definition,
Insertion, Deletion .Search Trees- Binary Search Trees, Definition, ADT, Implementation, Operations-
Searching, Insertion, Deletion.
UNIT V: Search Trees- AVL Trees, Definition, Height of AVL Tree, Operations-, Insertion, Deletion
and Searching, Introduction to Red-Black and Splay Trees, B-Trees, Height of B-Tree, Insertion, Deletion
and Searching, Comparison of Search Trees.
Text Books:
1. Data Structures: A Pseudo Code Approach, 2/e, Richard F.Gilberg, Behrouz
A. Forouzon and Cengage
2. Data Structures, Algorithms and Applications in java, 2/e, Sartaj Sahni,
University Press
Reference Books:
1. Data Structures and Algorithm Analysis, 2/e, Mark Allen Weiss, Pearson.
2. Data Structures and Algorithms, 3/e, Adam Drozdek, Cengage
3. C and Data Structures: A Snap Shot Oriented Treatise Using Live Engineering
Examples, N.B.Venkateswarulu, E.V.Prasad and S Chand & Co, 2009
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I Year - I Semester
3
0
0
3
Big Data Analytics ( MTCSE11XX)
Course Objectives: This course is aimed at enabling the students to
? To provide an overview of an exciting growing field of big data analytics.
? To introduce the tools required to manage and analyze big data like Hadoop, NoSQL, Map Reduce,
HIVE, Cassandra, Spark.
? To teach the fundamental techniques and principles in achieving big data analytics with scalability
and streaming capability.
? To optimize business decisions and create competitive advantage with Big Data analytics
Course Outcomes:
After the completion of the course, student will be able to
? Illustrate on big data and its use cases from selected business domains.
? Interpret and summarize on No SQL, Cassandra
? Analyze the HADOOP and Map Reduce technologies associated with big data analytics and
explore on Big Data applications Using Hive.
? Make use of Apache Spark, RDDs etc. to work with datasets.
? Assess real time processing with Spark Streaming.
UNIT I: What is big data, why big data, convergence of key trends, unstructured data, industry examples
of big data, web analytics, big data and marketing, fraud and big data, risk and big data, credit risk
management, big data and algorithmic trading, big data and healthcare, big data in medicine, advertising
and big data, big data technologies, introduction to Hadoop, open source technologies, cloud and big data,
mobile business intelligence, Crowd sourcing analytics, inter and trans firewall analytics.
UNIT II: Introduction to NoSQL, aggregate data models, aggregates, key-value and document data
models, relationships, graph databases, schema less databases, materialized views, distribution models,
sharding, master-slave replication, peer- peer replication, sharding and replication, consistency, relaxing
consistency, version stamps, Working with Cassandra ,Table creation, loading and reading data.
UNIT III: Data formats, analyzing data with Hadoop, scaling out, Architecture of Hadoop distributed file
system (HDFS), fault tolerance ,with data replication, High availability, Data locality , Map Reduce
Architecture, Process flow, Java interface, data flow, Hadoop I/O, data integrity, compression,
serialization. Introduction to Hive, data types and file formats, HiveQL data definition, HiveQL data
manipulation, Logical joins, Window functions, Optimization, Table partitioning, Bucketing, Indexing,
Join strategies.
UNIT IV: Apache spark- Advantages over Hadoop, lazy evaluation, In memory processing, DAG, Spark
context, Spark Session, RDD, Transformations- Narrow and Wide, Actions, Data frames ,RDD to Data
frames, Catalyst optimizer, Data Frame Transformations, Working with Dates and Timestamps, Working
with Nulls in Data, Working with Complex Types, Working with JSON, Grouping, Window Functions,
Joins, Data Sources, Broadcast Variables, Accumulators, Deploying Spark- On-Premises Cluster
Deployments, Cluster Managers- Standalone Mode, Spark on YARN , Spark Logs, The Spark UI- Spark
UI History Server, Debugging and Spark First Aid
UNIT V: Spark-Performance Tuning, Stream Processing Fundamentals, Event-Time and State full
Processing - Event Time, State full Processing, Windows on Event Time- Tumbling Windows, Handling
Late Data with Watermarks, Dropping Duplicates in a Stream, Structured Streaming Basics - Core
Concepts, Structured Streaming in Action, Transformations on Streams, Input and Output.
Text Books:
1. Big Data, Big Analytics: Emerging, Michael Minnelli, Michelle Chambers, and Ambiga Dhiraj
2. SPARK: The Definitive Guide, Bill Chambers & Matei Zaharia, O'Reilley, 2018 Edition
3. Business Intelligence and Analytic Trends for Today's Businesses", Wiley, 2013
4. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging World Polyglot
Persistence", Addison-Wesley Professional, 2012
5. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012
Reference Books:
1. "Hadoop Operations", O'Reilley, Eric Sammer, 2012
2. "Programming Hive", O'Reilley, E. Capriolo, D. Wampler, and J. Rutherglen, 2012
3. "HBase: The Definitive Guide", O'Reilley, Lars George, 2011
4. "Cassandra: The Definitive Guide", O'Reilley, Eben Hewitt, 2010
5. "Programming Pig", O'Reilley, Alan Gates, 2011
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I Year - I Semester
3
0
0
3
Digital Image Processing ( MTCSE11XX)
Course Objectives:
? Describe and explain basic principles of digital image processing.
? Design and implement algorithms that perform basic image processing (e.g. noise removal and image
enhancement).
? Design and implement algorithms for advanced image analysis (e.g. image compression, image
segmentation).
? Assess the performance of image processing algorithms and systems.
Course Outcomes:
After the completion of the course, student will be able to
? Demonstrate the components of image processing
? Explain various filtration techniques.
? Apply image compression techniques.
? Discuss the concepts of wavelet transforms.
? Analyze the concept of morphological image processing.
UNIT I: Introduction: Fundamental steps in Image Processing System, Components of Image
Processing System, Elements of Visual Perception, Image Sensing and acquisition, Image sampling &
Quantization, Basic Relationship between pixels. Image Enhancement Techniques: Spatial Domain
Methods: Basic grey level transformation, Histogram equalization, Image subtraction, image averaging.
UNIT II: Spatial filtering: Smoothing, sharpening filters, Laplacian filters, Frequency domain filters,
Smoothing and sharpening filters, Homomorphism is filtering. Image Restoration & Reconstruction:
Model of Image Degradation/restoration process, Noise models, Spatial filtering, Inverse filtering,
Minimum mean square Error filtering, constrained least square filtering, Geometric mean filter, Image
reconstruction from projections. Color Fundamentals, Color Models, Color Transformations.
UNIT III: Image Compression: Redundancies- Coding, Interpixel, Psycho visual; Fidelity, Source and
Channel Encoding, Elements of Information Theory; Loss Less and Lossy Compression; Run length
coding, Differential encoding, DCT, Vector quantization, Entropy coding, LZW coding; Image
Compression Standards-JPEG, JPEG 2000, MPEG; Video compression.
UNIT IV: Wavelet Based Image Compression: Expansion of functions, Multi-resolution analysis,
Scaling functions, MRA refinement equation, Wavelet series expansion, Discrete Wavelet Transform
(DWT), Continuous, Wavelet Transform, Fast Wavelet Transform, 2-D wavelet Transform, JPEG-2000
encoding.
UNIT V: Image Segmentation: Discontinuities, Edge Linking and boundary detection, Thresholding,
Region Based Segmentation, Watersheds; Introduction to morphological operations; binary morphology-
erosion, dilation, opening and closing operations, applications; basic gray-scale morphology operations;
Feature extraction; Classification; Object recognition. Digital Image Watermarking: Introduction, need
of Digital Image Watermarking, applications of watermarking in copyright protection and Image quality
analysis.
Text Books:
1. Digital Image Processing. 2nd ed. Gonzalez, R.C. and Woods, R.E. India: Person Education, (2009)
Reference Books:
1. Digital Image Processing. John Wiley, Pratt, W. K, (2001)
2. Digital Image Processing, Jayaraman, S., Veerakumar, T. and Esakkiranjan, S. (2009),Tata McGraw-
Hill
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I Year - I Semester
3
0
0
3
Advanced Operating Systems ( MTCSE11XX)
Course Objectives: This course is aimed at enabling the students to
? To provide comprehensive and up-to-date coverage of the major developments in distributed
Operating System, Multi-processor Operating System and Database Operating System and to cover
important theoretical foundations including Process Synchronization, Concurrency, Event ordering,
Mutual Exclusion, Deadlock, Agreement Protocol, Security, Recovery and fault tolerance.
Course Outcomes:
After the completion of the course, student will be able to
? Illustrate on the fundamental concepts of distributed operating systems, its architecture and
distributed mutual exclusion.
? Analyze on deadlock detection algorithms and agreement protocols.
? Make use of algorithms for implementing DSM and its scheduling.
? Apply protection and security in distributed operating systems.
? Elaborate on concurrency control mechanisms in distributed database systems.
UNIT-1: Architectures of Distributed Systems, System Architecture types, issues in distributed operating
systems, communication networks, communication primitives. Theoretical Foundations, inherent
limitations of a distributed system, lamp ports logical clocks, vector clocks, casual ordering of messages,
global state, cuts of a distributed computation, termination detection. Distributed Mutual Exclusion,
introduction, the classification of mutual exclusion and associated algorithms, a comparative performance
analysis.
UNIT-2:Distributed Deadlock Detection, Introduction, deadlock handling strategies in distributed
systems, issues in deadlock detection and resolution, control organizations for distributed deadlock
detection, centralized and distributed deadlock detection algorithms, hierarchical deadlock detection
algorithms. Agreement protocols, introduction-the system model, a classification of agreement problems,
solutions to the Byzantine agreement problem, and applications of agreement algorithms. Distributed
resource management: introduction-architecture, mechanism for building distributed file systems design
issues, log structured file systems.
UNIT- 3: Distributed shared memory, Architecture, algorithms for implementing DSM, memory
coherence and protocols, design issues. Distributed Scheduling, introduction, issues in load distributing,
components of a load distributing algorithm, stability, load distributing algorithm, performance
comparison, selecting a suitable load sharing algorithm, requirements for load distributing, task migration
and associated issues. Failure Recovery and Fault tolerance: introduction, basic concepts, classification of
failures, backward and forward error recovery, backward error recovery, recovery in concurrent systems,
consistent set of check points, synchronous and asynchronous check pointing and recovery, check
pointing for distributed database systems, recovery in replicated distributed databases.
UNIT- 4: Protection and security, preliminaries, the access matrix model and its implementations.-safety
in matrix model, advanced models of protection. Data security, cryptography: Model of cryptography,
conventional cryptography modern cryptography, private key cryptography, data encryption standard
public key cryptography, multiple encryptions, authentication in distributed systems.
UNIT-5: Multiprocessor operating systems, basic multiprocessor system architectures, inter connection
networks for multiprocessor systems, caching hypercube architecture. Multiprocessor Operating System,
structures of multiprocessor operating system, operating system design issues, threads, process
synchronization and scheduling. Database Operating systems: Introduction, requirements of a database
operating system Concurrency control :Theoretical aspects, introduction, database systems, a concurrency
control model of database systems, the problem of concurrency control, serializability theory, distributed
database systems, concurrency control algorithms, introduction, basic synchronization primitives, lock
based algorithms, timestamp based algorithms, optimistic algorithms, concurrency control algorithms,
data replication.
Text Books:
1. "Advanced concepts in operating systems: Distributed, Database and multiprocessor operating
systems", Mukesh Singhal, Niranjan and G.Shivaratri, TMH, 2001
Reference Books:
1. "Modern operating system", Andrew S.Tanenbaum, PHI, 2003
2. "Distributed operating system-Concepts and design", Pradeep K.Sinha, PHI, 2003
3. "Distributed operating system", Pearson education, AndrewS.Tanenbaum, 2003
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I Year - I Semester
3
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3
ADVANCED COMPUTER NETWORKS (MTCSE11YY)
Course Objectives: This course is aimed at enabling the students to
? The course is aimed at providing basic understanding of Computer networks starting with OSI
Reference Model, Protocols at different layers with special emphasis on IP, TCP & UDP and Routing
algorithms.
? Some of the major topics which are included in this course are CSMA/CD, TCP/IP implementation,
LANs/WANs, internetworking technologies, Routing and Addressing.
? Provide the mathematical background of routing protocols.
? Aim of this course is to develop some familiarity with current research problems and research
methods in advance computer networks.
Course Outcomes:
After the completion of the course, student will be able to
? Illustrate reference models with layers, protocols and interfaces.
? Describe the routing algorithms, Sub netting and Addressing of IP V4and IPV6.
? Describe and Analysis of basic protocols of computer networks, and how they can be used to assist in
network design and implementation.
? Describe the concepts Wireless LANS, WIMAX, IEEE 802.11, Cellular telephony and Satellite
networks
? Describe the emerging trends in networks-MANETS and WSN
Unit-I:Network layer: Network Layer design issues: store-and forward packet switching, services
provided transport layers, implementation connection less services, implementation connection oriented
services, comparison of virtual ?circuit and datagram subnets, Routing Algorithms-shortest path routing,
flooding, distance vector routing, link state routing, Hierarchical routing, congestion control algorithms
:Approaches to congestion control, Traffic aware routing, Admission control, Traffic throttling, choke
Packets, Load shedding, Random early detection, Quality of Service, Application requirements, Traffic
shaping, Leaky and Token buckets
Unit-II: Internetworking and IP protocols: How networks differ, How net works can be connected,
internetworking, tunneling, The network layer in the internet,IPV4 Protocol, IP addresses, Subnets,
CIDR, classful and Special addressing, network address translation (NAT),IPV6 Address structure
address space, IPV6 Advantages, packet format, extension Headers, Transition from IPV4 to IPV6 ,
Internet Control Protocols-IMCP, ARP, DHCP
Unit-III: Transport Layer Protocols: Introduction, Services, Port numbers,
User Datagram Protocol: User datagram, UDP services, UDP Applications, Transmission control
Protocol: TCP services, TCP features, Segment, A TCP connection, State transition diagram, Windows in
TCP, Flow control and error control, TCP Congestion control, TCP Timers, SCTP: SCTP services
SCTP features, packet format, An SCTP association, flow control, error control.
Unit- IV: Wireless LANS: Introduction, Architectural comparison, Access control, The IEEE 802.11
Project: Architecture, MAC sub layer, Addressing Mechanism, Physical Layer, Bluetooth: Architecture,
Bluetooth Layers Other Wireless Networks: WIMAX: Services, IEEE project 802.16, Layers in project
802.16, Cellular Telephony: Operations, First Generation (1G), Second Generation (2G), Third
Generation (3G), Fourth Generation (4G), Satellite Networks: Operation, GEO Satellites, MEO satellites,
LEO satellites.
Unit?V: Emerging trends in Computer networks:
Mobile computing: Motivation for mobile computing, Protocol stack issues in mobile computing
environment, mobility issues in mobile computing, security issues in mobile networks, MOBILE Ad Hoc
Networks: Applications of Ad Hoc Networks, Challenges and Issues in MANETS, MAC Layer Issues
Routing Protocols in MANET, Transport Layer Issues, Ad hoc Network Security. Wireless Sensor
Networks: WSN functioning, Operating system support in sensor devices, WSN characteristics, sensor
network operation, Sensor Architecture: Cluster management, Wireless Mesh Networks: WMN design ,
Issues in WMNs, Computational Grids, Grid Features, Issues in Grid construction design, Grid design
features,P2P Networks: Characteristics of P2P Networks, Classification of P2P systems, Gnutella,
BitTorrent, Session Initiation Protocol(SIP) , Characteristics and addressing, Components of SIP, SIP
establishment, SIP security.
Text Books:
1. Data communications and networking 4th edition Behrouz A Fourzan,TMH
2. Computer networks 4th edition Andrew S Tanenbaum, Pearson
3. Computer networks, Mayank Dave, CENGAGE
Reference Books:
1. Computer networks, A system Approach, 5th ed, Larry L Peterson and Bruce S Davie, Elsevier
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I Year - I Semester
3
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3
Internet of Things ( MTCSE11YY)
Course Objectives:
? To Understand Smart Objects and IoT Architectures.
? To learn about various IOT-related protocols
? To build simple IoT Systems using Arduino and Raspberry Pi.
? To understand data analytics and cloud in the context of IoT
? To develop IoT infrastructure for popular applications.
Course Outcomes:
After the completion of the course, student will be able to
? Summarize on the term 'internet of things' in different contexts.
? Analyze various protocols for IoT.
? Design a PoC of an IoT system using Rasperry Pi/Arduino
? Apply data analytics and use cloud offerings related to IoT.
? Analyze applications of IoT in real time scenario
UNIT I: FUNDAMENTALS OF IoT: Evolution of Internet of Things, Enabling Technologies, IoT
Architectures,oneM2M, IoT World Forum ( IoTWF ) and Alternative IoT models, Simplified IoT
Architecture and Core IoT Functional Stack, Fog, Edge and Cloud in IoT, Functional blocks of an IoT
ecosystem, Sensors, Actuators, Smart Objects and Connecting Smart Objects.
UNIT II: IoT PROTOCOLS: IT Access Technologies: Physical and MAC layers, topology and
Security of IEEE 802.15.4, 802.15.4g, 802.15.4e, 1901.2a, 802.11ah and Lora WAN, Network Layer: IP
versions, Constrained Nodes and Constrained Networks, Optimizing IP for IoT: From 6LoWPAN to 6Lo,
Routing over Low Power and Lossy Networks, Application Transport Methods: Supervisory Control and
Data Acquisition, Application Layer Protocols: CoAP and MQTT.
UNIT III: DESIGN AND DEVELOPMENT: Design Methodology, Embedded computing logic,
Microcontroller, System on Chips, IoT system building blocks, Arduino, Board details, IDE
programming, Raspberry Pi, Interfaces and Raspberry Pi with Python Programming.
UNIT IV: DATA ANALYTICS AND SUPPORTING SERVICES: Structured Vs Unstructured Data
and Data in Motion Vs Data in Rest, Role of Machine Learning ? No SQL Databases, Hadoop
Ecosystem, Apache Kafka, Apache Spark, Edge Streaming Analytics and Network Analytics, Xively
Cloud for IoT, Python Web Application Framework, Django, AWS for IoT, System Management with
NETCONF-YANG.
UNIT V: CASE STUDIES/INDUSTRIAL APPLICATIONS: Cisco IoT system, IBM Watson IoT
platform, Manufacturing, Converged Plant wide Ethernet Model (CPwE), Power Utility Industry, Grid
Blocks Reference Model, Smart and Connected Cities: Layered architecture, Smart Lighting, Smart
Parking Architecture and Smart Traffic Control.
Text Books:
1.IoT Fundamentals: Networking Technologies, Protocols and Use Cases for Internet of Things, David
Hanes, Gonzalo Salgueiro, Patrick Grossetete, Rob Barton and Jerome Henry, Cisco Press, 2017
Reference Books:
1. Internet of Things ? A hands-on approach, Arshdeep Bahga, Vijay Madisetti, Universities Press, 2015
2. The Internet of Things ? Key applications and Protocols, Olivier Hersent, David Boswarthick, Omar
Elloumi and Wiley, 2012 (for Unit 2).
3. "From Machine-to-Machine to the Internet of Things ? Introduction to a New Age of Intelligence",
Jan Ho? ller, Vlasios Tsiatsis, Catherine Mulligan, Stamatis, Karnouskos, Stefan Avesand. David
Boyle and Elsevier, 2014.
4. Architecting the Internet of Things, Dieter Uckelmann, Mark Harrison, Michahelles and Florian
(Eds), Springer, 2011.
5. Recipes to Begin, Expand, and Enhance Your Projects, 2nd Edition, Michael Margolis, Arduino
Cookbook and O'Reilly Media, 2011.
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I Year - I Semester
3
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3
Object Oriented Software Engineering (MTCSE11YY)
Course Objectives:
? To elicit, analyze and specify software requirements through a productive working relationship with
various stakeholders of the project.
? To understand the what software life cycle is, how software projects are planned and managed, types
of resources involved in software development projects, risks are identified and assessed, predictions
and assessments are made.
? To identify, formulate, and solve software engineering problems, including the specification, design,
implementation, and testing of software systems that meet specification, performance, maintenance
and quality requirements
Course Outcomes:
After the completion of the course, student will be able to
? Apply the Object Oriented Software-Development Process to design software
? Analyze and Specify software requirements through a SRS documents.
? Design and Plan software solutions to problems using an object-oriented strategy.
? Model the object oriented software systems using Unified Modeling Language (UML)
? Estimate the cost of constructing object oriented software.
UNIT I: Introduction to Software Engineering: Software, Software Crisis, Software Engineering
definition, Evolution of Software Engineering Methodologies, Software Engineering Challenges.
Software Processes: Software Process, Process Classification, Phased development life cycle, Software
Development Process Models, Process, use, applicability and Advantages/limitations.
UNIT II: Object oriented Paradigm, Object oriented Concepts, Classes, Objects, Attributes, Methods and
services, Messages, Encapsulation, Inheritance, Polymorphism, Identifying the elements of object model,
management of object oriented Software projects, Object Oriented Analysis, Domain Analysis, Generic
Components of OOA model,OOA Process, Object Relationship model, Object Behavior Model.
UNIT III: Object Oriented Design: Design for Object- Oriented systems, The Generic components of the
OO design model, The System design process, The Object design process, Design Patterns, Object
Oriented Programming.
UNIT IV: Object Oriented testing: Broadening the view of Testing, Testing of OOA and OOD models,
Object-Oriented testing strategies, Test case design for OO software, testing methods applicable at the
class level, Interclass test case design.
UNIT V: Technical Metrics for Object Oriented Systems: The Intent of Object Oriented metrics, The
distinguishing Characteristics, Metrics for the OO Design model, Class-Oriented metrics, Operation-
Oriented Metrics, Metrics foe Object Oriented testing, Metrics for Object Oriented projects. CASE Tools.
Text Books:
1. Object oriented and Classical Software Engineering, 7/e, Stephen R. Schach,
TMH.
2. Object oriented and Classical Software Engineering, Timothy Lethbridge,
Robert Laganiere, TMH
3. Software Engineering by Roger S Pressman, Tata McGraw Hill Edition.
Reference Books:
1. Component based software engineering: 7th International symposium, ivicaCrnkovic, Springer, CBSE
2004
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I Year - I Semester
2
0
0
2
RESEARCH METHODOLOGY AND IPR
UNIT 1:
Meaning of research problem, Sources of research problem, Criteria Characteristics of a good research
problem, Errors in selecting a research problem, Scope and objectives of research problem. Approaches
of investigation of solutions for research problem, data collection, analysis, interpretation, Necessary
instrumentations
UNIT 2:
Effective literature studies approaches, analysis Plagiarism, Research ethics, Effective technical writing,
how to write report, Paper Developing a Research Proposal, Format of research proposal, a presentation
and assessment by a review committee
UNIT 3:
Nature of Intellectual Property: Patents, Designs, Trade and Copyright. Process of Patenting and
Development: technological research, innovation, patenting, development. International Scenario:
International cooperation on Intellectual Property. Procedure for grants of patents, Patenting under PCT.
UNIT 4:
Patent Rights: Scope of Patent Rights. Licensing and transfer of technology. Patent information and
databases. Geographical Indications.
UNIT 5:
New Developments in IPR: Administration of Patent System. New developments in IPR; IPR of
Biological Systems, Computer Software etc. Traditional knowledge Case Studies, IPR and IITs.
REFERENCES:
(1) Stuart Melville and Wayne Goddard, "Research methodology: an introduction for science &
engineering students'"
(2) Wayne Goddard and Stuart Melville, "Research Methodology: An Introduction"
(3) Ranjit Kumar, 2nd Edition, "Research Methodology: A Step by Step Guide for beginners"
(4) Halbert, "Resisting Intellectual Property", Taylor & Francis Ltd ,2007.
(5) Mayall, "Industrial Design", McGraw Hill, 1992.
(6) Niebel, "Product Design", McGraw Hill, 1974.
(7) Asimov, "Introduction to Design", Prentice Hall, 1962.
(8) (8) Robert P. Merges, Peter S. Menell, Mark A. Lemley, " Intellectual Property in New
Technological Age", 2016.
(9) T. Ramappa, "Intellectual Property Rights Under WTO", S. Chand, 2008
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I Year - I Semester
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0
4
2
Advanced Data Structures & Algorithms Lab (MTCSE1106)
Course Objectives:
From the course the student will learn
? Knowing about oops concepts for a specific problem.
? Various advanced data structures concepts like arrays, stacks, queues, linked lists, graphs and trees.
Course Outcomes:
After the completion of the course, student will be able to
? Identify classes, objects, members of a class and relationships among them needed for a specific
problem.
? Examine algorithms performance using Prior analysis and asymptotic notations.
? Organize and apply to solve the complex problems using advanced data structures (like arrays,
stacks, queues, linked lists, graphs and trees.)
? Apply and analyze functions of Dictionary
Experiment 1:
Write a java program to perform various operations on single linked list
Experiment 2:
Write a java program for the following
a) Reverse a linked list
b) Sort the data in a linked list
c) Remove duplicates
d) Merge two linked lists
Experiment 3:
Write a java program to perform various operations on doubly linked list.
Experiment 4:
Write a java program to perform various operations on circular linked list.
Experiment 5:
Write a java program for performing various operations on stack using linked list.
Experiment 6:
Write a java program for performing various operations on queue using linked list.
Experiment 7:
Write a java program for the following using stack
a) Infix to postfix conversion.
b) Expression evaluation.
c) Obtain the binary number for a given decimal number.
Experiment 8:
Write a java program to implement various operations on Binary Search Tree
Using Recursive and Non-Recursive methods.
Experiment 9:
Write a java program to implement the following for a graph.
a) BFS
b) DFS
Experiment 10:
Write a java program to implement Merge & Heap Sort of given elements.
Experiment 11:
Write a java program to implement Quick Sort of given elements.
Experiment 12:
Write a java program to implement various operations on AVL trees.
Experiment 13:
Write a java program to perform the following operations:
a) Insertion into a B-tree
b) Searching in a B-tree
Experiment 14:
Write a java program to implementation of recursive and non-recursive functions to Binary tree
Traversals
Experiment 15:
Write a java program to implement all the functions of Dictionary (ADT) using Hashing.
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I Year - I Semester
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4
2
Advanced Computing Lab (MTCSE1107)
Course Objectives:
From the course the student will learn
? The student should have hands on experience in using various sensors like temperature, humidity,
smoke, light, etc. and should be able to use control web camera, network, and relays connected to the
Pi.
Course Outcomes:
After the completion of the course, student will be able to
? The student should have hands on experience in using various sensors like temperature, humidity,
smoke, light, etc. and should be able to use control web camera, network, and relays connected to
the Pi.
? Development and use of s IoT technology in Societal and Industrial Applications.
? Skills to undertake high quality academic and industrial research in Sensors and IoT.
? To classify Real World IoT Design Constraints, Industrial Automation in IoT.
Experiment 1: Start Raspberry Pi and try various Linux commands in command terminal window: ls, cd,
touch, mv, rm, man, mkdir, rmdir, tar, gzip, cat, more, less, ps, sudo, cron, chown, chgrp, ping etc.
Experiment 2: Study and Install IDE of Arduino and different types of Arduino.
Experiment 3: Study and Implement Zigbee Protocol using Arduino / RaspberryPi.
Experiment 4: Write a map reduce program that mines weather data. Weather sensors collecting data
every hour at many locations across the globe gather a large volume of log data, which is a good
candidate for analysis with Map Reduce, since it is semi structured and record-oriented.
Experiment 5: Data analytics using Apache Spark on Amazon food dataset, find all the pairs of items
frequently reviewed together.
Write a single Spark application that
? Transposes the original Amazon food dataset, obtaining a PairRDD of the type<user_id> <list of
the product_ ids reviewed by user_id>
? Counts the frequencies of all the pairs of products reviewed together.
? Writes on the output folder all the pairs of products that appear more than once and their frequencies.
The pairs of products must be sorted by frequency.
Experiment 6:
Write a program to Implement Bankers algorithm for Dead Lock Avoidance.
Experiment 7:
Write a program to Producer-consumer problem Using semaphores.
Experiment 8:
Write a program for an image enhancement using pixel operation.
Experiment 9:
Write a Program to enhance image using image arithmetic and logical operations.
Experiment 10:
Write a program of bit stuffing used by Data Link Layer.
Experiment 11:
Write a program to configure a Network using Distance Vector Routing protocol.
Experiment 12:
Write a program to perform the function oriented diagram: DFD and Structured chart.
Experiment 13:
Write a program to perform the system analysis: Requirement analysis, SRS.
Experiment 14:
Write a program to draw the structural view diagram: Class diagram, object diagram.
Experiment 15:
Write C programs for implementing the Demorgan's law.
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I Year - II Semester
3
0
0
3
Machine Learning
Course Objectives:
Machine Learning course will
? Develop an appreciation for what is involved in learning from data.
? Demonstrate a wide variety of learning algorithms.
? Demonstrate how to apply a variety of learning algorithms to data.
? Demonstrate how to perform evaluation of learning algorithms and model selection.
Course Outcomes:
After the completion of the course, student will be able to
Domain Knowledge for Productive use of Machine Learning and Diversity of Data.
Demonstrate on Supervised and Computational Learning
Analyze on Statistics in learning techniques and Logistic Regression
Illustrate on Support Vector Machines and Perceptron Algorithm
Design a Multilayer Perceptron Networks and classification of decision tree
Unit I: Introduction: Towards Intelligent Machines Well posed Problems, Example of Applications in
diverse fields, Data Representation, Domain Knowledge for Productive use of Machine Learning,
Diversity of Data: Structured / Unstructured, Forms of Learning, Machine Learning and Data Mining,
Basic Linear Algebra in Machine Learning Techniques.
Unit II: Supervised Learning: Rationale and Basics: Learning from Observations, Bias and Why
Learning Works: Computational Learning Theory, Occam's Razor Principle and Over fitting Avoidance
Heuristic Search in inductive Learning, Estimating Generalization Errors, Metrics for assessing
regression, Metris for assessing classification.
Unit III: Statistical Learning: Machine Learning and Inferential Statistical Analysis, Descriptive
Statistics in learning techniques, Bayesian Reasoning: A probabilistic approach to inference, K-Nearest
Neighbor Classifier. Discriminant functions and regression functions, Linear Regression with Least
Square Error Criterion, Logistic Regression for Classification Tasks, Fisher's Linear Discriminant and
Thresholding for Classification, Minimum Description Length Principle.
Unit IV: Support Vector Machines (SVM): Introduction, Linear Discriminant Functions for Binary
Classification, Perceptron Algorithm, Large Margin Classifier for linearly seperable data, Linear Soft
Margin Classifier for Overlapping Classes, Kernel Induced Feature Spaces, Nonlinear Classifier, and
Regression by Support vector Machines.
Learning with Neural Networks: Towards Cognitive Machine, Neuron Models, Network Architectures,
Perceptrons, Linear neuron and the Widrow-Hoff Learning Rule, The error correction delta rule.
Unit V: Multilayer Perceptron Networks and error back propagation algorithm, Radial Basis Functions
Networks. Decision Tree Learning: Introduction, Example of classification decision tree, measures of
impurity for evaluating splits in decision trees, ID3, C4.5, and CART decision trees, pruning the tree,
strengths and weakness of decision tree approach.
Textbooks:
1. Applied Machine Learning,1st edition, M.Gopal, McGraw Hill Education,2018
2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC) 1st
Edition-2014
Reference Books:
1. Machine Learning Methods in the Environmental Sciences, Neural Networks, William WHsieh,
Cambridge Univ Press. 1 edition (August 31, 2009)
2. Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley &SonsInc., 2nd
Edition-2001
3. Chris Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
4. Machine Learning by Peter Flach , Cambridge-1st Edition 2012
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I Year - II Semester
3
0
0
3
MEAN Stack Technologies
Course Objectives:
From the course the student will learn
? Translate user requirements into the overall architecture and implementation of new systems and
Manage Project and coordinate with the Client.
? Writing optimized front end code HTML and JavaScript.
? Monitor the performance of web applications & infrastructure and Troubleshooting web application
with a fast and accurate a resolution
? Design and implementation of Robust and Scalable Front End Applications.
Course Outcomes:
? After the completion of the course, student will be able to
? Identify the Basic Concepts of Web & Markup Languages.
? Develop web Applications using Scripting Languages & Frameworks.
? Make use of Express JS and Node JS frameworks
? Illustrate the uses of web services concepts like restful, react js.
? Adapt to Deployment Techniques & Working with cloud platform.
UNIT I: Introduction to Web: Internet and World Wide Web, Domain name service, Protocols: HTTP,
FTP, SMTP. Html5 concepts, CSS3, Anatomy of a web page. XML: Document type Definition, XML
schemas, Document object model, XSLT, DOM and SAX Approaches.
UNIT II: JavaScript: The Basic of JavaScript: Objects, Primitives Operations and Expressions, Control
Statements, Arrays, Functions, Constructors, Pattern Matching using Regular Expressions. Angular Java
Script Angular JS Expressions: ARRAY, Objects, $eval, Strings, Angular JS Form Validation & Form
Submission, Single Page Application development using Angular JS
UNIT III: Node.js: Introduction, Advantages, Node.js Process Model, Node JS Modules. Express.js:
Introduction to Express Framework, Introduction to Nodejs , What is Nodejs, Getting Started with
Express, Your first Express App, Express Routing, Implementing MVC in Express, Middleware, Using
Template Engines, Error Handling , API Handling , Debugging, Developing Template Engines, Using
Process Managers, Security & Deployment.
UNIT IV: RESTful Web Services: Using the Uniform Interface, Designing URIs,
Web Linking, Conditional Requests. React Js: Welcome to React, Obstacles and Roadblocks, React's
Future, Keeping Up with the Changes, Working with the Files, Pure React, Page Setup, The Virtual
DOM, React Elements, ReactDOM, Children, Constructing Elements with Data, React Components,
DOM Rendering, Factories
UNIT V: Mongo DB: Introduction, Architecture, Features, Examples, Database Creation & Collection in
Mongo DB. Deploying Applications: Web hosting & Domains, Deployment Using Cloud Platforms.
Text Books:
1. Programming the World Wide Web, Robet W Sebesta, 7ed, Pearson.
2. Web Technologies, Uttam K Roy, Oxford
3. Pro Mean Stack Development, ELadElrom, Apress
4. Restful Web Services Cookbook, Subbu Allamraju, O'Reilly
5. JavaScript & jQuery the missing manual, David sawyer mcfarland, O'Reilly
6. Web Hosting for Dummies, Peter Pollock, John Wiley Brand
Reference Books:
1. Ruby on Rails up and Running, Lightning fast Web development, Bruce Tate, Curt Hibbs, Oreilly
(2006)
2. Programming Perl, 4ed, Tom Christiansen, Jonathan Orwant, Oreilly (2012)
3. Web Technologies, HTML< JavaScript, PHP, Java, JSP, XML and AJAX, Black book, Dream Tech
4. An Introduction to Web Design, Programming, Paul S Wang, Sanda S Katila, Cengage Learning
5. Express.JS Guide, The Comprehensive Book on Express.js, Azat Mardan, Lean Publishing.
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I Year - II Semester
3
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0
3
Advanced Databases and Mining
Course Objectives:
?
This Subject deals with dealing data in the real world, maintaining data without any redundancy,
several techniques involved in DBMS to recover the problems caused due to redundancy, storing
data for quick insertion, manipulation and deletion operations in order to retrieve data from the
database.
?
This subject provides an introduction to multidisciplinary field of data mining, the general data
features, techniques for data preprocessing, general implementation of data warehouses and OLAP,
the relationship between data warehousing and other generalization methods
?
The concepts of data clustering includes a different methods of clustering such as k-means, k-
mediods, db scan algorithm, role of data mining in web mining.
Course Outcomes:
After the completion of the course, student will be able to
? Analyze on normalization techniques.
? Elaborate on concurrency control techniques and query optimization.
? Summarize the concepts of data mining, data warehousing and data preprocessing strategies.
? Apply data mining algorithms.
? Assess various classification & cluster techniques.
UNIT I: Introduction: Concepts and Definitions, Relational models, Data Modeling and Query
Languages, Database Objects. Normalization Techniques: Functional Dependency, 1NF, 2NF, 3NF,
BCNF; Multi valued Dependency; Loss-less Join and Dependency Preservation.
UNIT II: Transaction Processing: Consistency, Atomicity, Isolation and Durability, Serializable
Schedule, Recoverable Schedule, Concurrency Control, Time-stamp based protocols, Isolation Levels,
Online Analytical Processing,
Database performance Tuning and Query optimization: Query Tree, Cost of Query, Join, Selection
and Projection Implementation Algorithms and Optimization Database Security: Access Control, MAC,
RBAC, Authorization, SQL Injection Attacks.
UNIT III: Data Mining: stages and techniques, knowledge representation methods, data mining
approaches (OLAP, DBMS, Statistics and ML). Data warehousing: data warehouse and DBMS,
multidimensional data model, OLAP operations. Data processing: cleaning, transformation, reduction,
filters and discretization with weka.
UNIT IV: Knowledge representation: background knowledge, representing input data and output
knowledge, visualization techniques and experiments with weka. Data mining algorithms: association
rules, mining weather data, generating item sets and rules efficiently, correlation analysis.
UNIT V: Classification & Clustering: 1R algorithm, decision trees, covering rules, task prediction,
statistical classification, Bayesian network, instance based methods, linear models, Cluster/2, Cobweb, k-
means, Hierarchical methods. Mining real data: preprocessing data from a real medical domain, data
mining techniques to create a comprehensive and accurate model of data. Advanced topics: text mining,
text classification, web mining, data mining software.
Text Books:
1. Fundamentals of Database Systems, RamezElmasri, Shamkant B. Navathe, Addison-Wesley,6th
edition-
2. Data Mining: Concepts and Techniques, J. Han and M. Kamber, Morgan Kaufmann C.J. Date,
Database Systems, Pearson, 3rd edition-
Reference Books:
1. Principles of Distributed Database Systems, Prentice Hall, P. Valduriez, M. TamerOzsu 3rd edition-
2000
2. Database systems: Design, implementation and Management, C.M. Coronel, S. Morris, P. Rob,
Boston: Cengage Learning,9th edition-2011
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I Year - II Semester
3
0
0
3
Ad Hoc & Sensor Networks
Course Objectives:
? Architect sensor networks for various application setups.
? Devise appropriate data dissemination protocols and model links cost.
? Understandings of the fundamental concepts of wireless sensor networks and have a basic
knowledge of the various protocols at various layers.
? Evaluate the performance of sensor networks and identify bottlenecks
Course Outcomes:
After the completion of the course, student will be able to
? Explain the Fundamental Concepts and applications of ad hoc and wireless sensor networks
? Discuss the MAC protocol issues of ad hoc networks
? Enumerate the concept of routing protocols for ad hoc wireless networks with respect to TCP design
issues
? Analyze & Specify the concepts of network architecture and MAC layer protocol for WSN
? Discuss the WSN routing issues by considering QoS measurements
UNIT I: Introduction : Fundamentals of Wireless Communication Technology, The Electromagnetic
Spectrum, Radio propagation Mechanisms ,Characteristics of the Wireless channel mobile ad hoc
networks (MANETs), Wireless Sensor Networks (WSNs): concepts and architectures, Applications of
Ad Hoc and Sensor Networks, Design Challenges in Ad hoc and Sensor Networks.
UNIT II: MAC Protocols For Ad Hoc Wireless Networks: Issues in designing a MAC Protocol,
Issues in Designing a MAC Protocol for Ad Hoc Wireless Networks, Design Goals of a MAC Protocol
for Ad Hoc Wireless Networks, Classification of MAC Protocols, Contention based protocols, Contention
based protocols with Reservation Mechanisms, Contention based protocols with Scheduling Mechanisms,
Multi channel MAC - IEEE 802.11.
UNIT III: Routing Protocols And Transport Layer In Ad Hoc Wireless Networks: Routing Protocol:
Issues in designing a routing protocol for Ad hoc networks, Classification, proactive routing, reactive
routing (on-demand), hybrid routing, Transport Layer protocol for Ad hoc networks, Design Goals of a
Transport Layer Protocol for Ad Hoc Wireless Networks, Classification of Transport Layer solutions-
TCP over Ad hoc wireless, Network Security, Security in Ad Hoc Wireless Networks, Network Security
Requirements.
UNIT IV: Wireless Sensor Networks (WSNS) And Mac Protocols: Single node architecture -
hardware and software components of a sensor node, WSN Network architecture: typical network
architectures, data relaying and aggregation strategies, MAC layer protocols: self-organizing, Hybrid
TDMA/FDMA and CSMA based MAC -IEEE 802.15.4.
UNIT V: WSN Routing, Localization & Qos: Issues in WSN routing, OLSR, Localization, Indoor and
Sensor Network Localization, absolute and relative localization, triangulation, QOS in WSN, Energy
Efficient Design, Synchronization.
Text Books:
1. "Ad Hoc Wireless Networks: Architectures and Protocols ", C. Siva Ram Murthy, and B. S. Manoj,
Pearson Education, 2008
2. "Wireless Adhoc and Sensor Networks", Labiod. H, Wiley, 2008
3. "Wireless ad -hoc and sensor Networks: theory and applications", Li, X, Cambridge University Press,
2008.
Reference Books:
1. "Ad Hoc & Sensor Networks: Theory and Applications", 2nd edition, Carlos De Morais Cordeiro,
Dharma Prakash Agrawal ,World Scientific Publishing Company, 2011
2. "Wireless Sensor Networks", Feng Zhao and Leonides Guibas,Elsevier Publication.
3. "Protocols and Architectures for Wireless Sensor Networks", Holger Karl and Andreas Willig,Wiley,
2005 (soft copy available)
4. "Wireless Sensor Networks Technology, Protocols, and Applications", Kazem Sohraby, Daniel
Minoli, & TaiebZnati, John Wiley, 2007. (soft copy available)
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I Year - II Semester
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3
Soft Computing
Course Objectives:
? To introduce soft computing concepts and techniques and foster their abilities in designing appropriate
technique for a given scenario.
? To implement soft computing based solutions for real-world problems.
? To give students knowledge of non-traditional technologies and fundamentals of artificial neural
networks, fuzzy sets, fuzzy logic, genetic algorithms.
? To provide student a hand-on experience on MATLAB to implement various strategies.
Course Outcomes:
After the completion of the course, student will be able to
? Elaborate fuzzy logic and reasoning to handle uncertainty in engineering problems.
? Make use of genetic algorithms to combinatorial optimization problems.
? Distinguish artificial intelligence techniques, including search heuristics, knowledge representation,
planning and reasoning.
? Formulate and apply the principles of self-adopting and self organizing neuro fuzzy inference
systems.
? Evaluate and compare solutions by various soft computing approaches for a given problem
UNIT I: Fuzzy Set Theory: Introduction to Neuro, Fuzzy and Soft Computing, Fuzzy Sets, Basic
function and Terminology, Set-theoretic Operations, Member Function Formulation and
Parameterization, Fuzzy Rules and Fuzzy Reasoning, Extension Principle and Fuzzy Relations, Fuzzy
If-Then Rules, Fuzzy Reasoning, Fuzzy Inference Systems, Mamdani Fuzzy Models, Sugeno Fuzzy
Models, Tsukamoto Fuzzy Models, Input Space Partitioning and Fuzzy Modeling.
UNIT II: Optimization: Derivative based Optimization, Descent Methods, and The Method of Steepest
Descent, Classical Newton's Method, Step Size Determination, Derivative-free Optimization, Genetic
Algorithms, Simulated Annealing, and Random Search, Downhill Simplex Search.
UNIT III: Artificial Intelligence: Introduction, Knowledge Representation, Reasoning, Issues and
Acquisition: Prepositional and Predicate Calculus Rule Based knowledge Representation Symbolic
Reasoning Under Uncertainty Basic knowledge Representation Issues Knowledge acquisition, Heuristic
Search: Techniques for Heuristic search Heuristic Classification State Space Search: Strategies
Implementation of Graph Search based on Recursion Patent-directed Search Production System and
Learning.
UNIT IV: Neuro Fuzzy Modeling: Adaptive Neuro-Fuzzy Inference Systems, Architecture Hybrid
Learning Algorithm, Learning Methods that Cross-fertilize ANFIS and RBFN Coactive Neuro Fuzzy
Modeling, Framework Neuron Functions for Adaptive Networks Neuro Fuzzy Spectrum.
UNIT V: Applications Of Computational Intelligence: Printed Character Recognition, Inverse
Kinematics Problems, Automobile Fuel Efficiency Prediction, Soft Computing for Coloripe Prediction.
Text Books:
1. "Neuro-Fuzzy and Soft Computing", J.S.R.Jang, C.T.Sun and E.Mizutani, PHI, 2004, Pearson
Education 2004
2. Artificial Intelligence by Saroj Koushik, Cengage Learning
3. "Artificial Intelligence and Intelligent Systems", N.P.Padhy, Oxford University Press, 2006
Reference Books:
1. Artificial Intelligence, Second Edition, Elaine Rich & Kevin Knight, Tata McGraw Hill Publishing
Comp., New Delhi, , 2006
2. "Fuzzy Logic with Engineering Applications", Timothy J.Ross, McGraw-
Hill, 1997
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I Year - II Semester
3
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3
Cloud Computing
Course Objectives:
? To implement Virtualization
? To implement Task Scheduling algorithms.
? Apply Map-Reduce concept to applications.
? To build Private Cloud.
? Broadly educate to know the impact of engineering on legal and societal issues involved.
Course Outcomes: At the end of the course, student will be able to
? Interpret the key dimensions of the challenge of Cloud Computing.
? Examine the economics, financial, and technological implications for selecting cloud computing for
own organization.
? Assessing the financial, technological, and organizational capacity of employer's for actively
initiating and installing cloud-based applications.
? Evaluate own organizations' needs for capacity building and training in cloud computing-related IT
areas.
? To Illustrate Virtualization for Data-Center Automation.
UNIT I: Introduction: Network centric computing, Network centric content, peer-to ?peer systems,
cloud computing delivery models and services, Ethical issues, Vulnerabilities, Major challenges for cloud
computing. Parallel and Distributed Systems: Introduction, architecture, distributed systems,
communication protocols, logical clocks, message delivery rules, concurrency, model concurrency with
Petri Nets.
UNIT II: Cloud Infrastructure: At Amazon, The Google Perspective, Microsoft Windows Azure, Open
Source Software Platforms, Cloud storage diversity, Inter cloud, energy use and ecological impact,
responsibility sharing, user experience, Software licensing, Cloud Computing: Applications and
Paradigms: Challenges for cloud, existing cloud applications and new opportunities, architectural styles,
workflows, The Zookeeper, The Map Reduce Program model, HPC on cloud, biological research.
UNIT III: Cloud Resource virtualization: Virtualization, layering and virtualization, virtual machine
monitors, virtual machines, virtualization- full and para, performance and security isolation, hardware
support for virtualization, Case Study: Xen, vBlades, Cloud Resource Management and Scheduling:
Policies and Mechanisms, Applications of control theory to task scheduling, Stability of a two-level
resource allocation architecture, feedback control based on dynamic thresholds, coordination, resource
bundling, scheduling algorithms, fair queuing, start time fair queuing, cloud scheduling subject to
deadlines, Scheduling Map Reduce applications, Resource management and dynamic application scaling.
UNIT IV: Storage Systems: Evolution of storage technology, storage models, file systems and database,
distributed file systems, general parallel file systems. Google file system. Apache Hadoop, Big Table,
Megastore (text book 1), Amazon Simple Storage Service(S3) (Text book 2), Cloud Security: Cloud
security risks, security ? a top concern for cloud users, privacy and privacy impact assessment, trust, OS
security, Virtual machine security, Security risks.
UNIT V: Cloud Application Development: Amazon Web Services : EC2 ? instances, connecting
clients, security rules, launching, usage of S3 in Java, Installing Simple Notification Service on Ubuntu
10.04, Installing Hadoop on Eclipse, Cloud based simulation of a Distributed trust algorithm, Cloud
service for adaptive data streaming ( Text Book 1), Google: Google App Engine, Google Web Toolkit
(Text Book 2), Microsoft: Azure Services Platform, Windows live, Exchange Online, Share Point
Services, Microsoft Dynamics CRM (Text Book 2).
Text Books:
1. Cloud Computing, Theory and Practice, Dan C Marinescu, MK Elsevier
2. Cloud Computing, A Practical Approach, Anthony T Velte, Toby J Velte, Robert Elsenpeter, TMH
Reference book:
1. Mastering Cloud Computing, Foundations and Application Programming, Raj
Kumar Buyya, Christen vecctiola, S Tammarai selvi, TMH
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I Year - II Semester
3
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3
Principles of Computer Security
Course Objectives:
In the course the student will learn
? This course provides an overview of modern cryptographic theories and techniques, mainly focusing
on their application into real systems.
? Topics include Database and Cloud Security, Malicious Software, Denial-of-Service Attacks,
Software Security, Operating System Security, Wireless Network Security and mobile device
security.
Course Outcomes:
After the completion of the course, student will be able to
? Describe the key security requirements of confidentiality, integrity, and availability, types of security
threats and attacks and summarize the functional requirements for computer security.
? Explain the basic operation of symmetric block encryption algorithms, use of secure hash functions
for message authentication, digital signature mechanism.
? Discuss the issues involved and the approaches for user authentication and explain how access
control fits into the broader context that includes authentication, authorization, and audit.
? Explain the basic concept of a denial-of-service attack, nature of flooding attacks, distributed denial-
of-service attacks and describe how computer security vulnerabilities are a result of poor
programming practices.
? List the steps used to secure the base operating system, specific aspects of securing Unix/Linux
systems, Windows systems, and security in virtualized systems and describe the security threats and
countermeasures for wireless networks.
Unit I: Introduction: Computer Security Concepts, Threats, Attacks, and Assets, Security Functional
Requirements, Fundamental Security Design Principles, Attack Surfaces and Attack Trees, Computer
Security Strategy. Cryptographic Tools: Confidentiality with Symmetric Encryption, Message
Authentication and Hash Functions, Public-Key Encryption, Digital Signatures and Key Management,
Random and Pseudorandom Numbers.
Unit II: User Authentication: Electronic User Authentication Principles, Password-Based
Authentication, Token-Based Authentication, Biometric Authentication, Remote User Authentication,
Security Issues for User Authentication. Access Control: Access Control Principles, Subjects, Objects,
and Access Rights, Discretionary Access Control, UNIX File Access Control, Role-Based Access
Control, Attribute-Based Access Control, Identity, Credential, and Access Management, Trust
Frameworks.
Unit III: Database and Cloud Security: The Need For Database Security, Database Management
Systems, Relational Databases, Sql Injection Attacks, Database Access Control, Database Encryption,
Cloud Computing, Cloud Security Risks And Countermeasures, Data Protection In The Cloud, Cloud
Security As A Service. Malicious Software: Types of Malicious Software (Malware), Advanced
Persistent Threat, Propagation, Infected Content, Viruses, Propagation, Vulnerability Exploit, Worms,
Propagation, Social Engineering, Spam E-Mail, Trojans, Payload, System Corruption, Payload, Attack
Agent, Zombie, Bots, Payload, Information Theft, Key loggers, Phishing, Spyware, Payload, Stealthing,
Backdoors, Root kits, Countermeasures.
Unit IV: Denial-of-Service Attacks: Denial-of-Service Attacks, Flooding Attacks, Distributed Denial-
of-Service Attacks, Application-Based Bandwidth Attacks, Reflector and Amplifier Attacks, Defenses
Against Denial-of-Service Attacks, Responding to a Denial-of-Service Attack. Software Security:
Software Security Issues, Handling Program Input, Writing Safe Program Code, Interacting with the
Operating System and Other Programs.
Unit V: Operating System Security: Introduction To Operating System Security, System Security
Planning, Operating Systems Hardening, Application Security, Security Maintenance, Linux/Unix
Security, Windows Security, Virtualization Security. Wireless Network Security: Wireless Security,
Mobile Device Security, IEEE 802.11Wireless LAN Overview, IEEE 802.11i Wireless LAN Security.
Text Book:
1. Computer Security: Principles and Practices, 3e, William Stallings, Lawrie Brown, Pearson
Reference book:
1. Network Security Essentials, Principles and Practices, William Stallings, Pearson
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3
0
0
3
High Performance Computing
Course Objectives:
The objective of the subject is to
? Introduce the basic concepts related to HPC architecture and parallel computing.
? To discuss various computational techniques for studying soft matter systems.
? To apply these concepts to examine complex bimolecular/materials systems that generally require
large-scale HPC platform with hybrid CPU-GPU architectures.
Course Outcomes:
After the completion of the course, student will be able to
? Design, formulate, solve and implement high performance versions of standard single threaded
algorithms.
? Demonstrate the architectural features in the GPU and MIC hardware accelerators.
? Design programs to extract maximum performance in a multicore, shared memory execution
environment processor.
? Analyze Symmetric and Distributed architectures.
? Develop and deploy large scale parallel programs on tightly coupled parallel systems using the
message passing paradigm.
UNIT I: Graphics Processing Units: Introduction to Heterogeneous Parallel Computing, GPU
architecture, Thread hierarchy, GPU Memory Hierarchy.
UNIT II: GPU Programming: Vector Addition, Matrix Multiplication algorithms. 1D, 2D, and 3D
Stencil Operations, Image Processing algorithms ? Image Blur, Gray scaling. Histogramming,
Convolution, Scan, Reduction techniques.
UNIT III: Many Integrated Cores: Introduction to Many Integrated Cores. MIC, Xeon Phi architecture,
Thread hierarchy, Memory Hierarchy, Memory Bandwidth and performance considerations.
UNIT IV: Shared Memory Parallel Programming: Symmetric and Distributed architectures, OpenMP
Introduction, Thread creation, Parallel regions. Work sharing, Synchronization.
UNIT V: Message Passing Interface: MPI Introduction, Collective communication, Data grouping for
communication.
Text Books:
1. Programming Massively Parallel Processors A Hands-on Approach, 3e, Wen-Mei W Hwu, David B
Kirk and Morgan Kaufmann-2019
2. Intel Xeon Phi Coprocessor Architecture and Tools, Rezaur Rahman, Apress Open, 1st edition-2013
3. Using OpenMP, Barbara Chapman, Gabriele Jost, Rudd Vander Pas, MIT Press, 2008
Reference books:
1. "A Parallel Algorithm Synthesis Procedure for High-Performance Computer Architectures" by Dunn
Ian N, 2003
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0
4
2
Machine Learning with Python Lab
Course Objectives:
This course will enable students to
? To learn and understand different Data sets in implementing the machine learning algorithms.
? Implement the machine learning concepts and algorithms in any suitable
language of choice.
Course Outcomes(COs): At the end of the course, student will be able to
? Implement procedures for the machine learning algorithms
? Design Python programs for various Learning algorithms
? Apply appropriate data sets to the Machine Learning algorithms
? Identify and apply Machine Learning algorithms to solve real world problems
Experiment-1:
Exercises to solve the real-world problems using the following machine learning methods:
a) Linear Regression
b) Logistic Regression.
Experiment-2:
Write a program to Implement Support Vector Machines.
Experiment-3:
Exploratory Data Analysis for Classification using Pandas and Matplotlib.
Experiment-4:
Implement a program for Bias, Variance, and Cross Validation.
Experiment-5:
Write a program to simulate a perception network for pattern classification and function approximation.
Experiment-6:
Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate
data set for building the decision tree and apply this knowledge to classify a new sample.
Experiment-7:
Build an Artificial Neural Network by implementing the Back propagation algorithm and test the same
using appropriate data sets.
Experiment-8:
Write a program to implement the na?ve Bayesian classifier for Iris data set. Compute the accuracy of the
classifier, considering few test data sets.
Experiment-9:
Assuming a set of documents that need to be classified, use the na?ve Bayesian Classifier model to
perform this task. Built-in Java classes/API can be used to write the program. Calculate the accuracy,
precision, and recall for your data set.
Experiment-10:
Apply EM algorithm to cluster a Heart Disease Data Set. Use the same data set for clustering using k-
Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.
You can add Java/Python ML library classes/API in the program.
Experiment-11:
Write a program to implement k-Nearest Neighbor algorithm to classify the iris data set. Print both
correct and wrong predictions.
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0
0
4
2
MEAN Stack Technologies Lab
Course Objectives:
From the course the student will
? Learn the core concepts of both the frontend and backend programming course.
? Get familiar with the latest web development technologies.
? Learn all about SQL and Mongo databases.
? Learn complete web development process.
Course Outcomes: At the end of the course, student will be able to
? Identify the Basic Concepts of Web & Markup Languages.
? Develop web Applications using Scripting Languages & Frameworks.
? Creating & Running Applications using JSP libraries.
? Creating Our First Controller Working with and Displaying in Angular Js and Nested Forms with ng-
form.
? Working with the Files in React JS and Constructing Elements with Data.
Experiment-1:
Develop static pages (using only HTML) of an online Book store. The pages should resemble:
www.amazon.com. The website should consist of the following pages. Home page
? Registration and user Login
? User profile page
? Books catalog
? Shopping cart
? Payment by credit card Order Conformation
Experiment-2:
Write an HTML page including any required JavaScript that takes a number from text field in the range of
0 to 999 and shows it in words. It should not accept four and above digits, alphabets and special
characters.
Experiment-3:
Develop and demonstrate JavaScript with POP-UP boxes and functions for the following problems:
a) Input: Click on Display Date button using on click ( ) function Output: Display date in the textbox
b) Input: A number n obtained using prompt Output: Factorial of n number using alert
c) Input: A number n obtained using prompt Output: A multiplication table of numbers from 1 to 10 of n
using alert
d) Input: A number n obtained using prompt and add another number using confirm Output: Sum of the
entire n numbers using alert
Experiment-4:
Create a simple visual bean with a area filled with a color. The shape of the area depends on the property
shape. If it is set to true then the shape of the area is Square and it is Circle, if it is false. The color of the
area should be changed dynamically for every mouse click.
Experiment-5:
Create an XML document that contains 10 users information. Write a Java Program, which takes User Id
as input and returns the user details by taking the user information from XML document using DOM
parser or SAX parser.
Experiment-6:
Develop and demonstrate PHP Script for the following problems:
a) Write a PHP Script to find out the Sum of the Individual Digits.
b) Write a PHP Script to check whether the given number is Palindrome or not
Experiment-7:
Implement the following in CSS
a) Implementation of `get' and `post' methods.
b) Implementation in colors, boarder padding.
c) Implementation button frames tables, navigation bars.
Experiment-8:
Implement the web applications with Database using
a) PHP,
b) Servlets and
c) JSP.
Experiment-9:
Write a program to design a simple calculator using
a) JavaScript
b) PHP
c) Servlet and
d) JSP.
Experiment-10:
Create registration and login forms with validations using Jscript query.
Experiment-11:
Jscript to retrieve student information from student database using database connectivity.
Experiment-12:
Implement the following in React JS
a) Using React Js creating constructs data elements.
b) Using React Js implementations DoM.
Experiment-13:
Implement the following in Angular JS
a) Angular Js data binding.
b) Angular JS directives and Events.
c) Using angular Js fetching data from MySQL.
Experiment-14:
Develop and demonstrate Invoking data using Jscript from Mongo DB.
Experiment-15:
Create an Online fee payment form using JSCript and MangoDB.
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I Year - II Semester
2
0
0
2
Mini project with seminar
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II Year - I Semester
3
0
0
3
Deep Learning
Course Objectives:
At the end of the course, the students will be expected to:
? Learn deep learning methods for working with sequential data,
? Learn deep recurrent and memory networks,
? Learn deep Turing machines,
? Apply such deep learning mechanisms to various learning problems.
? Know the open issues in deep learning, and have a grasp of the current research directions.
Course Outcomes:
After the completion of the course, student will be able to
? Demonstrate the basic concepts fundamental learning techniques and layers.
? Discuss the Neural Network training, various random models.
? Explain different types of deep learning network models.
? Classify the Probabilistic Neural Networks.
? Implement tools on Deep Learning techniques.
UNIT I: Introduction: Various paradigms of learning problems, Perspectives and Issues in deep learning
framework, review of fundamental learning techniques. Feed forward neural network: Artificial Neural
Network, activation function, multi-layer neural network.
UNIT II: Training Neural Network: Risk minimization, loss function, back propagation,
regularization, model selection, and optimization.
Conditional Random Fields: Linear chain, partition function, Markov network, Belief propagation,
Training CRFs, Hidden Markov Model, Entropy.
UNIT III: Deep Learning: Deep Feed Forward network, regularizations, training deep models, dropouts,
Convolution Neural Network, Recurrent Neural Network, and Deep Belief Network.
UNIT IV: Probabilistic Neural Network: Hopfield Net, Boltzmann machine, RBMs, Sigmoid net,
Auto encoders.
UNIT V: Applications: Object recognition, sparse coding, computer vision, natural language
processing. Introduction to Deep Learning Tools: Caffe, Theano, Torch.
Text Books:
1. Goodfellow, I., Bengio,Y., and Courville, A., Deep Learning, MIT Press, 2016..
2. Bishop, C. ,M., Pattern Recognition and Machine Learning, Springer, 2006.
Reference Books:
1. Artificial Neural Networks, Yegnanarayana, B., PHI Learning Pvt. Ltd, 2009.
2. Matrix Computations, Golub, G.,H., and Van Loan,C.,F, JHU Press,2013.
3. Neural Networks: A Classroom Approach, Satish Kumar, Tata McGraw-Hill Education, 2004.
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II Year - I Semester
3
0
0
3
Social Network Analysis
Course Objectives:
? The learning objective of the course Social Network Analysis is to provide students with essential
knowledge of network analysis applicable to real world data, with examples from today's most
popular social networks.
Course Outcomes:
? After the completion of the course, student will be able to
? Demonstrate social network analysis and measures.
? Analyze random graph models and navigate social networks data
? Apply the network topology and Visualization tools.
? Analyze the experiment with small world models and clustering models.
? Compare the application driven virtual communities from social network Structure.
UNIT I: Social Network Analysis: Preliminaries and definitions, Erdos Number Project, Centrality
measures, Balance and Homophily.
UNIT II: Random graph models: Random graphs and alternative models, Models of network growth,
Navigation in social Networks, Cohesive subgroups, Multidimensional Scaling, Structural equivalence,
roles and positions.
UNIT III: Network topology and diffusion, Contagion in Networks, Complex contagion, Percolation
and information, Navigation in Networks Revisited.
UNIT IV: Small world experiments, small world models, origins of small world, Heavy tails, Small
Diameter, Clustering of connectivity, The ErdosRenyi Model, Clustering Models.
UNIT V: Network structure -Important vertices and page rank algorithm, towards rational dynamics in
networks, basics of game theory, Coloring and consensus, biased voting, network formation games,
network structure and equilibrium, behavioral experiments, Spatial and agent-based models.
Text Books:
1. S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications (Cambridge,
Cambridge University Press, 1994)
2. D. Easley and J. Kleinberg, Networks, Crowds and Markets: Reasoning about a highly connected
world-2010
Reference Books:
1. Social Network Analysis: Methods and Applications (Structural Analysis in the Social
Sciences) by Stanley Wasserman, Katherine Faust, 1994.
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IV Semester
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0
32
16
(DISSERTATION) DISSERTATION PHASE ? I AND PHASE ? II
Syllabus Contents:
The dissertation / project topic should be selected / chosen to ensure the satisfaction of the urgent need to
establish a direct link between education, national development and productivity and thus reduce the gap
between the world of work and the world of study. The dissertation should have the following
? Relevance to social needs of society
? Relevance to value addition to existing facilities in the institute
? Relevance to industry need
? Problems of national importance
? Research and development in various domain
The student should complete the following:
? Literature survey Problem Definition
? Motivation for study and Objectives
? Preliminary design / feasibility / modular approaches
? Implementation and Verification
? Report and presentation
The dissertation stage II is based on a report prepared by the students on dissertation allotted to them. It
may be based on:
? Experimental verification / Proof of concept.
? Design, fabrication, testing of Communication System.
? The viva-voce examination will be based on the above report and work.
Guidelines for Dissertation Phase ? I and II at M. Tech. (Electronics):
? As per the AICTE directives, the dissertation is a yearlong activity, to be carried out and
evaluated in two phases i.e. Phase ? I: July to December and Phase ? II: January to June.
? The dissertation may be carried out preferably in-house i.e. department's laboratories and
centers OR in industry allotted through department's T & P coordinator.
? After multiple interactions with guide and based on comprehensive literature survey, the student
shall identify the domain and define dissertation objectives. The referred literature should
preferably include IEEE/IET/IETE/Springer/Science Direct/ACM journals in the areas of
Computing and Processing (Hardware and Software), Circuits-Devices and Systems,
Communication-Networking and Security, Robotics and Control Systems, Signal Processing
and Analysis and any other related domain. In case of Industry sponsored projects, the relevant
application notes, while papers, product catalogues should be referred and reported.
? Student is expected to detail out specifications, methodology, resources required, critical issues
involved in design and implementation and phase wise work distribution, and submit the
proposal within a month from the date of registration.
? Phase ? I deliverables: A document report comprising of summary of literature survey, detailed
objectives, project specifications, paper and/or computer aided design, proof of
concept/functionality, part results, A record of continuous progress.
? Phase ? I evaluation: A committee comprising of guides of respective specialization shall assess
the progress/performance of the student based on report, presentation and Q &A. In case of
unsatisfactory performance, committee may recommend repeating the Phase-I work.
? During phase ? II, student is expected to exert on design, development and testing of the
proposed work as per the schedule. Accomplished results/contributions/innovations should be
published in terms of research papers in reputed journals and reviewed focused conferences OR
IP/Patents.
? Phase ? II deliverables: A dissertation report as per the specified format, developed system in
the form of hardware and/or software, a record of continuous progress.
? Phase ? II evaluation: Guide along with appointed external examiner shall assess the
progress/performance of the student based on report, presentation and Q &A. In case of
unsatisfactory performance, committee may recommend for extension or repeating the work
Course Outcomes:
At the end of this course, students will be able to
1. Ability to synthesize knowledge and skills previously gained and applied to an in-depth study
and execution of new technical problem.
2. Capable to select from different methodologies, methods and forms of analysis to produce a
suitable research design, and justify their design.
3. Ability to present the findings of their technical solution in a written report.
4. Presenting the work in International/ National conference or reputed journals.
AUDIT 1 and 2: ENGLISH FOR RESEARCH PAPER WRITING
Course objectives:
Students will be able to:
1. Understand that how to improve your writing skills and level of readability
2. Learn about what to write in each section
3. Understand the skills needed when writing a Title Ensure the good quality of paper at very first-
time submission
Syllabus
Units
CONTENTS
Hou
rs
1
Planning and Preparation, Word Order, Breaking up long sentences, 4
Structuring Paragraphs and Sentences, Being Concise
and Removing Redundancy, Avoiding Ambiguity and Vagueness
2
Clarifying Who Did What, Highlighting Your Findings, Hedging and 4
Criticising, Paraphrasing and Plagiarism, Sections of a Paper,
Abstracts. Introduction
3
Review of the Literature, Methods, Results, Discussion,
4
Conclusions, The Final Check.
4
key skills are needed when writing a Title, key skills are needed when 4
writing an Abstract, key skills are needed when writing an
Introduction, skills needed when writing a Review of the Literature,
5
skills are needed when writing the Methods, skills needed when writing the 4
Results, skills are needed when writing the Discussion,
skills are needed when writing the Conclusions
6
useful phrases, how to ensure paper is as good as it could possibly
4
be the first- time submission
Suggested Studies:
1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books)
2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press
3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.
Highman'sbook .
4. Adrian Wallwork , English for Writing Research Papers, Springer New York Dordrecht
Heidelberg London, 2011
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
AUDIT 1 and 2: DISASTER MANAGEMENT
Course Objectives: -Students will be able to:
1. learn to demonstrate a critical understanding of key concepts in disaster risk reduction and
humanitarian response.
2. critically evaluate disaster risk reduction and humanitarian response policy and practice from
multiple perspectives.
3. develop an understanding of standards of humanitarian response and practical relevance in specific
types of disasters and conflict situations.
4. critically understand the strengths and weaknesses of disaster management approaches, planning
and programming in different countries, particularly their home country or the countries
they work in
Syllabus
Units CONTENTS
Hours
1
Introduction
4
Disaster: Definition, Factors And Significance; Difference Between Hazard And
Disaster; Natural And Manmade Disasters: Difference,
Nature, Types And Magnitude.
2
Repercussions Of Disasters And Hazards: Economic Damage, Loss Of
4
Human And Animal Life, Destruction Of Ecosystem.
Natural Disasters: Earthquakes, Volcanisms, Cyclones, Tsunamis, Floods,
Droughts And Famines, Landslides And Avalanches, Man- made disaster:
Nuclear Reactor Meltdown, Industrial Accidents, Oil Slicks And Spills,
Outbreaks Of Disease And Epidemics, War And
Conflicts.
3
Disaster Prone Areas In India
4
Study Of Seismic Zones; Areas Prone To Floods And Droughts, Landslides And
Avalanches; Areas Prone To Cyclonic And Coastal Hazards With Special
Reference To Tsunami; Post-Disaster Diseases
And Epidemics
4
Disaster Preparedness And Management
4
Preparedness: Monitoring Of Phenomena Triggering A Disaster Or Hazard;
Evaluation Of Risk: Application Of Remote Sensing, Data From Meteorological
And Other Agencies, Media Reports:
Governmental And Community Preparedness.
5
Risk Assessment
4
Disaster Risk: Concept And Elements, Disaster Risk Reduction, Global And
National Disaster Risk Situation. Techniques Of Risk Assessment, Global Co-
Operation In Risk Assessment And Warning, People's
Participation In Risk Assessment. Strategies for Survival.
6
Disaster Mitigation
4
Meaning, Concept And Strategies Of Disaster Mitigation, Emerging
Trends In Mitigation. Structural Mitigation And Non-Structural Mitigation,
Programs Of Disaster Mitigation In India.
Suggested Readings:
1. R. Nishith, Singh AK, "Disaster Management in India: Perspectives, issues and strategies "'New Royal
book Company.
2. Sahni, PardeepEt.Al. (Eds.)," Disaster Mitigation Experiences And Reflections", Prentice Hall Of
India, New Delhi.
3. Goel S. L. , Disaster Administration And Management Text And Case Studies" ,Deep &Deep
Publication Pvt. Ltd., New Delhi.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
AUDIT 1 and 2: SANSKRIT FOR TECHNICAL KNOWLEDGE
Course Objectives
1. To get a working knowledge in illustrious Sanskrit, the scientific language in the world
2. Learning of Sanskrit to improve brain functioning
3. Learning of Sanskrit to develop the logic in mathematics, science & other subjects enhancing
the memory power
4. The engineering scholars equipped with Sanskrit will be able to explore the huge knowledge
from ancient literature
Syllabus
Unit Content
Hours
1
Alphabets in Sanskrit,
4
Past/Present/Future Tense,
Simple Sentences
2
Order
4
Introduction of roots
Technical information about Sanskrit Literature
3
Technical concepts of Engineering-Electrical,
4
4
Technical concepts of Engineering - Mechanical.
4
5
Technical concepts of Engineering - Architecture.
4
6
Technical concepts of Engineering ? Mathematics.
4
Suggested reading
1. "Abhyaspustakam" ? Dr.Vishwas, Samskrita-Bharti Publication, New Delhi
2. "Teach Yourself Sanskrit" Prathama Deeksha-Vempati Kutumbshastri, Rashtriya Sanskrit Sansthanam,
New Delhi Publication
3. "India's Glorious Scientific Tradition" Suresh Soni, Ocean books (P) Ltd., New Delhi.
Course Output
Students will be able to
1. Understanding basic Sanskrit language
2. Ancient Sanskrit literature about science & technology can be understood
3. Being a logical language will help to develop logic in students
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
AUDIT 1 and 2: VALUE EDUCATION
Course Objectives
Students will be able to
1. Understand value of education and self- development
2. Imbibe good values in students
3. Let the should know about the importance of character
Syllabus
Unit Content
Hours
1
Values and self-development ?Social values and individual attitudes. Work ethics, 4
Indian vision of humanism.
Moral and non- moral valuation. Standards and principles.
Value judgements
2
Importance of cultivation of values.
4
Sense of duty. Devotion, Self-reliance. Confidence, Concentration. Truthfulness,
Cleanliness.
Honesty, Humanity. Power of faith, National Unity.
Patriotism.Love for nature ,Discipline
3
Personality and Behavior Development - Soul and Scientific attitude. Positive 4
Thinking. Integrity and discipline.
Punctuality, Love and Kindness.
Avoid fault Thinking.
4
Free from anger, Dignity of labour.
4
Universal brotherhood and religious tolerance.
True friendship.
Happiness Vs suffering, love for truth.
Aware of self-destructive habits.
Association and Cooperation.
Doing best for saving nature
5
Character and Competence ?Holy books vs Blind faith.
4
Self-management and Good health.
Science of reincarnation.
Equality, Nonviolence ,Humility, Role of Women.
6
All religions and same message.
4
Mind your Mind, Self-control.
Honesty, Studying effectively
Suggested reading
1 Chakroborty, S.K. "Values and Ethics for organizations Theory and practice", Oxford University Press,
New Delhi
Course outcomes
Students will be able to 1.Knowledge of self-development
2.Learn the importance of Human values 3.Developing the overall personality
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
AUDIT 1 and 2: CONSTITUTION OF INDIA
Course Objectives:
Students will be able to:
1. Understand the premises informing the twin themes of liberty and freedom from a civil rights
perspective.
2. To address the growth of Indian opinion regarding modern Indian intellectuals' constitutional
role and entitlement to civil and economic rights as well as the emergence of nationhood in the
early years of Indian nationalism.
3. To address the role of socialism in India after the commencement of the Bolshevik Revolution
in 1917 and its impact on the initial drafting of the Indian Constitution.
Syllabus
Units
Content
Hours
History of Making of the Indian Constitution:
1
History
4
Drafting Committee, ( Composition & Working)
Philosophy of the Indian Constitution:
2
Preamble Salient Features
4
Contours of Constitutional Rights & Duties:
Fundamental Rights
Right to Equality
Right to Freedom
3
Right against Exploitation
4
Right to Freedom of Religion
Cultural and Educational Rights
Right to Constitutional Remedies
Directive Principles of State Policy
Fundamental Duties.
Organs of Governance:
Parliament
Composition
Qualifications and Disqualifications
Powers and Functions
4
Executive
4
President
Governor
Council of Ministers
Judiciary, Appointment and Transfer of Judges, Qualifications
Powers and Functions
Local Administration:
District's Administration head: Role and Importance,
5
Municipalities: Introduction, Mayor and role of Elected Representative, CE of
Municipal Corporation.
4
Pachayati raj: Introduction, PRI: ZilaPachayat.
Elected officials and their roles, CEO ZilaPachayat: Position and role.
Block level: Organizational Hierarchy (Different departments),
Village level: Role of Elected and Appointed officials,
Importance of grass root democracy
Election Commission:
Election Commission: Role and Functioning.
6
Chief Election Commissioner and Election Commissioners.
4
State Election Commission: Role and Functioning.
Institute and Bodies for the welfare of SC/ST/OBC and women.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Suggested reading
1. The Constitution of India, 1950 (Bare Act), Government Publication.
2. Dr. S. N. Busi, Dr. B. R. Ambedkar framing of Indian Constitution, 1st Edition, 2015.
3. M. P. Jain, Indian Constitution Law, 7th Edn., Lexis Nexis, 2014.
4. D.D. Basu, Introduction to the Constitution of India, Lexis Nexis, 2015.
Course Outcomes:
Students will be able to:
1. Discuss the growth of the demand for civil rights in India for the bulk of Indians before the arrival
of Gandhi in Indian politics.
2. Discuss the
intellectual
origins of
the
framework
of
argument
that
informed
the conceptualization of social reforms leading to revolution in India.
3. Discuss the circumstances surrounding the foundation of the Congress Socialist Party [CSP] under
the leadership of Jawaharlal Nehru and the eventual failure of the proposal of direct elections
through adult suffrage in the Indian Constitution.
4. Discuss the passage of the Hindu Code Bill of 1956.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
AUDIT 1 and 2: PEDAGOGY STUDIES
Course Objectives:
Students will be able to:
4. Review existing evidence on the review topic to inform programme design and policy
making undertaken by the DfID, other agencies and researchers.
5. Identify critical evidence gaps to guide the development.
Syllabus
Units Content
Hours
Introduction and Methodology:
Aims and rationale, Policy background, Conceptual framework and terminology
1
Theories of learning, Curriculum, Teacher education.
4
Conceptual framework, Research questions.
Overview of methodology and Searching.
Thematic overview: Pedagogical practices are being used by teachers in formal
2
and informal classrooms in developing countries.
4
Curriculum, Teacher education.
3
Evidence on the effectiveness of pedagogical practices
Methodology for the in depth stage: quality assessment of included studies.
How can teacher education (curriculum and practicum) and the school curriculum
and guidance materials best support effective pedagogy?
4
Theory of change.
Strength and nature of the body of evidence for effective pedagogical practices.
4
Pedagogic theory and pedagogical approaches.
4
Teachers' attitudes and beliefs and Pedagogic strategies.
Professional development: alignment with classroom practices and follow-up
support
Peer support
4
5
Support from the head teacher and the community.
Curriculum and assessment
Barriers to learning: limited resources and large class sizes
6
Research gaps and future directions
4
Research design
Contexts
Pedagogy
Teacher education
Curriculum and assessment
Dissemination and research impact.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Suggested reading
1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools, Compare, 31 (2):
245-261.
2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of
Curriculum Studies, 36 (3): 361-379.
3. Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher education
research project (MUSTER) country report 1. London: DFID.
4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of basic
maths and reading in Africa: Does teacher preparation count? International Journal Educational
Development, 33 (3): 272?282.
5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education.
Oxford and Boston: Blackwell.
6. Chavan M (2003) Read India: A mass scale, rapid, `learning to read' campaign.
7. www.pratham.org/images/resource%20working%20paper%202.pdf.
Course Outcomes:
Students will be able to understand:
1. What pedagogical practices are being used by teachers in formal and informal classrooms in
developing countries?
2. What is the evidence on the effectiveness of these pedagogical practices, in what conditions, and
with what population of learners?
3. How can teacher education (curriculum and practicum) and the school curriculum and guidance
materials best support effective pedagogy?
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
AUDIT 1 and 2: STRESS MANAGEMENT BY YOGA
Course Objectives
1. To achieve overall health of body and mind
2. To overcome stress
Syllabus
Unit Content
Hours
1 Definitions of Eight parts of yog. ( Ashtanga )
5
2 Yam and Niyam. Do`s and Don't's in life.
5
Ahinsa, satya, astheya, bramhacharya and aparigraha
3 Yam and Niyam. Do`s and Don't's in life.
5
Shaucha, santosh, tapa, swadhyay, ishwarpranidhan
4 Asan and Pranayam
5
Various yog poses and their benefits for mind & body
5 Regularization of breathing techniques and its effects-Types of pranayam
4
Suggested reading
1. `Yogic Asanas for Group Tarining-Part-I" : Janardan Swami YogabhyasiMandal, Nagpur
2. "Rajayoga or conquering the Internal Nature" by Swami Vivekananda, Advaita Ashrama
(Publication Department), Kolkata
Course Outcomes:
Students will be able to:
1. Develop healthy mind in a healthy body thus improving social health also
2. Improve efficiency
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
AUDIT 1 and 2: PERSONALITY DEVELOPMENT THROUGH LIFE ENLIGHTENMENT
SKILLS
Course Objectives
1. To learn to achieve the highest goal happily
2. To become a person with stable mind, pleasing personality and determination
3. To awaken wisdom in students
Syllabus
Unit
Content
Hours
1
Neetisatakam-Holistic development of personality
4
Verses- 19,20,21,22 (wisdom)
Verses- 29,31,32 (pride & heroism)
Verses- 26,28,63,65 (virtue)
2
Neetisatakam-Holistic development of personality
4
Verses- 52,53,59 (dont's)
Verses- 71,73,75,78 (do's)
3
Approach to day to day work and duties.
4
Shrimad Bhagwad Geeta : Chapter 2-Verses 41, 47,48,
4
Chapter 3-Verses 13, 21, 27, 35, Chapter 6-Verses 5,13,17, 23, 35,
4
Chapter 18-Verses 45, 46, 48.
5
Statements of basic knowledge.
4
Shrimad Bhagwad Geeta: Chapter2-Verses 56, 62, 68
Chapter 12 -Verses 13, 14, 15, 16,17, 18
6
Personality of Role model. Shrimad Bhagwad Geeta: Chapter2-Verses 4
17, Chapter 3-Verses 36,37,42,
Chapter 4-Verses 18, 38,39
Chapter18 ? Verses 37,38,63
Suggested reading
1. "Srimad Bhagavad Gita" by Swami Swarupananda Advaita Ashram (Publication Department), Kolkata
2. Bhartrihari's Three Satakam (Niti-sringar-vairagya) by P.Gopinath, Rashtriya Sanskrit Sansthanam,
New Delhi.
Course Outcomes
Students will be able to
1. Study of Shrimad-Bhagwad-Geeta will help the student in developing his personality and achieve the
highest goal in life
2. The person who has studied Geeta will lead the nation and mankind to peace and prosperity
3. Study of Neetishatakam will help in developing versatile personality of students
This post was last modified on 16 March 2021