Download JNTU Kakinada (Jawaharlal Nehru Technological University, Kakinada) M.Tech (Master of Technology) R19 CSE M.Tech Information Technology Course Structure And Syllabus
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
COURSE STRUCTURE M.Tech CSE for
INFORMATION TECHNOLOGY PROGRAMME
(Applicable for batches admitted from 2019-2020)
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
I-SEMESTER
S.N
Course
Cate
Courses
L T P C
o
Code
gory
Program Core-1
1
MTIT1101
PC
3
0 0
3
Discrete Mathematical Structures
Program Core-2
2
MTIT1102
PC
3
0 0
3
Advanced Data Structures
Program Elective-1
1. Artificial Intelligence
2. Service Oriented Architectures and Web Security
3
MTIT1103
PE
3
0 0
3
3. Internet of Things
4. Optimization Techniques
5. Parallel Computer Architecture
Program Elective-2
1. Big Data Analytics
2. Principles of Cryptography
4
MTIT1104
PE
3
0 0
3
3. Cluster and Grid Computing
4. Imaging and Multimedia Systems
5. Advanced Graph Theory
5
MTIT1105 Research Methodology and IPR
CC
0
2
Laboratory-1
6
MTIT1106
LB
0
0 4
2
Advanced Data Structures Lab
Laboartory-2
7
MTIT1107
LB
0
0 4
2
Computing Lab
8
MTIT1108 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
Courses
L
T P
C
Code
Gory
Program Core-3
1
MTIT1201
PC
3
0
0
3
Advanced Algorithms
Program Core-4
2
MTIT1202
PC
3
0
0
3
Full Stack Technologies
Program Elective-3
1. Machine Learning
2. DevOps
3
MTIT1203
PE
3
0
0
3
3. Advanced Network Principles and Protocols
4. Distributed Computing
5. Social Network Analytics
Program Elective-4
1. Digital Image Processing
2. Block Chain Technologies
4
MTIT1204
PE
3
0
0
3
3. Data Science
4. Soft Computing
5. Natural Language Processing
Laboratory-3
5
MTIT1205
LB
0
0
4
2
Advance Algorithms Lab
Laboartory-4
6
MTIT1206
LB
0
0
4
2
Full Stack Technologies Lab
7
MTIT1207
Mini Project with Seminar
MP
2
0
0
2
8
MTIT1208
Audit Course-2 *
AC
2
0
0
0
Total Credits
18
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
*Student has to choose any one audit course listed below.
Audit Course 1 & 2:
1. English for Research Paper Writing
5. Constitution of India
2. Disaster Management
6. Pedagogy Studies
3. Sanskrit for Technical Knowledge
7. Stress Management by Yoga
4. Value Education
8. Personality Development through Life
Enlightenment Skills
III SEMESTER
Course
Cate
S.No
Courses
L
T P
C
Code
gory
Program Elective-5
PE
1. Deep Learning
2. Embedded Computing
3. Ethical Hacking
1
MTIT2101
4. Digital Marketing
3
0
0
3
5. MOOCs-1 through NPTEL/SWAYAM- 12 Week
Program related to the programme which is not listed
in the course structure
Open Elective
OE
1. MOOCs-2 through NPTEL/ SWAYAM - any 12 week
2
MTITS2102
course on Engineering/ Management/ Mathematics
3
0
0
3
offered by other than parent department
2.
Course offered by other departments in the college
3
MTIT2103
Dissertation-I/Industrial Project#
PJ 0
0 20 10
Total Credits
16
#Students going for Industrial Project/Thesis will complete these courses through MOOCs
M. Tech. (IT) IV SEMESTER
Course
Cate
S.No
Courses
L T P C
Code
gory
1
MTIT2201
Dissertation-II
PJ
0
0 32 16
Total Credits
16
Open Electives offered to other departments:
1. Python Programming
2. Web Technologies
3. Artificial Intelligence
4. Internet of Things
5. Machine Learning
6. Advanced Data Structures
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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I Year - I Semester
3
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0
3
Discrete Mathematical Structures (MTIT1101)
Course Objective:
? To understand the mathematical fundamentals that is prerequisites for a 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
information technology like machine learning, programming language design, and concurrency.
? To study various sampling and classification problems.
Course Outcomes:
After completion of course, students would be able to:
? To understand the basic notions of discrete and continuous probability.
? To understand the methods of statistical inference, and the role that sampling distributions play in those
methods.
? To be able to perform correct and meaningful statistical analyses of simple to moderate complexity.
Unit I
Probability mass, density, and cumulative distribution functions, parametric families of distributions, Expected
value, variance, conditional expectation, Applications of the univariate and multivariate Central Limit Theorem,
Probabilistic inequalities, Markov chains
Unit II
Random samples, sampling distributions of estimators, Methods of Moments and Maximum Likelihood
Unit III
Statistical inference, Introduction to multivariate statistical models: regression and
classification problems, principal components analysis, The problem of over fitting
Model assessment.
Unit IV
Graph Theory: Isomorphism, Planar graphs, graph coloring, Hamilton circuits and
Euler cycles. Permutations and Combinations with and without repetition. Specialized techniques to solve
combinatorial enumeration problems
Unit V
Recurrence Relation: Order and Degree of Recurrence Relation, Linear Homogeneous and Non-Homogeneous
Recurrence Relations, Solutions of Linear Recurrence Relation with Constant Coefficients, Homogeneous
Solutions, Particular Solutions, Generating functions, Counting (Combinatorial) Method, Trees: Introduction, Tree,
Labeled Tree, Some diagrams of Directed and Undirected Trees, Review of Basic Properties of a Tree, Sequential
Representation of a Binary Tree, Operations on Tree.
Text Books:
1. John Vince, Foundation Mathematics for Computer Science, Springer.
2. K. Trivedi.Probability and Statistics with Reliability, Queuing, and Computer Science
Applications. Wiley.
3. Discrete Mathematics -Swapna kumar chakraborty, Bikash kanti sarkar, Oxford
Higher Education
Reference Books:
1. M. Mitzenmacher and E. Upfal.Probability and Computing: Randomized Algorithms and Probabilistic Analysis.
2. Alan Tucker, Applied Combinatorics, Wiley
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I Year - I Semester
3
0
0
3
Advanced Data Structures
( MTIT1102)
Course Objectives:
? The student should be able to choose appropriate data structures, understand the ADT/libraries, and use it
to design algorithms for a specific problem.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
? Students should be able to understand the necessary mathematical abstraction to solve problems.
? To familiarize students with advanced paradigms and data structure used to solve algorithmic
problems.
? Student should be able to come up with analysis of efficiency and proofs of correctness.
Course Outcomes:
After completion of course, students would be able to:
? Understand the implementation of symbol table using hashing techniques.
? Develop and analyze algorithms for red-black trees, B-trees and Splay trees.
? Develop algorithms for text processing applications.
? Identify suitable data structures and develop algorithms for computational geometry problems.
Unit I:
Dictionaries: Definition, Dictionary Abstract Data Type, Implementation of Dictionaries. Hashing: Review of
Hashing, Hash Function, Collision Resolution Techniques in Hashing, Separate Chaining, Open Addressing, Linear
Probing, Quadratic Probing, Double Hashing, Rehashing, Extendible Hashing.
Unit II:
Skip Lists: Need for Randomizing Data Structures and Algorithms, Search and Update Operations on Skip Lists,
Probabilistic Analysis of Skip Lists, Deterministic Skip Lists
Unit III:
Trees: Binary Search Trees, AVL Trees, Red Black Trees, 2-3 Trees, B-Trees, Splay
Trees
Unit IV:
Text Processing: Sting Operations, Brute-Force Pattern Matching, The Boyer-Moore Algorithm, The Knuth-
Morris-Pratt Algorithm, Standard Tries, Compressed Tries, Suffix Tries, The Huffman Coding Algorithm, The
Longest Common Subsequence Problem (LCS), Applying Dynamic Programming to the LCS Problem.
Unit V:
Computational Geometry: One Dimensional Range Searching, Two Dimensional Range Searching, Constructing
a Priority Search Tree, Searching a Priority Search Tree, Priority Range Trees, Quad trees, k-D Trees. Recent
Trends in Hashing, Trees, and various computational geometry methods for efficiently solving the new evolving
problem
Text Books:
1. Mark Allen Weiss, Data Structures and Algorithm Analysis in C++, 2nd Edition, Pearson, 2004.
2. M T Goodrich, Roberto Tamassia, Algorithm Design, John Wiley, 2002.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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I Year - I Semester
3
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3
Artificial Intelligence ( MTIT11XX)
Course Objectives:
? Gain a historical perspective of AI and its foundations.
? Become familiar with basic principles of AI toward problem solving, inference, perception, knowledge
representation, and learning.
? Investigate applications of AI techniques in intelligent agents, expert systems, artificial neural networks and
other machine learning models.
? Experience AI development tools such as an `AI language', expert system shell, and/or data mining tool.
Experiment with a machine learning model for simulation and analysis.
? Explore the current scope, potential, limitations, and implications of intelligent systems.
Course Outcomes: At the end of the course, student will be able to
? Demonstrate knowledge of the building blocks of AI as presented in terms of intelligent agents
? Analyze and formalize the problem as a state space, graph, design heuristics and select amongst different
search or game based techniques to solve them.
? Develop intelligent algorithms for constraint satisfaction problems and also design intelligent systems for
Game Playing
? Attain the capability to represent various real life problem domains using logic based techniques and use
this to perform inference or planning.
? Solve problems with uncertain information using Bayesian approaches.
UNIT-I:
Introduction to artificial intelligence: Introduction , history, intelligent systems, foundations of AI, applications,
tic-tac-tie game playing, development of AI languages, current trends in AI, Problem solving: state-space search
and control strategies: Introduction, general problem solving, characteristics of problem, exhaustive searches,
heuristic search techniques, iterative-deepening a*, constraint satisfaction
UNIT-II:
Problem reduction and game playing: Introduction, problem reduction, game playing, alpha-beta pruning, two-
player perfect information games, Logic concepts: Introduction, propositional calculus, proportional logic, natural
deduction system, axiomatic system, semantic tableau system in proportional logic, resolution refutation in
proportional logic, predicate logic
UNIT-III:
Knowledge representation: Introduction, approaches to knowledge representation, knowledge representation
using semantic network, extended semantic networks for KR, knowledge representation using frames, advanced
knowledge representation techniques: Introduction, conceptual dependency theory, script structure, cyc theory,
case grammars, semantic web, Expert system and applications: Introduction phases in building expert systems,
expert system versus traditional systems, rule-based expert systems blackboard systems truth maintenance systems,
application of expert systems, list of shells and tools
UNIT-IV:
Uncertainty measure: probability theory: Introduction, probability theory, Bayesian belief networks, certainty
factor theory, dempster-shafer theory , Fuzzy sets and fuzzy logic: Introduction, fuzzy sets, fuzzy set operations,
types of membership functions, multi valued logic, fuzzy logic, linguistic variables and hedges, fuzzy propositions,
inference rules for fuzzy propositions, fuzzy systems.
UNIT-V:
Machine learning paradigms: Introduction, machine learning systems, supervised and unsupervised learnings,
inductive learning, deductive learning, clustering, support vector machines, case based reasoning and learning,
Artificial neural networks: Introduction, artificial networks, single layer feed forward networks, multi layered
forward networks, design issues of artificial neural networks
Text Books:
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
1. Artificial Intelligence- Saroj Kaushik, CENGAGE Learning,
2. Artificial intelligence, A modern Approach, 2nd ed, Stuart Russel, Peter Norvig, PEA
3. Artificial Intelligence- 3rd ed, Rich, Kevin Knight, Shiv Shankar B Nair, TMH
4. Introduction to Artificial Intelligence, Patterson, PHI
Reference Books:
1. Artificial intelligence, structures and Strategies for Complex problem solving,
5th ed, George F Lugar, PEA
2. Introduction to Artificial Intelligence, Ertel, Wolf Gang, Springer
3. Artificial Intelligence, A new Synthesis, Nils J Nilsson, Elsevier
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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I Year - I Semester
3
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3
Service Oriented Architecture and Web Security ( MTIT11XX)
Course Objective:
? To provide an overview of XML Technology and modeling databases in XML
? To provide an overview of Service Oriented Architecture and Web services and their importance
? To introduce Security solutions in XML and Web Services and to introduce Security standards for
Web Services
Course Outcome:
? The students will understand the basics of XML
? The students will learn the concepts of SOA and Web services, some of the prevailing standards
and technologies of Web Services
? The students will also learn the approaches for providing security for XML documents as well as
messages exchanged among Web Services
UNIT I :
XML Technology: XML ? XML and Web - Name Spaces ? XML Document Structure - Structuring
with Schemas and DTD - Modeling Databases in XML ? XQuery
UNIT II:
SOA Basics : Service Oriented Architecture (SOA) ? Comparing SOA with Client-Server and
Distributed architectures - Characteristics of SOA ? Benefits of SOA -- Principles of Service orientation ?
Service layers - Business Process management
UNIT III :
Web Services (WS) : SOA and Web Services ? Web Services Protocol Stack ? Service descriptions ?
WSDL ? Messaging with SOAP ? Service discovery ? UDDI. Service-Level Interaction patterns ? XML
and Web Services - Enterprise Service Bus - .NET and J2EE Interoperability.
UNIT IV
WS Technologies and Standards : Web Services Technologies - JAX-RPC, JAX-WS. Web Service
Standards ? WS-RM, WS-Addressing, WS-Policy. Service Orchestration and Choreography ?
Composition Standards - BPEL. Service Oriented Analysis and Design.
UNIT V :
XML and WS Security : XML Security Overview ? Canonicalization ? XML Security Framework ?
XML Encryption ? XML Signature ? XKMS Structure. Web Services Security - XACML - WS-Security.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Text Books:
1. Ron Schmelzer et al. "XML and Web Services", Pearson Education, 2008. (Unit 1 and 3)
2. Thomas Erl, " Service Oriented Architecture: Concepts, Technology, and Design", Pearson Education,
2005 (Unit 2, 3, 4, and 5)
3. Frank P.Coyle, "XML, Web Services and the Data Revolution", Pearson Education, 2002 (Unit 5)
Reference Books:
1. Eric Newcomer, Greg Lomow, "Understanding SOA with Web Services", Addison Wesley, 2005.
2. James McGovern, Sameer Tyagi, Michael E Stevens, Sunil Mathew, "Java Web Services
Architecture", Elsevier, 2011.
3. Mark O' Neill, et al., "Web Services Security", Tata McGraw-Hill Edition, 2003.
4. Sandeep Chatterjee and James Webber, "Developing Enterprise Web Services: An Architect's Guide",
Prentice Hall, 2004
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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I Year - I Semester
3
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3
Internet of Things ( MTIT11XX)
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
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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I Year - I Semester
3
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3
Optimization Technique ( MTIT11XX)
Course Objectives:
? To understand the theory of optimization methods and algorithms developed for solving various types of
optimization problems.
? To develop and promote research interest in applying optimization techniques in problems of Engineering and
Technology.
? To apply the mathematical results and numerical techniques of optimization theory to concrete Engineering
problems
Course Outcomes:
At the end of the course, the student should be able to
? Students should able to apply the dynamic programming to solve problems of discreet and continuous
variables.
? Students should able to apply the concept of non-linear programming
? Students should able to carry out sensitivity analysis
? Student should able to model the real world problem and simulate it.
Unit 1:
Optimization Techniques, Model Formulation, models, General L.R Formulation, Simplex Techniques, Sensitivity
Analysis, Inventory Control Models
Unit 2
Formulation of a LPP - Graphical solution revised simplex method - duality theory - dual simplex method -
sensitivity analysis - parametric programming
Unit 3:
Nonlinear programming problem - Kuhn-Tucker conditions min cost flow problem - max flow problem -
CPM/PERT
Unit 4
Scheduling and sequencing - single server and multiple server models - deterministic inventory models -
Probabilistic inventory control models - Geometric Programming.
Unit 5
Competitive Models,Single and Multi-channel Problems, Sequencing Models, Dynamic Programming, Flow in
Networks, Elementary Graph Theory, Game Theory Simulation
Text Books:
1. H.A. Taha, Operations Research, An Introduction, PHI, 2008
2. H.M. Wagner, Principles of Operations Research, PHI, Delhi, 1982.
Reference Books:
1. J.C. Pant, Introduction to Optimisation: Operations Research, Jain Brothers, Delhi, 2008
2. Hitler Libermann Operations Research: McGraw Hill Pub. 2009
3. Pannerselvam, Operations Research: Prentice Hall of India 2010
4. Harvey M Wagner, Principles of Operations Research: Prentice Hall of India 2010
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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I Year - I Semester
3
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3
Parallel Computer Architecture ( MTIT11XX)
Course Objective
? To understand the principles of parallel computer architecture
? To understand the design of parallel computer systems including modern parallel architectures
? To assess the communication and computing possibilities of parallel system architecture and to
predict the performance of parallel applications
Course Outcome:
? Students accustomed with the representation of data, addressing modes, and instructions sets.
? Students able to understand parallelism both in terms of a single processor and multiple processors
? Technical knowhow of parallel hardware constructs to include instruction-level parallelism for
multi core processor design
Unit ? I : Fundamentals of Computer Design
Defining Computer Architecture ? Trends in Technology ? Trends in Power in Integrated Circuits ?
Trends in Cost ? Dependability ? Measuring, Reporting and Summarizing Performance ? Quantitative
Principles of Computer Design ? Basic and Intermediate concepts of pipelining ? Pipeline Hazards ?
Pipelining Implementation issues.
Unit ? II: Instruction-Level Parallelism and Its Exploitation
Instruction-Level Parallelism: Concepts and Challenges ? Basic Compiler Techniques for Exposing ILP ?
Reducing Branch Costs with Prediction ? Overcoming Data Hazards with Dynamic Scheduling ?
Dynamic Scheduling: Algorithm and Examples ? Hardware-Based Speculation ? Exploiting ILP Using
Multiple Issue and Static Scheduling ? Exploiting ILP Using Dynamic Scheduling, Multiple Issue and
Speculation ? Studies of the Limitations of ILP ? Limitations on ILP for Realizable Processors ?
Hardware versus Software Speculation ? Using ILP Support to Exploit Thread-Level Parallelism
Unit ? III : Data-Level and Thread-Level Parallelism
Vector Architecture ? SIMD Instruction Set Extensions for Multimedia ? Graphics Processing Units ?
Detecting and Enhancing Loop-Level Parallelism ? Centralized Shared-Memory Architectures ?
Performance of Shared-Memory Multiprocessors ? Distributed Shared Memory and Directory Based
Coherence ? Basics of Synchronization ? Models of Memory Consistency ? Programming Models and
Workloads for Warehouse-Scale Computers ? Computer Architecture of Warehouse-Scale Computers ?
Physical Infrastructure and Costs of Warehouse-Scale Computers
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Unit ? IV: Memory Hierarchy Design
Cache Performance ? Six Basic Cache Optimizations ? Virtual Memory ? Protection and
Examples of Virtual Memory ? Ten Advanced Optimizations of Cache Performance ? Memory
Technology and Optimizations ? Protection: Virtual Memory and Virtual Machines ? The Design of
Memory Hierarchies
Unit ? V: Storage Systems & Case Studies
Advanced Topics in Disk Storage ? Definition and Examples of Real Faults and Failures ? I/O
Performance, Reliability Measures and Benchmarks ? Designing and Evaluating an I/O System ? The
Internet Archive Cluster Case Studies / Lab Exercises: INTEL i3, i5, i7 processor cores, NVIDIA GPUs,
AMD, ARM processor cores ? Simulators ? GEM5, CACTI, SIMICS, Multi2sim and INTEL Software
development tools.
Text Books:
1. David.A.Patterson, John L.Hennessy, "Computer Architecture: A Quantitative approach", Elsevier, 5 th
Edition 2012.
2. K.Hwang, Naresh Jotwani, "Advanced Computer Architecture, Parallelism, Scalability,
Programmability", Tata McGraw Hill, 2 nd Edition 2010
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Big Data Analytics ( MTIT11XX)
Course Objectives:
The main objective of this course is to
? Provide an overview of an exciting growing field of Big Data analytics
? Introduce the tools required to manage and analyze big data like Hadoop, MapReduce etc.,
Course Outcomes(COs): At the end of the course, student will be able to
? Understand the programming requirements viz., generic types and methods to perform data analysis
? Understand the existing technologies and the need of distributed files systems to analyze the big data
? To understand and analyze Map-Reduce programming model for better optimization
? Collect, manage, store, query, and analyze big data; and identify the need of interfaces to perform I/O
operations in Hadoop
? Identify the need based tools, viz., Pig and Hive and to handle
? Formulate an effective strategy to implement a successful Data analytics project
UNIT-I:
Data structures in Java: Linked List, Stacks, Queues, Sets, Maps; Generics: Generic classes and Type
parameters, Implementing Generic Types, Generic Methods, Wrapper Classes, Concept of Serialization
UNIT-II:
Working with Big Data: Google File System, Hadoop Distributed File System (HDFS), Building
blocks of Hadoop (Name node, Data node, Secondary Name node, Job Tracker, Task Tracker),
Introducing and Configuring Hadoop cluster (Local, Pseudo-distributed mode, Fully Distributed mode),
Configuring XML files.
UNIT-III:
Writing Map Reduce Programs: A Weather Dataset, Understanding Hadoop API for Map Reduce
Framework (Old and New), Basic programs of Hadoop Map Reduce: Driver code, Mapper code,
Reducer code, Record Reader, Combiner, Partitioner
UNIT-IV
Hadoop I/O: The Writable Interface, Writable Comparable and comparators, Writable Classes:
Writable wrappers for Java primitives, Text, Bytes Writable, Null Writable, Object Writable and
Generic Writable, Writable collections, Implementing a Custom Writable: Implementing a Raw
Comparator for speed, Custom comparators
UNIT-V
Pig: Hadoop Programming Made Easier, Admiring the Pig Architecture, Going with the Pig Latin
Application Flow, Working through the ABCs of Pig Latin, Evaluating Local and Distributed Modes of
Running Pig Scripts, Checking out the Pig Script Interfaces, Scripting with Pig Latin, Applying
Structure to Hadoop Data with Hive: Saying Hello to Hive, Seeing How the Hive is Put Together,
Getting Started with Apache Hive, Examining the Hive Clients, Working with Hive Data Types, Creating
and Managing Databases and Tables, Seeing How the Hive Data Manipulation Language Works,
Querying and Analyzing Data
Text Books:
1. Big Java 4th Edition, Cay Horstmann, Wiley John Wiley & Sons, INC
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
2.
Hadoop: The Definitive Guide by Tom White, 3rd Edition, O'Reilly
3.
Hadoop in Action by Chuck Lam, MANNING Publ
4. Hadoop for Dummies by Dirk deRoos, Paul C.Zikopoulos, Roman B.Melnyk,Bruce Brown and
Rafael Coss
Reference Books:
1. Hadoop in Practice by Alex Holmes, MANNING Publ
2. Hadoop MapReduce Cookbook, Srinath Perera, Thilina Gunarathne
Web Resources:
1. Hadoop:https://hadoop.apache.org/
2. Hive: https://cwiki.apache.org/confluence/display/Hive/Home/
3. Piglatin: https://pig.apache.org/docs/r0.7.0/tutorial.html
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Principles of Cryptography ( MTIT11XX)
Course Objective :
? To gain knowledge about the mathematics of the cryptographic algorithms.
? To get an insight into the working of different existing cryptographic algorithms.
? To learn how to use cryptographic algorithms in security.
Course Outcome :
? Building a new unbreakable cryptosystem
? Blending the existing cryptographic algorithms with the existing communication protocols
? Analyzing and application of cryptography for secure eCommerce and other secret transactions
Unit-I
Algebra: Group, cyclic group, cyclic subgroup, field, probability. Number Theory: Fermat's theorem ,
Cauchy 's theorem, Chinese remainder theorem, primality testing algorithm, Euclid's algorithm for
integers, quadratic residues, Legendre symbol, Jacobi symbol etc..
Unit-II
Cryptography and cryptanalysis, Classical Cryptography, substitution cipher, different type of attack:
CMA,CPA,CCA etc, Shannon perfect secrecy, OTP, Pseudo random bit generators, stream ciphers and
RC4.
Unit-III
Block ciphers: Modes of operation, DES and its variants, AES, linear and differential cryptanalysis.
Unit-IV
One-way function , trapdoor one-way function, Public key cryptography, RSA cryptosystem,
DiffieHellman key exchange algorithm, Elgamal Cryptosystem.
Unit-V
Cryptographic hash functions, secure hash algorithm, Message authentication, digital signature, RSA
digital signature, Elgamal digital signature.
Textbook:
1. Stinson. D. Cryptography: Theory and Practice, third edition, Chapman & Hall/CRC, 2010.
Reference Books:
1. W. Stallings, Cryptography and Network Security Principles and practice, 5/e, Pearson Education Asia,
2012.
2. Behrouz A. Forouzan and Debdeep Mukhopadhyay, Cryptography and Network Security, second
edition, Tata McGraw Hill, 2011
3. Thomas Koshy, Elementary Number Theory with applications, Elsevier India, 2005.
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Cluster and Grid Computing( MTIT11XX)
Course Objective
? The course will provide an insight for achieving cost efficient high performance system.
? The course will deal with design and architecture of grid and cluster computing.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Course Outcomes
? At the end of the course student will have knowledge of Grid Computing, Web Services,
and Service-oriented architecture, Architecture for grid computing, Cluster Computing, process
scheduling and load balancing.
Unit I:
Introduction: Cluster and Grid computing, Meta-computing, Web services and Grid Computing, e-
Governance and the Grid Technologies and Architectures for Grid Computing: Issues in Data Grids,
Functional requirements in Grid Computing, Standards for Grid Computing, Recent technology trends in
Large Data Grids. Web Services and the Service Oriented Architecture: Service Oriented Architecture,
SOAP and WSDL, Creating Web Services, Server Side.
Unit II:
OGSA and WSRF: OGSA for Resource Distribution, Stateful Web Services in OGSA, WSRF, WSRF
Specification, Globus Toolkit: History, version, Applications, Approaches and Benefits, Infrastructure
Management, Monitoring and Discovery, Security, Data Choreography and Coordination, GT4
Architecture, GT4 Containers. The Grid and Databases: Requirements, Storage Request Broker,
Integration of Databases with the Grid, Architecture of OGSADAI for offering Grid Database services.
Unit III:
Cluster Computing: Approaches to Parallel Computing, Definition and Architecture of a Cluster,
Categories of clusters. Cluster Middleware: Levels and Layers of Single System Image, Design
objectives, Resource Management and Scheduling, Cluster programming Environment and Tools.
Networking, Protocols & I/O for clusters: Networking and Interconnection/Switching Devices, Design
Issues, Design Architecture, HiPPI, ATM, Myrinet, Memory Channel
Unit IV:
Setting Up and Administering a Cluster: Setup of simple cluster, setting up nodes, clusters of clusters,
System monitoring, Global Clocks Sync. Cluster Technology for High Availability: High availability
clusters, high availability parallel computing, types of failures and errors, cluster architectures and
configurations for high availability, Failure/Recovery clusters.
Unit V:
Process Scheduling: Job management System, Resource management system, policies of resource
utilization, Scheduling policies. Load Sharing and Load Balancing: Introduction, Strategies for load
balancing, Modelling parameters. Recent trends: technologies and attributes in Cluster and Grid
computing. Case study of various cluster architectures, load balancing and scheduling policies.
Text Books:
1. Grid and Cluster Computing , CSR Prabhu, PHI
Refernces:
2. Distributed and Cloud Computing: From Parallel Processing to the Internet of Things, K. Hwang,
G. Fox and J. Dongarra, MK Publishers
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Imaging and Multimedia Systems ( MTIT11XX)
Course Objective :
? To understand the basics of image processing and image security techniques
? To study various compression and file formats used in imaging and multimedia systems
? To analyze different media and design issues related to multimedia systems
Course Outcome:
? Technical know to develop new compression standards
? Acquire skill set to handle all multimedia components efficiently
? Develop Integrated and Collaborative multimedia systems
Unit I: Introduction
Introduction to Image Processing: Steps in Image Processing Systems ?Image Acquisition ? Sampling
and Quantization ? Pixel Relationships ? Colour Fundamentals and Models. Introduction to Multimedia:
Multimedia Elements ? Multimedia applications ? Multimedia System Architecture ? Evolving
technologies for Multimedia ? Defining objects for Multimedia systems ? Multimedia Data interface
standards ? Multimedia Databases.
Unit II :Compression and File Formats
Compression and Decompression: Need for Data Compression ? Types of Compression ? Binary Image
Compression Schemes ? Image Compression ? Video Compression ? Audio Compression. Data and File
Format Standards: Rich Text Format ? TIFF File Format ? Resource Interface File Format ? MIDI File
Format - JPEG DIB File Format ? AVI Indeo File Format ? MPEG Standards ?TWAIN.
Unit III :Image Computing and Security
Image computing: The basics of processing 2D images- Thresholding -Convolution-Edge detection-
Mathematical Morphology and Shape Descriptors-Noise Reduction- Image Fusion. Image Security:
Image Forensics - Steganography -Image Cryptography Techniques-Chaos based and Non-Chaos based
methods.
Unit IV: I/O Technologies
Input and Output Technologies: Multimedia I/O Technologies: Image Scanners ? Digital Voice and
Audio ? Digital Camera ? Video Images and Animation ? Full Motion Video -Video Motion Analysis.
Unit V: Application Design
Multimedia Application Classes ? Types of Multimedia Systems ? Virtual Reality ? Components of
Multimedia Systems -Multimedia Authoring Systems ? Multimedia Authoring Tools - User Interface
Design- Mobile Messaging ? Hypermedia Message Components -Hypermedia Linking and embedding.
Text Books :
1. Rafael C Gonzalez, Richard E Woods 2nd Edition, Digital Image Processing - Pearson Education,
2011.
2. Ralf Steinmetz, Klara Steinmetz, "Multimedia Computing, Communications & Applications", Pearson
education, 2009.
Reference Books :
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
1. A.K. Jain, Fundamentals of Digital Image Processing ,PHI, New Delhi, 2001.
2. William K Pratt, Digital Image Processing, John Willey , 2012.
3. Prabat K Andleigh and Kiran Thakrar, "Multimedia Systems and Design", Prentice Hall India,
2007,New Delhi.
4. Tay Vaughan, "Multimedia Making It Work", McGraw Hill, 2011. 5. Parekh R "Principles of
Multimedia" Tata McGraw-Hill, 2006.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
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Advanced Graph Theory ( MTIT11XX)
Course Objectives:
From the course the student will learn
? All elementary concepts such as coloring, covering, hamiltonicity, planarity, connectivity and so
on, it will also introduce the students to some advanced concepts.
? The student will know the definitions of relevant vocabulary and various algorithms from graph
theory.
Course Outcomes:
? Demonstrate basic concepts in graph theory: coloring, planar graphs.
? Evaluate precise and accurate mathematical definitions of objects in graph theory.
? Determine and solve some real time problems using concepts of graph theory (e.g., scheduling
problems).
? Build some classical graph algorithms in order to find sub graphs with desirable properties.
? Compile and deduce properties of chromatic numbers and polynomials and identify certain
problems as graph colouring problems.
UNIT?I: Basic Concepts- Graphs and digraphs, incidence and adjacency matrices, isomorphism, the
automorphism group, Trees- Equivalent definitions of trees and forests, Cayley's formula, the Matrix-
Tree theorem.
UNIT?II: Connectivity- Cut vertices, cut edges, bonds, the cycle space and the bond space, blocks,
Menger's theorem, Paths and Cycles- Euler tours, Hamilton paths and cycles, theorems of Dirac, Ore,
Bondy and Chvatal, circumference, the Chinese Postman Problem, the Travelling Salesman problem,
diameter and maximum degree.
UNIT?III: Matchings- Berge's Theorem, perfect matchings, Hall's theorem, Tutte's theorem, Konig's
theorem, Petersen's theorem, algorithms for matching and weighted matching (in both bipartitie and
general graphs), factors of graphs (decompositions of the complete graph), Tutte's f-factor theorem,
Extremal problems- Independent sets and covering numbers
UNIT?IV: Colorings- Brooks theorem, the greedy algorithm, the Welsh-Powell bound, critical graphs,
chromatic polynomials, girth and chromatic number, Vizing's theorem, Graphs on surfaces- Planar
graphs, duality, Euler's formula, Kuratowski's theorem, toroidal graphs, 2-cell embeddings, graphs on
other surfaces.
UNIT?V: Directed graphs- Tournaments, directed paths and cycles, connectivity and strongly connected
digraphs, Networks and flows- Flow cuts, max flow min cut theorem, Selected topics- Dominating sets,
the reconstruction problem.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Text Books:
1. Introduction to Graph Theory, Douglas B. West, Prentice Hall of India
2. Graph Theory with Applications to Engineering and Computer Science, Narsingh Deo, Prentice-Hall
Reference Books:
1. Graph Theory, Frank Harary, Narosa
2. Network Flows: Theory, Algorithms, and Applications, R.Ahuja, T. Magnanti, and J. Orlin, Prentice-
Hall
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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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
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Advanced Data Structures Lab ( MTIT1106)
List of Experiments:
1. Perform various operations on AVL Trees
2. Perform various operations on BST
3. Implementation of Static Hashing
4. Implementation of Huffman coding
5. Implementation of B Tree.
6. Consider telephone book database of N clients. Make use of a hash table implementation to
quickly look up client`s telephone number.
7. Implement all the functions of a dictionary (ADT) using hashing. Data: Set of (key, value) pairs,
Keys are mapped to values, Keys must be comparable, Keys must be unique Standard Operations:
Insert(key, value), Find(key), Delete(key)
8. For given set of elements create skip list. Find the element in the set that is closest to some given
value.
9. Implement KMP algorithm for Pattern Matching
10. Implement Boyer-Moore algorithm for Pattern Matching
11. Implement Na?ve string matching algorithm.
12. Implement insertion, deletion, display and search operation in m-way B tree (i.e. a non-leaf node
can have atmost m children) for the given data as integers (Test the program for m=3, 5, 7).
13. Implementation of Skiplists
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Computing Lab( MTIT1107)
Note: First SIX experiments are mandatory. Remaining experiments can be done based on the students choice
of any one specialization.
Common Experiments:
1. (a) Write a python program to print the multiplication table for the given number?
(b) Write a python program to check whether the given number is prime or not?
(c) Write a python program to find factorial of the given number?
2. Write a python program to implement simple Chatbot?
3. (a) Write a python program to implement List operations (Nested List, Length,
Concatenation, Membership, Iteration, Indexing and Slicing)?
(b) Write a python program to implement List methods (Add, Append, Extend & Delete).
4. (a) Write a python program to Illustrate Different Set Operations?
(b) Write a python program to generate Calendar for the given month and year?
(c) Write a python program to implement Simple Calculator program?
5. (a) Write a python program to Add Two Matrices.
(b) Write a python program to Transpose a Matrix.
6. (a) Write a python program to remove punctuations from the given string?
(b) Write a python program to sort the sentence in alphabetical order?
Artificial Intelligence Specialization:
1. Write a python program to implement Breadth First Search Traversal?
2. Write a python program to implement Water Jug Problem?
3. Write a program to implement Hangman game using python.
4. Write a program to implement Tic-Tac-Toe game using python.
5.
(a) Write a python program to remove stop words for a given passage from a text file using NLTK?
(b) Write a python program to implement stemming for a given sentence using NLTK?
(c) Write a python program to POS (Parts of Speech) tagging for the give sentence using NLTK?
Big Data Specialization:
1. (i)Perform setting up and Installing Hadoop in its three operating modes:
Standalone,
Pseudo distributed,
Fully distributed
(ii)Use web based tools to monitor your Hadoop setup.
2. Implement the following file management tasks in Hadoop:
? Adding files and directories
? Retrieving files
? Deleting files
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
3. Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm.
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 MapReduce, since it is semi structured and
record-oriented.
5. Implement Matrix Multiplication with Hadoop Map Reduce
6. Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter Your data.
7. Install and Run Hive then use Hive to create, alter, and drop databases, tables, views, functions, and
indexes
Cryptography Specialization:
Exercise ?1:
Write a Java program to perform encryption and decryption using the following algorithms:
a) Ceaser Cipher
b) Substitution Cipher
c) Hill Cipher
Exercise ? 2:
Write a Java program to implement the 3 DES and AES algorithms.
Exercise ? 3:
Write a JAVA program to implement the BlowFish algorithm
Exercise-4 :
Implement MD-5 using Java
Exercise-5:
Write a Java program to implement RSA (2048 Key Length) Algorithm.
Exercise-6:
Implement the Diffie-Hellman Key Exchange mechanism using HTML and JavaScript. Consider the end
user as one of the parties (Alice) and the JavaScript application as other party (bob).
Exercise-7:
Calculate the message digest of a text using the SHA-2 algorithm in JAVA.
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Advanced Algorithms
Course Objectives:
? Introduce students to the advanced methods of designing and analyzing algorithms.
? The student should be able to choose appropriate algorithms and use it for a specific problem.
? To familiarize students with basic paradigms and data structures used to solve advanced
algorithmic problems.
? Students should be able to understand different classes of problems concerning their computation
difficulties.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
? To introduce the students to recent developments in the area of algorithmic design.
Course Outcomes:
After completion of course, students would be able to:
? Analyze the complexity/performance of different algorithms.
? Determine the appropriate data structure for solving a particular set of problems.
? Categorize the different problems in various classes according to their complexity.
? Students should have an insight of recent activities in the field of the advanced data structure.
UNIT I: Sorting: Review of various sorting algorithms, topological sorting Graph: Definitions and
Elementary Algorithms: Shortest path by BFS, shortest path in edge-weighted case (Dijkasra's), depth-
first search and computation of strongly connected components, emphasis on correctness proof of the
algorithm and time/space analysis, example of amortized analysis.
UNIT II: Matroids: Introduction to greedy paradigm, algorithm to compute a maximum weight maximal
independent set. Application to MST. Graph Matching: Algorithm to compute maximum matching.
Characterization of maximum matching by augmenting paths, Edmond's Blossom algorithm to compute
augmenting path.
UNIT III: Flow-Networks: Maxflow-mincut theorem, Ford-Fulkerson Method to compute maximum
flow, Edmond-Karp maximum-flow algorithm.
Matrix Computations: Strassen's algorithm and introduction to divide and conquer paradigm, inverse of
a triangular matrix, relation between the time complexities of basic matrix operations, LUP-
decomposition.
UNIT IV: Shortest Path in Graphs: Floyd-Warshall algorithm and introduction to dynamic
programming paradigm. More examples of dynamic programming. Modulo Representation of
integers/polynomials: Chinese Remainder Theorem, Conversion between base-representation and
modulo-representation. Extension to polynomials. Application: Interpolation problem.
Discrete Fourier Transform (DFT): In complex field, DFT in modulo ring. Fast Fourier Transform
algorithm. Schonhage-Strassen Integer Multiplication algorithm
UNIT V: Linear Programming: Geometry of the feasibility region and Simplex algorithm. NP-
completeness: Examples, proof of NP-hardness and NP-completeness. One or more of the following
topics based on time and interest Approximation algorithms, Randomized Algorithms, Interior Point
Method,Advanced Number Theoretic AlgorithmRecent Trands in problem solving paradigms using
recent searching and sorting techniques by applying recently proposed data structures.
Text Books:
1. "Introduction to Algorithms",3rd Edition, by Cormen, Leiserson, Rivest, Stein, PHP,
2017.
2. "The Design and Analysis of Computer Algorithms" 1st edition by Aho, Hopcroft,
Ullman,pearson,1974.
3."Algorithms " Robert Sedgewick, Addison wisely publications,1983
Reference Books:
1."Algorithm Design"1st Edition by Kleinberg and Tardos, Pearson 2013.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Full 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.
? Write backend code in Python/Java, PHP languages and Writing optimized front end code HTML
and JavaScript.
? Understand, create and debug database related queries and Create test code to validate the
applications against client requirement.
? Monitor the performance of web applications & infrastructure and Troubleshooting web application
with a fast and accurate a resolution.
Course Outcomes(COs): 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
UNIT ? I: HTML
Web Essentials: Clients, Servers, and Communication. The Internet-Basic Internet Protocols -The World
Wide Web-HTTP request message-response message-Web Clients Web Servers. Markup Languages:
XHTML an Introduction to HTML, History, Versions, Basic, XHTML Syntax and Semantics Some
Fundamental HTML Elements-Relative URLs-Lists-tables-Frames-Forms-HTML 5.0.
UNIT ? II: Cascading Style Sheets (CSS)
Style Sheets: CSS-Introduction to Cascading Style Sheets-Features-Core Syntax-Style Sheets and HTML-
Style Rule Cascading and Inheritance-Text Properties-Box Model Normal Flow Box Layout beyond the
Normal Flow-CSS3.0, Boot strap basics, Boot strap CSS3, Introduction to Java Script, Jscript basics,
JScripts objects, JSON, Don.
UNIT ? III: Jscript
Separating Programming and Presentation: JSP Technology, Introduction to JSP and Servlets-Running
JSP Applications, Basic JSP-JavaBeans Classes and JSP-Tag Libraries and Files-Support for the Model-
View-Controller Paradigm- Mongo DB, JQuery, Mean stack Fundamentals
UNIT ? IV: Angular Js
Introducing AngularJS, Starting Out with AngularJS, Basic AngularJS, Directives and Controllers,
AngularJS Modules, Creating First Controller, working with and Displaying, Arrays, more Directives,
working with ng-repeat, Unit Testing in AngularJS, Forms, Inputs, and Services, Working with ng-model,
Working with Forms, Leverage Data-Binding and Models, Form Validation and States, Error Handling
with Forms, ngModelOptions, Nested Forms with ng-form, Other Form Controls.
UNIT ? V: React JS
Introduction to React, Obstacles and Roadblocks, 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
Text Books:
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
1.
Jeffrey C. Jackson, "Web Technologies--A Computer Science Perspective", Pearson
Education, 2006
2. Robert. W. Sebesta, "Programming the World Wide Web", Fourth Edition, Pearson Education, 2007
3. AngularJS: Up and Running Enhanced Productivity with Structured Web Apps By Brad
Green, Shyam Seshadri Publisher: O'Reilly Media
Reference Books:
1. Learning React Functional Web Development with React and Redux By Alex Banks, Eve Porcello
Publisher: O'Reilly Media
2. Head First Java, 2nd Edition by Bert Bates, Kathy Sierra Publisher: O'Reilly Media, Inc
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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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,
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.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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, M. Gopal, McGraw Hill Education
2. Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press,2012
3. The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman,
Springer 2009 (freely available online)
Reference Books:
1. Pattern Recognition and Machine Learning, Christopher Bishop, Springer,2007
2. Programming Collective Intelligence: Building Smart Web 2.0 Applications - Toby Segaran
3. Building Machine Learning Systems with Python - WilliRichert, Luis Pedro Coelho
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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DevOps
Course Objectives:
DevOps improves collaboration and productivity by automating infrastructure and workflows and
continuously measuring applications performance
Course Outcomes (COs): At the end of the course, student will be able to
? Understand the principles of continuous development and deployment, automation of
configuration management, inter-team collaboration, and IT service agility
? Describe DevOps & DevSecOps methodologies and their key concepts
? Explain the types of version control systems, continuous integration tools, continuous monitoring
tools, and cloud models
? Set up complete private infrastructure using version control systems and CI/CD tools
UNIT I:
Phases of Software Development life cycle. Values and principles of agile software development.
UNIT II:
Fundamentals of DevOps: Architecture, Deployments, Orchestration, Need, Instance of applications,
DevOps delivery pipeline, DevOps eco system.
UNIT III:
DevOps adoption in projects: Technology aspects, Agiling capabilities, Tool stack implementation,
People aspect, processes
UNIT IV:
CI/CD: Introduction to Continuous Integration, Continuous Delivery and Deployment , Benefits of
CI/CD, Metrics to track CICD practices
UNIT V:
Devops Maturity Model: Key factors of DevOps maturity model, stages of Devops maturity model,
DevOps maturity Assessment
Text Books:
1. The DevOPS Handbook: How to Create World-Class Agility, Reliability, and Security in Technology
Organizations by Gene Kim , John Willis , Patrick Debois , Jez Humb,O'Reilly publications
2. What is Devops? Infrastructure as code By in Mike Loukides ,O'Reilly publications.
3. Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment Automation,
by Jez Humble and David Farley
4. Achieving DevOps: A Novel About Delivering the Best of Agile, DevOps, and Microservices by
Dave Harrison, Knox Lively
Reference Books:
1. Building a DevOps Culture by Mandi Walls, O'Reilly publications
2. The DevOps 2.0 Toolkit: Automating the Continuous Deployment Pipeline With Containerized
Microservices by Viktor Farcic
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Advanced Network Principles and Protocols
Course Objectives:
? Understand the architecture of the Internet protocols as a layered model
? To understand the fundamentals of data transmission, encoding and multiplexing
? To understand how the various components of wide area networks and local area networks work
together
Course Outcomes:
? Familiarization of the different layers of TCP/IP protocol stack
? Analyze the Concepts of Network media and topologies, Network security concepts and Network
management
? Understanding of the working principle of different protocols at different layers
Unit-I
Introduction to Networks - Application of Networks - Architecture Topology Switching - SLIP, PPP -
ALOHA protocols, CSMA/CD, IEEE 802.3, 802.4, 802.5
Unit-II
Network Layer Issues- Routing, Congestion control- Internetworking - Issues, Address Learning Bridges,
Spanning tree, Source routing, Bridges, Routers, Gateway.
Unit-III
Network Protocol- IP datagram - hop by hop routing, ARP, RARP, DHCP -Sub net Addressing, Address
Masking, ICMP, RIP, RIPV2, OSPF, DNS, LAN and WAN Multicast.
Unit-IV
Transport Layer- Design issues, Connection Management, Transmission Control Protocol (TCP) - User
Datagram Protocol (UDP).
Unit-V
Application Layer Protocol- Telnet - TFTP - FTP - SMTP - Ping Finger, Bootstrap Network Time
Protocol- SNMP.
Text Books :
1. Andrew S. Tanenbaum and David J. Wetherall, "Computer Networks", 5th Edition, Pearson, 2011
2. William Stallings, "Data and Computer Communications", 9th Edition, Pearson, 2011
Reference Book :
1. W Richard Stevens and G. Gabrani, "TCP/IP Illustrated - Volume I, The protocols", Pearson
Education, 2009
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
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Distributed Computing
Course Objectives:
? To understand the foundations of distributed systems.
? To learn issues related to clock Synchronization and the need for global state in distributed
systems.
? To learn distributed mutual exclusion and deadlock detection algorithms.
? To understand the significance of agreement, fault tolerance and recovery protocols in Distributed
Systems.
? To learn the characteristics of peer-to-peer and distributed shared memory systems.
Course Outcomes:
At the end of this course, the students will be able to:
? Elucidate the foundations and issues of distributed systems
? Understand the various synchronization issues and global state for distributed systems.
? Understand the Mutual Exclusion and Deadlock detection algorithms in distributed systems
? Describe the agreement protocols and fault tolerance mechanisms in distributed systems.
? Describe the features of peer-to-peer and distributed shared memory systems
UNIT I- INTRODUCTION :
Introduction: Definition, Relation to computer system components, Motivation, Relation to parallel
systems, Message-passing systems versus shared memory systems, Primitives for distributed
communication, Synchronous versus asynchronous executions, Design issues and challenges. A model of
distributed computations: A distributed program, A model of distributed executions, Models of
communication networks, Global state, Cuts, Past and future cones of an event, Models of process
communications. Logical Time: A framework for a system of logical clocks, Scalar time, Vector time,
Physical clock synchronization: NTP.
UNIT II -MESSAGE ORDERING & SNAPSHOTS:
Message ordering and group communication: Message ordering paradigms, Asynchronous execution with
synchronous communication, Synchronous program order on an asynchronous system, Group
communication, Causal order (CO), Total order. Global state and snapshot recording algorithms:
Introduction, System model and definitions, Snapshot algorithms for FIFO channels
UNIT III- DISTRIBUTED MUTEX & DEADLOCK :
Distributed mutual exclusion algorithms: Introduction, Preliminaries, Lamport`s algorithm, Ricart-
Agrawala algorithm, Maekawa`s algorithm, Suzuki?Kasami`s broadcast algorithm. Deadlock detection in
distributed systems: Introduction, System model, Preliminaries, Models of deadlocks, Knapp`s
classification, Algorithms for the single resource model, the AND model and the OR model.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
UNIT IV- RECOVERY & CONSENSUS:
Check pointing and rollback recovery: Introduction, Background and definitions, Issues in failure
recovery, Checkpoint-based recovery, Log-based rollback recovery, Coordinated check pointing
algorithm, Algorithm for asynchronous check pointing and recovery. Consensus and agreement
algorithms: Problem definition, Overview of results, Agreement in a failure ?free system, Agreement in
synchronous systems with failures.
UNIT V -P2P & DISTRIBUTED SHARED MEMORY :
Peer-to-peer computing and overlay graphs: Introduction, Data indexing and overlays, Chord ? Content
addressable networks, Tapestry. Distributed shared memory: Abstraction and advantages, Memory
consistency models, Shared memory Mutual Exclusion.
Text Books:
1. Distributed computing: principles, algorithms, and systems, Kshemkalyani, Ajay D., and Mukesh
Singhal. Cambridge University Press, 2011.
2, Distributed Systems Concepts and Design, George Coulouris, Jean Dollimore and Tim Kindberg ,
Fifth Edition, Pearson Education, 2012.
Reference Books:
1. "Distributed Operating Systems: Concepts and Design", Pradeep K Sinha, Prentice Hall of India, 2007.
2. Advanced concepts in operating systems, Mukesh Singhal and Niranjan G. Shivaratri McGraw-Hill,
Inc., 1994.
3. Distributed Systems: Principles and Paradigms, Tanenbaum A.S., Van Steen M., Pearson Education,
2007.
4. Distributed Computing, Principles and Applications, Liu M.L., Pearson Education, 2004.
5. Distributed Algorithms, Nancy A Lynch, Morgan Kaufman Publishers, USA, 2003.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
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Social Network Analytics
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
Reference Books:
1. Social Network Data Analytics, Aggarwal, Charu C. (Ed.), Springer Publisher, 2011
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Digital Image Processing
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.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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3
Block Chain Technologies
Course Objectives:
By the end of the course, students will be able to
? Understand how block chain systems (mainly Bit coin and Ethereum) work,
? To securely interact with them,
? Design, build, and deploy smart contracts and distributed applications,
? Integrate ideas from block chain technology into their own projects.
Course Outcomes:
At the end of the course, student will be able to
? Demonstrate the foundation of the Block chain technology and understand the processes in payment
and funding.
? Identify the risks involved in building Block chain applications.
? Review of legal implications using smart contracts.
? Choose the present landscape of Blockchain implementations and Understand Crypto currency
markets
? Examine how to profit from trading cryptocurrencies.
UNIT ? I:The consensus problem ,Asynchronous Byzantine Agreement ,AAP protocol and its analysis
,Nakamoto Consensus on permission-less, nameless, peer-to-peer network - Abstract Models for
BLOCKCHAIN - GARAY model ,RLA Model - Proof of Work ( PoW) as random oracle - formal
treatment of consistency, liveness and fairness - Proof of Stake ( PoS) based Chains - Hybrid models (
PoW + PoS).
UNIT ? II:Cryptographic basics for cryptocurrency - a short overview of Hashing, signature schemes,
encryption schemes and elliptic curve cryptography.
UNIT ? III:Bitcoin, Wallet, Blocks, Merkley Tree, hardness of mining,transaction verifiability -
anonymity - forks - double spending - mathematical analysis of properties of Bitcoin.
UNIT ? IV:Ethereum - Ethereum Virtual Machine ( EVM) ,Wallets for Ethereum - Solidity , Smart
Contracts , some attacks on smart contracts.
UNIT ? V:(Trends and Topics) - Zero Knowledge proofs and protocols in Blockchain, Succinct non
interactive argument for Knowledge (SNARK), pairing on Elliptic curves, Zcash.
Text Books:
1. Aravind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, and Steven Goldfeder. Bitcoin
and cryptocurrency technologies: a comprehensive introduction. Princeton University Press, 2016.
(Free download available)
Reference Books:
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
1.
Joseph Bonneau et al, SoK: Research perspectives and challenges for Bitcoin and
cryptocurrency, IEEE Symposium on security and Privacy, 2015 ( article available for free
download) { curtain raiser kind of generic article, written by seasoned experts and pioneers}.
2. J. A. Garay et al, The bitcoin backbone protocol - analysis and applications EUROCRYPT 2015
LNCS VOl 9057, ( VOLII ), pp 281-310. (Also available at eprint.iacr.org/2016/1048). (serious
beginning of discussions related to formal models for bitcoin protocols).
3. R. Pass et al, Analysis of Blockchain protocol in Asynchronous networks, EUROCRYPT 2017,
(eprint.iacr.org/2016/454). A significant progress and consolidation of several principles).
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Data Science
Course Objective:
? Provide you with the knowledge and expertise to become a proficient data scientist.
? Demonstrate an understanding of statistics and machine learning concepts that are vital for data
science.
? Produce Python code to statistically analyze a dataset.
? Critically evaluate data visualizations based on their design and use for communicating stories from
data.
Course Outcomes: After the completion of the course, student will be able to
? Explain how data is collected, managed and stored for data science.
? Understand the key concepts in data science, including their real-world applications and the toolkit
used by data scientists.
? Implement data collection and management scripts using MongoDB.
Unit -I: Introduction to core concepts and technologies: Introduction, Terminology, data science process,
data science toolkit, Types of data, Example applications.
Unit -II: Data collection and management: Introduction, Sources of data, Data collection and APIs,
Exploring and fixing data, Data storage and management, Using multiple data sources
Unit -III: Data analysis: Introduction, Terminology and concepts, Introduction to statistics, Central
tendencies and distributions, Variance, Distribution properties and arithmetic, Samples/CLT, Basic
machine learning algorithms, Linear regression, SVM, Naive Bayes.
Unit -IV: Data visualization-Introduction, Types of data visualization, Data for
Visualization- Data types, Data encodings, Retinal variables, Mapping variables
to encodings, Visual encodings.
Unit -V: Applications of Data Science, Technologies for visualization, Bokeh (Python), Recent trends in
various data collection and analysis techniques, various visualization techniques, application development
methods of used in data science.
Text Books:
1. Cathy O'Neil and Rachel Schutt. Doing Data Science, Straight Talk From The
Frontline. O'Reilly.
2. Jure Leskovek, Anand Rajaraman and Jeffrey Ullman. Mining of Massive
Datasets. v2.1, Cambridge University Press.
Reference Books:
1. Data Science for dummies, 2nd Edition, Lillian Louise Pierson
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Soft Computing
Course Objectives:
? Develop the skills to gain a basic understanding of neural network theory and fuzzy logic theory.
? Introduce students to artificial neural networks and fuzzy theory from an engineering perspective
Course Outcomes:
? Comprehend the fuzzy logic and the concept of fuzziness involved in various systems and fuzzy
set theory.
? Understand the concepts of fuzzy sets, knowledge representation using fuzzy rules, approximate
reasoning, fuzzy inference systems, and fuzzy logic
? To understand the fundamental theory and concepts of neural networks, Identify different neural
network architectures, algorithms, applications and their limitations
? Understand appropriate learning rules for each of the architectures and learn several neural
network paradigms and its applications
? Reveal different applications of these models to solve engineering and other problems.
UNIT-I: Fuzzy Set Theory: Introduction to Neuro ? Fuzzy and Soft Computing, Fuzzy Sets, Basic
Definition 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, Surgeon Fuzzy
Models, Tsukamoto Fuzzy Models, Input Space Partitioning and Fuzzy Modeling.
UNIT-II: Optimization: Derivative based Optimization, Descent Methods, 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 Color Recipe
Prediction.
Text Books:
1. J.S.R.Jang, C.T.Sun and E.Mizutani, "Neuro-Fuzzy and Soft Computing",
PHI, 2004, Pearson Education 2004.
2.N.P.Padhy, "Artificial Intelligence and Intelligent Systems", Oxford University
Press, 2006.
Reference Books:
1. Elaine Rich & Kevin Knight, Artificial Intelligence, Second Edition, Tata Mcgraw Hill Publishing
Comp., 2006, New Delhi.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
2.
Timothy J.Ross, "Fuzzy Logic with Engineering Applications", McGraw-Hill, 1997.
3.
Davis E.Goldberg, "Genetic Algorithms: Search, Optimization and Machine Learning",
Addison Wesley, N.Y., 1989.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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3
Natural Language Processing
Course Objectives:
? Make them understand the concepts of morphology, syntax, semantics and pragmatics of the language
and that they are able to give the appropriate examples that will illustrate the above mentioned
concepts.
? Teach them to recognize the significance of pragmatics for natural language understanding.
? Enable students to be capable to describe the application based on natural language processing and to
show the points of syntactic, semantic and pragmatic processing.
Course Outcomes:
? Explain approaches to syntax and semantics in NLP.
? Demonstrate approaches to discourse, generation, dialogue and summarization within NLP.
? Explain current methods for statistical approaches to machine translation.
? Identify machine learning techniques used in NLP, including hidden Markov models and probabilistic
? Explain context-free grammars, clustering and unsupervised methods, log-linear and discriminative
models, and the EM algorithm as applied within NLP
UNIT-I: Introduction:
NLP tasks in syntax, semantics, and pragmatics. Applications such as information extraction, question
answering, and machine translation. The problem of ambiguity. The role of machine learning. Brief
history of the field.
UNIT-II:
N-gram Language Models: The role of language models, Simple Ngram models. Estimating parameters
and smoothing. Evaluating language models. Part of Speech Tagging and Sequence Labeling: Lexical
syntax. Hidden Markov Models. Maximum Entropy Models. Conditional Random Fields
UNIT-III: Syntactic parsing:
Grammar formalisms and tree banks. Efficient parsing for context-free grammars (CFGs). Statistical
parsing and probabilistic CFGs (PCFGs). Lexicalized PCFGs.
UNIT-IV: Semantic Analysis:
Lexical semantics and word-sense disambiguation. Compositional semantics. Semantic Role Labeling
and Semantic Parsing.
UNIT- V: Information Extraction (IE) and Machine Translation (MT):
Named entity recognition and relation extraction. IE using sequence labeling. Basic issues in MT.
Statistical translation, word alignment, phrase based translation, and synchronous grammars. Dialogues:
Turns and utterances, grounding, dialogue acts and structures Natural Language Generation: Introduction
to language generation, architecture, discourse planning (text schemata, rhetorical relations).
Text Books:
1. D. Jurafsky & J. H. Martin ? "Speech and Language Processing ? An introduction to Language
processing, Computational Linguistics, and Speech Recognition", Pearson Education
References:
1. Allen, James. 1995. ? "Natural Language Understanding". Benjamin/ Cummings, 2ed.
2. Bharathi, A., Vineet Chaitanya and Rajeev Sangal. 1995. Natural Language Processing- "A Pananian
Perspective". Prentice Hll India, Eastern Economy Edition.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
3.
Eugene Cherniak: "Statistical Language Learning", MIT Press, 1993.
4.
Manning, Christopher and Heinrich Schutze. 1999. "Foundations of Statistical Natural
Language Processing". MIT Press.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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2
Advance Algorithms Lab
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:
? 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
List of Experiments:
Experiment 1:
Implement Multi stacks.
Experiment 2:
Implement Double Ended Queue (Dequeues) & Circular Queues.
Experiment 3:
Implement various Recursive operations on Binary Search Tree.
Experiment 4:
Implement various Non-Recursive operations on Binary Search Tree.
Experiment 5:
Implement BFS for a Graph
Experiment 6:
Implement DFS for a Graph.
Experiment 7:
Implement Merge & Heap Sort of given elements.
Experiment 8:
Implement Quick Sort of given elements.
Experiment 9:
Implement various operations on AVL trees.
Experiment 10:
Implement B: Tree operations.
Experiment 11:
Implementation of Binary trees and Traversals (DFT, BFT)
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Experiment 12:
Implement Krushkal's algorithm to generate a min-cost spanning tree.
Experiment 13:
Implement Prim's algorithm to generate a min-cost spanning tree.
Experiment 14:
Implement functions of Dictionary using Hashing.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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Full 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(COs): 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
List of Experiments:
1. Implementation of `get' and `post' methods.
2. CSS implementation in colors, boarder padding.
3. CSS3 implementation button frames tables, navigation bars.
4. Create registration and login forms with validations using Jscript query.
5. Jscript to retrieve student information from student database using database connectivity.
6. Angular Js data binding
7. Angular JS directives and Events
8. Using angular Js fetching data from MySQL.
9. Using React Js creating constructs data elements.
10. Using React Js implementations DoM
11. Invoking data using Jscript from Mongo DB.
12. Create an Online fee payment form using JSCript and MangoDB
Reference/ Preferred Text Books:
1. Jeffrey C. Jackson, "Web Technologies--A Computer Science Perspective", Pearson Education, 2006
2. Robert. W. Sebesta, "Programming the World Wide Web", Fourth Edition, Pearson Education, 2007
3. Angular JS: Up and Running Enhanced Productivity with Structured Web Apps By Brad
Green, Shyam Seshadri Publisher: O'Reilly Media
4. Learning React Functional Web Development with React and Redux By Alex Banks, Eve Porcello
Publisher: O'Reilly Media
5. Head First Java, 2nd Edition by Bert Bates, Kathy Sierra Publisher: O'Reilly Media, Inc
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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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,
? Apply such deep learning mechanisms to various learning problems.
? Learn deep Turing machines, 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. Deep Neural Networks: Difficulty of training deep neural networks,
Greedy layer wise training.
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: Tensor Flow, 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. Yegnanarayana, B., Artificial Neural Networks PHI Learning Pvt. Ltd, 2009.
2. Golub, G.,H., and Van Loan,C.,F., Matrix Computations, JHU Press,2013.
3. Satish Kumar, Neural Networks: A Classroom Approach, Tata McGraw-Hill Education, 2004
4. Neural Networks: A Systematic Introduction, Ra?l Rojas, 1996
5. Pattern Recognition and Machine Learning, Christopher Bishop, 2007
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Embedded Computing
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Course Objectives:
? To demonstrate the basic functions of the Linux and embedded system.
? To demonstrate about Linux Kernel
? To demonstrate about I/O and File Systems
? To develop client server models using TCP socket programming
Course Outcomes:
By the end of the course, Students are able to
? Knowledge and understanding of Embedded Linux OS Architecture, Linux Kernel Modules
? Describes the differences between the general computing system and the embedded
computing system.
? Write client server program using TCP sockets
UNIT ? I: Linux and Embedded Systems: An Introduction- What is an Embedded System?,
Embedded system components, Basic software, Operating systems for embedded systems, Why Linux-
based embedded systems?, Linux evolution, Linux-based Embedded System Component Stack-
Linux-based embedded system components, Reference hardware model, Reference hardware model
implementations, CPU memory map, The role of the bootloader, Possible scenarios, An example of
bootloader operations.
UNIT ? II: Linux kernel, Device tree, System programs, Application, Typical layout of the root
filesystem, Anatomy of a Linux-based System - Linux architecture, Conceptual view of the kernel,
Process scheduler, Memory manager, Memory manager external interfaces, Memory manager
architecture, Virtual file system, i-node, i-node interface, File interface, Virtual file system architecture,
Inter-process communication, Inter-process communication architecture
UNIT ? III: Introduction to Linux kernel modules- Introduction, CPU ? I/O interface, CPU ? I/O
interface with polling, CPU ? I/O interface with interrupt, CPU ? I/O interface, CPU ? I/O interface
latency, Direct memory access (DMA) architecture, DMA transfer modes, I/O taxonomy, Typical
operations, Linux devices, The Virtual File System (VFS) abstraction, VFS? an example, VFS functions
? include/linux/fs.h, The device file concept, Linux kernel modules ? the initialization function, the cdev
data structure, the initialization function, the clean-up function, custom VFS functions,
UNIT ? IV: File Handling: Memory Mapping- Page Alignment, Establishing Memory Mappings,
Unmapping Regions, Syncing Memory Regions to Disk, Locking Memory Regions, File Locking, Lock
Files, Record Locking, Mandatory Locks, Leasing a File
UNIT ? V: Networking with Sockets: Protocol Support, Nice Networking, Real Networking, Making
Reality Play Nice, Addresses, Utility Functions, Basic Socket Operations, Creating a Socket, Establishing
Connections, Binding an Address to a Socket, Waiting for Connections, Connecting to a Server, Finding
Connection Addresses, Networking Machines with TCP/IP, Byte Ordering, IPv4 Addressing, IPv6
Addressing, Manipulating IP Addresses, Turning Names into Addresses, Turning Addresses into Names,
Listening for TCP Connection, TCP Client Applications
Text Books:
1. Embedded Linux Systems with the Yocto Project by Rudolf K. Sterif
2. Linux Application Development, Michael K. Johnson, Erik W. Troan, O' Reilly, 2004
Reference Books:
1. Peter Barry and Patrick Crowley, "Modern Embedded Computing", 1st Edition., Elsevier/Morgan
Kaufmann, 2012.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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II Year - I Semester
3
0
0
3
Ethical Hacking
Course Objectives:
The main objectives of the course are
? To gain knowledge about Ethical hacking and penetration testing.
? To learn about various types of attacks, attackers and security threats and
vulnerabilities present in the computer system.
? To examine how social engineering can be done by attacker to gain access of
useful & sensitive information about the confidential data.
? To learn about cryptography, and basics of web application attacks.
? To gain knowledge of the tools , techniques and ethical issues likely to face the
domain of ethical hacking and ethical responsibilities.
Course Outcomes:
By the end of the course students will
? Learn various hacking methods.
? Perform system security vulnerability testing.
? Perform system vulnerability exploit attacks.
? Produce a security assessment report
? Learn various issues related to hacking.
UNIT I:
Hacking Windows: BIOS Passwords, Windows Login Passwords, Changing Windows Visuals, Cleaning
Your Tracks, Internet Explorer Users, Cookies, URL Address Bar, Netscape Communicator, Cookies
URL History, The Registry, Baby Sitter Programs.
UNIT II:
Advanced Windows Hacking: Editing your Operating Systems by editing Explorer.exe, The Registry,
The Registry Editor, Description of .reg file, Command Line Registry Arguments, Other System Files,
Some Windows & DOS Tricks, Customize DOS, Clearing the CMOS without opening your PC, The
Untold Windows Tips and Tricks Manual, Exiting Windows the Cool and Quick Way, Ban Shutdowns: A
Trick to Play, Disabling Display of Drives in My Computer, Take Over the Screen Saver, Pop a Banner
each time Windows Boots, Change the Default Locations, Secure your Desktop Icons and Settings.
UNIT III:
Getting Past the Password: Passwords: An Introduction, Password Cracking, Cracking the Windows
Login Password, The Glide Code, Windows Screen Saver Password, XOR, Internet Connection
Password, Sam Attacks, Cracking Unix Password Files, HTTP Basic Authentication, BIOS Passwords,
Cracking Other Passwords.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
UNIT IV:
The Perl Manual: Perl: The Basics, Scalars, Interacting with User by getting Input, Chomp() and Chop(),
Operators, Binary Arithmetic Operators, The Exponentiation Operator(**), The Unary Arithmetic
Operators, Other General Operators, Conditional Statements, Assignment Operators. The : Operator,
Loops, The While Loop, The For Loop, Arrays, THE FOR EACH LOOP: Moving through an Array,
Functions Associated with Arrays, Push() and Pop(), Unshift() and Shift(), Splice(), Default Variables,
$_, @ARGV, Input Output, Opening Files for Reading, Another Special Variables.
UNIT V:
Virus Working, Boot Sector Viruses (MBR or Master Boot Record), File or Program Viruses,
Multipartite Viruses, Stealth Viruses, Polymorphic Viruses, Macro Viruses, Blocking Direct Disk Access,
Recognizing Master Boot Record (MBR) Modifications, Identifying Unknown Device Drivers, making
own Virus, Macro Viruses, Using Assembly to Create your own Virus, Modifying a Virus so Scan won't
Catch it, Creating New Virus Strains, Simple Encryption Methods.
Text Books:
1. Patrick Engbreston: "The Basics of Hacking and Penetration Testing: Ethical Hacking and
Penetration Testing Made Easy",1st Edition, Syngress publication,2011.
2. Ankit Fadia : "Unofficial Guide to Ethical Hacking", 3rd Edition , McMillan India Ltd,2006.
Reference Books:
1. Simpson/backman/corley, "HandsOn Ethical Hacking & Network Defense International", 2nd
Edition,Cengageint,2011.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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II Year - I Semester
3
0
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3
Digital Marketing
Course Objectives:
Digital marketing aims at being SMART (Specific, Measurable, Achievable, Relevant and Time Related)
so that people can withstand against competitors.
Course Outcomes(COs): At the end of the course, student will be able to
? Explain about web pages with basic HTML5, DHTML tags using CSS and XML, the overview of
W3C DOM.
? Discuss the key elements of a digital Java Scripts.
? Apply search engine optimization techniques to a website.
? Illustrate how the effectiveness of a digital marketing campaign can be measured
? Demonstrate advanced practical skills in common digital marketing tools such as SEO, SEM, Social
media and Blogs
UNIT I:
HTML: Introduction, HTML5, Audio Elements, Video Elements, Organizing Elements. Scripting
Documents: Dynamic Document content, Document properties, Legacy DOM, Document Collections,
Overview of the W3C DOM, Traversing a Document, Finding Elements in a Document, Modifying a
Document, Adding Content to a Document Example
UNIT II:
Cascading Style Sheets and Dynamic HTML: Overview of CSS, CSS for DHTML Scripting inline
Styles, Scripting computed styles, Scripting CSS Classes, Scripting Style Sheets, Java Script and XML:
Obtaining XML Documents, Manipulating XML with the DOM API, Transforming XML with XSLT
querying XML with X path, Serializing XML, Example, XML and Web services.
UNIT III:
Search Engine Optimization (SEO): Searching Engine Marketing, Search Engine Optimization,
Measuring SEO Success, Mapping with SEO Journey, Search Advertising: Online Advertising Payment
Models, Search Advertising (Desktop & Mobile Devices), Planning & Executing a search Advertising
Camping, Strategic Implications of Advertising on the search Network.
UNIT IV:
Search Media Marketing: What is Social Media? Social Media Marketing, Social Media Marketing
Strategy, Adopting Social Media in Organizations: Internal Learning, Paid-Owned-Earned Media, Social
CRM, Mobile Marketing: Mobile Internet in India, What is Mobile Marketing? Email Marketing
Strategy, Forms of Mobile Marketing, Mobile Advertising, M-Commerce.
UNIT V:
E-Mail Marketing: E-Mail Marketing in India, What is E-Mail Marketing? E-Mail Marketing Strategy,
Executing E-Mail Marketing, Internet Marketing: Internet Marketing Strategy, Content Marketing,
Content Marketing in India.
Text Books:
1. The Art of Digital Marketing: The Definitive Guide to Creating Strategic, Targeted, and Measurable
Online Campaigns, Ian Dodson, Wiley, 2016
2. Programming the World Wide Web, Robet W Sebesta, Pearson, 8th edition, 2015
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Reference Books:
1. Fundamentals of Digital Marketing, Second Edition, Pearson Paperback, 2019
2. Internet Marketing- A Practical approach in the India Context by Moutusy Maity, Oxford
3. Java Script: The Definite Guide David Flanagan, O' Reilly Publisher
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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II Year - I Semester
3
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0
3
Python Programming
Course Objectives:
? Knowledge and understanding of the different concepts of Python.
? Using the GUI Programming and Testing in real-time applications.
? Using package Python modules for reusability.
Course Outcomes: At the end of the course, student will be able to
? Understand and comprehend the basics of python programming.
? Demonstrate the principles of structured programming and be able to describe, design, implement,
and test structured programs using currently accepted methodology.
? Explain the use of the built-in data structures list, sets, tuples and dictionary.
? Make use of functions and its applications.
? Identify real-world applications using oops, files and exception handling provided by python.
UNIT?I: Introduction- History of Python, Python Language, Features of Python, Applications of
Python, Using the REPL (Shell), Running Python Scripts, Variables, Assignment, Keywords, Input-
Output, Indentation.
UNIT?II: Types, Operators and Expressions-Types - Integers, Strings, Booleans; Operators-
Arithmetic Operators, Comparison (Relational) Operators, Assignment Operators, Logical Operators,
Bitwise Operators, Membership Operators, Identity Operators, Expressions and order of evaluations,
Control Flow- if, if-elif-else, for, while, break, continue, pass.
UNIT?III: Data Structures-Lists - Operations, Slicing, Methods; Tuples, Sets, Dictionaries, Sequences,
Comprehensions.
UNIT?IV: Functions- Defining Functions, Calling Functions, Passing Arguments, Keyword Arguments,
Default Arguments, Variable-length arguments, Anonymous Functions, Fruitful Functions (Function
Returning Values), Scope of the Variables in a Function - Global and Local Variables, Modules: Creating
modules, import statement, from.. import statement, name spacing, Python packages, Introduction to PIP,
Installing Packages via PIP, Using Python Packages Error and Exceptions: Difference between an error
and Exception, Handling Exception, try except block, Raising Exceptions, User Defined Exceptions.
UNIT?V: Object Oriented Programming OOP in Python-Classes, 'self variable', Methods,
Constructor Method, Inheritance, Overriding Methods, Datahiding, Brief Tour of the Standard Library -
Operating System Interface - String Pattern Matching, Mathematics, Internet Access, Dates and Times,
Data Compression, Multithreading, GUI Programming, Turtle Graphics, Testing: Why testing is required
?, Basic concepts of testing, Unit testing in Python, Writing Test cases, Running Tests.
Text Books:
1. Introduction to Python, Kenneth A. Lambert, Cengage
2. Learning Python, Mark Lutz, Orielly
Reference Books:
1. Think Python, Allen Downey, Green Tea Press
2. Core Python Programming, W. Chun, Pearson
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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II Year - I Semester
3
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0
3
Web Technologies
Course Objective:
To develop the web applications for different end users by using set of development tools like XHTML,
CSS, JavaScript, XML, Ajax, PHP, PERL and Ruby Rails.
Course Outcomes:
Upon completion of the course the student will be able to,
1. Understand the concepts of Java Script and develop a dynamic webpage by the use of Java Script.
2. Write a well formed / valid XML document and describe the concepts of Ajax.
3. Creating & Running PHP script and also to connect & working with DBMS such as MySql.
4. Understand the concepts PERL and develop the web applications by using PERL.
5. Understand the concepts RUBY and develop the web applications by using RUBY.
UNIT-I:
Javascript : The Basic of Javascript: Objects, Primitives Operations and Expressions, Screen Output and
Keyboard Input, Control Statements, Object Creation and Modfication, Arrays, Functions, Constructors,
Patttern Matching using Regular Expressions
UNIT-II:
XML: Document type Definition, XML schemas, Document object model, XSLT, DOM and SAX
Approaches, AJAX A New Approach: Introduction to AJAX, Integrating PHP and AJAX.
UNIT-III:
PHP Programming: Introducing PHP: Creating PHP script, Running PHP script.Working with
variables and constants: Using variables, Using constants, Data types, Operators. Controlling program
flow: Conditional statements, Control statements, Arrays, functions. Working with forms and Databases
such as MySQL.
UNIT-IV:
PERL: Introduction to PERL, Operators and if statements, Program design and control structures,
Arrays, Hashs and File handling, Regular expressions, Subroutines, Retrieving documents from the web
with Perl.
UNIT-V:
RUBY: Introduction to Ruby, Variables, types, simple I/O, Control, Arrays, Hashes, Methods, Classes,
Iterators, Pattern Matching. Overview of Rails.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Text Books:
1. Programming the World Wide Web, Robet W Sebesta, 7ed, Pearson.
2. Web Technologies, Uttam K Roy, Oxford
3. The Web Warrior Guide to Web Programming, Bai, Ekedahl, Farrelll, Gosselin, Zak, Karparhi,
Maclntyre, Morrissey, Cengage
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. http://www.upriss.org.uk/perl/PerlCourse.html
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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II Year - I Semester
3
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3
Artificial Intelligence
Course Objectives:
? Gain a historical perspective of AI and its foundations.
? Become familiar with basic principles of AI toward problem solving, inference, perception,
knowledge representation, and learning.
? Investigate applications of AI techniques in intelligent agents, expert systems, artificial neural
networks and other machine learning models.
? Experience AI development tools such as an `AI language', expert system shell, and/or data mining
tool. Experiment with a machine learning model for simulation and analysis.
? Explore the current scope, potential, limitations, and implications of intelligent systems.
Course Outcomes: At the end of the course, student will be able to
? Demonstrate knowledge of the building blocks of AI as presented in terms of intelligent agents
? Analyze and formalize the problem as a state space, graph, design heuristics and select amongst
different search or game based techniques to solve them.
? Develop intelligent algorithms for constraint satisfaction problems and also design intelligent
systems for Game Playing
? Attain the capability to represent various real life problem domains using logic based techniques
and use this to perform inference or planning.
? Solve problems with uncertain information using Bayesian approaches.
UNIT-I:
Introduction to artificial intelligence: Introduction , history, intelligent systems, foundations of AI,
applications, tic-tac-tie game playing, development of AI languages, current trends in AI, Problem
solving: state-space search and control strategies: Introduction, general problem solving,
characteristics of problem, exhaustive searches, heuristic search techniques, iterative-deepening a*,
constraint satisfaction
UNIT-II:
Problem reduction and game playing: Introduction, problem reduction, game playing, alpha-beta
pruning, two-player perfect information games, Logic concepts: Introduction, propositional calculus,
proportional logic, natural deduction system, axiomatic system, semantic tableau system in proportional
logic, resolution refutation in proportional logic, predicate logic
UNIT-III:
Knowledge representation: Introduction, approaches to knowledge representation, knowledge
representation using semantic network, extended semantic networks for KR, knowledge representation
using frames, advanced knowledge representation techniques: Introduction, conceptual dependency
theory, script structure, cyc theory, case grammars, semantic web, Expert system and applications:
Introduction phases in building expert systems, expert system versus traditional systems, rule-based
expert systems blackboard systems truth maintenance systems, application of expert systems, list of shells
and tools
UNIT-IV:
Uncertainty measure: probability theory: Introduction, probability theory, Bayesian belief networks,
certainty factor theory, dempster-shafer theory , Fuzzy sets and fuzzy logic: Introduction, fuzzy sets,
fuzzy set operations, types of membership functions, multi valued logic, fuzzy logic, linguistic variables
and hedges, fuzzy propositions, inference rules for fuzzy propositions, fuzzy systems.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
UNIT-V:
Machine learning paradigms: Introduction, machine learning systems, supervised and unsupervised
learnings, inductive learning, deductive learning, clustering, support vector machines, case based
reasoning and learning, Artificial neural networks: Introduction, artificial networks, single layer feed
forward networks, multi layered forward networks, design issues of artificial neural networks
Text Books:
1. Artificial Intelligence- Saroj Kaushik, CENGAGE Learning,
2. Artificial intelligence, A modern Approach, 2nd ed, Stuart Russel, Peter Norvig, PEA
3. Artificial Intelligence- 3rd ed, Rich, Kevin Knight, Shiv Shankar B Nair, TMH
4. Introduction to Artificial Intelligence, Patterson, PHI
Reference Books:
1. Artificial intelligence, structures and Strategies for Complex problem solving,
5th ed, George F Lugar, PEA
2. Introduction to Artificial Intelligence, Ertel, Wolf Gang, Springer
3. Artificial Intelligence, A new Synthesis, Nils J Nilsson, Elsevier
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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II Year - I Semester
3
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3
Internet of Things
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. Bluetooth Smart Connectivity-Overview,
Key Versions, BLE-Bluetooth Low Energy Protocol, Low Energy Architecture.
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:
Arm Based Embedded System Design: ARM Cortex-A class processor, Embedded Devices-ARM
Cortex-M Class processor, Networking-Bluetooth Smart Technology
Introduction to embedded systems: CPUs vs MCU's vs Embedded Systems, Examples, Options for
Building Embedded Systems, Features of Embedded Systems, Building Embedded Systems, Building
Embedded Systems using MCUs, Introduction to mbedTM Platform
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.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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
2. The Definative Guide to ARM Cortex-MR3 and M4 Processor, 3rd Edition, Joseph Yiu
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.
6. Cortex?A
series
Programmer's
Guide
for
ARMv7-A
by
Arm
http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.den0013d/index.htmlc
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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II Year - I Semester
3
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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,
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, M. Gopal, McGraw Hill Education
2. Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press,2012
3. The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer
2009 (freely available online)
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
Reference Books:
1. Pattern Recognition and Machine Learning, Christopher Bishop, Springer,2007
2. Programming Collective Intelligence: Building Smart Web 2.0 Applications - Toby Segaran
3. Building Machine Learning Systems with Python - WilliRichert, Luis Pedro Coelho
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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II Year - I Semester
3
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3
Advanced Data Structures
Course Objectives:
? The student should be able to choose appropriate data structures, understand the ADT/libraries,
and use it to design algorithms for a specific problem.
? Students should be able to understand the necessary mathematical abstraction to solve problems.
? To familiarize students with advanced paradigms and data structure used to solve algorithmic
problems.
? Student should be able to come up with analysis of efficiency and proofs of correctness.
Course Outcomes:
After completion of course, students would be able to:
? Understand the implementation of symbol table using hashing techniques.
? Develop and analyze algorithms for red-black trees, B-trees and Splay trees.
? Develop algorithms for text processing applications.
? Identify suitable data structures and develop algorithms for computational geometry problems.
Unit I:
Dictionaries: Definition, Dictionary Abstract Data Type, Implementation of Dictionaries. Hashing:
Review of Hashing, Hash Function, Collision Resolution Techniques in Hashing, Separate Chaining,
Open Addressing, Linear Probing, Quadratic Probing, Double Hashing, Rehashing, Extendible Hashing.
Unit II:
Skip Lists: Need for Randomizing Data Structures and Algorithms, Search and Update Operations on
Skip Lists, Probabilistic Analysis of Skip Lists, Deterministic Skip Lists
Unit III:
Trees: Binary Search Trees, AVL Trees, Red Black Trees, 2-3 Trees, B-Trees, Splay
Trees
Unit IV:
Text Processing: Sting Operations, Brute-Force Pattern Matching, The Boyer-Moore Algorithm, The
Knuth-Morris-Pratt Algorithm, Standard Tries, Compressed Tries, Suffix Tries, The Huffman Coding
Algorithm, The Longest Common Subsequence Problem (LCS), Applying Dynamic Programming to the
LCS Problem.
Unit V:
Computational Geometry: One Dimensional Range Searching, Two Dimensional Range Searching,
Constructing a Priority Search Tree, Searching a Priority Search Tree, Priority Range Trees, Quad trees,
k-D Trees. Recent Trends in Hashing, Trees, and various computational geometry methods for efficiently
solving the new evolving problem
Text Books:
1. Mark Allen Weiss, Data Structures and Algorithm Analysis in C++, 2nd Edition, Pearson, 2004.
2. M T Goodrich, Roberto Tamassia, Algorithm Design, John Wiley, 2002.
References Books:
1. Data structures and algorithms in C++, by Adam Drozdek, Mc Graw Hill
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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
Hours
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.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, 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
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
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.
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