Master of Technology Computer Science & Engineeringtmu.ac.in/college-of-computing-sciences-and-it/... · UNIT V Distributed Databases: Introduction to distributed databases, Distributed

Syllabus of M.Tech.(CSE) – College of Computing Sciences & IT, TMU Moradabad .

Study & Evaluation Scheme

of

Master of Technology

Computer Science & Engineering Based on Choice Based Credit System

[Applicable w.e.f. the Academic Session 2019-20 till revised]

COLLEGE OF COMPUTING SCIENCES &

INFORMATION TECHNOLOGY

TEERTHANKER MAHAVEER UNIVERSITY Delhi Road, Moradabad, Uttar Pradesh-244001

Website: w ww.tmu.ac.in

……………………………………………………………………………………………………………

Syllabus Applicable w. e. f. Academic Session 2019-20

1

Syllabus of M.Tech.(CSE) – College of Computing Sciences &IT, TMU Moradabad .

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TEERTHANKER MAHAVEER UNIVERSITY (Established under Govt. of U. P. Act No. 30, 2008)

Study & Evaluation Scheme

Of

Master of Technology (Computer Science & Engineering) SUMMARY

Programme

Duration

Medium

Minimum Required Attendance

Maximum Credit

Minimum Credit

Assessment

Duration of Examination :

Internal Evaluation (Theory Courses):

Evaluation of Seminar/ Dissertation:

: M.Tech.(Computer Science & Engineering)

: Two years Regular (Four Semesters)

: English

: 75%

: 78

: 74

:

External Internal

3 hrs. 1.5hrs.

Class

Test

I

Class

Test

I I

Class

Test

I I I

Attendance Assignment Total

Best two out of the three

10 10 10 10 10 40

To qualify the course a student is required to secure a minimum of 45% marks in aggregate including the semester examination and teachers continuous evaluation. (i.e. both internal and external). A candidate who secures less than 45% of marks in a course shall be deemed to have failed in that course. The student should have secured at least 45% marks in aggregate to clear the semester.

Internal External Total

40 60 100

Internal External Total

50 50 100


………………………………………………………………………………………………………………………………………………………………… 3


Practical Course Evaluation (50 marks)

The Internal evaluation would also be done by the Internal Examiner based on the experiment

performed during the internal examination.

EXPERIMENT

(30 MARKS)

ATTENDANCE

(10 MARKS)

VIVA

(10 MARKS)

TOTAL

INTERNAL (50 MARKS)

External Evaluation (50 marks)

The external evaluation would also be done by the External Examiner based on the experiment

performed during the examination.

EXPERIMENT

(30 MARKS)

FILE WORK

(10 MARKS)

VIVA

(30 MARKS)

TOTAL EXTERNAL

(50 MARKS)


………………………………………………………………………………………………………………………………………………………………… 4


TEERTHANKER MAHAVEER UNIVERSITY

Bagarpur, Delhi Road (N.H. 24)

Moradabad – 244001 (U.P)

G UIDELINES FOR SETTING QUESTION PAPER

Structure of Question paper

Question paper shall have two sections and examiner shall set questions specific to respective section.

Section wise details shall be as mentioned under;

Section-1: It shall consist of short answer type questions (answer should not exceed 50 words). This

section will essentially assess COs related to Remembering & Understanding. This section will

contain five questions and every question shall have an “or” option. (Questions

should be from each unit and the “or” option question should also be from the

same unit) each question shall have equal weightage of 2 Marks and total weightage of this section

shall be 10 Marks.

Section-2: It shall consist of long answer type questions. This section will also contain five

questions and every question should assess an specific CO and should have an

“or” option (Questions should be from the entire syllabus and the “or” option

question should assess the same CO). Each question shall have equal weightage of 10

Marks and total weightage of this section shall be 50 Marks.

There must be at least one question from the entire syllabus to assess the specific element of the

Higher Level of Learning (Thinking). Every question in this section must essentially assess at least one of

the following aspects of learning: Applying, Analyzing, Evaluating and Creating/ Designing/ Developing.


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The question must be designed in such a way that it assesses the concerned CO in entirety. It means a

question could have multiple parts depending upon the requirement of the Specific Course Outcome.

Progression:

There is no restriction to earn minimum credits for progression from semester to semester and

year to year.

Maximum Duration:

The maximum duration to award the degree is N+2, where N is the minimum number of years

to complete the programme. (For M.Tech CSE N=2)

Important Note:

Student must earn minimum 74 credits required for the award of M.Tech CSE Degree out of

maximum 78 credits offered in the programme. However it is compulsory to earn all the credits

for Core Courses. The categories of course are mentioned below:

Credits Summary

Category Code Category Name Credits

PCC Professional Core Course 66

PEC Professional Elective

Course

12

TOTAL 78


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Study and Evaluation Scheme Course: M.Tech. (CSE)

SEMESTER I

S.N

Category

Code

Course

Code

Course Name

Periods

Credits

Evaluation Scheme

L

T

P

Internal

External

Total

1

PCC

MCS107 Data Warehousing and Mining

3

1

0

4

40

60

100

2

PCC

MCS 111

Advanced Database System

3

1

0

4

40

60

100

3

PCC

MCS 112 Advanced Data Structure and Algorithms.

3

1

0

4

40

60

100

4

PEC1

Professional Elective Course I

Select any one out of three.

3

1

0

4

40

60

100

5

PCC

MCS 153

Data Warehousing and

Mining Lab

0

0

4

2

50

50

100

6

PCC

MCS 154

Advanced Database System

Lab

0

0

4

2

50

50

100

7

PCC

MCS 155

Advanced Data Structure

and Algorithms Lab

0

0

4

2

50

50

100

Total

12

4

12

22

310

390

700


………………………………………………………………………………………………………………………………………………………………… 7


SEMESTER II

S.N.

Category

Code

Course

Code

Course Name

Periods Credits Evaluation Scheme

L

T

P

Internal

External

Total

1

PCC

MCS 202

Advanced Computer

Network

3

1

0

4

40

60

100

2

PCC

MCS 204

Big Data Analytics and Business Intelligence

3

1

0

4

40

60

100

3

PCC

MCS 205

Semantic Web and Social

Networks

3

1

0

4

40

60

100

4

PEC II

Professional Elective Course II Select any one out of five.

3

1

0

4

40

60

100

5

PCC

MCS 252

Advanced Computer

Network Lab

0

0

4

2

50

50

100

6

PCC

MCS 253

Big Data Lab

0

0

4

2

50

50

100

7

PCC

MCS 291

Seminar

0

0

0

2

50

50

100

Total

12

4

8

22

310

390

700


………………………………………………………………………………………………………………………………………………………………… 8


Scheme of Professional Elective Course (PEC)

Professional Elective Courses(PEC)I- Select any one Course from serial no.1 given below:

Semester I

1

Professional Elective

Course (PEC)-1

MCS102

Advanced Computer Architecture

MCS104

Advanced Software Engineering

MCS106

Advanced Real Time Operating System

Professional Elective Courses(PEC) II- Select any one Course from serial no.2 given below:

Semester II

2


Course (PEC)-II

MCS 231 Pattern Recognition and Image Processing

MCS 232

Neural Networks

MCS 235 Genetic Algorithms

MCS 238

Natural Language Processing

MCS 240

Software Testing

Professional Elective Courses(PEC) III- Select any one Course from serial no.3 given below:

Semester III

3


Course (PEC)-III

MCS 337 Distributed and Parallel Computing

MCS 344 Computational Technique using Matlab

MCS 345 Digital Image Processing


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M.Tech- Semester I

DATA WAREHOUSING AND MINING

Course Code: MCS 107 L-3, T-1, P-0, C-4

Objective: Syllabus deals with importance of Data Warehousing and Mining. Course provides data mining classification, clustering.

UNIT-I

Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data

Mining systems, Major issues in Data Mining, Data Warehouse and OLAP Technology for Data

Mining Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Data

Warehouse Implementation, Further Development of Data Cube Technology, From Data

Warehousing to Data Mining.

Data Preprocessing: Needs Preprocessing the Data, Data Cleaning, Data Integration and

Transformation, Data Reduction, Discretization and Concept Hierarchy Generation, Online Data

Storage. (Lecture 09)

UNIT-II

Data Mining Primitives, Languages, and System Architectures: Data Mining Primitives, Data

Mining Query Languages, Designing Graphical User Interfaces Based on Data Mining Query

Language Architectures of Data Mining Systems. Concepts Description: Characterization and Comparison: Data Generalization and Summarization-Based Characterization, Analytical Characterization: Analysis of Attribute Relevance, Mining Class Comparisons: Discriminating between Different Classes, Mining Descriptive Statistical Measures in Large Databases. (Lecture 09)

UNIT-III Mining Association Rules in Large Databases: Association Rule Mining, Mining

Single-Dimensional Boolean Association Rules from Transactional Databases, Mining Multilevel

Association Rules from Transaction Databases, Mining Multidimensional Association Rules from

Relational Databases and Data Warehouses, From Association Mining to Correlation Analysis,

Constraint-Based Association Mining.

Classification and Prediction: Issues Regarding Classification and Prediction, Classification

By decision tree induction, Bayesian Classification, Classification by Back propagation, Classification Based on Concepts from Association Rule Mining, Other Classification

(Lecture 09)

UNIT-IV Cluster Analysis Introduction :Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods, Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods, Outlier Analysis. (Lecture 09)


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UNIT-V

Mining Complex Types of Data: Multidimensional Analysis and Descriptive Mining of Complex, Data Objects, Mining Spatial Databases, Mining Multimedia Databases, Mining Time-Series and Sequence Data, Mining Text Databases, Mining the World Wide Web. (Lecture 09)

TEXT BOOKS:

1. Data Mining – Concepts and Techniques - JIAWEI HAN & MICHELINE Harcourt India.

2. Data Mining Techniques – ARUN K PUJARI, University Press

3. Building the Data Warehouse- W. H. Inman, Wiley Dreamtech India Pvt. Ltd.

REFERENCE BOOKS:

1. Data Warehousing in the Real World – Sam Anahory & Dennis Murray Pearson Edn. Asia.

2. Data Warehousing Fundamentals –Paulraj Ponnaiah Wiley Student EDITION.

3. The Data Warehouse Life cycle Tool kit – Ralph Kimball Wiley Student EDITION.

4. Data Mining Introductory and advanced topics –Margaret H Dunham, PEARSON EDUCATION.

*Latest editions of all the suggested books are recommended.


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M.Tech- Semester I

ADVANCED DATABASE SYSTEM


Objective: To understand the basic concepts and terminology related to DBMS and Relational

Database Design.To Understand the Distributed Databases. To understand about Recovery and

management using Oracle.

UNIT 1

Elementary Database Concepts: Hierarchical, Relational, Network and OO Database

Architectures and their comparison. Data Modeling. Relational model – Concept, Algebra and

Constraints. Use of SQL as a relational database language in data definition & query formulation.

(Lecture 09)

UNIT II

Comparison of DBMS: MySQL, DB2, MS SQL Server, Oracle 8i/9i/10g - their strengths &

weaknesses. Summary of Normalization techniques used with RDBMS – relative comparison and

applications. Concept and use of Indexes. (Lecture 09)

UNIT III

Database Backup: Recovery and management using Oracle RMAN.DBMS performance tuning,

goals, principles & benchmarks, DBMS Storage management. Oracle Enterprise Manager: Console

functions, Database Administration tools –DBA Studio. (Lecture 09)

UNIT IV

Oracle Enterprise Security Manager: User authentication & privilege management. Integrity

Management – Locking techniques; implementation using Latches. Database Replication

Management – Multiple master technique; types of propagation & replication; conflict resolution.

Programmatic interfaces to Oracle RDBMS; Case Study of SQLJ, JDBC and related Java

capabilities in Oracle. (Lecture 09)

UNIT V

Distributed Databases: Introduction to distributed databases, Distributed DBMS architectures,

Storing data in a distributed DBMS, Distributed catalog management, Distributed query processing

Updating distributed data, Distributed transactions, Distributed concurrency control, Distributed

recovery. (Lecture 09)


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TEXT BOOKS:

1. Database Management Systems, Raghurama Krishnan, Johannes Gehrke, TATA McGraw-Hill 3 Edition

2. Data base System Concepts, Silberschatz, Korth, McGraw hill, IV edition.

REFERENCE BOOKS: 1. Introduction to Database Systems, C.J.Date Pearson Education 2. Data base Management System, ElmasriNavate Pearson Education



………………………………………………………………………………………………………………………………………………………………… 13


M.Tech- Semester I

ADVANCED DATA STRUCTURE AND ALGORITHMS


Objective: Syllabus deals with understanding of how several fundamental algorithms work. To

learn the advanced concepts of data structure and algorithms and its implementation.

UNIT I

Introduction to Basic Data Structures: Importance and need of good data structures and

algorithms, Arrays, Linked lists, Stacks, Queues, Priority queues, Heaps; Strategies for choosing the

appropriate data structures. Advanced Data Structures: AVL Trees, Red-Black Trees, Splay Trees,

B-trees, Fibonacci heaps, Data Structures for Disjoint Sets, Augmented Data Structures. Algorithms

Complexity and Analysis: Probabilistic Analysis, Amortized Analysis, Competitive Analysis.

(Lecture 09)

UNIT II

Graphs & Algorithms: Representation, Type of Graphs, Paths and Circuits: Euler Graphs,

Hamiltonian Paths & Circuits; Cut-sets, Connectivity and Separability, Planar Graphs,

Isomorphism,Graph Coloring, Covering and Partitioning,, Depth- and breadth-first traversals,

Minimum Spanning Tree: Prim’s and Kruskal’s algorithms, Shortest-path Algorithms: Dijkstra’s

and Floyd’s algorithm, Topological sort, Max flow: Ford-Fulkerson algorithm, max flow – min cut.

String Matching Algorithms: Suffix arrays, Suffix trees, tries, Rabin-Karp, KnuthMorris-Pratt,

Boyer-Moore algorithm. (Lecture 09)

UNIT III

Approximation algorithms: Need of approximation algorithms: Introduction to P, NP, NP-Hard

and NP-Complete; Deterministic, non-Deterministic Polynomial time algorithms; Knapsack, TSP,

Set Cover, Open Problems. (Lecture 09)

UNIT IV

Randomized Algorithm: Introduction to probability and randomized algorithms. Examples of 8

randomized algorithms. Basic inequalities, Random variables. max-cut and derandomization.

Permutation routing in a hypercube. 8 Basic Chernoff bound. Markov chains and random walks (2-

SAT example, random walk on a path example). (Lecture 09)


………………………………………………………………………………………………………………………………………………………………… 14


UNIT V

Parallel Algorithms: Flynn‟s Classifications – List Ranking – Prefix computation – Array Max –

Sorting on EREW PRAM – Sorting on Mesh and Butterfly – Prefix sum on Mesh and Butterfly –

Sum on mesh and butterfly – Matrix Multiplication – Data Distribution on EREW, Mesh and

Butterfly

TEXT BOOKS:

1. Computer Algorithms/C++, E. Horowitz, S. Sahani and S. Rajasekharan, Galgotia Publishers pvt. Limited.

2. Data Structures and Algorithm Analysis in C++, 2nd Edition, Mark Allen Weiss, Pearson education.

3. Introduction to Algorithms, 2nd Edition, T.H.Cormen, C.E.Leiserson, R.L.Rivest, and C.Stein, PHI pvt.Ltd./ Pearson Education.

REFERENCE BOOKS:

1. Design and Analysis of algorithms, Aho, Ullman and Hopcroft, Pearson Education.

2. Introduction to the Design and Analysis of Algorithms, A.Levitin, Pearson Education.



………………………………………………………………………………………………………………………………………………………………… 15


M.Tech- Semester I

ADVANCED COMPUTER ARCHITECTURE


Objective: Computer architecture deals with the physical configuration, logical structure, formats, protocols, and operational sequences for processing data, controlling the configuration, and controlling the operations over a computer.

UNIT-I Fundamentals of Computer Design- Technology trends- cost-measuring and reporting performance quantitative principles of computer design.

Instruction set principles and examples- classifying instruction set-memory addressing-type and size

of operands- addressing modes for signal processing-operations in the instruction set- instructions

for control flow- encoding an instruction set.-the role of compiler (Lecture 09)

UNIT-II

Instruction Level Parallelism (ILP) - Over coming data hazards-reducing branch costs –

high performance instruction delivery-hardware based speculation-limitation of ILP (Lecture 09)

UNIT-III

ILP Software Approach- compiler techniques, static branch protection-VLIW approach-,H.W support for more ILP at compile time,H.W verses S.W solutions, Memory hierarchy design,

cache performance, reducing cache misses penalty and Miss rate, virtual memory-protection and examples of VM. (Lecture 09)

UNIT-IV

Multiprocessors and Thread Level Parallelism- symmetric shared memory architectures, distributed shared memory, Synchronization, multi threading.

Storage systems-Types, Buses, RAID- errors and failures, bench marking a storage device,

designing, I/O system. (Lecture 09)

UNIT-V

Inter Connection Networks and Clusters- interconnection network media, practical issues in

interconnecting networks, examples, clusters, designing a cluster. (Lecture 09)


………………………………………………………………………………………………………………………………………………………………… 16


TEXT BOOKS:

1. Computer Architecture A quantitative approach 3 edition John L. Hennessy & David A. Patterson Morgan Kufmann (An Imprint of Elsevier)

REFERENCE BOOKS:

1. “Computer Architecture and parallel Processing” Kai Hwang and A. Briggs International Edition McGraw-Hill.

2. Advanced Computer Architectures, DezsoSima, Terence Fountain, Peter Kacsuk, Pearson.



………………………………………………………………………………………………………………………………………………………………… 17


M.Tech- Semester I

ADVANCED SOFTWARE ENGINEERING


Objective: The purpose of this course is to teach component-based systems, based on contemporary methods of development, software architectures and middleware platforms.

UNIT-I

Introduction to Software Engineering: The evolving role of software, Changing Nature of

Software, Software myths. A Generic view of process: Software engineering- A layered technology, a process framework, The Capability Maturity Model Integration (CMMI), Process patterns, process assessment, personal and team process models.

Process models: The waterfall model, Incremental process models, Evolutionary process models,

The Unified process. Overview of agile process and aspect oriented programming.

(Lecture 09)

UNIT-II Software Requirements: Functional and non-functional requirements, User requirements, System requirements, Interface specification, the software requirements document. Requirements engineering process: Feasibility studies, Requirements elicitation and analysis, Requirements validation, Requirements management.

System models: Context Models, Behavioral models, Data models, Object models, structured methods. (Lecture 09)

UNIT-III

Design process and Design quality: Design concepts, the design model.

Creating an architectural design: software architecture, Data design, Architectural styles and

patterns, Architectural Design. Object-Oriented Design: Objects and object classes, An Object-Oriented design process, Design evolution. (Lecture 09)

UNIT-IV

Performing User interface design: Golden rules, User inter face analysis and design,

interface analysis, interface design steps, Design evaluation. Testing Strategies: A strategic approach to software testing, test strategies for conventional software, Black-Box and White-Box testing, Validation testing, System testing, the art of Debugging.


………………………………………………………………………………………………………………………………………………………………… 18


Software Quality Assurance: Software Configuration Management, Overview of Software Quality

Control and Quality Assurance, ISO 9000 Certification for Software Industry, SEI Capability

Maturity Model (CMM) and Comparison between ISO & SEI CMM. (Lecture 09)

UNIT-V

Technical Metrics for Software: A Framework for Technical Software Metrics, Metrics for the Analysis Model, Metrics for Design Model, Metrics for Source Code, Metrics for Testing, Metrics for Maintenance. CASE (Computer Aided Software Engineering): CASE and its Scope, CASE support in Software Life Cycle, Documentation Support, Architecture of CASE Environment .

(Lecture 09)

TEXT BOOKS: 1. Software Engineering, A practitioner’s Approach-Roger S. Pressman, 6th editions. McGraw Hill International Edition. 2. Software Engineering- Sommerville, 7 edition, Pearson education.

3. Software Testing Techniques – Loveland, Miller, Prewitt, Shannon, Shroff Publishers & Distribution Pvt. Ltd.,

REFERENCE BOOKS:

1. Software Engineering- K.K. Agarwal & Yogesh Singh, New Age International

Publishers

2. Software Engineering, an Engineering approach- James F. Peters, WitoldPedrycz, John

Wiley.

3. Systems Analysis and Design- Shelly Cashman Rosenblatt, Thomson Publications.



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M.Tech- Semester I

ADVANCED REAL TIME OPERATING SYSTEM


Objective: Syllabus deals with issues in real time operating systems, importance of deadlines and

concept of task scheduling. Student will be able to understand and design real time operating

systems which are backbone of embedded industry.

UNIT I

Introduction to Real time systems: Issues in real time computing, Structure of real time system, Need for RTOS, Task classes. Performance measures for real time system: Properties, traditional performance measures, performability, cost functions and hard deadlines, and Estimating program run times. Introduction LINUX/ UNIX OS. (Lecture: 09)

UNIT II

Embedded software and Task Scheduling: Examples of embedded system, their characteristics and their typical hardware components, embedded software architectures, Scheduling algorithms: round robin, round robin with interrupts, function queue scheduling real time operating system selection, CPU scheduling algorithms: Rate monotonic, EDF, MLF. Priority Scheduling, Priority Ceiling and Priority inheritance Real time operating system: Tasks and task states, shared data and reentrancy semaphores and shared data, use of semaphores protecting shared data. (Lecture: 09)

UNIT III

Features of Real Time Operating System: Messages, queues, mailboxes, pipes, timer function, events memory management, Interrupt basic system design using an RT (OS design principles, interrupt routines, task structures and priority.) Current research in RTOS. Case Studies: Vx Works and Micro OS-II. (Lecture: 09)

UNIT IV

Real Time Databases: Real time v/s general purpose, databases, main memory databases, transaction priorities, transaction aborts, concurrency control issues: pessimistic concurrency control and optimistic concurrency control, Disk scheduling algorithms. (Lecture: 09)

UNIT V

Fault Tolerance Techniques: Causes of failure, Fault Types, Fault detection, Fault and error Containment, Redundancy: hardware redundancy, software redundancy, Time redundancy, information redundancy, Data diversity, Integrated failure handling. (Lecture: 09)


………………………………………………………………………………………………………………………………………………………………… 20


TEXT BOOKS

1. Charles Crowley, “Operating Systems-A Design Oriented approach”, McGraw Hill 1997.

2. C.M. Krishna, Kang, G.Shin, “Real Time Systems”, McGraw Hill, 1997.

3. Tanenbaum, “Distributed Operating Systems”, Pearson Education.

4. Raymond J.A.Bhur, Donald L.Bailey, “An Introduction to Real Time Systems”, PHI 1999.

REFERENCE BOOKS

1. Computers as Components Principles of Embedded Computing System Design by

2. Real Times Systems Theory and Practice by Rajib Mall (Pearson Education)

3. Real-Time Systems , Krisha & Shin, McGraw Hill

4. Embedded/Real time systems programming, Dr. KV K K Prasad, (Dreamtech)



………………………………………………………………………………………………………………………………………………………………… 211


M.Tech- Semester I

Data Warehousing and Mining Lab


1. Build Data Warehouse and Explore WEKA

2. Data Mining Query Languages

3. Perform data preprocessing tasks and Demonstrate performing association rule mining on

data sets

4. Demonstrate performing classification on data set.

5. Classification by decision tree induction

6. Bayesian Classification

7. Classification by Back propagation

8. Demonstrate performing clustering on data sets

9. Demonstrate performing Regression on data sets

10. Demonstration of clustering rule process on dataset iris.arff using simple k-means

11. Partitioning Methods

12. Density-Based Method

13. Grid-Based Methods


………………………………………………………………………………………………………………………………………………………………… 212


M.Tech- Semester I

ADVANCED DATABASE SYSTEM LAB


The following operation has to be performed using any database tools:

1. Granting Roles and Privileges.

2. Implementation of various constraints.

3. performance tuning

4. Creation of Index.

5. Storage Management

6. Recovery

7. Hands on Testing with Database Administration tools- DBA studio

8. Locking techniques

9. Database Replication Management

10. Distributed catalog management

11. Distributed query processing

12. Updating distributed data

13. Distributed transactions

14. Distributed concurrency control

15. Distributed recovery.


………………………………………………………………………………………………………………………………………………………………… 213


M.Tech- Semester I

Advanced Data Structure and Algorithms Lab


Write a C/C++ program to perform the following operations:

1 Write a program to implement the following using an array

a) Stack ADT b) Queue ADT

2 Write a program to implement the following using a singly linked list

a. Stack ADT

b. Queue ADT.

3 Write a Program to implement the DEQUE (double ended queue) ADT

using arrays.

4 Write a program to perform the following operations:

a) Insert an element into a binary search tree.

b) Delete an element from a binary search tree.

c) Search for a key element in a binary search tree.

5 Write a program that use recursive functions to traverse the given

Binary tree in

a) Preorder b) In order and c) Post order

6 Write a program that use non –recursive functions to traverse the

given binary tree in

a) Preorder b) In order and c) Post order

7 Write C programs for the implementation of BFS and DFS for a given graph.

8 Write C programs for implementing the following sorting methods:

a) Merge Sort b) Heap Sort.


………………………………………………………………………………………………………………………………………………………………… 214


9 Write a program to perform the following operations.

a) Insertion into a B-tree b) Deletion from a B-tree

10 Write a program to perform the following operations.

a) Insertion into an AVL-tree b) Deletion from an AVL-tree

11 Write a Program to implement all the functions of Dictionary (ADT) using hashing.

.


………………………………………………………………………………………………………………………………………………………………… 215


M.Tech- Semester II

ADVANCED COMPUTER NETWORK


Objective: This course covers advanced fundamentals principles of computer networks and techniques for networking. Briefly, the topics include advanced network architecture and design principles, protocol mechanisms, implementation principles and software engineering practices, network algorithmic, network simulation techniques and tools.

UNIT-I

Introduction Introduction to Network models-ISO-OSI, SNA, Apple talk and TCP/IP models.

Review of Physical layer and Data link layers, Review of LAN (IEEE 802.3, 802.5, 802.11b/a/g,

FDDI) and WAN (Frame Relay, ATM, ISDN) standards. (Lecture: 09)

UNIT-II

Network layer ARP, RARP, Internet architecture and addressing, internetworking, IPv4, overview

of IPv6, ICMP, Routing Protocols- RIP, OSPF, BGP, IP over ATM. (Lecture: 09)

UNIT-III

Transport layer Design issues, Connection management, Transmission Control Protocol (TCP),

User Datagram Protocol (UDP), Finite state machine model. (Lecture: 09)

UNIT-IV

Application layer: WWW, DNS, e-mail, SNMP, RMON (Lecture: 09)

UNIT-V

Network Security: Cryptography, Firewalls, Secure Socket Layer (SSL) and Virtual Private Networks (VPN).

Case study: Study of various network simulators, Network performance analysis using NS2.

(Lecture: 09)

TEXT BOOKS: 1. Behrouz A. Forouzan, “TCP/IP Protocol Suit”, TMH, 2000.

2. Tananbaum A. S., “Computer Networks”, 3rd Ed., PHI, 1999.

REFERENCE BOOKS:

1. Black U, “Computer Networks-Protocols, Standards and Interfaces”, PHI, 1996. 2. Stallings W., “Data and Computer Communications”, 6th Ed., PHI, 2002.

3. Stallings W., “SNMP, SNMPv2, SNMPv3, RMON 1 & 2”, 3rd Ed., Addison Wesley, 1999. 4. Laurra Chappell (Ed), “Introduction to Cisco Router Configuration”, Techmedia, 1999.


………………………………………………………………………………………………………………………………………………………………… 216


M.Tech- Semester II

BIG DATA ANALYTICS AND BUSINESS INTELLIGENCE


Objective: To have an advanced level of understanding of most recent advancements in Big

Data and using insights, statistical models, visualization techniques for its effective application

in Business intelligence.

UNIT I

Introduction to Data Analytics: Data and Relations, Data Visualization, Correlation, Regression,

Forecasting, Classification, Clustering. (Lecture 09)

UNIT II

Big Data Technology Landscape: Fundamentals of Big Data Types, Big data Technology

Components, Big Data Architecture, Big Data Warehouses, Functional vs. Procedural Programming

Models for Big Data. (Lecture 09)

UNIT III

Introduction to Business Intelligence: Business View of IT Applications, Digital Data, OLTP vs.

OLAP, BI Concepts, BI Roles and Responsibilities, BI Framework and components, BI Project Life

Cycle, Business Intelligence vs. Business Analytics. (Lecture 09)

UNIT IV

Big Data Analytics: Big Data Analytics, Framework for Big Data Analysis, Approaches for

Analysis of Big Data, ETL in Big Data, Introduction to Hadoop Ecosystem, HDFS, Map-Reduce

Programming, Understanding Text Analytics and Big Data, Predictive analysis on Big Data, Role of

Data analyst. (Lecture 09)

UNIT V

Business implementation of Big Data: Big Data Implementation, Big Data workflow, Operational

Databases, Graph Databases in a Big Data Environment, Real-Time Data Streams and Complex

Event Processing, Applying Big Data in a business scenario, Security and Governance for Big Data,

Big Data on Cloud, Best practices in Big Data implementation, Latest trends in Big Data, Latest

trends in Big Data, Big Data Computation, More on Big Data Storage, Big Data Computational

Limitations. (Lecture 09)


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TEXT BOOKS:

1. M., Dhiraj A., Big Data, Big Analytics: Emerging Business

2. Intelligence and Analytic Trends for Today's Businesses, Wiley CIO Series (2013), 1sted.

REFERENCE BOOKS:

1. White T., Hadoop: The Definitive Guide, O’ Reilly Media (2012), 3rd Ed.



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M.Tech- Semester II

SEMANTIC WEB AND SOCIAL NETWORKS


Objective: The objective of this course is to make students to learn Web Intelligence,

Knowledge Representation for the Semantic Web. Learn Ontology Engineering; Learn

Semantic Web Applications, Services and Technology. Learn Social Network Analysis and

semantic web

UNIT I

Web Intelligence

Thinking and Intelligent Web Applications, The Information Age ,The World Wide Web,

Limitations of Today’s Web, The Next Generation Web, Machine Intelligence, Artificial

Intelligence, Ontology, Inference engines, Software Agents, Berners-Lee www, Semantic Road

Map, Logic on the semantic Web. (Lecture 09)

UNIT II

Knowledge Representation for the Semantic Web

Ontologies and their role in the semantic web, Ontologies Languages for the Semantic Web –

Resource Description Framework(RDF) / RDF Schema, Ontology Web Language(OWL), UML,

XML/XML Schema. (Lecture 09)

UNIT-III

Ontology Engineering Ontology Engineering, Constructing Ontology, Ontology Development Tools, Ontology Methods,

Ontology Sharing and Merging, Ontology Libraries and Ontology Mapping, Logic, Rule and

Inference Engines. (Lecture 09)

UNIT-IV

Semantic Web Applications, Services and Technology Semantic Web applications and services, Semantic Search, e-learning, Semantic Bioinformatics,

Knowledge Base ,XML Based Web Services, Creating an OWL-S Ontology for Web Services,

Semantic Search Technology, Web Search Agents and Semantic Methods. (Lecture 09)

UNIT-V

Social Network Analysis and semantic web What is social Networks analysis, development of the social networks analysis, Electronic Sources

for Network Analysis – Electronic Discussion networks, Blogs and Online Communities, Web

Based Networks. Building Semantic Web Applications with social network features. (Lecture 09)


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TEXT BOOKS

1. Berners Lee, Godel and Turing , ”Thinking on the Web”, Wiley , 2008.

2. Peter Mika, “Social Networks and the Semantic Web, Springer, 2007.

REFERENCE BOOKS:

1. J.Davies, R.Studer, P.Warren, Semantic Web Technologies, Trends and Research in Ontology

Based Systems,John Wiley & Sons,2006.

2. Liyang Lu Chapman ,Semantic Web and Semantic Web Services ,Hall/CRC Publishers,2007.

3. Heiner Stucken Schmidt, Frank Van Harmelen, Information Sharing on the semantic Web,

Springer Publications,2006.



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M.Tech- Semester II

PATTERN RECOGNITION AND IMAGE PROCESSING


Objective: This paper provides a broad overview of the major elements of pattern recognition and image processing (PRIP).

UNIT-I Introduction: Machine perception, pattern recognition example, pattern Recognition systems, the design cycle, learning and adaptation.

Bayesian Decision Theory: Introduction, continuous features – two categories classifications,

minimum error-rate classification-zero–one loss function, classifier s, discriminate functions, and

decision surfaces. (Lecture: 09)

UNIT-II

Normal Density: Univariate and multivariate density, discriminant functions for the normal

density-different cases, Bayes decision theory – discrete features, compound Bayesian decision

theory and context.

Maximum likelihood and Bayesian parameter estimation: Introduction, maximum likelihood

estimation, Bayesian estimation, Bayesian parameter estimation–Gaussian case (Lecture: 09)

UNIT-III

Un-supervised learning and clustering: Introduction, mixture densities and identifiability,

maximum likelihood estimates, application to normal mixtures, K-means clustering. Date

description and clustering – similarity measures, criteria function for clustering. Pattern recognition using discrete hidden Markov models: Discrete-time Markov process, Extensions to hidden Markov models, three basic problems of HMMs, types of HMMs.

(Lecture: 09)

UNIT-IV

Continuous hidden Markov models: Continuous observation densities, multiple mixtures per

state, speech recognition applications.

Digital image fundamentals: Introduction, an image model, sampling and quantization, basic

relationships between pixels, image geometry. (Lecture: 09)

UNIT V

Image enhancement: Back ground, enhancement by point processing histogram processing,

spatial filtering, introduction to image transforms, image enhancement in frequency domain.

Image Segmentation and Edge Detection: Region Operations, Crack Edge Detection, Edge

Following, Gradient operators, Compass and Laplace operators. Threshold detection methods,

optimal thresholding, multispectral thresholding, thresholding in hierarchical data structures; edge

based image segmentation- edge image thresholding, edge relaxation, border tracing, border

detection. (Lecture: 09)


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TEXT BOOKS:

1. Pattern classifications, Richard O. Duda, Peter E. Hart, David G. Stroke. Wiley student edition, Second Edition.

2. Fundamentals of speech Recognition, Lawrence Rabiner, Biing – Hwang Juang Pearson education.

3. R.C Gonzalez and R.E. Woods, “Digital Image Processing”, Addison Wesley, 1992.

REFERENCE BOOKS:

1. A.K.Jain, “Fundamentals of Digital Image Processing”, Prentice Hall of India. 2. Digital Image Processing – M.Anji Reddy, BS Publications. 3. Pattern Recognition and Image Analysis – Earl Gose, Richard John baugh, PHI,2000



………………………………………………………………………………………………………………………………………………………………… 33


M.Tech- Semester II

NEURAL NETWORKS


Objective: This paper contains biological neural network which is composed of a group or groups of chemically connected or functionally associated neurons. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling.

UNIT I

INTRODUCTION What is a neural network, Human Brain, Models of a Neuron, Neural networks

viewed as Directed Graphs, Network Architectures, Knowledge Representation, Artificial

Intelligence and Neural Networks.

LEARNING PROCESS – Error Correction learning, Memory based learning, Hebbian learning,

Competitive, Boltzmann learning, Credit Assignment Problem, Memory, Adaption, Statistical

nature of the learning process. (Lecture: 09)

UNIT II

SINGLE LAYER PERCEPTRONS Adaptive filtering problem, Unconstrained Organization

Techniques, Linear least square filters, least mean square algorithm, learning curves, Learning

rate annealing techniques, perceptron –convergence theorem, Relation between perceptron and

Bayes classifier for a Gaussian Environment

MULTILAYER PERCEPTRON – Back propagation algorithm XOR problem, Heuristics, Output

representation and decision rule, Computer experiment, feature detection. (Lecture: 09)

UNIT III

BACK PROPAGATION back propagation and differentiation, Hessian matrix, Generalization,

Cross validation, Network pruning Techniques, Virtues and limitations of back propagation

learning, Accelerated convergence, supervised learning. (Lecture: 09)

UNIT IV

SELF ORGANIZATION MAPS Two basic feature mapping models, Self organization map,

SOM algorithm, properties of feature map, computer simulations, learning vector quantization,

Adaptive patter classification. (Lecture: 09)

UNIT V

NEURO DYNAMICS Dynamical systems, stability of equilibrium states, attractors,

neurodynamical models, manipulation of attractors as a recurrent network paradigm

HOPFIELD MODELS – Hopfield models, computer experiment. (Lecture: 09)


………………………………………………………………………………………………………………………………………………………………… 34


TEXT BOOKS:

1. Neural networks A comprehensive foundations, Simon Hhaykin, Pear son Education edition 2004.

REFERENCE BOOKS: 1. Artificial neural networks - B.Vegnanarayana Prentice Hall of India P Ltd 2005 2. Neural networks in Computer intelligence, Li Min Fu TMH 2003 3. Neural networks James A Freeman David M S kapurapearson education 2004



………………………………………………………………………………………………………………………………………………………………… 35


M.Tech- Semester II

GENETIC ALGORITHMS


Objective: This paper provide a search technique used in computing to find exact or approximate solutions to optimization and search problems.

UNIT-I Introduction A brief history of evolutionary computation, Elements of Genetic Algorithms, A simple genetic algorithm, Applications of genetic algorithms Genetic Algorithms in Scientific models Evolving computer programs, data analysis &

prediction, evolving neural networks, modeling interaction between learning & evolution,

modeling sexual selection, measuring evolutionary activity. (Lecture: 09)

UNIT-II

Theoretical Foundation of genetic algorithm Schemas & Two-Armed and k-armed problem,

royal roads, exact mathematical models of simple genetic algorithms, Statistical- Mechanics

Approaches. (Lecture: 09)

UNIT-III Computer Implementation of Genetic Algorithm Data structures, Reproduction, crossover & mutation, mapping objective functions to fitness form, fitness scaling, coding, a multiparameter, mapped, fixed point coding, discretization and constraints. (Lecture: 09)

UNIT-IV Some applications of genetic algorithms The risk of genetic algorithms, De Jong & function optimization, Improvement in basic techniques, current application of genetic algorithms .

(Lecture: 09)

UNIT-V

Advanced operators & techniques in genetic search Dominance, duplicity, & abeyance,

inversion & other reordering operators, other micro operators, Niche & speciation, multi

objective optimization, knowledge based techniques, genetic algorithms & parallel processors.

(Lecture: 09)


………………………………………………………………………………………………………………………………………………………………… 36


TEXT BOOKS:

1. David E. Goldberg, “Genetic algorithms in search, optimization & Machine Learning” Pearson Education, 2006.

REFERENCE BOOKS:

1. Melanie Mitchell, “An introduction to genetic algorithms”, Prentice Hall India, 2002.

2. Michael D. Vose, “The simple genetic algorithm foundations and theory, Prentice Hall India, 1999.


………………………………………………………………………………………………………………………………………………………………… 37


M.Tech- Semester II

NATURAL LANGUAGE PROCESSING


Objective: To understand the advanced concepts of Natural Language Processing and to be

able to apply the various concepts of NLP in other application areas.

UNIT I

Introduction: Origin of Natural Language Processing (NLP), Challenges of NLP, NLP

Applications, Processing Indian Languages. (Lecture 09)

UNIT II

Words and Word Forms: Morphology fundamentals; Morphological Diversity of Indian

Languages; Morphology Paradigms; Finite State Machine Based Morphology; Automatic

Morphology Learning; Shallow Parsing; Named Entities; Maximum Entropy Models; Random

Fields, Scope Ambiguity and Attachment Ambiguity resolution. (Lecture 09)

UNIT III

Machine Translation: Need of MT, Problems of Machine Translation, MT Approaches, Direct

Machine Translations, Rule-Based Machine Translation, Knowledge Based MT System, Statistical

Machine Translation, UNL Based Machine Translation, and Translation involving Indian

Languages. (Lecture 09)

UNIT IV

Meaning: Lexical Knowledge Networks, WorldNet Theory; Indian Language Word Nets and

Multilingual Dictionaries; Semantic Roles; Word Sense Disambiguation; WSD and Multilinguality;

Metaphors. (Lecture 09)

UNIT V

Speech Recognition: Signal processing and analysis method, Articulation and acoustics,

Phonology and phonetic transcription, Word Boundary Detection; Argmax based computations;

HMM and Speech Recognition. Other Applications: Sentiment Analysis; Text Entailment; Question

Answering in Multilingual Setting; NLP in Information Retrieval, Cross-Lingual IR. (Lecture 09)

TEXT BOOKS:

1. Siddiqui and Tiwary U.S., Natural Language Processing and Information Retrieval, Oxford

University Press (2008).

2. Allen J., Natural Language understanding, Benjamin/Cunnings, (1987).


………………………………………………………………………………………………………………………………………………………………… 38


REFERENCE BOOKS:

1. Jensen K., Heidorn G.E., Richardson S.D., Natural Language Processing: The PLNLP

Approach, Springer (2013). 4. Roach P., Phonetics, Oxford University Press (2012).



………………………………………………………………………………………………………………………………………………………………… 39


M.Tech- Semester II

Software Testing


Objective: To explore the basics and goals of software testing. To discuss various types of

software testing and its techniques .To discuss various methods and evaluation procedures for

improving the quality Models.

UNIT I

Introduction: Testing Process, Terminologies: Error, Fault, Failure, Test Cases, Testing Process,

Limitations of Testing, Graph Theory: Graph, Matrix representation, Paths and Independent paths,

Generation of graph from program, Identification of independent paths. Functional Testing:

Boundary Value Analysis, Equivalence Class Testing, Decision Table Based Testing, Cause Effect

Graphing Technique. (Lecture 09)

Unit - II

Structural Testing: Control flow testing, Path testing, Data Flow Testing, Slice based testing,

Mutation Testing Software Verification: Verification methods, SRS verification, SDD verification,

Source code reviews, User documentation verification, and Software project audit.

(Lecture 09)

Unit- III

Creating Test Cases from Requirements and use cases: Use case diagram and use cases,

Generation of Test cases from use cases, Guidelines for generating validity checks, Strategies for

data validating, Database testing, Regression Testing: What is Regression Testing?, Regression test

cases selection, Reducing the number of test cases, Risk analysis, Code coverage prioritization

technique Software Testing Activities: Levels of Testing, Debugging, Software Testing Tools, and

Software test Plan.

(Lecture 09)

Unit- IV

Object oriented Testing: What is Object orientation?, What is Object Oriented testing?, Path

Testing, State Based Testing, Class Testing, Testing Web Applications: What is Web testing?,

Functional Testing, User interface Testing, Usability Testing, Configuration and Compatibility

Testing, Security Testing, Performance Testing, Database testing, Post Deployment Testing

(Lecture 09)

UNIT V

Basic concepts of Test Management :Testing, debugging goals, policies – Test planning – Test

plan components – Test plan attachments – Locating test items – Reporting test results – The role of


………………………………………………………………………………………………………………………………………………………………… 40


three groups in test planning and policy development – Process and the engineering disciplines –

Introducing the test specialist – Skills needed by a test specialist – Building a testing group.

(Lecture 09)

TEXT BOOKS:

1. Yogesh Singh, “Software Testing”, Cambridge University Press, New York, 2012

2. CemKaner, Jack Falk, Nguyen Quoc, “Testing Computer Software”, Second Edition, Van

Nostrand Reinhold, New York, 1993.

Reference Books: 1. William Perry, “Effective Methods for Software Testing”, John Wiley

& Sons, New York, 1995.

3. K.K. Aggarwal&Yogesh Singh, “Software Engineering”, New Age International Publishers,

New Delhi, 2005

4. Louise Tamres, “Software Testing”, Pearson Education Asia, 2002

5. Roger S. Pressman, “Software Engineering – A Practitioner’s Approach”, Fifth Edition,

McGraw-Hill International Edition, New Delhi, 2001.

REFERENCE BOOKS:

1. Marc Roper, “Software Testing”, McGraw-Hill Book Co., London, 1994.

2. Watts Humphrey, “Managing the Software Process”, Addison Wesley Pub. Co. Inc.,

Massachusetts, 1989.

3. Boris Beizer, “Software Testing Techniques”, Second Volume, Second Edition, Van

Nostrand Reinhold, New York, 1990. 12. Louise Tamres, “Software Testing”, Pearson

Education Asia, 2002



………………………………………………………………………………………………………………………………………………………………… 41


M.Tech- Semester II

ADVANCED COMPUTER NETWORK LAB


Network Programming

1. Socket Programming

a. TCP Sockets

b. UDP Sockets

c. Applications using Sockets

2. Simulation of Sliding Window Protocol

3. Simulation of Routing Protocols


………………………………………………………………………………………………………………………………………………………………… 42


M.Tech- Semester II

BIG DATA LAB


LIST OF EXPERIMENTS:

1. Introduction, use and assessment of most recent advancements in Big Data technology

along with their usage and implementation with relevant tools and technologies.

2. Map Reduce application for word counting on Hadoop cluster.

3. Unstructured data into NoSQL data and do all operations such as NoSQL query with

API.

4. K-means clustering using map reduce.

5. Page Rank Computation.

6. Data retrieval from AQL.

7. Data Retrieval from JQL

6. Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data

Analytics


………………………………………………………………………………………………………………………………………………………………… 43


M.Tech- Semester II

SEMINAR


Course Objectives: This course is designed to help the student obtain skills to discuss or present something. Seminar Course is an outcome of study, exploration, survey and analysis of a particular topic.

Selection of Topic: All students pursuing M.Tech shall submit the proposed topic of the seminar in the first week of the semester to the course coordinator. Care should be taken that the topic selected does not directly relate to the subject of the courses being pursued or thesis work, if any. The course coordinator shall then forward the list to the concerned department coordinators who will vet the list and add some more topics in consultation with the faculty of the department .The topics will then be allocated to the students along with the name of the faculty guide.

Preparation of the Seminar

1. The student shall meet the guide for the necessary guidance for the seminar work.

2. During the next two to four weeks the student should read the primary literature germane to the seminar topic. Reading selection should continuously be informed to the guide.

3. After necessary collection of data and literature survey, the students must prepare a report. The report shall be arrange in the sequence consisting of the following:-

a. Top Sheet of transparent plastic.

b. Top cover.

c. Preliminary pages.

(i) Title page

(ii) Certification page.

(iii) Acknowledgment.

(iv) Abstract.

(v) Table of Content.

(vi) List of Figures and Tables.

(vii) Nomenclature.

d. Chapters (Main Material).

e. Appendices, If any.

f. Bibliography/ References. g. Evaluation Form.

h. Back Cover (Blank sheet).

i. Back Sheet of Plastic (May be opaque or transparent).


………………………………………………………………………………………………………………………………………………………………… 44


4. T op Cover-The sampled top cover shall be as Under:

Title of the seminar

Submitted in Partial fulfillment of the requirement for the degree of

MASTER OF TECHNOLOGY

In

COMPUTER SCIENCE & ENGINEERING by

Name of Student in capital Letters (Roll No.)

Under the guidance of

GUIDE NAME

Designation, CCSIT,

TMU, Moradabad.

COLLEGE OF COMPUTING SCIENCES AND INFORMATION TECHNOLOGY

TEERTHANKER MAHAVEER UNIVERSITY N.H. 24, BAGARPUR, MORADABAD-244001

MONTH AND YEAR


………………………………………………………………………………………………………………………………………………………………… 45


5. T itle Page:- The sampled title page shall be as Under:

Title of the seminar

Submitted in Partial fulfillment of the requirement for the degree of

MASTER OF TECHNOLOGY

In

COMPUTER SCIENCE & ENGINEERING by

Name of Student in capital Letters (Roll No.)

Under the guidance of

GUIDE NAME

Designation, CCSIT,

TMU, Moradabad.

COLLEGE OF COMPUTING SCIENCES AND INFORMATION TECHNOLOGY TEERTHANKER MAHAVEER UNIVERSITY

N.H. 24, BAGARPUR,

MORADABAD-244001

MONTH AND YEAR


………………………………………………………………………………………………………………………………………………………………… 46


6. Certification page:- This shall be as under

College of Computing Sciences and Information Technology

Teerthanker Mahaveer University

Moradabad-244001

The seminar Report and Title “Topic of the Seminar.”

Submitted by Mr. /Ms. (Name of the student) (Roll No.) may be accepted for being evaluated

Date Signature

Place (Name of guide)

F or Guide If you Choose not to sign the acceptance certificate above, please indicate reasons for the same from amongst those given below:

i) The amount of time and effort put in by the student is not sufficient;

ii) The amount of work put in by the student is not adequate;

iii) The report does not represent the actual work that was done / expected to be done;

iv) Any other objection (Please elaborate)

7. Acknowledgement:- This shall be as under

I am highly thankful to the almighty who gave me the strength and health for

completing my seminar. It gives me pleasure to express my thanks and gratitude to my

seminar guide Guide Name who gave me the opportunity to do this seminar. His/Her

guidance and support helped me to complete my seminar.

I would like to thanks Principal Sir ……, HOD ……. and my course coordinator ……..

for their motivation and encouragement.

I am also thankful to everyone who has helped me in making this report. I am really

thankful to them. Their encouragement really helped me to frame this seminar in a

better manner. I also, whole heartedly, thank my parents and friends for their support

and helpfulness.

Place: Moradabad Name of the Student


………………………………………………………………………………………………………………………………………………………………… 47


8. Abstract- A portion of the seminar evaluation will be based on the abstract. The abstract will be evaluated according to the adherence to related technical field and according to the format described below.

The seminar abstract is an important record of the coverage of your topic and provides a valuable source of leading references for students. Accordingly, the abstract must serve as an introduction to your seminar topic. The abstract will be limited to 500 words, excluding figures and tables (if any). The abstract will include references to the research articles upon which the seminar is based as well as research articles that have served as key background material.

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