Department of
COMPUTER-SCIENCE






Syllabus for
Master of Science (Computer Science)
Academic Year  (2019)

 
1 Semester - 2019 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCS131 PROGRAMMING IN JAVA 4 4 100
MCS132 UNIX OPERATING SYSTEM 4 4 100
MCS133 DIGITAL LOGIC AND ASSEMBLY LANGUAGE PROGRAMMING 4 4 100
MCS134 ADVANCED DATABASE MANAGEMENT SYSTEM 4 4 100
MCS135 DISCRETE MATHEMATICAL STRUCTURES 4 4 100
MCS136 RESEARCH METHODOLOGY 4 4 100
MCS151 JAVA PROGRAMMING LAB 4 2 100
MCS152 UNIX LAB 4 2 100
2 Semester - 2019 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCS231 DATA STRUCTURES 4 4 100
MCS232 MOBILE APPLICATION 4 4 100
MCS241A DIGITAL IMAGE PROCESSING 4 4 100
MCS241B SOFTWARE DEFINED NETWORKS 4 4 100
MCS241C SOFTWARE QUALITY AND TESTING 4 4 100
MCS241D DATA WAREHOUSING AND DATA MINING 4 4 100
MCS241E COMPUTER GRAPHICS UISING OPEN GL 4 4 100
MCS241F COMPUTER ARCHITECTURE 4 4 100
MCS242A CYBER LAW AND IT SECURITY 4 4 100
MCS242B OOAD USING UML 4 4 100
MCS242C WEB ENGINEERING 4 4 100
MCS242D COMPILER DESIGN 4 4 100
MCS242E WIRELESS AND MOBILE NETWORKS 4 4 100
MCS242F THEORY OF COMPUTATION 4 4 100
MCS242G DATA ANALYTICS 4 4 100
MCS243A MATLAB PROGRAMMING 4 4 100
MCS243B R PROGRAMMING 4 4 100
MCS243C NETWORK SIMULATION USING NS3 4 4 100
MCS243D HADOOP 4 4 100
MCS243E PYTHON PROGRAMMING 4 4 100
MCS251 DATA STRUCTURES LAB 4 2 100
MCS252 MOBILE APPLICATION LAB 4 02 100
MCS271 SEMINAR 2 1 50
MCS281 ADBMS PROJECT LAB 4 2 100
3 Semester - 2018 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCS331 DIGITAL IMAGE PROCESSING 4 4 100
MCS332 MOBILE APPLICATIONS 4 04 100
MCS333 DESIGN AND ANALYSIS OF ALGORITHMS 4 04 100
MCS341A DATA WAREHOUSING AND DATA MINING 4 4 100
MCS341B MACHINE LEARNING 4 4 100
MCS341C PARALLEL COMPUTING WITH OPEN CL 4 4 100
MCS341D SOFTWARE PROJECT MANAGEMENT 4 04 100
MCS341E CLOUD COMPUTING 4 4 100
MCS341F ARTIFICIAL INTELLIGENCE 4 04 100
MCS341G STORAGE AREA NETWORK 4 4 100
MCS351 DIGITAL IMAGE PROCESSING LAB 4 2 100
MCS352 MOBILE APPLICATION LAB 4 02 100
MCS353 SPECIALIZATION PROJECT LAB 4 2 100
MCS371 RESEARCH - MODELING / IMPLEMENTATION 4 2 50
MCS372 SEMINAR 2 1 50
4 Semester - 2018 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCS451 INDUSTRY PROJECT 2 6 300
MCS471 RESEARCH PUBLICATION 4 4 100
        

  

Assesment Pattern

Theory Assessment

 

Component

Mode of Assessment

Parameters

Points

CIA I

Written Assignment/

Class test/

Problem based assignment

Basic and conceptual

10

CIA II

Mid-semester Examination

Conceptual and analytical knowledge of the subject

25

CIA III

Quiz/ Seminar/ Group Presentation/ Test

Mastery of the core concepts

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject with core concepts

50

 

Total

100

 
Examination And Assesments
  • Continuous Internal assessment (CIA) forms 50% and the end semester examination forms the other 50% of the marks in both theory and practical.
  • The MSE & ESE for each theory paper is of two and three hours respectively.
  • The CIA for the practical sessions are done on a day-to-day basis depending upon their performance in the pre-lab, the conduct of the experiment, viva questions etc. Only those who qualify with minimum require attendance and CIA will be allowed to appear for the ESE.
Department Overview:
Department of Computer Science of CHRIST(Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation?s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.
Mission Statement:
The Department of Computer Science endeavors to imbibe the vision of the University Excellence and Service. The department is committed to this philosophy which pervades every aspect and functioning of the department. Mission: To develop IT professionals with ethical and human values. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their career. The department also moulds the students to be sociall
Introduction to Program:
MSc Computer Science is a 4-semester programme which includes the core areas of Computer Science. The objective of the course is to mould students to acquire analytical, creative and problem solving skills to meet the industry standards and be well prepared for research activities.
Program Objective:
PSO 1:PROBLEM ANALYSIS AND DESIGN: Ability to identify, analyze and solve research based interdisciplinary computational challenges. PSO 2 :CONDUCT INVESTIGATION: Undertake quality research to contribute academia and industry. PSO 3:MODERN TOOL USAGE: Apply their knowledge and experience on modern computing tools and platforms for continuing professional development. PSO 4:LIFE LONG LEARNING: Adapt to the continuous technological change in computational science and update themselves through life-long learning process. PSO 5:SOCIETAL AND ENVIRONMENTAL CONCERN: Utilize the computational knowledge efficiently for societal and environmental concerns . PSO 6:INNOVATION AND ENTREPRENEURSHIP : Produce innovative IT products and services based on global needs and trends.

MCS131 - PROGRAMMING IN JAVA (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To introduce the concepts and principles of Java Programming language and to design, implement object-oriented solutions to simple and complex problems. To give students experience in Java Programming and program development within an integrated development environment.

Learning Outcome

Upon completion of the course, the student will able to

CO 1: Understand the principles of object-oriented programming concepts

CO 2: Apply logical and critical thinking skills in programming  to address the dynamic requirements coming from the Industry

CO 3: Design/Develop highly professional applications in Java targeting to the Industry standard

 

Unit-1
Teaching Hours:12
Introduction to Java Programming, Language Fundamentals
 

History of Java, Characteristics of Java, The Java Environment – JVM, JDK & JRE, Different versions of Java, OOP Principles, Comparison of Java with C and C++.

Unit-1
Teaching Hours:12
Language Fundamentals
 

Data Types, Expressions, Keywords, Operators and Control Flow Statements, Structure of Java Program, Creating and Running Java Programs, Arrays.

Unit-1
Teaching Hours:12
Class and Objects
 

Creating class and objects, methods, this keyword, Constructors, Garbage Collection, the finalize () method, Access Control, Static Blocks, Finals, Nested and Inner Classes, String Class and String Buffer Class, Command Line Arguments.

Unit-2
Teaching Hours:13
Inheritance in Java
 

Inheritance in classes, using super, Method overriding, Dynamic Method Dispatch.Abstract Classes, Using final with inheritance, the Object Class.

Unit-2
Teaching Hours:13
Interfaces and Packages
 

Inheritance in java with Interfaces – Defining Interfaces, Implementing Interfaces, Extending Interfaces, Creating Packages, CLASSPATH variable, Access protection, Importing Packages, Interfaces in a Package. 

Unit-2
Teaching Hours:13
Exception Handling in Java
 

try-catch-finally mechanism, throw statement, throws statement, Packages and Classes for Exception Handling.

Unit-3
Teaching Hours:13
Input / Output in java
 

java.io package, I/O Streams, Readers and Writers, Using various I/O classes: Reader, Writer, Input Stream and Output Stream, Serialization of objects.

Unit-3
Teaching Hours:13
Multithreading
 

Life cycle of a thread, Java Thread priorities, Runnable interface and Thread Class, sharing limited resources, shared Object with synchronization.

Unit-3
Teaching Hours:13
Applets
 

Life cycle of Applet, Applet Architecture, Applet restrictions, Creation and Execution of Java Applets, Animation in Applets, Advantages of Applets, Applets vs. Applications.

Unit-4
Teaching Hours:11
GUI Components (AWT & SWING)
 

GUI concepts in Java, Basic GUI Components in AWT, Container Classes, Layout   Managers, Flow Layout, Border Layout, Card Layout, Box Layout, Difference between AWT and SWING.

Unit-4
Teaching Hours:11
Self-Learning SWING
 

Java foundation Classes – javax. swing and Model View Controller, creating a Frame in Swing, Displaying Image in Swing, JComponent class methods – Creating components in Swing, Writing GUI programs in java (with AWT or SWING), Event Handling – Handling Keyboard Events and Mouse Events.

Unit-5
Teaching Hours:11
Database and Client Server Communication
 

Creating a server that sends data, creating a client that receives data, two way communication between server and client, Stages in a JDBC program, Registering the driver, connecting to a database, Preparing SQL statements, Improving the performance of a JDBC program.

Text Books And Reference Books:

[1] Schildt Herbert, Java: The Complete Reference, Tata McGraw-Hill, 10th Edition, 2017.

Essential Reading / Recommended Reading

[1] Deitel&Deitel, Java How to Program, Pearson Education Asia, 8th Edition, 2010.

[2] Rao Nageswara, Core Java, An Integrated Approach, Dreamtech press, 2nd Edition, 2010.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS132 - UNIX OPERATING SYSTEM (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The course provides comprehensive understanding of the philosophy and development of the layered architecture of UNIX operating system. It also provides learning opportunities to appreciate the principles and system calls for File System, Process Management, Inter Process Communication, Memory Management and IO System.

Learning Outcome

Upon completion of the course,student will be able to

CO1: Understand the UNIX Operating System Architecture

CO2: Analyze the kernel level data structures used for keeping track of different entities

CO3: Identify system calls for file system, process management and Inter Process Communication

CO4: Apply memory management concept and IO system principles

Unit-1
Teaching Hours:12
Conceptual Foundation of UNIX
 

History - System Structure - User Perspective - Operating System Services -Architecture of UNIX - System Concepts - Kernel Data Structures - Buffer Cache - Principles of Computation through C – Steps in compilation, storage class of variables, memory layout, address binding, address space – logical Vs physical – Concept of a process – Process Control Block.

Unit-2
Teaching Hours:12
File System
 

Internal representation of files – inodes - Structure of a regular files –Directories - Conversion of path name to an inode - Super block - inode assignment to a file - Allocation of disk blocks - System calls for file system -open, read, write, lseek, close, file creation, stat and fstat, dup, link, unlink and mounting.

Unit-3
Teaching Hours:12
Process Management
 

Process states and transitions - Layout of system memory - Process context - Process Creation – Process Termination – Waiting for a process – Invoking other programs – System Boot and Init Process – Process Scheduling – Concept of a thread - Thread creation - Thread termination - waiting for a thread.

Unit-4
Teaching Hours:12
Inter Process Communication
 

Process Tracing – Pipe – FIFO - System V IPC - Message queue, Shared memory, Semaphore - Network communications – Socket – Thread Synchronization – Mutex, Condition Variables, Semaphores.

Unit-5
Teaching Hours:12
Memory Management and IO Subsystem
 

Swapping- allocation of swap space, swapping processes out, Swapping processes in - Demand Paging- Data structures for demand paging, Page fault - Hybrid System.

Driver Interfaces – System calls and driver interface, Interrupt handlers, Disk Drivers - Terminal Drivers – Clists, Terminal driver in canonical mode, Terminal driver in raw mode, Terminal polling – Streams.

Text Books And Reference Books:

1. Bach M.J., “The Design of the Unix Operating System”, Prentice Hall India, reprint 2009.

2. Richard Stevens, “Advanced programming in the UNIX environment “, Addison Wesley, 3rd edition, 2013

Essential Reading / Recommended Reading

1. Sumitabha Das,” Unix Concepts and Applications”, Tata McGraw-Hill, 4th edition, 2017.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS133 - DIGITAL LOGIC AND ASSEMBLY LANGUAGE PROGRAMMING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The proposed course provides a foundation in design and analysis of the operation of digital gates. Design and implementation of combinational and sequential logic circuits. Concepts of Boolean algebra, Karnaugh maps, flip-flops, registers, and counters along with various logic families and comparison of their behavior and characteristics are discussed. This course also covers microprocessor basic architecture, various instruction sets.

Learning Outcome

Upon successful completion of the course, students should be able to:

CO 1: Understand the number systems and conversions from one to another.

CO 2: Apply Boolean algebra and methods to simplify the expression using K- maps and design logic circuits.

CO 3: Understand the concepts and working of sequential circuits and combinational circuits,

CO 4: Identify the basic element, functions, and architecture of 8085 microprocessor. 

CO 5: Develop a microprocessor-based assembly language program.

Unit-1
Teaching Hours:12
Introduction
 

Number System (Binary, Hexadecimal, Octal, Decimal, Binary Arithmetic, Hexadecimal addition & subtraction, BCD addition & subtraction – 1's and 2's Compliment addition and subtraction, Basic gates, Universal gates, Positive and negative logic, Boolean Algebra, Simplification using Boolean Laws. 

DeMorgan’s Theorem, Karnaugh map, sum of products, Pairs-Quads-Octets, Karnaugh map simplifications, Don't care conditions. Multiplexers, Demultiplexers, Decoders.

Unit-2
Teaching Hours:12
Sequential Circuits
 

RS Flip-Flops, Edge-Triggered RS, D, JK Flip-Flops, Flip-Flop Timing, JK Master-Slave  Flip-Flops. Types of Registers, Serial in-Serial out, Serial in-Parallel out, Parallel in-Serial out, Parallel in-Parallel out. Asynchronous Counters, Synchronous Counters, Decade Counters.

 

Unit-3
Teaching Hours:12
Introduction to Microprocessors
 

Introduction to Microprocessors      

Microprocessor Architecture and its operations – Address Bus, Data Bus, Control Bus, Internal data operations and Registers, The 8085 MPU – Architecture, Communication and Bus Timings, Demultiplexing the Bus, Generating Control Signals.    

Self-learning

Memory, I/O Devices.

Unit-4
Teaching Hours:12
8085 Programming model
 

8085 Programming model                  

The 8085 programming model, Instruction classification, Instruction and Data Format, Data Transfer Operations, Arithmetic Operations, Logic Operations, Branch Operations, Writing ALP Programs. 

Looping, Counting, Indexing, Additional Data Transfer and 16-Bit Arithmetic Instructions, Arithmetic operations related to memory, Rotate and Compare of Logic operations. Assembly Language Programming – Addition of two 8-bit Hexadecimal numbers, Addition of N Hexadecimal numbers, Interchange N one byte numbers, etc.

Unit-5
Teaching Hours:12
Time delays and Interrupts
 

Counters and Time Delays, Stack, Subroutines, Restart –Simulate a decade counter to count up to 99. The 8085 Interrupt – RST, SIM and RIM Instructions, Multiple Interrupts and Priorities, 8085 Vectored Interrupts – TRAP, RST 7.5, 6.5, 5.5.

Text Books And Reference Books:

[1] Mano, Morris M and Kime, Charles R, Logic and Computer Design Fundamentals, Pearson Education, 4th Edition, 2013

[2] Ramesh.S. Goankar Microprocessor Architecture, Programming & Applications With 8085, Penram International, 5th Edition, 2011. 

 

Essential Reading / Recommended Reading

[1] Tokheim, Digital Electronics Principles and Applications, Tata McGraw-Hill, 6th Edition, 2010.

[2] Charles M Gilmore, Pal Ajit, Microprocessor Principles and Applications, Tata McGraw-Hill, 2nd Edition, 1998. 

[3] Hall. D.V, Microprocessor and Digital System, McGraw Hill Publishing Company, 2nd Edition, 1990.

Evaluation Pattern

CIA-50%

ESE-50%

MCS134 - ADVANCED DATABASE MANAGEMENT SYSTEM (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The Course focus on providing  strong foundation for database design and application development and helps in understanding the underlying core database concepts and emerging database technologies. Practical concepts of PLSQL and Document based databases are discussed.

Learning Outcome

The successful completion of this course will enable the students to: 

CO 1: Develop  basic understanding of  Database management system concepts.

CO 2: Apply SQL and PLSQL concepts to solve real world problems.

CO3: Analyze the relevance of various advanced database systems and their applications in real world.

 

Unit-1
Teaching Hours:12
Conceptual Modeling and Database Design
 

Using High-Level Conceptual Data Models for Database Design - Entity Types, Entity Sets, Attributes, and Keys - Relationship Types, Relationship Sets, Roles, and Structural Constraints - Weak Entity Types - ER Diagrams, Naming Conventions, and Design Issues - Relationship Types of Degree Higher than Two - Subclasses, Superclasses, and Inheritance – Enhanced Entity Relationship Model - Relational Database Design by ER- and EER-to-Relational Mapping - Role of Information Systems in Organizations - Database Design and Implementation Process

Unit-2
Teaching Hours:12
Normalization, File Organization and Indexing
 

Design Guidelines for Relation Schemas - Functional Dependencies - Normal Forms Based on Primary Keys - Second and Third Normal Forms - Boyce-Codd Normal Form - Multivalued Dependency and Fourth Normal Form - Join Dependencies and Fifth Normal Form - Inference Rules, Equivalence and Minimal Cover - Properties of Relational Decompositions - Nulls and Dangling Tuples - File Organization - Organization of Records in Files - Ordered Indices - B+ Tree Index Files - Static Hashing - Bitmap Indices

Unit-3
Teaching Hours:12
PL/SQL Programming
 

PL/SQL Block Structure – Identifiers – Literals – Comments - Conditional and Sequential Control - Iterative Processing with Loops - Exception Handlers – Data Retrieval with Cursors - Procedures, Functions, and Parameters – Packages

Unit-4
Teaching Hours:12
Document-Oriented Database
 

Introduction - Documents and Collections - Data Types - Create, Read, Update and Delete Operations - Querying using Find - Query Criteria – Type-Specific Queries – Where Queries

Unit-5
Teaching Hours:12
Emerging Database Technologies and Applications
 

Mobile databases – Multimedia Databases – Geographic Information Systems – Genome Databases

Text Books And Reference Books:

[1] Elmasri, Navathe, Fundamentals of database systems, Pearson, Sixth Edition, 2014.

[2]Korth, Sudershan, Database System Concepts, McGraw Hill, Sixth Edition, 2013.

[3]Kristina Chodorow, MongoDB: The Definitive Guide, O’Reilly, Second Edition, 2013.

[4]Steven Feuerstei, Oracle PL/SQL Programming, O’Reilly, Sixth Edition, 2014.

Essential Reading / Recommended Reading

[1] Ramakrishnan and Gehrke, Database Management System, McGraw-Hill, 3rd  Edition, 2003.

[2] ShashankTiwari, Professional NoSQL, Wrox, Second Edition, 2013.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS135 - DISCRETE MATHEMATICAL STRUCTURES (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course description: This is a fundamental course in Discrete Mathematics involving Mathematical Logic, functions, Divisibility, Congruences, Graph Theory and Group Theory.

Course objectives: To prepare the students for a background in abstraction, notation, and critical thinking in the Discrete Mathematics topics which are closely related  to Computer Science.

Learning Outcome

The successful completion of this course will enable the students to:

CO1: construct mathematical arguments using logical connectives and quantifiers.

CO2: verify the correctness of an argument using propositional and predicate logic and truth tables.

CO3: understand how Graphs  are used as tools and Mathematical Models in  the study of networks.

CO4: apply algorithms and use definitions to solve problems to prove statements in elementary number theory.

CO5: perform operations on discrete structures such as sets, relations and functions and be familiar with concepts like Groups and Rings.

Unit-1
Teaching Hours:15
Foundations
 

How to do Mathematics? – Compound statements – Existential and Universal statements – Proof techniques – Logical operations – Logical equivalence- Conditional statements – Universal and Existential quantifiers – Concept of a function – Types of functions – Composition of functions.  

Unit-2
Teaching Hours:15
Techniques
 

Introduction to numbers – Divisibility – Greatest common divisor – Existence and uniqueness of prime factorization – Partition of a set – Partition of a positive integer –Even and odd permutations – modular arithmetic – Latin squares.

Unit-3
Teaching Hours:15
Networks
 

Types of relations – Graphs as network – Types of graphs-Representation of graphs – Representation of relations through graphs – Paths and Cycles- Eulerian and Hamiltonian properties of paths – Equality of graphs – Trees – Coloring of graphs – Max-Flow –Min-Cuttheorem.

Unit-4
Teaching Hours:15
Algebraic Structures
 

Groups – Axiom of a group – Examples and basic algebra in groups – Order of an element of a group – Isomorphism of groups – Cyclic groups – Subgroups – Cosets and Lagrange’s theorem – Rings-Fields. 

Text Books And Reference Books:

N L Biggs, Discrete Mathematics, Oxford University Press, New Delhi,  2nd  Edition, 2003. 

Essential Reading / Recommended Reading

R. P. Grimaldi,  Discrete and Combinatorial Mathematics, Pearson education, 5th Edition, 2004.

B. Kolman, R. C. Busby and S. C. Ross,  Discrete Mathematical Structures, Pearson Education,  5th Edition, 2004.

T. Koshy, Discrete Mathematics with Applications, Elsevier Academic Press,London,  2004.

K. H. Rosen,  Discrete Mathematics and Its Applications,  Tata McGraw-Hill, 6th  Edition, 2006.

G.S. Rao,  Discrete Mathematical Structures,  New Age International, 2009.

J. P. Trembly and R. Manohar,  Discrete Mathematics with Applications  to Computer Science,Tata McGraw-Hill, 2003.

Evaluation Pattern

Component

Mode of Assessment

Parameters

Points

CIA I

Written Assignment

Reference work 

Mastery of the core concepts 

 

10

CIA II

Mid-semester Examination

Basic, conceptual and analytical knowledge of the subject

 

25

CIA III

Written Assignment

Class Test

Problem solving skills, Familiarity with the proof techniques

10

Attendance

Attendance

Regularity and Punctuality

05

ESE

 

Basic, conceptual and analytical knowledge of the subject

50

Total

100

MCS136 - RESEARCH METHODOLOGY (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The research methodology module is intended to assist students in planning and carrying out research projects. The students are exposed to the principles, procedures and techniques of implementing a research project. The course starts with an introduction to research and carries through the various methodologies involved. The process involeved in research from Identifying and wrinting problem statement, conducting organized literature survey, basic statistics required for research and ends with linear regression.

 

Learning Outcome

Upon completion of the course, the student will be able to

CO1: Understand the acquired knowledge to select and formulate research problem statement effectively.

CO2: Analyze and apply the concept of collected relevant literature.

CO3: Investigate various statistical techniques to propose a research design to find a solution for a research problem

CO4: Demonstrate the proficiency to write an appropriate research article.

Unit-1
Teaching Hours:12
Research Methodology
 

Defining research problem - selecting the problem - necessity of defining the problem - techniques involved in defining a problem- Ethics in Research

Unit-2
Teaching Hours:12
Research Design
 

Principles of experimental design:  Working with Literature Importance, finding literature, using your resources, managing the literature, keep track of references, using the literature, literature review

On-line Searching: Database – SciFinder – Scopus - Science Direct - Searching research articles - Citation Index - Impact Factor - H-index etc,

Unit-3
Teaching Hours:12
Research Data
 

Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation.

Unit-4
Teaching Hours:12
Statistics
 

Descriptive Statistics: Measures of Central Tendency, Measures of Dispersion, Measure of Skewness, Kurtosis, Measure of Relationship. Linear Regression Analysis: Dependent and Independent variables, Simple Linear Regression Model.

Unit-5
Teaching Hours:12
Report Writing
 

Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, text, tables, figures, equations, citations, referencing, and templates (IEEE style), paper writing for international journals, Writing scientific report.

Text Books And Reference Books:

[1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014.

[2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.

Essential Reading / Recommended Reading

[1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th. ed. SAGE Publications, 2014.

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010.

Evaluation Pattern

Exercises:

•  Review 1 Pre-Selected Papers  (U: Unique for each student)

•  Review 1 papers in areas of scholars choice (U)

•  Using an IEEE MS Word Template and convert literature review done in previous paper reviews. Follow Reference Styles

•   Gather data from Wikipedia and populate a spreadsheet. (U)

•   Using the data collected, analyze the data using 7 different spreadsheet statistics functions.

•   Script a paper in IEEE LATEX template(U)

 Evaluation Pattern

CIA for this course is similar to other regular courses in the curriculum as per university guidelines. Additionally, students have to maintain a record of the following:

•   Papers reviewed (minimum 5 papers)

•   Data collected (7 different graphs)

•   Analysis of data

•   Statistical functions

 

ESE is conducted by the department during which each student should come up with a research proposal of any domain of his/her interest. Students should incorporate the research objectives and methodologies along with justification for why those methodologies were suggested to achieve the objectives. Each student is evaluated based on a presentation that highlights the application of various research methodology concepts learnt in the semester.

Evaluation Rubrics

S. NoCriteria for Evaluation                                       Marks

1CIA I, Mid Semester Examination (MSE), CIA III          45

2End Semester Examination (ESE)                                     50

3Attendance                                                                           5                 

MCS151 - JAVA PROGRAMMING LAB (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

To introduce the concepts and principles of Java Programming language and to design, implement object-oriented solutions to simple and complex problems. To give students experience in Java Programming and program development within an integrated development environment. 

Learning Outcome

Upon completion of the course, the student will able to

CO 1: Understand the features of Object Oriented Programming Languages

CO 2: Design / Develop highly professional applications in Java targeting to the Industry standards

CO 3: Apply logical and critical thinking skills in developing applications to address the dynamic requirements coming from the Industry 

Unit-1
Teaching Hours:60
Section A
 

1. Demonstrate various data types and operators.

2. Demonstrate method overloading and constructor overloading.

3. Demonstrate the usage of static keyword in Java – use static data and static block.

4. Demonstrate the final keyword with respect to variable, method, and class.

5. Demonstrate inner classes in Java.

6. Demonstrate multilevel inheritance and usage of the keywords this & super.

7. Demonstrate an abstract class.

8. Demonstrate the usage of interface for multiple inheritances.

9. Differentiate the usage of throw, throws, and try-catch-finally by writing a Java program.

 

Unit-1
Teaching Hours:60
Section B
 

10. Demonstrate various I/O streams in Java.

11. Demonstrate the Reader/Writer classes in Java.

12. Demonstrate the multithreading concept by implementing Runnable interface.

13. Demonstrate the multithreading concept by extending Thread class.

14. Make some graphics in an applet program using paint function.

15. Demonstrate the usage of different Layouts in Java.

16. Demonstrate various GUI components in Java (AWT / SWING) with appropriate Event Handling.

17. Implement two way communication between server and client.

18. Retrieve data from the table of the database.

 

 

Text Books And Reference Books:

[1] Schildt Herbert, Java: The Complete Reference, Tata McGraw-Hill, 10th Edition, 2017.

 

Essential Reading / Recommended Reading

[1] Deitel&Deitel, Java How to Program, Pearson Education Asia, 8th Edition, 2010.

[2] Rao Nageswara, Core Java, An Integrated Approach, Dreamtech press, 2nd Edition, 2010.

 

 

Evaluation Pattern

CIA-50%

ESE-50%

MCS152 - UNIX LAB (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

The course provides comprehensive understanding of the philosophy and development of the layered architecture of UNIX operating system. It also provides learning opportunities to appreciate the principles and system calls for File System, Process Management, Inter Process Communication, Memory Management and IO System.

 

Learning Outcome

Upon completion of the course , the student will be able to

CO1: Understand the UNIX Operating System Architecture

CO2: Analyse system calls for file system, process management ,kernel working and Inter Process Communication

CO3: Apply memory management concepts and  IO system principles

Unit-1
Teaching Hours:60
Section A
 

1.     Write a shell script to print prime numbers up to a given range using arguments

2.     Write a shell script to find out the factorial of a number using functions

3.     Write a shell script to count the number of lines in a file accepted as command line argument

4.     Write a shell script which merge the contents of file1, file2, file3, sort them and display the sorted output on the screen page by page

5.     Write a shell script to locate users who have logged in today or earlier but have not logged out and mail the list to root. Users who have logged more than once should appear in the list only once

6.     Write a program to demonstrate open, read, write and close system calls

7.     Write a program to demonstrate dup, link, unlink and lseek system calls

8.     Write a program to demonstrate fork and wait / waitpid system calls

9.     Write a program to demonstrate exec family of system calls

10.  Write a program to demonstrate thread creation, waiting and termination of threads

 

Unit-1
Teaching Hours:60
Section B
 

1.     Write program to demonstrate file descriptor behavior in fork and exec family system calls

2.     Write a program to demonstrate two-way data transfer between processes using pipes

3.     Write a program to demonstrate two-way data transfer between processes using FIFOs

4.     Write a program to demonstrate two-way data transfer between processes using message queues

5.     Write a program to demonstrate two-way data transfer between processes using shared memory

6.     Write a program to implement a iterative server and client using TCP sockets

7.     Write a program to implement a forked concurrent server and client using TCP sockets

8.     Write a program to implement a iterative server and client using UDP sockets

9.     Write a program to demonstrate thread synchronization using mutex

10.  Write a program to demonstrate thread synchronization using semaphore

Text Books And Reference Books:

1. Bach M.J., “The Design of the Unix Operating System”, Prentice Hall India, reprint 2009.

2. Richard Stevens, “Advanced programming in the UNIX environment “, Addison Wesley, 3rd edition, 2013

 

Essential Reading / Recommended Reading

1. Sumitabha Das,” Unix Concepts and Applications”, Tata McGraw-Hill, 4th edition, 2017.

 

Evaluation Pattern

CIA-50%

ESE-50%

MCS231 - DATA STRUCTURES (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Data Structure is considered as one of the fundamental subjects towards a more comprehensive understanding of programming and application development. Student is expected to work towards a sound theoretical understanding of Data Structures and also compliment the same with hands on implementing experience 

Learning Outcome

The successful completion of this course will enable the students to: 

CO 1: Understand the need for Data Structures when building applications.

CO 2: Analyse the efficiency to optimize the algorithms

CO 3: Apply  algorithms for linear and non-linear data structures.

CO 4: Implement various data structures and their algorithms for real time applications.

Unit-1
Teaching Hours:12
Introduction and overview, Stacks and Queues
 

Introduction and overview        

Introduction, Basic Terminology, Data Structures, Operations, Algorithms: Time & Space Complexity, Algorithmic Notation, Abstract Data Types. Programming standards and ethics.

Stacks and Queues         

Stacks, Array Representation, Arithmetic Expressions, Polish Notation, Application of Stacks, Recursion, Towers of Hanoi, Implementation of Recursive procedures by Stack, Queues, Queue Array RepresentationIntroduction and overview        

 

Introduction, Basic Terminology, Data Structures, Operations, Algorithms: Time & Space Complexity, Algorithmic Notation, Abstract Data Types. Programming standards and ethics.

 

Stacks and Queues         

Stacks, Array Representation, Arithmetic Expressions, Polish Notation, Application of Stacks, Recursion, Towers of Hanoi, Implementation of Recursive procedures by Stack, Queues, Queue Array Representation.

Unit-2
Teaching Hours:11
Linked Lists
 

Introduction, Linked lists and Memory Representation, Traversing, Searching, Memory Allocation, Garbage Collection, Insertion, Deletion, Circular Linked list, Two-way Lists (Doubly). Linked List Implementation of Stack and Queue

Self Learning

Infix to Prefix

 

Unit-3
Teaching Hours:12
Sorting, Searching
 

Sorting          

Introduction, Sorting, Insertion Sort, Selection Sort, Shell Sort, Merging, Merge-Sort, Quick Sort, Radix Sort, External Sorting.

Searching 

Hashed List Searches: Hashing Methods - Direct method, Subtraction Method, Modulo-division Method, Digit-extraction Method, Midsquare Method, Folding Method, Rotation Method, Pseudorandom Hashing.

Collision Resolution – Open addressing, Linear Probe, Quadratic Probe, Pseudorandom Collison Resolution, Linked List Collision Resolution, Bucket Hashing, Combination Approaches.  Text Searching using Knuth-Morris-Pratt algorithm.

Unit-4
Teaching Hours:12
Trees, Balanced Tree
 

Trees 

Introduction, Binary Trees, Representing Binary Trees in memory, Traversing Binary Trees, Traversal Algorithms, Binary Search Trees, Searching, Inserting and deleting in Binary Search Trees, Heap, Heap sort, Huffman’s Algorithm.

Balanced Tree 

AVL Trees: AVL Balance Factor, Balancing Trees, AVL node structure, AVL Tree Rotate Algorithms.

Self learning

Heap, Heap Sort, splay and Red Black tree.

Unit-5
Teaching Hours:13
Multiway Search Trees, B-Trees, Graphs
 

Multiway Search Trees, B-Trees          

B-Trees: B-Tree insertion, Deletion, Traversal and Search algorithm, Simplified B Trees, 2-3 Tree, 2-3-4 Tree, Variations of B Tree - B+ Tree, B* Tree.

Graphs          

Graph Theory Terminology, Sequential representation of Graphs, Adjacency matrix, Path matrix, Linked representation of a Graph, Operations on Graphs, Depth First and Breadth First Traversing a Graph, Minimum Spanning Tree Algorithm.

 

Text Books And Reference Books:

[1] Gilberg, F Richard &Forouzan, A Behrouz, Data Structures A Pseudocode approach with C,2nd Edition, Cengage, 2008.

 

 

 

Essential Reading / Recommended Reading

[1] Horowitz Sahni Anderson-Freed, Fundamental of Data Structures in C, Universities Press, Reprint 2008.

[2] Richard Johnsonbaugh, Algorithms, Pearson Education, 2nd Edition, 2008

[3] Robert Sedgwick, Algorithm in C++, Addison-Wesley Publishing Company. [4] Knuth, Donald E, Art of Computer Programming, Sorting & Searching, Addison-Wesley, 2005.

 

Evaluation Pattern

CIA-50%

ESE-50%

MCS232 - MOBILE APPLICATION (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This Course Description aimed at helping learners create applications using Google's Android™ open- source platform. The Course Description explains what Android™ is and how it compares to other mobile environments, the setup of the Android™ Eclipse-based development tools, the Android™ SDK, all essential features, as well as the advanced capabilities and APIs such as background services, accelerometers, graphics, and GPS. 

Learning Outcome

Upon successful completion of the course student will be able to

CO 1: Build your own Android apps.

CO 2: Explain the differences between Android and other mobile development environments.

CO 3: Design and develop useful Android applications with compelling user interfaces by using extending and creating your own layouts and views and using menus.

CO 4: Secure, tune, package and deploy Android Applications.

Unit-1
Teaching Hours:12
Introduction
 

Brief History of Embedded Device Programming, Introduction to Android, Get to know the required tools, Creating your first Android application, Anatomy of android Application. Understanding Activities, linking Activities using intents, fragments, calling Built-in Applications using Intents, Displaying Notifications.

Unit-2
Teaching Hours:12
User Interface and Designing with views
 

Understanding the components of a screen, adapting to display orientation, managing changes to screen orientation, Utilizing the Action Bar, Creating the user Interface programmatically,

Listening for UI Notifications. Using Basic Views, Using Picker views, Using List views to display lists, Understanding specialized fragments.

Unit-3
Teaching Hours:12
Displaying with views, Data persistence and Content Providers
 

Using Image Views to display pictures, using menus with views, some additional views. Saving and loading user preferences, persisting Data Files, Creating and using Databases. Sharing Data in Android, using content provider, creating your own content providers, using content providers.

Unit-4
Teaching Hours:12
Messaging and Location based services
 

SMS Messaging, Sending E-mail, Displaying Maps, Getting Location Data, Monitoring a Location. Hands on project: Building a Location Tracker

Unit-5
Teaching Hours:12
Creating Services
 

Creating your own services, Establishing Communications between a service and an activity, binding activities to services

Self Learning

understanding Threads, Preparing for Publishing, Deploying APK Files.

 

Text Books And Reference Books:

[1] Wei-Meng Lee, “Beginning android 4 application Development, John Wiley & sons, Inc, 2012.

Essential Reading / Recommended Reading

[1] Paul Deitel-Harvey Deitel-Abbey Deitel-Michael Morgano,” Android for Programmers An App-Driven Approach”, Pearson Education Inc., 2012.

[2] Jerome (J.F) DiMarzio, "Android - A programmer's Guide", Tata McGraw Hill,2010, ISBN: 9780071070591

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

 

 

 

 

 

MCS241A - DIGITAL IMAGE PROCESSING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The main objective of this course is to give strong foundation on concepts and algorithms widely used in Digital image processing. It provides hands-on experience for processing images using various techniques.

Learning Outcome

Upon successful completion of the course the student would:

CO1: Comprehend the knowledge of image processing and image analysis techniques
CO2: Analyze image processing techniques in both the spatial and frequency domain for two-dimensional images.  
CO3: Design algorithms to solve real time problems for Classification and compression.

Unit-1
Teaching Hours:12
Introduction and Digital Image Fundamentals
 

The origins of Digital Image Processing, Fundamental Steps in Image Processing, Elements of Digital Image Processing System, Image Sampling and Quantization, Basic relationships: Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations.

Unit-2
Teaching Hours:12
Image Enhancement in Spatial Domain & Frequency Domain
 

Image Enhancement in Spatial Domain

Gray Level Transformations, Histogram Processing, Histogram equalization, Histogram specification, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters.

Image Enhancement in Frequency Domain 

Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening, Frequency Domain Filters.

Self Learning

Homomorphic Filtering

Unit-3
Teaching Hours:12
Image Restoration and Image Compression
 

A model of The Image Degradation / Restoration Process, Noise Models, Restoration in the presence of Noise, Periodic Noise Reduction by Frequency Domain Filtering.  Image Compression models: Huffman coding, Run length coding, LZW coding.

Unit-4
Teaching Hours:12
Image Segmentation and Representation
 

Point, Line and Edge detection. Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method. Region Based Segmentation – Region Growing and Region Splitting and Merging. Representation – Chain codes,

 Self Learning

Polygonal approximations using minimum perimeter polygons.

Unit-5
Teaching Hours:12
Description and Object Recognition
 

Boundary descriptors – Fourier descriptors. Regional descriptors –Topological descriptors and Moment invariants. Introduction to Patterns and Pattern Classes. Decision-Theoretic Methods – Minimum distance classifier, K-NN classifier and Bayes’

Self Learning

classifier

Text Books And Reference Books:

[1] R. C. Gonzalez & R. E. Woods, Digital Image Processing, 3rd Edition.Pearson Education, 2009.

[2] A.K. Jain, Fundamental of Digital Image Processing, 4th Edition.P HI, 2011.

[3] Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, Digital Image Processing Using MATLAB, 2nd Edition. PHI, 2009.

Essential Reading / Recommended Reading

[1] M. A. Joshi, Digital Image Processing: An algorithmic approach, 2nd Edition.  PHI 2009.

[2] B. Chanda, D. Dutta Majumdar, Digital Image Processing and analysis, 1st Edition, PHI,2011.

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCS241B - SOFTWARE DEFINED NETWORKS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The purpose of this course is to introduce the students with basics of computer networks. The concepts covered throughout the semester includes topics related to SDN (Software Defined Networking), a new era in networking that is influencing the modern network as well as changing the future of networks and services. During the study of this course, student will learn how SDN made a paradigm shift in telecommunications and networking that is one of the rare tectonic shifts in an industry in the last 30 years. One of the goals of this subject is to prepare attendees for a market that is going to demand computer scientists and software engineers to deliver the next generation of software centric networks.

Learning Outcome

CO1: Understand the fundamentals of SDN and the characteristics of SDN controllers and their implications.

CO2: Apply SDN for security and scalability.

CO3: Dvelop SDN for real time applications.

Unit-1
Teaching Hours:12
Introduction to Computer Networks, Protocols and Network Programming
 

Introduction to the OSI stack and network protocols. Understand the mechanisms of network elements (switches, routers). How do legacy networks work? Introduction to VPN and MPLS. Identify the shortcomings of today’s legacy networks. Birth of network programmability and SDN.  

Unit-2
Teaching Hours:12
Introduction to SDN Networks
 

 

SDN Architecture. OpenFlow switch. Traffic matching with traditional networks. OpenFlow. Ports. Control and data plane separation. Centralization of control. Introduction to controllers. Southbound interfaces (e.g. OpenFlow). Virtualization. NBI. SBI. Abstraction. PCEP. What is NFV? Spanning Tree.

Unit-3
Teaching Hours:12
SDN Tools
 

VMware, Controllers, Ryu, OpenDaylight, Floodlight, ONOX,POX. Analyzing tool: Wireshark and Gnuplot. Topology creation using Mininet. Customizing topology with own python script to run your own network topology and connect to the controller. Capture OpenFlow message format and message sequence. Testing communication between the hosts. IPerf and Gnuplot.

Unit-4
Teaching Hours:12
Large scale Wireless SDNs:
 

Running OFM on top of ODL. ACL with help of OFM. Large topology generation using GNS3, Docker container and VM. Multiple controllers on single network. Using WiFi-Mininet generate wireless SDN Networks. SDWAN

Unit-5
Teaching Hours:12
SDN Applications
 

Real World SDN –Google, Real World SDN – Microsoft, Real World SDN – NSA, Real World SDN – Facebook, Linux on switches, Analyze which SDN Controller should be used in which scenario.

Text Books And Reference Books:

1. Software Defined Networking, Network Function Virtualization, and Quality of Experience: Foundations of Modern Networking, William Stallings, Addison-Wesley, (2015).

2. Software Defined Networks: A Comprehensive Approach, Paul Goransson, Chuck Black, Morgan Kaufmann; 1 edition (June 6, 2014).

Essential Reading / Recommended Reading

1. Network Programmability and Automation, Volume 1 (Networking Technology), by Khaled Abuelenain and Karim Okasha, (Jan,2019).

2. SDN a Complete Guide, by Gerardus Blokdyk, 2019 edition (Dec 21, 2018).

 

Web Resources:

1.https://www.opennetworking.org/sdn-definition/

2.https://www.sdxcentral.com/sdn/definitions/what-the-definition-of-software-defined-networking-sdn/

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCS241C - SOFTWARE QUALITY AND TESTING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This Course helps the student to understand the need for Software Quality, Tools Used and Metrics involved. It focus on the learning of  software testing principles and methods to detect the ever changing software technological changes.

 

Learning Outcome

Student will be able to :

CO1: Understand the concepts of Software Quality framework,standards and Measurement theory.

CO 2: Apply the different Software Quality Tools.

CO 3: Analyze  the advantages of Extreme Testing and High Order Testing

CO 4: Create effective test plan and test cases.

 

 

 

Unit-1
Teaching Hours:12
Introduction to Software Quality, Framework and Quality Standards
 

 

Quality: popular view, Quality: professional view, software quality, total quality management, The defect prevention process, process maturity framework and quality standards (CMM, SPR Assessment, Malcolm Bridge, ISO9000)

Unit-2
Teaching Hours:12
Fundamentals in Measurement Theory and Metrics
 

Fundamentals in Measurement Theory  

Levels of measurement some basic measures, reliability and validity

Software quality metrics

Product Quality Metrics, in-process quality process, example of Metrics Program – Motorola, HP

Unit-3
Teaching Hours:12
Basic Quality Tools and Defect Removal
 

Seven Basic Quality Tools      

Ishikawas’ seven basic tools, checklist, pareto diagram, histogram, runchart, scatter diagram control chart cause and effect diagram.

Defect Removal Effectiveness

Literature review, a close look at DRE, defect removal effectiveness and quality planning

 

 

Unit-4
Teaching Hours:13
Fundamentals of Software Testing
 

 

Software Testing Principles, Economics of Testing Inspection and walkthrough, code inspection, an error checklist for Inspection, Walkthroughs, Desk Checking, Peer Rating Module Testing

Self Study

Two testing tools

Unit-5
Teaching Hours:12
High Order Testing, Debugging and Extreme Testing
 

 

High Order Testing - Debugging by Brute Force, Induction, Deduction, Backtracking Extreme Programming basics, Extreme Testing, Extreme Testing Applied

Java Unit Testing, Automatic testing

Text Books And Reference Books:

[1] Stepen H Kan, Metrics and Models in Software Quality Engineering, 2nd Edition, reprint 2006

[2] S A Kelkar, Software Quality and Testing, PHI, 1st Edition, 2012.

 

Essential Reading / Recommended Reading

[1] GlenfordJ.Myers, The Art of Software Testing” John Wiley and Sons publications, 2004

 

 

Evaluation Pattern

CIA-50%

ESE-50%

MCS241D - DATA WAREHOUSING AND DATA MINING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

 This course is designed to introduce the core concepts of data mining, techniques, implementation, benefits, and outcome expectations from this new technology and to understand the concepts of Data Warehouse system.

Learning Outcome

Upon successful completion of the course, the Students will able to

CO1:Demonstrate basic data mining algorithms, methods, tools and OLAP.
CO2:Apply data mining principles and techniques.

CO3:Build the Data warehouse for real time applications.

Unit-1
Teaching Hours:12
Introduction to data Mining
 

Data Mining – Process and architecture - Kinds of Data to be mined - Data Mining Functionalities, Classification of Data Mining Systems, Data Mining Task Primitives, Major Issues in Data Mining.

Unit-1
Teaching Hours:12
Data Preprocessing
 

Preprocessing - Descriptive Data Summarization – Measuring the central tendency- Measuring the dispersion of data.

Unit-2
Teaching Hours:12
Data Mining Algorithms
 

Association Rule Mining: Basic Concepts, Efficient and Scalable Frequent Item set Mining Methods – Apriori algorithm.

Unit-2
Teaching Hours:12
Data Preprocessing (cont.,)
 

Data Cleaning - Missing Values – Noisy Data - Data Cleaning as a Process - Data Integration and Transformation - Data Reduction-Data Cube Aggregation-Attribute Subset Selection.

Demo: Preprocessing can be done using WEKA tool.

Unit-3
Teaching Hours:12
Generating Rules
 

Generating Rules – Improving efficiency – Mining frequent item set without candidate generation. Classification and Prediction: Issues Regarding Classification and Prediction, Accuracy and Error Measures.

Unit-3
Teaching Hours:12
Cluster Analysis
 

Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods – K-Means and K-Medoids, Hierarchical Methods – Agglomerative and Divisive.

Unit-3
Teaching Hours:12
Demo
 

Classification and clustering analysis can be done using WEKA tool.

Unit-4
Teaching Hours:12
Building a data warehouse
 

Business considerations, Design considerations, technical considerations, implementation considerations, integrated solutions, benefits of data warehousing, Relational data base technology for data warehouse, database architectures for parallel processing, parallel RDBMS features, alternative technologies.

Unit-5
Teaching Hours:12
DBMS schemas for decision support
 

Data layout for best access, multidimensional data model, star schema, STARjoin and STARindex, bitmapped indexing, column local storage, complex data types, Data extraction, clean up and transformation tools-tool requirements, vendor approaches, access to legacy data, vendor solutions, transformation engines

Text Books And Reference Books:

 [1] Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, USA, 2nd Edition, 2012.

 [2]  Kimball, Ralph & et al, The Data Warehouse Lifecycle Toolkit, John Wiley &Sons, 2013.

Essential Reading / Recommended Reading

[1] Berson Alex, Stephen J Smith, “Data Warehousing, Data Mining and OLAP”, TATA McGraw-Hill, 13th reprint 2017.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS241E - COMPUTER GRAPHICS UISING OPEN GL (2019 Batch)

Total Teaching Hours for Semester:100
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To familiarize the students with the concepts of computer graphics like line, circle drawing algorithms, transformations, clipping, projection, color models, curves. To make the students understand how to implement the computer graphics concepts using OpenGL.

Learning Outcome

At the conclusion of course, students are able to:

CO 1: Demonstrate the concepts of Computer Graphics. 

CO 2:Analyze the different algorithms and models in Graphics.

 

CO 3: Implement the Graphics concepts using OpenGL.

Unit-1
Teaching Hours:12
Introduction to Computer Graphics
 

Applications, Overview of Graphics Systems – Video display devices, Raster-scan systems, Graphics software, Introduction to OpenGL.

Graphics Output Primitives

Coordinate Reference Frames, Two-Dimensional frame in OpenGL, Point Functions, Line Functions, Line-Drawing Algorithms – DDA, Bresenhams, Curve Functions, Midpoint Circle Algorithm, and Display-window reshape function.

 

 

Self-Learn: Area filling, Display lists, Basic colors, Attribute functions.

 

Unit-2
Teaching Hours:12
Geometric Transformations
 

Basic two-dimensional geometric transformations, Homogeneous Coordinates, Composite transformations, Geometric transformations in three-dimensional space, Translation, Rotation, scaling, composite three-dimensional transformations, OpenGL geometric transformation functions.

Self-Learn: Reflection, shear.

Unit-3
Teaching Hours:12
Illumination and Color Models
 

Light sources, Basic illumination models, transparent surfaces, OpenGL illumination functions. Color Models, Standard primaries and chromaticity diagrams, RGB color model, HSV color model. OpenGL color functions.

Self-Learn: Ray-tracing and Texture mapping.

 

Unit-4
Teaching Hours:12
Viewing
 

Two-dimensional viewing pipeline, clipping window, Normalization and viewport transformations, 2D viewing functions, Clipping Algorithms – Line clipping – Cohen- Sutherland and Liang-Barsky Line clipping, polygon clipping – Sutherland-Hodgman algorithm.

Three-dimensional viewing concepts – Projections, Three-dimensional viewing pipeline, Projection transformation, Parallel and Perspective projection matrices. 3D viewing functions. Self-Learn: Other clipping algorithms, Text clipping, and Projection derivations.

Unit-5
Teaching Hours:12
Three-dimensional Object Representations
 

Spline representations, Cubic spline interpolation methods, Bezier curves and B-Spline curves. OpenGL approximation-Spline functions.

 

Text Books And Reference Books:

[1] D. Hearn, M. Pauline Baker, Computer Graphics with OpenGL. PHI, 3rd Edition, New Delhi, 2011.

Essential Reading / Recommended Reading

[1] Foley, Vandam&Feiner, Hughes, Computer Graphics Principles &Practice, in C, Pearson Education (Singapore Pvt Ltd, Indian Branch, Delhi), 6th Indian Reprint 2001.

[2] Richard S Wright, Jr. Michael Sweet, Open GL Super Bible, 2nd Edition. 

[3] Woo, Mason and Neider, Jackie, Open GL Programming guide.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS241F - COMPUTER ARCHITECTURE (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To enable the students to learn the basic functions, principles and concepts of Computer Architecture, learn the fundamental aspects of Computer Architecture and design, focus on processor design, control unit design techniques and study on I/O interfacing.

 

Learning Outcome

On successful completion of the course the students should have

CO 1: Demonstrate the fundamentals of  Computer Architecture.

CO 2: Understand the working of  number systems, I/O, Registers and Memory.

CO 3: Analyze the concepts of  processor design, control unit design and I/O interfacing.

 

Unit-1
Teaching Hours:13
Computer system and Memory
 

Computer components – computer function – instruction fetch and execute – interrupts – I/O functions – interconnection structures – Bus interconnection – Bus structure – multiple bus hierarchies – elements of bus design.

Memory

Computer memory system overview – characteristics of memory system – memory hierarchy – cache memory principles – elements of cache design- cache size – mapping function – replacement algorithms – write policy – internal memory semiconductor memory – organization – DRAM and SRAM – types of ROM – chip logic – external memory – magnetic disk magnetic read write mechanisms – data organization and formatting – physical characteristics – disk performance parameters – RAID – optical memory.

Self Learning:

Introduction to Magnetic disks, optical memory.

 

Unit-2
Teaching Hours:11
Input/output organization
 

External devices – I/O modules – programmed I/O – interrupt driven I/O – DMA – I/O processor  – interface circuits – serial port – parallel port – standard I/O interfaces – PCI bus, SCSI bus, USB bus.

Unit-3
Teaching Hours:12
Computer arithmetic
 

The arithmetic and logic unit – integer arithmetic – negation – addition – subtraction – multiplication and division – floating point representation – principles – IEEE standard for binary floating point representation – floating point arithmetic addition and subtraction – multiplication and division – precision considerations.

Unit-4
Teaching Hours:12
Central processing unit
 

Instruction sets characteristics – types of operands – types of operations – addressing modes – instruction formats - processor organization – register organization – instruction cycle – instruction pipelining- reduced instruction set architecture – RISC verses CISC Case study: Pentium and power PC data types – operation types – addressing modes.  

 

Unit-5
Teaching Hours:12
Control unit
 

Control unit operations – micro operations – fetch cycle – indirect cycle – interrupt cycle – execute cycle – instruction cycle – control of the processor  – functional requirements – control signals –  hardwired implementation – control unit inputs and control unit logic – micro programmed control Basic concepts – Micro instructions – micro programmed control unit – micro instruction sequencing  design considerations – sequencing techniques – address generation –micro instruction execution – micro instruction encoding.

 

Service learning

E-waste Management

 

Text Books And Reference Books:

[1] William Stallings, Computer Architecture and Organization, Pearson Education, 7th Edition, 2010.

Essential Reading / Recommended Reading

[1] Carl Hamacher, ZvonkoVranesic and SafwatZaky, Computer Organization, 5th Edition, Tata McGraw Hill, 2011.

[2] David A. Patterson and John L. Hennessy, Computer Organization and Design: The Hardware/Software Interface, Elsevier, 2008.

[3] John P. Hayes, Computer Architecture and Organization, McGraw Hill, 3rd Edition, 2002.

[4] Vincent P. Heuring and Harry F. Jordan, Computer Systems Design and Architecture, Pearson Education, 2nd Edition, 2004.

[5] M. Morrris Mano, Computer system architecture, Pearson Education, 3rd  Edition, 2005.

                                                                   

 

Evaluation Pattern

CIA       ESE

50%      50%

MCS242A - CYBER LAW AND IT SECURITY (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To understand the dynamics of cyber security and cyber law matrix, Creating techno-legal professionals with the blended skills of law and technology.

Learning Outcome

The Course will assist students in

CO 1: Understand the principles of  cyber security and cyber law matrix.

CO 2: Analyzing the legal issues involved in various problems and conflicts commonly encountered online

CO 3: Identify the E-governance and thedigital signature concepts

Unit-1
Teaching Hours:12
Cryptography
 

Object and Scope of the IT Act, Genesis, Object, Scope of the Act, Encryption Symmetric Cryptography, Asymmetric Cryptography, RSA Algorithm, Public Key, Encryption. 

Unit-2
Teaching Hours:12
Digital Signature
 

Digital Signature: Technology behind Digital Signature, Creating a Digital Signature, Verifying a Digital Signature, Digital Signature and PKI, Digital Signature and the Law

Unit-3
Teaching Hours:12
E-Governance
 

E-Governance and IT Act 2000: Legal recognition of electronic records, Legal recognition of digital signature, Use of electronic records and digital, signatures in Government and its agencies.

Certifying Authorities: Need of Certifying Authority and Power, Appointment, function of Controller, Who can be a Certifying Authority? Digital Signature Certifications, Generation, Suspension and Revocation of Digital Signature Certificate.

Unit-4
Teaching Hours:12
Domain Name Disputes and Trademark Law
 

Concept of Domain Names, New Concepts in Trademark, Jurisprudence, Cyber squatting, Reverse Hijacking, Meta tags, Framing, Spamming, Jurisdiction in Trademark Dispute.

Cyber Regulations Appellate Tribunal, Establishment & Composition of Appellate Tribunal, Powers of Adjudicating officer to Award Compensation, Powers of Adjudicating officer to Impose Penalty.

Unit-5
Teaching Hours:12
The Cyber Crimes
 

Tampering with Computer Source Documents, Hacking with Computer System, Publishing of Information Which is Obscene in Electronic Form, Offences: Breach of Confidentiality & Privacy, Offences: Related to Digital Signature Certificate.

Text Books And Reference Books:

[1] David Baumer, J.Poindexter, Cyberlaw and E-Commerce: A Primer, McGraw-Hill Publishing Co, 2005, ISBN-13: 978-0071123006

[2] Cyber Law in India by Farooq Ahmad – Pioneer Books,2017.

Essential Reading / Recommended Reading

[1] Law relating to computers, Internet and e-commerce: A guide to cyber laws / Nandan Kamath. -Delhi: Universal Law Publishing Co. Pvt. Ltd., 2000.

[2] Cyber laws: For every netizen in India (With Information Technology Bill 1999) / NA Vijayashankar. - Bangalore: Ujvala Consultants Pvt. Ltd., 1999.

[3] Researching the legal web: A guide to legal resources on the Internet Nick Holmes and Dalia Venables. - 2nd Ed. - London: Butterworths, 1999.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS242B - OOAD USING UML (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Object Oriented Analysis and Design Using UML course provides instruction and practical experience focusing on the effective use of object-oriented technologies and the judicious use of software modeling as applied to a software development process.

Learning Outcome

Upon the completion of the course,the student will able to 

CO 1: Understand the object-oriented life cycle.

CO 2: Identify the objects, relationships, services and attributes through UML.

CO 3: Apply the use-case diagrams.

CO 4: Implement software quality and usability.

Unit-1
Teaching Hours:12
Complexity
 

The inherent complexity of software, The Structure of complex systems, Bringing order to chaos, on designing complex systems, Categories of analysis and Design methods.

Unit-1
Teaching Hours:12
The Object Model
 

The evolution of object model, Elements of object model, applying the object model, Foundations of the object model.

Unit-2
Teaching Hours:13
Classification
 

The importance of proper classification, Identifying classes and objects, Key abstraction and mechanisms, A problem of classification.

Unit-2
Teaching Hours:13
Classes and Objects
 

The nature of an object, Relationship among objects, the nature of a class, Relationship among classes, The interplay of classes and objects, On building quality classes and objects, invoking a method.  

Unit-3
Teaching Hours:12
Notation
 

Basic Behavioural Modelling, Basic elements, class diagram, object, state Transition diagram, Interactions, Use Case Diagrams, Activity, module and process diagrams.

Unit-4
Teaching Hours:10
Process
 

Principles, Micro and macro development process, Pragmatics- Management and planning, staffing, Release management, Reuse, Quality Assurance Metrics, Documentation, Tools, The benefits and risks and Object-oriented development.

Unit-5
Teaching Hours:13
Architectural Modeling
 

Components, Deployment, Collaborations, Pattern and Frameworks, Component Diagram, Deployment Diagrams, Systems and Models.

Case Study:  A domain based   analysis and design using rational rose can be made.

Text Books And Reference Books:

[1] Mahesh P. Matha, Object-Oriented analysis and Design Using UML, PHI,3rd  reprint, 2012.

[2] Grady Booch, Object-Oriented Analysis And Design With Applications, Pearson Education, 3rd edition, 2009.

Essential Reading / Recommended Reading

[1] Grady Booch, James Rumbaugh and Ivar Jacobson, The Unified Modeling Languages User Guide, Addison Wesley, 4th Edition, Reprint 2000.

[2] Mike O’Docherty, Object Oriented Analysis and Design Understanding system development with UML2.0, John Wiley and Sons, 1st Edition, 2005.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS242C - WEB ENGINEERING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The World Wide Web has become a major delivery platform for information resources.  Many applications continue to be developed in an ad-hoc way, contributing to problems of usability, maintainability, quality and reliability. This Course Description examines systematic, disciplined and quantifiable approaches to developing of high-quality, reliable and usable web applications.

 

Learning Outcome

At the conclusion of course, students are expected to be able to:

CO 1: Understand the concepts, principles and methods of Web engineering.

CO 2: Apply the concepts, principles, and methods of Web engineering to Web applications development.

CO 3: Implement the Web application development software tools and environments currently available on the market.

Unit-1
Teaching Hours:12
Requirements Engineering and Modeling
 

RE Fundamentals and Specifics - Principles for RE - Adapting RE Methods - Modeling Fundamentals and Specifics - Modeling Requirements - Content Modeling - Hypertext Modeling - Presentation Modeling - Customization Modeling

Unit-2
Teaching Hours:12
Web Application Architectures and Design
 

Fundamentals and Specifics – Components - Layered Architectures - Data-aspect Architectures - Evolutionary Perspective - Presentation Design - Interaction Design - Functional Design - Outlook

 

Unit-3
Teaching Hours:12
Testing, Operation and Maintenance
 

Fundamentals and Specifics of Testing - Test Approaches and Schemes - Test Methods and Techniques - Test Automation – Challenges in Launch of a Web Application - Promoting a Web Application - Content Management - Usage Analysis

Unit-4
Teaching Hours:12
Performance and Security
 

Characteristics of Performance - Definition and Indicators – Workload - Analytical Techniques - Representing and Interpreting Results - Performance Optimization Methods - Aspects of Security - Encryption, Digital Signatures and Certificates - Secure Client/Server-Interaction - Client Security Issues - Service Provider Security Issues

 

Unit-5
Teaching Hours:12
Technologies for Web Applications and Semantic Web
 

Fundamentals - Client/Server Communication - Client-side Technologies  - Ajax - Document-specific Technologies - Server-side Technologies - Fundamentals and Specifics of Semantic Web - Technological Concepts and Tools.

 

Text Books And Reference Books:

[1].Web Engineering: The Discipline of Systematic Development of Web Applications by GertiKappel, 2012.

 

Essential Reading / Recommended Reading

[1]. Unleashing Web 2.0: From Concepts to Creativity by Diane Cerra, 2010.

 

Evaluation Pattern

CIA-50%

ESE-50%

MCS242D - COMPILER DESIGN (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course is to learn the theory and practice behind building automatic translators and compilers for higher level programming languages and to engineer and build key phases of a compiler.

 

Learning Outcome

Upon  completion,the student will able to 

CO1: Develop and implement compilers.

CO2: Design LL and LR parsers.

CO3: Perform code optimization.

CO4: Design algorithms to generate machine code.

 

Unit-1
Teaching Hours:12
Introduction to Compilers
 

Structure of a compiler - Lexical Analysis: Role of Lexical Analyzer - Input Buffering - Specification of Tokens - Recognition of Tokens - The Lexical-Analyzer Generator Lex - Finite Automata - From Regular Expressions to Automata - Design of a Lexical-Analyzer Generator - Optimization of DFA-Based Pattern Matchers.

                                   

Unit-2
Teaching Hours:12
Syntax Analysis
 

Role of Parser - Grammars - Error Handling - Context-free grammars - Writing a grammar - Top-Down Parsing - Bottom-Up Parsing - LR Parsing - General Strategies Recursive Descent Parser Predictive Parser - Parser Generators.    

Unit-3
Teaching Hours:12
Intermediate Code Generation
 

Syntax Directed Definitions - Evaluation Orders for Syntax Directed Definitions - Evaluation Orders for SDD’s - Applications of Syntax-Directed Translation - Syntax-Directed Translation Schemes - Intermediate Languages: Syntax Tree - Three-Address Code - Types and Declarations - Translation of Expressions - Type Checking.

Unit-4
Teaching Hours:12
Run-Time Environment and Code Generation
 

Storage Organization - Stack Allocation Space - Access to Non-local Data on the Stack - Heap Management - Garbage Collection and Trace-Based Collection. Issues in Design of Code Generator - The Target Language - Addresses in the Target Code - Basic Blocks and Flow Graphs - Optimization of Basic Blocks - A Simple Code Generator - Register Allocation and Assignment - Dynamic Programming Code-Generation.    

 

Unit-5
Teaching Hours:12
Machine Independent Code Optimization
 

Principal Sources of Optimization - Introduction to Data-Flow Analysis - Foundations of Data-Flow Analysis - Constant Propagation - Partial Redundancy Elimination - Loops in Flow Graphs - Region-Based and Symbolic Analysis.

                                   

Text Books And Reference Books:

1. Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman, Compilers: Pearson New International Edition: Principles, Techniques, and Tools, Second Edition, Pearson Education Limited, 2013.

 

 

Essential Reading / Recommended Reading

[1]. Keith D Cooper and Linda Torczon, Engineering a Compiler, Morgan Kaufmann Publishers Elsevier Science, 2004.

[2].V. Raghavan, Principles of Compiler Design‖, Tata McGraw Hill Education Publishers, 2010.

[3].Terence Halsey, Compiler Design: Principles, Techniques and Tools, Larsen & Keller education, 2018.

 

Web Resources:

1.http://www.cs.cmu.edu/~fp/courses/15411-f14/resources.html

2.https://www.tutorialspoint.com/compiler_design/

Evaluation Pattern

CIA-50%

ESE-50%

MCS242E - WIRELESS AND MOBILE NETWORKS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course aims at providing a comprehensive overview of the important issues related to Wireless communication and Mobile Computing. They are grouped into four main areas: basic issues and problems, mobile communications, cellular networks and protocols. Different mobile communications methods and latest communication technologies are discussed. Protocols such as Mobile IP, medium access control and mobility management strategies that are needed to support mobile devices are covered under the specified topic. 

 

Learning Outcome

Upon completion of the course the students will be able to

 

CO 1: Understand the basic issues and problems in mobile computing.

CO 2: Assess the transmission mechanisms and characteristics of different mobile/wireless communication.

CO 3: Analyse the strengths and limitations of different types of mobile/wireless networks.

CO 4: Evaluate different mechanisms for supporting mobility. 

Unit-1
Teaching Hours:12
Wireless Systems and its Evolution
 

Wireless Telecommunications Systems and Networks

History and Evolution of Wireless Radio Systems, Development of Modern Telecommunications Infrastructure, Overview of Existing Network Infrastructure, Wireless Network Applications: Wireless Markets.

Evolution and Deployment of Cellular Telephone Systems

Different Generations of Wireless Cellular Networks, 1G Cellular Systems, 2G Cellular Systems, 2.5G Cellular Systems, 3G Cellular Systems, 4G Cellular Systems and Beyond, Wireless Standards Organizations.

Unit-2
Teaching Hours:12
Cellular System and Architecture
 

Common Cellular System Components

Common Cellular Network Components, Hardware and Software Views of the Cellular Network, 3G Cellular System Components, Cellular Component Identification, Cell establishment.

Wireless Network Architecture and Operation

The Cellular Concept, Cell Fundamentals, Capacity Expansion Techniques, Mobility Management, Wireless Network Security.

 

Unit-3
Teaching Hours:12
GSM and TDMA Technology
 

 

Introduction to GSM and TDMA, GSM Network and System Architecture, GSM Channel Concept, GSM Identities, GSM System Operations, GSM Infrastructure Communications.

Unit-4
Teaching Hours:12
CDMA and CDPD
 

CDMA Technology

Introduction to CDMA, CDMA Network and System Architecture, CDMA Channel Concept, CDMA System Operations.

CDPD and Edge Data Networks

CDPD, GPRS, GPRS Networks, GPRS Network Details, GPRS Network Layout and Operation, GPRS Packet Data Transfer, GPRS Protocol Reference Model, GPRS Logical Channels, GPRS Physical Channels, GSM/GPRS/Edge Technology.

Unit-5
Teaching Hours:12
Wireless LAN/Wireless PANs/IEEE 802.15x
 

 

Introduction to wireless LAN 802.11X technologies, Evolution of Wireless LAN, Introduction to IEEE 802.15x Technologies, Wireless PAN Applications and Architecture, Bluetooth, Introduction to Broadband wireless MAN,802.16 technologies.

 

Text Books And Reference Books:

[1] Mullett, Wireless Telecommunications Systems and Networks, Cengage Learning, 2013.

 

Essential Reading / Recommended Reading

[1] Raj Kamal, Mobile Computing, Oxford University Press, 2012.

[2] Stallings William, Wireless Communications and Networks, Pearson Education Asia, 2nd Edition, 2009.

[3] Theodore S Rappaport, Wireless Communications: Principles and Practice, Pearson Education Asia, 2nd Edition, 2009. [4] Jochen Schiller, Mobile Communication, Addison-Wesley, 2nd Edition, 2011.

Evaluation Pattern

CIA-50%

ESE-50%

MCS242F - THEORY OF COMPUTATION (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course serves as an introduction to the basic theory of Computer Science, formal methods of computation and in-depth understanding of the compilation process. Topics include finite automata, regular expressions and languages; push‐down automata, context‐free languages; selected advanced language theoretical topics; emphasis on technique and compiler theory as well.

Learning Outcome

Upon successful completion of this course students should be able to:

CO 1: Analyze and design finite automata, pushdown automata, Turing machines, formal languages, and grammars.

CO 2: Demonstrate their understanding of key notions, such as algorithm, computability, decidability, and complexity through problem solving.

CO 3: Assess the basic results of the Theory of Computation.

CO 4: Explain the relevance of the Church-Turing thesis.

 

Unit-1
Teaching Hours:12
Finite Automata and Regular Expressions
 

Alphabets, Strings and Languages, Deterministic and Non-Deterministic Finite Automata, Finite Automata with ε -moves, regular expressions equivalence of NFA and DFA, two-way finite automata, Moore and Mealy machines, applications of finite automata.

Unit-2
Teaching Hours:12
Push Down Automata Theory
 

Context-Free Languages and Derivation Trees Ambiguity in Context-Free Grammars Chomsky Normal Form Greibach Normal Form, Push Down Automata Definition, Acceptance by Push Down Automata, Push Down Automata and Context Free Languages, properties of CFL.

Unit-3
Teaching Hours:12
Introduction to Compiler
 

Compilers Analysis of the source program Phases of a compiler, Compiler construction tools, Role of Lexical Analyzer Input Buffering Specification of Tokens.

 

Unit-4
Teaching Hours:12
Basic Parsing Techniques
 

Shift reduce parsing- operator precedence parsing, Recursive descend parsing, predictive parsing, LR parsing, Simple LR parsing, canonical LR parsing, LALR parsing.

Unit-5
Teaching Hours:12
Intermediate Code Generation
 

Intermediate languages Declarations, Assignment Statements, Boolean Expressions, Case Statements, Back patching Procedure calls. Code Optimization: Principle Sources of optimization, Loop Optimization, DAG Representation of basic blocks, Global Data Flow Analysis, Code Generation, Problems in code generation Register allocation and assignment, Code Generation from DAG s, Peephole-Optimization.

Text Books And Reference Books:

[1] John E. Hopcroft and Jeffrey D. Ullman, Introduction to Automata Theory, Languages and Computation, Narosa Publishers, 2011.

 

 

Essential Reading / Recommended Reading

[1] Alfred Aho, Ravi Sethi, Jeffrey D Ullman, Compilers Principles, Techniques and Tools, Pearson Education Asia, 2008.
[2] Tremblay, A.S., and Sorenson, P.G., 'The Theory and Practice of Compiler Writing', McGraw-Hill Int. Edition, 1985.
[3] Michael Sipser, Introduction to the Theory of Computations, Brooks/Cole, Thomson Learning, 1997.

[4] Mishra&Chandrashekharan: Theory of Computer Science, Automata Languages & computation, 2nd Ed PHI, New Delhi.
[5] John c. Martin, Introduction to Languages and the Theory of Computation, Tata McGraw-Hill, 2003.

 

Evaluation Pattern

 

CIA      ESE

50%     50%

MCS242G - DATA ANALYTICS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Data Science is the latest buzz word in the modern era of cloud and big data in academic research and corporate world. Data Science experts must acquire analytical skill set for pursuing research and generating new knowledge in the business process. Data Analytics course delivers various techniques to discover new and hidden knowledge from the data set. This course provides insight into the complete research process in phases as research methodology, data exploration, modeling, evaluation and visualization. R programming, MATLAB and Excel are the suggestive tools for implementation.

Learning Outcome

Upon successful completion of the course students will be able to :

CO1: Identify appropriate preprocessing techniques to clean the data

CO2: Apply various supervised and unsupervised algorithms to real world problems

CO3: Interpret the results of developed models using different visualization techniques

Unit-1
Teaching Hours:12
Introduction to Data Analytics
 

Introduction to data, types of data, Knowledge discovery process, Big Data Platform – Challenges of conventional systems – Web data – Evolution of Analytic scalability, Types of analytics:  Confirmatory analytics, Descriptive analytics, exploratory analytics and Predictive analytics, analytic processes and tools, Statistical concepts: Sampling distributions, re-sampling, statistical inference, prediction error.

Unit-2
Teaching Hours:12
Classification Techniques
 

Bayesian modeling, inference and Bayesian networks, Support vector and kernel methods, nonlinear dynamics – Rule induction – Neural networks, competitive learning, neural networks and deep learning.

 

Unit-3
Teaching Hours:12
Association Rule Mining and Clustering
 

Mining Frequent item-sets – Market based model – Apriori Algorithm – FP growth algorithm, Handling large data sets in Main memory – Limited Pass algorithm – Counting frequent item-sets in a stream – Clustering Techniques – Hierarchical – K- Means – DBSCAN.

Unit-4
Teaching Hours:12
Mining Data Streams
 

Introduction to Streams Concepts – Stream data model and architecture – Stream Computing, Sampling data in a stream – Filtering streams – Counting distinct elements in a stream –linear systems analysis, stock market predictions.

Unit-5
Teaching Hours:12
Framework and Visualizations
 

MapReduce – Hadoop, Hive, MapR – Sharding – NoSQL Databases – S3 – Hadoop Distributed file system for big data – Visualizations – Visual data analysis techniques, interaction techniques; Systems and applications.

 

Text Books And Reference Books:

[1] Runkler, Thomas. A, Data Analytics: Models and Algorithms for Intelligent Data Analysis, Springer, 2012.

[2] Lillean Pearson, Data Science For Dummies, John Wiley and Sons, 2015.

 

Essential Reading / Recommended Reading

[1] Jain P and Sharma P, Behind Every Good Decision: How Anyone Can Use Business Analytics to Turn Data into Profitable Insight, Amacom, 2014.

[2] John W Foreman, Data Smart: Using Data Science to Transform Information into Insight, Wiley, 2013.

Evaluation Pattern

CIA 50%

ESE 50%

MCS243A - MATLAB PROGRAMMING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course introduces students to basic Matlab programming concepts. At the end of the course, students should be able to use Matlab in their own work and be prepared to develop the skills necessary to handle research related projects in the area of Science effectively.

 

Learning Outcome

On completion of the course the student will acquire the following skills in Matlab:

CO 1: Understand the use of procedural statements--assignments, conditional statements, loops, function calls, arrays and files.

CO 2: Design, code, and test small programs using GUI.

CO 3: Develop competency in using graphics tools.

 

Unit-1
Teaching Hours:12
An Introduction to Matlab
 

An Introduction to Matlab – Matlab Installation, Features of Matlab - Some basics of using Matlab - The order of precedence, Algebraic functions, special characters, functions, Variables, Different types of variables

Matrices, vectors and scalars - Creating matrices, Addressing parts of matrices, Changing parts of a matrix, Some special commands for handling matrices, More about matrices

 

Unit-2
Teaching Hours:12
Mathematical operations with matrices &Importing and exporting data
 

Mathematical operations with matrices - Functions that operate element-by-element, Elementary mathematical functions that operate column-wise, Matrix algebra, Solving systems of linear equations, Finding linear regression coefficients.

Importing and exporting data - Preparing data to import, Copy-and-paste importing, Importing using the Import Wizard, Importing using commands, Exporting to Excel files with commands

 

Unit-3
Teaching Hours:12
Graphics and Programming
 

Graphics- Useful commands for two-dimensional plotting, Time series plotting, Plotting a function, Several graphs in one window and other types of graphs, Other two-dimensional graphs, Plotting tools.

Programming in Matlab – Scripts, The Editor, Writing a script, The search path, User interaction with the script

User defined functions - About the differences between scripts and user defined functions, More about functions.

 

Unit-4
Teaching Hours:12
Flow control and Numerical analysis
 

Flow control – Loops, Relational and logical operators, Conditional statements, More about flow control.

Numerical analysis and curve fitting - Solving equations, Finding a function minimum point, Numerical integration, Curve fitting, More about numerical analysis

 

Unit-5
Teaching Hours:12
File Handling and GUI
 

File Handling - fopen, fclose functions, Reading and Writing Text files and Binary Files.

Graphical User Interface- GUI Development Environment, GUI Components, Dialog Boxes, File Dialog Box, Creating Simple GUI.

Text Books And Reference Books:

[1]   Krister Ahlersten, An Introduction to Matlab, Book Boon, 2nd Edition, 2015. (e-book)

[2]   Y. Kirani Singh & BB Chaudhuri, Matlab Programming, PHI Publications, 2007.

Essential Reading / Recommended Reading

[1]   Cesar Perez Lopez, Matlab Programming for Numerical Analysis, Springer publications, 2014

[2]   Delores M Etter & David C Kuncicky, Introduction to Matlab, 2nd Ed, Pearson Publications, 2004.

[3]   RudraPratap, Getting Started with MATLAB 7, A Quick introduction for Scientist and Engineers”, Oxford University Press (2006). 

 

Evaluation Pattern

CIA-50%

ESE-50%

MCS243B - R PROGRAMMING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course is used to provide an introduction to R, statistical language and environment that also provides more flexible graphics capabilities than other popular statistical packages. The course also covers the basics of R for statistical programming, computation, graphics, and modeling.

 

Learning Outcome

 

Students will able to:

CO 1:  Understand about usage of R for statistical programming, computation, graphics and modeling,

CO 2: Apply R in an efficient way, fit some basic types of statistical models.

CO 3:  Implement R in their own research work.

 

Unit-1
Teaching Hours:12
R-Introduction & Installation
 

Introduction to R-Benefits of using R-Unique features of R-Exploring R-Install-Packages- Working with code editor-First R session-navigating the workspace R-objects-Atomic Vectors- Attributes Matrices-Arrays-Class-Lists-Data Frames--Loading and Saving data.

 

Unit-2
Teaching Hours:12
Control Structures
 

 

If-else-For Loops-While Loops-Repeat-Next, Break, Functions, Symbol Binding-R Scoping Rules-Optimization-Coding Standards-Dates and Time- Loop Functions-lapply(), apply(), mapply(), tapply(),split-Debbuging-Problem diagnosis-Reading Errors and Warnings-Reading error messages-Caring about warnings-Going Bug Hunting-Calculating the logit()-Knowing where an error comes from-Looking inside a function

Unit-3
Teaching Hours:12
Graphics
 

 

Basic plotting-Manipulating the plotting window-Advanced plotting using lattice library- Saving plots.

 

Unit-4
Teaching Hours:12
Models
 

 

Central Tendency-Measuring Variability-Covariance and Correlation-Measuring Symmetry-PCA.Model formulae and model options-Output and extraction from fitted models.  Models considered: Linear regression: lm () – Logistic regression: glm () – Poisson regression: glm () – Survival analysis: Surv (), coxph () – Linear mixed models: lme () 

 

 

Unit-5
Teaching Hours:12
Data Processing using R
 

Entering data in the R text editor-Using the Clipboard to copy and paste-Reading data in CSV files, Reading data from Excel-Working with other data types. Manipulating and Processing Data-Deciding on the Most Appropriate Data Structure, Creating Subsets of Data, Adding Calculated Fields to Data, Combining and Merging Data Sets, Sorting and Ordering Data, Traversing Data with the Apply Functions, Getting to Know the Formula Interface, Working with Tables.

Text Books And Reference Books:

[1] Andrie De Vries, JorisMeys, R Programming for Dummies. ISBN 978-1-119-96284-7. John Wiley & Sons, 2012

[2] Grolemund, Forword, Hadley Wickham, Garrett, Hands-On Programming with R,OREILLY Publishers. June 2014.

 

Essential Reading / Recommended Reading

[1] Robert I. Kabacoff, R in Action, Data Analysis and Graphics with R, ISBN: 9781935182399, August 2011.

[2]ViswaViswanathan, ShanthiViswanathan, R Data Analysis Cookbook. ISBN 10: 1783989068, 2015.

 

Evaluation Pattern

CIA-50%

ESE-50%

MCS243C - NETWORK SIMULATION USING NS3 (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

 To study about basics of computer networks and to understand the NS3 Simulator architecture to simulate network components, topologies, network models, protocols and algorithms.

Learning Outcome

Upon completion,the student will be able to

CO 1: Understand the complex structure of networks protocol hierarchy

CO 2: Apply NS3 for communication network performance evaluation.

CO 3: Develop simulations for network components.

Unit-1
Teaching Hours:12
Basics of Computer Networks
 

Reference Models (OSI, TCP/IP); Data Link protocols: Sliding Window protocols: Go back N and Selective repeat sliding window protocol, Channel Allocation; Network Layer Protocols: Routing Algorithms, Congestion Control Algorithms; Transport layer protocols: Connection Establishment, Connection Release, Flow Control and Buffering.

Unit-2
Teaching Hours:12
NS-3 Introduction & Installation
 

NS3 introduction, NS-3 installation (Linux OS), Fundamental aspects of Network simulator-3, Working with netanim, working with gnuplot Logging level details, Debugging using gdb tool, Tracing and Monitoring of packet loss, delay and drop, Flow Monitoring.

Unit-3
Teaching Hours:12
Network Dynamics
 

Packet analysis using Wireshark, Building Topologies, Mesh Network, Simple Wireless Scenario, Status of Energy Model, WIFI and Adhoc Network, Broadcast Communication, Network Dynamics and mixed network management.

Unit-4
Teaching Hours:12
NS-3 Simulation
 

Implementation of OLSR, AODV, DSDV, DSR, TORA, Worm Model, Simulating point-to-point and CSMA networks in ns-3, Using PyViz for visualizations, Simulating BSS and IBSS in ns-3, Using Wireshark for tracing.

Unit-5
Teaching Hours:12
NS-3 Emulation and Encryption
 

Simulating a hidden node problem in ns-3, Using NetAnim for visualizations, Network Emulation with NS3 over Testbed and Grid Network, Simulating Encryption-Decryption in wireless networks.

Text Books And Reference Books:

[1]    Jack L. Burbank, "Introduction to Network Simulator NS3", Wiley-Blackwell ,2016

[2]   Andrew S Tanenbaum, Computer Networks, PHI publications, 5th Edition, 2012.

Essential Reading / Recommended Reading

[1]   Kevin Fall & KannanVaradhan, "The NS Manual", VINT Project – 2011.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS243D - HADOOP (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The subject is intended to give the knowledge of Big Data evolving in every real-time application and how they are manipulated using the emerging technologies. This course breaks down the walls of complexity in processing Big Data by providing a practical approach to developing Java applications on top of the Hadoop platform. It describes the Hadoop architecture and how to work with the Hadoop Distributed File System (HDFS) and HBase in Ubuntu platform.

Learning Outcome

Upon completion of the course the students will be able to

 

CO 1: Understand the Big Data concepts in real time scenario.

CO 2: Develop a map reduce program and to implement the program in cloud.

CO 3: Analyze the Hadoop Distributed File System (HDFS) and HBase.

CO 4: Create an application involving Big Data Analytics.

Unit-1
Teaching Hours:12
Introduction
 

Distributed file system – Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, Big data applications. Algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce.

Unit-1
Teaching Hours:12
Apache Hadoop
 

Moving Data in and out of Hadoop – Understanding inputs and outputs of MapReduce - Data Serialization. Problems with traditional large-scale systems-Requirements for a new approach- Hadoop – Scaling - Distributed Framework - Hadoop v/s RDBMS - Brief history of Hadoop.

Unit-2
Teaching Hours:12
Developing MapReduce Programs
 

Using languages other than Java with Hadoop, Analyzing a large dataset.

Unit-2
Teaching Hours:12
Understanding MapReduce
 

Key/value pairs, The Hadoop Java API for MapReduce, Writing MapReduce programs, Hadoop-specific data types, Input/output.

Unit-2
Teaching Hours:12
Setting up Hadoop on a local Ubuntu host
 

Prerequisites, downloading Hadoop, setting up SSH, configuring the pseudo-distributed mode, HDFS directory, NameNode, Examples of MapReduce, Using Elastic MapReduce, Comparison of local versus EMR Hadoop.

Unit-2
Teaching Hours:12
Configurations of Hadoop
 

Hadoop Processes (NN, SNN, JT, DN, TT) - Temporary directory-UI-Common errors when running Hadoop cluster, solutions.

Unit-3
Teaching Hours:12
Hadoop Streaming
 

How Streaming Works -   Streaming Command Options - Specifying a Java Class as the Mapper/Reducer - Packaging Files with Job Submissions - Specifying Other Plug-ins for Jobs.

Unit-3
Teaching Hours:12
Hadoop configuration properties
 

Setting up a cluster, Cluster access control, managing the NameNode, Managing HDFS, MapReduce management, Scaling.

Unit-3
Teaching Hours:12
Advanced MapReduce Techniques
 

Simple, advanced, and in-between Joins, Graph algorithms, using language-independent data structures.

Unit-4
Teaching Hours:12
HIVE & PIG
 

Architecture, Installation, Configuration, Hive vs RDBMS, Tables, DDL & DML, Partitioning & Bucketing, Hive Web Interface, Pig, Use case of Pig, Pig Components, Data Model, Pig Latin.

Unit-5
Teaching Hours:12
Hands On
 

Single Node Hadoop Cluster Set up in any cloud service provider - How to create instance. How to connect that Instance Using putty. Installing Hadoop framework on this instance. Run sample programs which come with Hadoop framework.

Unit-5
Teaching Hours:12
HBase
 

RDBMS Vs NoSQL, HBasics, Installation, Building an online query application – Schema design, Loading Data, Online Queries, Successful service.

Text Books And Reference Books:

[1]   Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, Wiley, ISBN: 9788126551071, 2015.

[2]   Tom White, “Hadoop: The Definitive Guide”, Publisher: O’Reilly Media, Inc., 2015 Edn.

[3]   Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013 edn.

Essential Reading / Recommended Reading

[1]  Pethuru Raj, Anupama Raman, Dhivya Nagaraj, Siddhartha Duggirala, “High-Performance Big-Data Analytics: Computing Systems and Approaches”, Springer, 2015.

[2]   Jonathan R. Owens, Jon Lentz, Brian Femiano, “Hadoop Real-World Solutions Cookbook”, Packt Publishing, 2013 edn.

[3]   Tom White, “HADOOP: The definitive Guide”, O Reilly 2012.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS243E - PYTHON PROGRAMMING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This Course helps in understanding the programming paradigms brought in by Python with a focus on Regular Expressions, List and Dictionaries.It enables learning Python case studies on Data Mining, Image and Data Processing to appreciate the fast-changing landscape of programming.

Learning Outcome

 

 Upon completion of the course,the student will able to

CO 1: Understand the Python Programming Paradigm.

CO 2: Implement the concepts of Regular Expression ,Text Processing and File handling.

CO 3 Apply Python for Data and Image processing.

CO 4: Develop hands on experience in Cluster Analysis using Python.

Unit-1
Teaching Hours:12
Programming Fundamentals
 

Introduction, Python Objects, Built-in Functions, Numbers and Strings, Conditionals and Loops, Functions, Passing Arguments, String Functions

Unit-2
Teaching Hours:12
Lists, Tuples, Files
 

Operators, Built-in Functions, List Type Built-in Methods, Special Features of Lists, Tuples, Tuple Operators and Built-in Functions, Special Features of Tuples

File Objects, File Built-in Function, File Built-in Methods, File Built-in Attributes, Standard Files, Command-line Arguments, File System, File Execution, Persistent Storage Modules

Unit-3
Teaching Hours:12
Regular Expressions, Dictionaries
 

Introduction/Motivation, Special Symbols and Characters for REs, REs and Python

Introduction to Dictionaries, Operators, Built-in Functions, Built-in Methods, Dictionary Keys.

Unit-4
Teaching Hours:12
Data Processing: Case Study
 

Storing in List and Strings, Dispersion, Central Tendency, Mean Median Mode, Frequency Distribution, Standard Deviation.

Using Files for large dataset, statistics with real data, reading data from internet, Accessing Stock Market Data, Correlating Stock data.

Unit-5
Teaching Hours:12
Image Processing and Data Mining: Case Study
 

Introduction, RGB Color Model, Object for Image Processing, Image Processing (Negative Images, Gray Scale, Resizing, Stretching, Flipping, Edge Detection)

What is Data Mining? Implementing Cluster Analysis on Simple Data, Distance between two points, Clusters and Centroids, K-Means cluster Analysis, File Processing, Visualization

Text Books And Reference Books:

[1]   Chun, J Wesley, Core Python Programming, Second Edition, Pearson, 2007 Reprint 2010

[2]   Bradley N Miller, David L Ranum, Python Programming in Context, Second Edition, 2014

Essential Reading / Recommended Reading

[1]   Barry, Paul, Head First Python, 2nd Edition, O Rielly, 2010

[2] Lutz, Mark, Learning Python, 4th Edition, O Rielly, 2009 

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS251 - DATA STRUCTURES LAB (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

This Course helps the students to implement the theoretical concepts of Data Structures and also compliment the same with hands-on experience effectively. 

Learning Outcome

The successful completion of this course will enable the students to: 

CO 1: Understand the need for Data Structures when building an application using an optimized algorithm  

CO 2: Apply Code efficiency and Optimization techniques.

CO 3: Implement algorithms like Huffman, Quick Sort, Shortest Path etc

CO 4: Develop programming skills

Unit-1
Teaching Hours:60
List of programs
 

1. Implement sequential search and binary search techniques.

2. Implement Selection sort.

3. Implement Insertion sort.

4. Implement Stacks.

5. Implement Queues.

6. Implement linked lists and some operations on linked lists.

7. Write a program to convert an infix expression to the postfix form.

8. Write a program to evaluate a postfix expression.

9. Implement Quick sort.

10. Implement Merge sort for array.

11. Merge Sort a file contents (without loading the content into an internal data structure).

12. Implement Two-Way linked lists.

13. Implement Circular linked lists.

14. Implement Binary Search Tree.

15. Implement Shell sort.

16. Implement Heap sort.

17. Implement Radix sort.

18. Implement Depth First Search for Graphs.

19. Implement Breadth First Search for Graphs.

Text Books And Reference Books:

[1] Gilberg, F Richard &Forouzan, A Behrouz, Data Structures A Pseudocode approach with C,2nd Edition, Cengage, 2008.

Essential Reading / Recommended Reading

[1] Horowitz Sahni Anderson-Freed, Fundamental of Data Structures in C, Universities Press, Reprint 2008.

[2] Richard Johnsonbaugh, Algorithms, Pearson Education, 2nd Edition, 2008

[3] Robert Sedgwick, Algorithm in C++, Addison-Wesley Publishing Company. [4] Knuth, Donald E, Art of Computer Programming, Sorting & Searching, Addison-Wesley, 2005.

Evaluation Pattern

 

CIA (Weightage)

ESE (Weightage)

50%

50%

 

MCS252 - MOBILE APPLICATION LAB (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:02

Course Objectives/Course Description

 

Aimed at helping learners create applications using Google's Android™ open- source platform. Learn, Understand, create and deploy  advanced capabilities and APIs such as background services, accelerometers, graphics, and GPS. 

Learning Outcome

Upon completion of the Course,student will be able to

CO1:Build your own Android apps.

CO2:Explain the differences between Android and other mobile development environments.

CO3Design and develop useful Android applications with compelling user interfaces by using extending and creating your own layouts and views and using menus.

CO4:Secure, tune, package and deploy Android Applications

Unit-1
Teaching Hours:60
Program List
 

1. Installation – Android Studio

2.Develop an application that uses GUI components, Font and Colors.

3.Develop an application that uses Layout Managers and event listeners.

4.Develop a native calculator application

5. Develop an Application that used the camera in phone and allows to click the photo.

6.Develop an application that uses that demonstrates the use of Fragments

7.Develop a Music player – Basic controls to play, pause and stop the MP3 file

8.Implement an application that writes data to the SD card.

9.Write a mobile application that creates alarm clock.

10.Develop an application that makes use of database.

11.  Creating application using App Inventor

12.  Packaging and Deploying the APK file in Google Play store

Text Books And Reference Books:

[1] Wei-Meng Lee, “Beginning android 4 application Development, John Wiley & sons, Inc, 2012.

Essential Reading / Recommended Reading

[1] Paul Deitel-Harvey Deitel-Abbey Deitel-Michael Morgano,”Android for Programmers An App-Driven Approach”,Pearson Education Inc., 2012.

[2] Jerome (J.F) DiMarzio , "Android - A programmer's Guide", TataMcgraw Hill,2010, ISBN: 9780071070591

Evaluation Pattern

CIA-50%

ESE-50%

MCS271 - SEMINAR (2019 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

Students will be giving presentations on any advanced concepts and technologies in computer science.

 

Learning Outcome

Upon completion of Course,student will be able to 

CO1: Understand new and latest trends in computer science

CO2: Identify advanced concepts in IT

CO3: Apply the acquired knowledge in their Research.

 

Unit-1
Teaching Hours:30
Seminar
 

Identification of Topic.

Presentation of Topic.

Submission of Seminar Report.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA-50%

ESE-50%

MCS281 - ADBMS PROJECT LAB (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

The main aim of the course is to develop the practical knowledge of the students on building a project using ADBMS concepts. The student identifies a real world problem, design, and creates the model to solve it. At the end of the semester the students should develop the working project using ADBMS concepts.

Learning Outcome

The following outcomes are expected from the students:       

CO1: Understand the practical concepts and the technical issues related to the development of ADBMS project and identify the problem.

CO2: Analyze the problem, identify the solution, various front end and backend tools required for the project and apply them as per the requirement.

CO3: Create a working project that satisfies the need of the end user.

CO4: Develop communication skills, ethics and leadership qualities as an individual and as a leader.

Unit-1
Teaching Hours:60
ADBMS Project Lab
 

1. DBMS Lab includes an application project. The backend of the project may be any one of the following:

         MS-SQL Server

         Oracle 

         DB2

         MySql

2. User interface could be made with any one of the front end tools available.

3. Students should have in-depth knowledge of the front and backend tool, which they are using.

4. Database tables are required to be normalized, at least to the second level.

5. There need to be independent forms for data entry operations.

6. All the forms in the project need to have similar look and feel in terms of background/foreground color, arrangement of controls, spacing and sizing of the controls, size of forms, etc.

7. There could be separate forms for searching purposes.

8. Master table data entry forms may include navigational buttons along with Add, Save, Delete etc.   

9. Reports should be generated dynamically. 

 

Note: Project should be developed by following software engineering process.

 

Solicitation, Source Selection, Contract Administration.

Text Books And Reference Books:

[1] Bob Hughes, Mike Cotterell, Software Project Management, Tata McGraw-Hill, 5th Ed.,2011

[2] Pankaj Jalote, Software Project Management in Practice, Pearson Education, 3rd Ed., 2010.

[3] Kathy Schwalbe, Information Technology Project Management, THOMSON Course Technology, International Student Edition, 2003.

[4] Elaine Marmel, Microsoft Office Project 2003 Bible, Wiley Publishing Inc.

Essential Reading / Recommended Reading

[1]  Maylor, H., Project Management, PHI, 3rd Ed., 2002.

[2] Robert T. Futrell, Quality Software Project Management, Pearson, 2010.

[3] Bentley C., PRINCE2: A Practical Handbook, NCC Blackwell, 2002.

[4] Robert T. Futrell, Quality Software Project Management, Pearson, 2010.

[5] S.A. Kelkar, Software Project Management - A Concise Study, PHI, Revised Edition.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCS331 - DIGITAL IMAGE PROCESSING (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To provide strong foundation on concepts and algorithms widely used in Digital image processing. It also focuses on compression and restoration of images using various techniques.

Learning Outcome

Upon successful completion of the course the student would:

Co1: Comprehend the knowledge of image processing and image analysis techniques.
CO2: Analyze image processing techniques in both the spatial and frequency domain for two-dimensional images.  
CO3: Design algorithms to solve real time problems for Classification and compression.

Unit-1
Teaching Hours:12
Introduction
 

The origins of Digital Image Processing, Fundamental Steps in Image Processing, Elements of Digital Image Processing System

Unit-1
Teaching Hours:12
Digital Image Fundamentals
 

Image Sampling and Quantization, Basic relationships: Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations.

Unit-2
Teaching Hours:12
Image Enhancement in Spatial Domain
 

Gray Level Transformations, Histogram Processing, Histogram equalization, Histogram specification, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters.

Unit-2
Teaching Hours:12
Image Enhancement in Frequency Domain
 

Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening, Frequency Domain Filters.

Self Learning topic : Homomorphic Filtering

Unit-3
Teaching Hours:12
Image Compression
 

Image Compression models: Huffman coding, Run length coding, LZW coding.

Unit-3
Teaching Hours:12
Image Restoration
 

A model of The Image Degradation / Restoration Process, Noise Models, Restoration in the presence of Noise, Periodic Noise Reduction by Frequency Domain Filtering.

Unit-4
Teaching Hours:12
Region based Segmentation and Representation
 

Region Based Segmentation – Region Growing and Region Splitting and Merging. Representation – Chain codes

Self Learning topic:  Polygonal approximations using minimum perimeter polygons.

Unit-4
Teaching Hours:12
Image Segmentation
 

Point, Line and Edge detection.Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method.

Unit-5
Teaching Hours:12
Object Recognition
 

Introduction to Patterns and Pattern Classes. Decision-Theoretic Methods – Minimum distance classifier, K-NN classifier and Bayes.

Self Learning topic : Classification

Unit-5
Teaching Hours:12
Descriptiors
 

Boundary descriptors – Fourier descriptors.Regional descriptors –Topological descriptors and Moment invariants.

Text Books And Reference Books:

[1] R. C. Gonzalez & R. E. Woods, Digital Image Processing, 3rd Edition.PearsonEducation, 2009.

[2] A.K. Jain, Fundamental of Digital Image Processing, 4th Edition.PHI, 2011.

[3] Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, Digital Image Processing Using MATLAB, 2nd Edition. PHI, 2009.

Essential Reading / Recommended Reading

[1] M. A. Joshi, Digital Image Processing: An algorithmic approach, 2nd Edition.  PHI 2009.

[2] B.Chanda, D. DuttaMajumdar, Digital Image Processing and analysis, 1st Edition, PHI,2011.

Evaluation Pattern

 

CIA (Weightage)

ESE (Weightage)

50%

50%

MCS332 - MOBILE APPLICATIONS (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

This Course Description aimed at helping learners create applications using Google's Android™ open- source platform. The Course Description explains what Android™ is and how it compares to  other mobile environments, the setup of the Android™ Eclipse-based development tools, the Android™ SDK, all essential features, as well as the advanced capabilities and APIs such as background services, accelerometers, graphics, and GPS.

Learning Outcome

Upon successful completion of the course, the student will be able to

 

CO1: Build your own Android apps.

 

CO2: Explain the differences between Android and other mobile development environments.

 

CO3: Design and develop useful Android applications with compelling user interfaces by using extending and creating your own layouts and views and using menus.

 

CO4: Deploy Android Applications and package.

Unit-1
Teaching Hours:12
Introduction
 

Brief History of Embedded Device Programming, Introduction to Android, Get to know the required tools,Creating your first Android application, Anatomy of android Application. Understanding Activities, linking Activities using intents, fragments, calling Built-in Applications using Intents, Displaying Notifications.

Unit-2
Teaching Hours:12
User Interface and Designing with views
 

Understanding the components of a screen, adapting to display orientation, managing changes to screen orientation, Utilizing the Action Bar, Creating the user Interface programmatically,

Listening for UI Notifications. Using Basic Views, Using Picker views, Using List views to display lists, Understanding specialized fragments.

Unit-3
Teaching Hours:12
Displaying with views, Data persistence and Content Providers
 

Using Image Views to display pictures, using menus with views, some additional views. Saving and loading user preferences, persisting Data Files, Creating and using Databases. Sharing Data in Android, using content provider, creating your own content providers, using content providers.

Unit-4
Teaching Hours:12
Messaging and Location based services
 

SMS Messaging, Sending E-mail, Displaying Maps, Getting Location Data, Monitoring a Location. Hands on project: Building a Location Tracker.

Unit-5
Teaching Hours:12
Services
 

Creating your own services, Establishing Communications between a service and an activity, binding activities to services 

 

Self Learning

 

understanding Threads, Preparing for Publishing, Deploying APK Files.

 

Text Books And Reference Books:

[1] Wei-Meng Lee, “Beginning android 4 application Development, John Wiley & sons, Inc, 2012.

Essential Reading / Recommended Reading

[1] Paul Deitel-Harvey Deitel-Abbey Deitel-Michael Morgano,”Android for Programmers An App-Driven Approach”,Pearson Education Inc., 2012.

[2] Jerome (J.F) DiMarzio , "Android - A programmer's Guide", TataMcgraw Hill,2010, ISBN: 9780071070591.

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCS333 - DESIGN AND ANALYSIS OF ALGORITHMS (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

The objective of the course to teach various algorithm design techniques for effective problem solving in computing. Students will be able to analyze the efficiency of algorithms in terms of its complexity and also to design effectivly.

Learning Outcome

Upon successful completion of the course student will be able to

CO1: Apply appropriate algorithm design techniques to solve computing problems

CO2:Analyze the running time of algorithms for problems in various domains.

CO3: Design efficient algorithms to solve real world problems.

Unit-1
Teaching Hours:12
Introduction
 

The role of Algorithms in Computing – Algorithms, Algorithms as a technology. Getting Started – Insertion sort, Analyzing algorithms, Designing

Algorithms.Growth of Functions – Asymptotic Notations. Recurrences – The Substitution method, Recursion Tree method and Master method.

 

Unit-2
Teaching Hours:12
Greedy Method
 

Knap Sack Problem, Minimum Spanning Trees , Prims algorithm and Kruskal’s algorithm.

Unit-2
Teaching Hours:12
Divide and Conquer
 

General Method, Binary Search, Finding the Maximum and Minimum, Merge Sort, Quick Sort, Selection sort, Strassens Matrix Multiplication.

Unit-3
Teaching Hours:12
Dynamic programming Method
 

Optimal Binary Search Trees, Traveling Salesman Problem, Longest Common Subsequence 

Unit-3
Teaching Hours:12
Back Tracking
 

Introduction - The 8-queens problem, Sum of Subsets

Unit-3
Teaching Hours:12
Branch n Bound
 

General Method- Traveling Salesman Problem 

Unit-4
Teaching Hours:12
Graph Algorithms
 

Representation of Graph, Depth First Search, Breadth first search.Single Source shortest path – Dijkstra’s Algorithm and Bellman Ford Algorithm. All Pair Shortest Path – Floyd-Warshall Algorithm.

Unit-4
Teaching Hours:12
Lower Bound Theory
 

Comparison trees for sorting and searching.

 

 

Self Learning

Representation of graphs (from discrete mathematics)

Unit-5
Teaching Hours:12
NP-Hard and NP-Complete problems
 

Basic Concepts, NP_Hard graph problems, NP-Hard Scheduling problems, NP- Hard code generation problems, some simplified  NP-Hard problems.

 

Amortized Analysis-Case studies.    

 

Text Books And Reference Books:

[1] Coremen T H, Leiserson  C E, Rivest  R L and Stein, Clifford, Introduction to algorithms, PHI, 2nd Edition, 2009.

[2] Horowitz  E and Sahni S. Fundamentals of Computer Algorithms, Computer SciencePress,2008.

Essential Reading / Recommended Reading

[1] Gelder Van Allen and Baase Sara, Computer Algorithms – Introduction to Design and Analysis, Addison Wesley, 3rd Edition, 2002.

[2] Aho A V, Hopcroft J E and Ullman J D., The Design and Analysis of Computer  Algorithms, Addison Wesley Publishing House, 1983.

[3] Dromey, R.G., How to solve it by Computer, Prentice-Hall International, 2006.

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCS341A - DATA WAREHOUSING AND DATA MINING (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The main objective of the course is designed to introduce the core concepts of data mining and data warehousing techniques and implementation.

Learning Outcome

Upon successful completion of the course, the Students will able to

CO1:Demonstrate basic data mining algorithms, methods, tools and OLAP.
CO2:Apply data mining principles and techniques.

CO3:Build the Data warehouse for real time applications.

Unit-1
Teaching Hours:12
Introduction to Data Warehouse and OLAP
 

Basic elements of the Data Warehouse: Source system-Data staging Area-Presentation Server-Dimensional Model-Business process-Data Mart-Data warehouse-Operational Data Store-OLAP: ROLAP, MOLAP and HOLAP.

Unit-1
Teaching Hours:12
Data Warehouse Design
 

The case for dimensional modeling – Putting Dimensional modeling together: the data warehouse bus architecture – Basic dimensional modeling techniques.

Unit-2
Teaching Hours:12
Data Staging
 

Data staging overview – Plan effectively – Dimension Table staging – Fact Table loads and warehouse operations – Data quality and cleansing – issues.

Unit-2
Teaching Hours:12
Data Warehouse Architecture
 

The value of architecture – An architectural framework and approach – Technical architecture overview – Back room data stores – Back room services. Back Room Services.

Unit-3
Teaching Hours:12
Introduction to data Mining
 

Data Mining – Process and architecture - Kinds of Data to be mined - Data Mining Functionalities, Classification of Data Mining Systems, Data Mining Task Primitives, Major Issues in Data Mining.

Unit-3
Teaching Hours:12
Data Preprocessing
 

Preprocessing - Descriptive Data Summarization – Measuring the central tendency- Measuring the dispersion of data - Data Cleaning - Missing Values – Noisy Data - Data Cleaning as a Process - Data Integration and Transformation - Data Reduction-Data Cube Aggregation-Attribute Subset Selection. Demo: Preprocessing can be done using WEKA tool.

Unit-4
Teaching Hours:12
Cluster Analysis
 

Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods – K-Means and K-Medoids, Hierarchical Methods  Agglomerative and Divisive

Demo: Classification and clustering analysis can be done using WEKA tool.

Unit-4
Teaching Hours:12
Data Mining Algorithms
 

Association Rule Mining: Basic Concepts, Efficient and Scalable Frequent Item set Mining Methods – Apriori algorithm, Generating Rules – Improving efficiency – Mining frequent item set without candidate generation. Classification and Prediction: Issues Regarding Classification and Prediction, Accuracy and Error Measures.

Unit-5
Teaching Hours:12
Mining Time-Series and Spatial Data
 

Mining Time-Series Data – Trend analysis – Similarity search, Spatial Data Mining-Spatial Data Cube Construction and Spatial OLAP- Mining Spatial Association and Co-location Patterns-Spatial Clustering, Classification Methods-Mining Raster Databases.

Unit-5
Teaching Hours:12
Applications and Trends in Data Mining
 

Data Mining Applications, Data Mining System Products and Research Prototypes, Social Impacts of Data Mining.

Text Books And Reference Books:
  1. Kimball, Ralph & et al, The Data Warehouse Lifecycle Toolkit, John Wiley & Sons, 2006.
  2. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, USA, 2nd Edition, 2011.
Essential Reading / Recommended Reading

  1. K P Soman,Shyam Diwakar, V. Ajay, Insight into Data Mining-Theory and  Practice, 6th Reprint, PHI, 2012.
  2. Inmon W H, Building the Data Warehouse, John Wiley & Sons, 3rd edition, 2005.
  3. Margaret H. Dunham, Data mining-Introductory and Advanced topics, Pearson Education,2003.
  4. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, 2005.
Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCS341B - MACHINE LEARNING (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To acquire basic knowledge in machine learning techniques and learn to apply the techniques in the area of pattern recognition and data analytics.

Learning Outcome

CO1: Understand the basic principles of machine learning.

CO2: Differentiate between predictive and descriptive machine learning techniques and their applications.

CO3: Apply machine learning techniques to solve real time problems. 

CO4: Analyze machine learning algorithms with respect to evolving data types and analysis paradigms

Unit-1
Teaching Hours:12
Introduction
 

Machine Learning, types of machine learning, examples. Supervised Learning: Learning class from examples, VC dimension, PAC learning, noise, learning multiple classes, regression, model selection and generalization, dimensions of a supervised learning algorithm. Parametric Methods: Introduction, maximum likelihood estimation, evaluating estimator, Bayes’ estimator, parametric classification.

 

 

Unit-2
Teaching Hours:12
Dimensionality reduction
 

Introduction, subset selection, principal component analysis, factor analysis, multidimensional scaling, linear discriminant analysis.

 

Clustering: Introduction, mixture densities, k-means clustering, expectation-maximization algorithm, hierarchical clustering, choosing the number of clusters. Non-parametric: Introduction, non-parametric density estimation, non-parametric classification.

Unit-3
Teaching Hours:10
Decision Trees
 

Introduction, univariate trees, pruning, rule extraction from trees, learning rules from data.

 

Multilayer perceptron: Introduction, training a perceptron, learning Boolean functions, multilayer perceptron, backpropogation algorithm, training procedures.

Unit-4
Teaching Hours:14
Kernel Machines
 

Introduction, optical separating hyperplane, v-SVM, kernel tricks, vertical kernel, defining kernel, multiclass kernel machines, one-class kernel machines.

Bayesian Estimation: Introduction, estimating the parameter of a distribution, Bayesian estimation, Gaussian processes.

 

Hidden Markov Models: Introduction, discrete Markov processes, hidden Markov models, basic problems of HMM, evaluation problem, finding the state sequence, learning model parameters, continuous observations, HMM with inputs, model selection with HMM.

Unit-5
Teaching Hours:12
Graphical Models
 

Introduction, canonical cases for conditional independence, d-separation, Belief propagation, undirected graph: Markov random field.

Reinforcement Learning: Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partiIntroduction, canonical cases for conditional independence, d-separation, Belief propagation, undirected graph: Markov random field.

 

 

Reinforcement Learning: Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partially observed state.ally observed state.

Self Learning            

 

Clustering , Decision tree 

Text Books And Reference Books:
  1.   Alpaydin, Introduction to Machine Learning. 2nd MIT Press, 2009.
Essential Reading / Recommended Reading
  1. K. P. Murphy, Machine Learning: A Probabilistic Perspective. MIT Press, 2012.
  2. P. Harrington, Machine Learning in Action. Manning Publications, 2012
  3. C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2011.
  4. S. Marsland, Machine Learning: An Algorithmic Perspective. 1st Ed. Chapman and Hall, 2009.
  5. T. Mitchell, Machine Learning. McGraw-Hill, 1997.
Evaluation Pattern

CIA - 50%

ESE - 50%

 

MCS341C - PARALLEL COMPUTING WITH OPEN CL (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description