Department of
COMPUTER-SCIENCE






Syllabus for
MS Computer Science
Academic Year  (2019)

 
1 Semester - 2019 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCSA131 PROGRAMMING IN JAVA 4 4 100
MCSA132 DIGITAL LOGIC AND COMPUTER ORGANISATION 4 4 100
MCSA133 ADVANCED DATABASE MANAGEMENT SYSTEM 4 04 100
MCSA134 DATA ANALYTICS 4 4 100
MCSA151 PROGRAMMING LAB - I 4 2 100
2 Semester - 2019 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MCSA231 DATA STRUCTURES AND ALGORITHMS 4 04 100
MCSA232 DATA COMMUNICATION AND NETWORK SECURITY 4 4 100
MCSA233 ADVANCED OPERATING SYSTEM 4 4 100
MCSA234 BUSINESS INTELLIGENCE 4 04 100
MCSA251 PROGRAMMING LAB - II 4 02 100
3 Semester - 2018 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MSP331 COMPUTER GRAPHICS 4 04 100
MSP332 WEB ENGINEERING 4 4 100
MSP341A DIGITAL IMAGE PROCESSING 4 04 100
MSP341B MULTIMEDIA SYSTEM AND APPLICATION 4 04 100
MSP341C SOFTWARE PROJECT MANAGEMENT 4 04 100
MSP341D DISTRIBUTED SYSTEMS 4 4 100
MSP341E NEURAL NETWORKS 4 4 100
MSP341F IOT AND WIRELESS SENSOR NETWORKS 4 04 100
MSP342A HIGH PERFORMANCE COMPUTING 4 4 100
MSP342B OOAD WITH UML 4 04 100
MSP342C PRINCIPLES OF USER INTERFACE DESIGN 4 04 100
MSP342D DATA MINING AND DATA WAREHOUSING 4 04 100
MSP342E MACHINE LEARNING 4 4 100
MSP342F RISK ANALYSIS 4 04 100
MSP351 MINI PROJECT 4 2 100
MSP371 RESEARCH (RESEARCH PROBLEM IDENTIFICATION AND DATA COLLECTION) 2 01 50
4 Semester - 2018 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MSP431 CLOUD COMPUTING 4 4 100
MSP441A KNOWLEDGE MANAGEMENT 4 04 100
MSP441B WIRELESS AND MOBILE NETWORKS 4 4 100
MSP441C SOFTWARE QUALITY AND TESTING 4 04 100
MSP441D STORAGE AREA NETWORK 4 4 100
MSP441E COMPUTER VISION 4 04 100
MSP442A INFORMATION RETRIEVAL AND WEB MINING 4 04 100
MSP442B DATABASE ADMINISTRATION 4 4 100
MSP442C BIOINFORMATICS 4 4 100
MSP442D SOFTWARE ARCHITECTURE 4 4 100
MSP442E AGENT BASED COMPUTING 4 4 100
MSP451 MAIN PROJECT 20 04 200
MSP471 RESEARCH (IMPLEMENTATION AND PUBLICATION) 2 1 50
        

  

Assesment Pattern

Question paper has to be set for the total marks of 100.

Examination duration is 3 hours.

Each full question carries 10 marks.

Answer any 10 questions out of 14.

Examination And Assesments

Evaluation Pattern: 60% CIA + 40% ESE 2. Tutorials / Assignments / Tests / Quiz / Seminar. 3. Attendance is part of the CIA component. 4. Minimum percentage to pass in each paper is 50% (CIA + 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:
Vision 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 t
Introduction to Program:
MSc programme is offered by the University for the professionals working in the software industry or related fields. This program is intended to enhance their existing academic foundations with comprehensive understanding of the use and application of information technology. The programme focuses on Advanced Operating Systems, Data Structures, Software Project Management, Networks, Data Warehousing and Data Mining.
Program Objective:
Programme Objective ? The program is designed to help software professionals who are already employed to further their knowledge in their respective domains. ? To enhance the project management skills. ? To facilitate software professionals to take lead roles. ? To understand and assimilate knowledge and skills to apply in their industry. ? To introduce contemporary theoretical concepts about the processes, standards and practices in software development life cycle. ? To assist in career advancement by acquiring additional degree.

MCSA131 - 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 and 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 successful completion of the course the student will be able to

CO1: Recognize the principles and practice of object oriented programming in the construction of robust maintainable programs.

CO2: Show competence in the use of Java Programming language in the development of small to medium sized application programs that demonstrate professionally acceptable coding and performance standards.

CO3: Design real-time applications in various domains.

Unit-1
Teaching Hours:12
Fundamentals of Java Programming
 

Review of the fundamentals of Java Programming, Class and Objects. Inheritance in Java - Inheritance in classes, Using super, Method overriding, Dynamic Method Dispatch. Abstract Classes, Using final with inheritance, the Object Class.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.Exception Handling in Java - try-catch-finally mechanism, throw statement, throws statement. Classes for Exception Handling

Unit-2
Teaching Hours:12
Input / Output in java, Multi threading, Applets
 

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 Multithreading - Life cycle of a thread, Java Thread priorities, Runnable interface and Thread Class. Sharing limited Resources, Shared Object with Synchronization. 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-3
Teaching Hours:12
GUI Components (awt& swing) , Swing, Servlets
 

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.Event Handling-Handling Keyboard Events and Mouse Events.Handling Sessions and Cookies.Servlet Model - Overview, Environment Setup, Life Cycle,  Examples - Client Request, Server Response.

Unit-4
Teaching Hours:12
Database and client server communication
 

Networking - Creating a server that sends data- Creating a client that receives data -two way communication between server and client. Difference between Server Socket and Socket.RMI. JDBC - Using MS-Sql Server Stages in a JDBC program- Registering the driver- Connecting to database - Transaction and Non-Transactional Events - Preparing SQL statements - various methods of statements and differences. Improving the performance of a JDBC program.

Unit-5
Teaching Hours:12
JSP Basics, Directive Elements, Custom Tags
 

Java Server Pages - The Problem with Servlets, Life Cycle of JSP Page, JSP Processing, JSP Application Design with MVC, Setting Up the JSP. Environment - JSP Directives, JSP Action, JSP Implicit Objects, JSP Form Processing, JSP Session and Cookies Handling, JSP Session. Tracking - JSP Database Access, JSP Standard Tag Libraries, JSP Custom Tag, JSP Expression Language, JSP Exception Handling, JSP XML Processing.

Text Books And Reference Books:

[1] Schildt Herbert, Java Eighth Edition: The Complete Reference, Tata McGraw-Hill, 2011

[2] Kathy walrath, Black Book : Java server programming” J2EE, 1st ed., Dream Tech Publishers,  2015

Essential Reading / Recommended Reading

[1] Deitel & Deitel, Java How to Program, Pearson Education Asia, 10th Edition, 2015.

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

[3] James Keogh, Complete Reference J2EE, mcgraw publication, 2015.

Evaluation Pattern

CIA - 60%

ESE - 40%

MCSA132 - DIGITAL LOGIC AND COMPUTER ORGANISATION (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 fundamental aspects of computer architecture and design in terms of digital logic elements and circuits, central processing unit and memory unit.

Learning Outcome

Upon the completion of course the student will be able to

CO1: Understand different number system, binary codes and digital logic elements

CO2: Acquaint with elementary postulates of Boolean algebra and methods for simplifying Boolean expressions

CO3: Illustrate the procedures for the analysis and design of sequential and combinational circuits

CO4: Demonstrate the basic structure and operation of processing unit and get familiarize with different types of memory systems

Unit-1
Teaching Hours:12
Number System and Binary Coding
 

Number system- Decimal number system- Binary number system- octal number system- hexadecimal number system- number system conversion- number representation- unsigned representation – signed number representation-1’s complement – 2’s complement- 9’s complement – 10’s complement- binary arithmetic operation- binary addition- binary subtraction- Binary multiplication- binary division- Binary codes- weighted codes and unweighted codes

Unit-2
Teaching Hours:12
Digital Logic Elements
 

Introduction- Boolean algebra- Boolean operators- truth table- laws of Boolean algebra- De Morgan’s Law- Logic gates- Description of logic gates- Universal properties- Simplification of logic functions- Simplification using NAND and NOR  gate- logic expression- minterm - maxterm- SOP - POS expression- minimization techniques- Karnaugh Map

Self learning: Implementation using simulator

Unit-3
Teaching Hours:12
Digital Combinational Circuits
 

Digital circuits- Combinational circuits- Half Adder – Full adder- Half subtractor-Full subtractor- Encoder- Decoder-BCD to seven segment display- Multiplexer- Demultiplexer

Unit-4
Teaching Hours:12
Digital Sequential Circuits
 

Sequential circuits-  Latches- SR- Latch- Flip Flop- SR flip flop- D flip flop- JK flip flop- master slave JK FF - Timing diagrams-Registers- Shift Register- SISO-SIPO-PISO-PIPO- Counters- Synchronous counters- Asynchronous counters- Decade counter- Mod counters- Timing diagrams

Unit-5
Teaching Hours:12
Computer Organization
 

Basic Structure of Computers: Basic Operational Concepts- Bus Structures – Processor Clock - Clock Rate- Instruction set: CICS and RISC.

Basic Processing Unit: Some Fundamental Concepts, Multiple Bus Organization, Hard-wired Control, Micro programmed Control.

The memory system: Semiconductor RAM memories-Internal organization of memory chips- static memories-ROM-Cache memories

Text Books And Reference Books:

[1] Donald P Leach, Albert Paul Malvino, Goutam Saha, Digital Principles and Applications, 8th Edition, Tata Mc Graw-Hill, 2013

[2]. V.Carl Hamacher, Zvonko G. Varanesic and Safat G. Zaky, Computer Organisation, 6th  edition, Mc Graw-Hill Inc, 2013.

Essential Reading / Recommended Reading

[1] Mano, Morris M and Kime Charles R., Logic and Computer Design Fundamentals, Pearson education, 2nd edition, 2014.

[2] Bartee, Thomas C, Digital Computer Fundamentals, Tata Mc Graw-Hill, 6th edition, 2013.

[3] William Stallings, Computer Architecture and Organization, PHI, Eigth  Edition, 2015.

[4] David A. Patterson and John L.Hennessey, Computer Organization and Design, Morgan Kauffman / Elsevier, Fifth edition, 2014.

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

MCSA133 - ADVANCED DATABASE MANAGEMENT SYSTEM (2019 Batch)

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

Course Objectives/Course Description

 

This course concentrates on introduction, principles, design and implementation of advanced database concepts.Objective of the course is to provide strong foundation of database concepts and develop skills for the design,   storage and retrieval in relational databases, XML and NoSQL databases.

Learning Outcome

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

CO1: Understand the fundamental and advanced concepts of relational databases.

CO2: Demonstrate storage and retrieval in XML and NoSQL.

CO3: Design Database Application using CRUD operations.

Unit-1
Teaching Hours:12
Introduction to Relational Databases
 

Database system applications, Purpose of database systems, View of data, Data models, Database languages, Database storage and querying, Transaction management, Database architecture, Database users and administrators.

Unit-2
Teaching Hours:12
ER Model and Relational Database Design
 

Structure of relational databases, Database schema, Keys, Schema diagrams, Design process, ER model, Constraints, ER diagrams, Aspects of database design, Atomic domains and 1NF, Decomposition using functional dependencies, Functional dependency theory

Unit-3
Teaching Hours:12
Database Storage and Indexing
 

File organization, Organization of records in files, Data dictionary storage, Basic indexing concepts, Ordered indexes, B+ tree index, Static hashing, Dynamic hashing, Bitmap index.

Unit-4
Teaching Hours:12
XML Data Model
 

Motivation, Structure of XML Data, XML Document Schema, Querying and Transformation, Application Program Interfaces to XML, Storage of XML Data, XML Applications.

Unit-5
Teaching Hours:12
NoSQL
 

Definition and introduction, Document databases – MongoDB, Storing data and accessing data from MongoDB, Querying MongoDB, Document store internals, MongoDB reliability and durability, Horizontal scaling, CRUD operations in MongoDB, Creating and using indexes in MongoDB.

Text Books And Reference Books:

[1].      Abraham Silberschatz, Henry Korth, Sudarshan, “Database System Concepts”, McGraw-Hill, 6th Edition, 2011.

[2].      ShashankTiwari, “Professional NoSQL”, John-Wiley, 2011.

Essential Reading / Recommended Reading

[1]   Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”, McGraw-Hill, 3rd Edition, 2014.

[2]   RamezElmasri, ShamkantNavathe, “Fundamentals of Database Systems”, Addison-Wesley, 6th Edition, 2011.

[3]   Kristina Chodorow, “MongoDB: The Definitive Guide”, O'Reilly, 2nd Edition, 2013.

Evaluation Pattern

CIA:  60%

ESE:  40%

MCSA134 - 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: Collect data from various sources

CO2: Explore data using tools

CO3: Build analytical models

CO4: Interpret results based on the choice of domain

Unit-1
Teaching Hours:12
Introduction and Data Exploration
 

Introduction, Data and Relations-Matrix representation, variable measures, sequential relation, sampling and quantization. Data Pre-processing: Cleaning, Transformation, Basic Visualization-PCA, multidimensional scaling, Histograms, Correlation.

Unit-2
Teaching Hours:12
Predictive Modeling and Optimization
 

Linear and non-linear regression, Feature Selection. Forecasting - Recurrent Models, Classification-Rules, Trees, Naïve Bayes, SVM, Vector Quantization. Evaluation Metrics-Validation and Interpretation.

Unit-3
Teaching Hours:12
Optimization and Clustering
 

Optimization Methods – With derivatives, Gradient Descent. Clustering - Cluster Partition, Sequential, Prototype-Based, Relational, Cluster Validity and Self Organizing Map.

Unit-4
Teaching Hours:12
Mathematical Modeling and Spatial Data
 

Introduction to Multi-criteria Decision Making, Using Numerical Methods in Data Science, Mathematical Modeling with Markov Chains. Modeling Spatial Data with Statistics- Getting predictive surfaces from special point data, Using trend surface analysis on spatial data.

Unit-5
Teaching Hours:12
Visualization
 

Principles of Visualization-Understanding the type, Design Style, Data Graphic Type, Web-based Applications for Visualization Design, Best practices in dashboards, Making maps for Spatial Data.

Self Learning: Additional Exploration and Modeling Algorithms

Service based learning: Building models for social relevance issues

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 (Weightage): 60%

ESE (Weightage): 40%

MCSA151 - PROGRAMMING LAB - I (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 builds the logical thinking in the students with the help of the programming. It also facilitates the students to build applications using java programming. The database concepts help the student to learn advance database connectivity and usage.

Learning Outcome

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

CO1: Demonstrate the skills for identifying logic in the problem

CO2: Analyze the given problem and write the algorithm, flowchart

CO3: Write structured java programs and implement the advance database concepts

Unit-1
Teaching Hours:60
Section A - Java Programming
 

1. Demonstrate objects and classes (constructor, access specifier, method overloading)

2. Demonstrate static block, static variables and static methods

3. Demonstrate inheritance in java

4. Demonstrate use of super and this

5. Demonstrate abstract class

6. Demonstrate interfaces in java

7. Demonstrate exception handling in java

8. Demonstrate multithreading in java

9. Demonstrate applets in java

10. Demonstrate two way communication between server and client

Unit-1
Teaching Hours:60
Section B - Advanced Database Management System
 

1. Select queries and DML

2. PL/SQL

3. Data manipulation with MongoDB

Text Books And Reference Books:

[1] Schildt Herbert, Java Eighth Edition: The Complete Reference, Tata McGraw-Hill, 2011

[2] Black Book “ Java server programming” J2EE, 1st ed., Dream Tech Publishers, Kathywalrath, 2015.

Essential Reading / Recommended Reading

[1] Deitel & Deitel, Java How to Program, Pearson Education Asia, 10th Edition, 2015.

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

[3] Complete Reference J2EE by James Keogh mcgraw publication, 2015.

Evaluation Pattern

1. Evaluation Pattern: 60% CIA + 40% ESE

 

MCSA231 - DATA STRUCTURES AND ALGORITHMS (2019 Batch)

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

Course Objectives/Course Description

 

This course provides a more comprehensive understanding of data structure and algorithm development.

Learning Outcome

Upon completion of the course, student will be able to 

CO1: Understand the need and working  of advanced search and sorting technique.

CO2: Calculate and measure efficiency of algorithm.

CO3: Appreciate some interesting algorithms like Huffman, Quick Sort, and Shortest Path etc.

Unit-1
Teaching Hours:12
Stacks and Queues
 

Basic Operations, Implementation, Stack applications, Recursion- A Case Study, Queue operations, implementation, Applications of a queue.Linked Lists: Linked list algorithms- Processing a linked list, Linked list algorithms (Create, Traverse, Insert, Delete, Search, Destroy), doubly linked lists structures and its operations, Applications of linked lists. Algorithmic efficiency in terms of space and time complexity

Unit-2
Teaching Hours:12
Trees
 

Basic tree concepts, Binary trees, Binary tree traversals, Expression trees, General trees- Changing general tree to binary tree, General tree insertion, Search trees, Binary search trees, Operations, Traversals-BFS and DFS methods, Searching a BST, Algorithms for and traversing and searching. AVL trees, AVL Balance factor, Balancing trees, AVL Insert, AVL Delete

Unit-3
Teaching Hours:12
Multiway trees
 

M-Way search trees, B Trees, B-Insertion, B-Tree Deletion, B –Tree Traversal-Tree Search, Simplified B-Trees - 2-3 Tree, 2-3-4 Tree, B-Tree Variations–B * Trees, B+ Trees

Unit-4
Teaching Hours:12
Graphs
 

Terminology, operations, Graph storage structures – Adjacency Matrix, Adjacency lists, Graph algorithms- Create insert vertex, Delete vertex, Retrieve vertex, Depth first traversal and Breadth First Traversal, Networks- Minimum spanning tree, Shortest Path algorithm

Unit-5
Teaching Hours:12
Advanced Sorting & Searching concepts
 

General sort concepts, O Notation, Sort Algorithms-Quick Sort, Heap sort, Sorting using a Heap, Shell sort, Merge sort, radix sort, merging two sorted lists. Efficiency considerations, comparative study

Text Books And Reference Books:

[1]  Richard F. Gilberg, Behrouz A. Forouzan, “Data Structure. A Pseudocode Approach with C”, 3rd Edition, Thomson Publications, reprint 2006.

[2]  A M Tanenbaum, Y Langsam and M. J. Augenstein, “Data Structure using C”, 2nd Edition, Prentice- Hall, India, 2007.

Essential Reading / Recommended Reading

[1]  Robert Kruse, Tondo C L, Bruce Leung, Data Structures & program Design In C, Pearson Education, 2nd Edition, 2004.

[2]  U.A.Deshpande and O. G. Kakde, Data Structures and Algorithms, ISTE- learning.

Evaluation Pattern

CIA: 60%

ESE: 40%

MCSA232 - DATA COMMUNICATION AND NETWORK SECURITY (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 to set the foundation for Data Communication and Network Security by introducing the network components, topologies, network models, layers, protocols, and some of the security aspect.

Learning Outcome

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

CO1: Comprehend knowledge about Network Architecture and its functionality. 

CO2: Evaluate network protocols for data transmission in various types of networks.

C03: Understand the working of Algorithm in Cryptography

CO4: Design solution to real time problems related to Network Security and compression. 

Unit-1
Teaching Hours:12
Data Communications
 

Data Communications- Data Transmission: Concepts and Terminology, Analog and Digital Data Transmission, Transmission Impairments, Channel Capacity; Transmission Media: Guided Transmission Media, Wireless Transmission, Wireless Propagation, Line-of-Sight Transmission; Signal Encoding Techniques: Digital Data, Digital Signals, Digital Data, Analog Signals, Analog Data, Digital Signals, Analog Data, Analog Signals.

Unit-2
Teaching Hours:12
Digital Data Communication
 

Digital Data Communication Techniques- Asynchronous and Synchronous Transmission, Types of Errors, Error Detection, Error Correction, Line Configurations; Multiplexing: Frequency, Division Multiplexing, Synchronous Time-Division Multiplexing, Statistical Time-Division Multiplexing, Asymmetric Digital Subscriber Line, Switched Communications Networks, Circuit Switching Networks, Circuit Switching Concepts, Softswitch Architecture, Packet-Switching Principles.

Unit-3
Teaching Hours:12
Congestion Control
 

Congestion Control in Data Networks- Effects of Congestion, Congestion Control, Traffic Management, Congestion Control in Packet-Switching Networks; Cellular Wireless Networks: Principles of Cellular Networks, First Generation Analog, Second Generation CDMA, Third Generation Systems; High-Speed LANs: The Emergence of High-Speed LANs, Ethernet, Fibre Channel; Wireless LANs: IEEE 802.11 Architecture and Services; Internetwork Protocols - Internetwork Protocols: Internet Protocol, IPv6; Transport Protocols: Connection-Oriented Transport Protocol Mechanisms, TCP, TCP Congestion Control, UDP.

Unit-4
Teaching Hours:12
Cryptography and Network Security
 

The need for security, Security Approaches, Security Attacks, Security Services, Security Mechanisms, A Model for Network Security. Symmetric Cipher Models, Substitution Techniques, Transposition Techniques and other Symmetric key approaches. Data Encryption Standard, AES Cipher. Public Key Cryptosystems, RSA Algorithm and Diffie-Hellman Key Exchange

Unit-5
Teaching Hours:12
Cryptographic Hash Function
 

Application of Cryptographic Hash Function, Brute Force Attack, Secure Hash Algorithm (SHA-2 & SHA-3), Message authentication code, HMAC, Digital Signatures (DSS). User Authentication: Kerberos Federated Identity Management. E-Mail Security, Pretty Good Privacy, S/MIME, SSL, IP Security Overview.

Text Books And Reference Books:

[1]     Stallings William, “Data and Computer Communications”, PHI, 9th Edition, 2011.

[2]     William Stallings, “Cryptography and Network Security”, Prentice Hall, 6th Edition, 2014.

Essential Reading / Recommended Reading

[1]      Forouzan, Behrouz A., “Data Communications and Networking”, Tata McGrawHill publishing Company Limited, 5th Edition, 2013.

[2]      AtulKahate, “Cryptography and Network Security”, Tata McGraw-Hills, 2010.

[3]      Brijendra Singh, “Network Security and Management”, PHI, 3rd Edition, 2013.

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

MCSA233 - ADVANCED 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 will expose few advanced topics in operating system and concepts related to recent developments in operating system.Objectives of the course are to understand the main concepts of parallel processing systems, distributed systems, real time systems etc., to have an insight into UNIX and MACH operating system and to know the components and management aspects of Real time, Mobile Operating Systems.

Learning Outcome

Upon successful completion of the course, the student

CO1: Analyse the requirements of Operating System.

CO2: Understand the concept of distributed operating system and concepts.

CO3: Demonstrate the advanced OS concepts of Real time OS and Mobile OS.

Unit-1
Teaching Hours:12
Overview
 

General Overview of the System - System Structure – Operating System Services – Introduction to kernel-architecture of unix operating system-introduction to system concepts kernel data structures. The Buffer cache - Buffer Headers – Structure of the buffer pool – Retrieval of a buffer – scenarios for retrieval of a Buffer-Reading and writing disk blocks - Advantages and disadvantages of the buffer cache. Internal Representation of files - Inodes, structure of a regular file, directories, conversion of a path to an inode, Super Block, inode assignment to a New file-Allocation of Disk Blocks-other file Types.

Unit-2
Teaching Hours:12
Process Management
 

UNIX Process Management - The Structure of Processes: Process States and Transitions - Layout of system memory - Context of a process – Sleep – Implementation of System Calls. Process Control - Process Creation – Signals – Process Termination – Invoking other programs – PID & PPID – Changing the size of a process – The shell – System Boot and the init process - Implementation of System Calls.

Unit-3
Teaching Hours:12
Memory Management
 

Memory Management: Swapping – Segmentation – Demand Paging – A Hybrid System with Swapping and Demand Paging. The I/O Subsystem: Driver Interfaces – Disk Drivers – Terminal Drivers – Streams. Inter Process Communication (IPC): Process Tracing – System V IPC – Network Communications – Sockets. Multiprocessor Systems: Problem with Multiprocessor Systems – Master and Slave processors – Semaphores

Unit-4
Teaching Hours:12
Distributed system and RPC
 

Introduction to Distributed system- Remote Procedure Call – Logical clocks- Vector clocks -  Distributed mutual exclusion – Non token based algorithms – Token based algorithms – Deadlock algorithms – Election algorithms – Byzantine agreement problem – Load distributing algorithms  - Performance comparison. Distributed File system Design-the file service interface-the directory server interface-semantics of file sharing.Distributed file system implementation-file usage-system structure0-caching-replication-an example sun’s network file system

Unit-5
Teaching Hours:12
Real Time Systems
 

Real time and Mobile Operating Systems – Basic Model of Real Time Systems – Characteristics – Applications of Real Time Systems – Real time Task Scheduling –Handling resource sharing . Mobile Operating System – Micro Kernel Design – Client Server Resource Access – Processes and Threads – Memory Management - - File System.Case study MACH: Introduction to MACH - Process management in MACH-processes-thread scheduling- memory management in MACH-Virtual memory-memory sharing

Text Books And Reference Books:

[1].      Maurice J Bach, “The Design of Unix Operating System”, Prentice Hall of India Pvt. Ltd.,New Delhi, Reprint 2007.

[2].      Andrew S Tanenbaum, “Distributed Operating Systems”, PHI, reprint 2006.

[3]     Rajib Mall, “Real Time Systems: Theory and Practice”, Pearson Education, India, 2006

Essential Reading / Recommended Reading

[1].      Stan-Kelly-Bootle, “Understanding Unix”, BPB Publications, New Delhi,reprint,2006

[2].      Arnold Robbins, “UNIX in a Nutshell”, In a Nutshell series, 3rd Edition, reprint 2007.

[3].      George Coulouris, Jean Dollimore and Tim Indberg, “Distributed Systems Concepts andDesign”, 3rd Edition, Pearson Education,      2002

[4].   Pradeep K Sinha, “Distributed Operating Systems – Concepts and Design”, PHI, 2006

Evaluation Pattern

1.      Evaluation Pattern: 60% CIA + 40% ESE

2.      Tutorials / Assignments / Tests / Quiz / Seminar.

3.      Attendance is part of the CIA component.

4.      Minimum percentage to pass in each paper is 50% (CIA + ESE).

MCSA234 - BUSINESS INTELLIGENCE (2019 Batch)

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

Course Objectives/Course Description

 

Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications support the activities of decision support, query and reporting, online analytical processing (OLAP) and analysis.

Learning Outcome

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

CO1: Understand the Technical components of BI

O2: Analyze the process involved in BI

CO3: Visualize the data and Generate Reports using report builder and power pivot

Unit-1
Teaching Hours:12
Requirements, Realities and Architecture
 

Defining Business Requirements: Introduction, Uncovering Business Value, Prioritizing the Business Requirements. Designing the Business Process Dimensional Model: Concepts and Terminology, Additional Design Concepts and Techniques. The Toolset: Microsoft DW/BI Toolset, Architecture and Overview of the Toolset.

Unit-2
Teaching Hours:12
Building and Populating the Databases
 

Creating the Relational Data Warehouse: Getting started, completing the physical design, Define storage and create constraints and supporting objects.

Master Data Services: Managing Master Reference Data, Introducing SQL Server MDS, Creating a Simple Application.

Design and Develop the ETL System: Developing the ETL Plan, Introducing SQL Server Integration Services, Extracting Data, Cleaning and Confirming Data, Delivering Data for Presentation.

Unit-3
Teaching Hours:12
Analysis Services
 

Core Analysis Services OLAP Database: Overview, Design the OLAP structure-Planning, getting started, Data source view, Dimension design, Editing dimension, Editing Cube, Physical Design Consideration.

Unit-4
Teaching Hours:12
Developing the BI Applications
 

Building the BI Applications in Reporting Services: Overview, High Level Architecture for Reporting, System Design and Development Process, Building and Delivering Reports, Reporting Options.

Unit-5
Teaching Hours:12
BI using Excel
 

Power Pivot and Excel: Using Excel for Analysis and Reporting, Architecture, Creating and using Power Pivot Databases, Power pivot Monitoring and Management.

Case study: Any Two Applications (eg. Healthcare, Retail Industry)

Text Books And Reference Books:

[1]. Joy Mundy, Warren Thornthwaiteand  Ralph Kimball, “The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset”, John Wiley & Sons, 2nd edition, 2011.

Essential Reading / Recommended Reading

[1]. Gert H.N. Laursen and JesperThorlund , “Business Analytics for Managers: Taking Business Intelligence beyond Reporting Paperback” , 2013

[2]. Mike Biere,“Business Intelligence for the Enterprise” , second edition, 2009

Evaluation Pattern

Weightage:

CIA 60%

ESE 40%

MCSA251 - PROGRAMMING LAB - II (2019 Batch)

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

Course Objectives/Course Description

 

This course provides different ways to solve business intelligence problems using various data structures and writing programs for these solutions

Learning Outcome

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

CO1: Understand the concepts of data structure

CO2: Implement basic data structures like arrays and linked lists

CO3: Demonstrate optimal approaches to solve sorting and graph problems

CO4: Analyse the process involved in Business Intelligence

Unit-1
Teaching Hours:60
Data Structures
 

1. Implementation of insertion, selection and merge sorting Methods

2. Implementation of stacks

3. Implementation of queues

4. Implementation of Linked list

5. Implementation of two way linked list

6. Implementation of circular linked List

7. Implementation of Binary search tree

8. Implementation of radix and heap sort

9. Implementation of DFS for graphs

10. Implementation of BFS for graphs

Unit-1
Teaching Hours:60
Business Intelligence
 

Pre-Lab: Software Installation and Configuration

1. Create a Relational Data bases

2. Design an ETL System using SSIS

3. Design a dimensional model using SSAS

4. Design reports using SSRS

5. Design a dashboard using power pivot

Text Books And Reference Books:

1. Richard F. Gilberg, Behrouz A. Forouzan, “Data Structure. A Pseudocode Approach with C”, 3rd Edition, Thomson Publications, reprint 2006.

2. Joy Mundy, Warren Thornthwaite and Ralph Kimball, “The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset”, John Wiley & Sons, 2nd edition, 2011.

Essential Reading / Recommended Reading

1. Robert Kruse, Tondo C L, Bruce Leung, Data Structures & program Design In C, Pearson Education, 2nd Edition, 2004.

2.Gert H. N. Laursen and Jesper Thorlund, “Business Analytics for Managers: Taking Business Intelligence beyond Reporting Paperback” , 2013

Evaluation Pattern

60% CIA

40% ESE

MSP331 - COMPUTER GRAPHICS (2018 Batch)

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

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

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

CO1: Understand the basic concepts of Computer Graphics.

CO2: Apply geometric conversions on graphical objects.

CO3: 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

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:

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.

Evaluation Pattern

60% CIA

40% ESE

MSP332 - WEB ENGINEERING (2018 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 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:

CO1: Understand the concepts, principles and methods of Web engineering.

CO2: Apply the concepts, principles, and methods of Web engineering to Web applications  development.

CO3: Correlate with current web technologies.

CO4: Collaborate with 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].      GertiKappel and and Birgit Proll, “Web Engineering: The Discipline of Systematic Development of Web Applications”, John Wiley & Sons, 2012.

Essential Reading / Recommended Reading

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

Evaluation Pattern

60% CIA + 40% ESE

MSP341A - DIGITAL IMAGE PROCESSING (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 will cover the basic theory and algorithms that are widely used in Digital image processing. Develop hands-on experience in using computers to process images with Matlab image processing toolbox.

 

Learning Outcome

Upon successful completion of the course the student would  

CO1: Understand the theoretical background of Image processing. 

CO2: Apply image enhancement, restoration, compression and segmentation in both frequency   and spatial domain. 

CO3: Represent and recognize objects through patterns in application.

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
 

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 and R. E. Woods, “Digital Image Processing”, 3rd Edition. Pearson Education, 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): 60%

ESE (Weightage):  40%

 

MSP341B - MULTIMEDIA SYSTEM AND APPLICATION (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 will provide the students with an overview of multimedia technologies and the latest developments in multimedia systems. Students will be able to gain valuable hands on experience in multimedia systems and applications. Issues in effectively representing, processing, and retrieving multimedia data will also be addressed. Recent multimedia papers or technique reports will be presented or assigned as homework.

Learning Outcome

Upon successful completion of course the student will be able to

CO1:  Comprehend multimedia system fundamentals 

CO2:  Design multimedia application with creativity

CO3: Identify a problem in certain multimedia area and provide a reasonable solution.

 

Unit-1
Teaching Hours:12
Multimedia Information representation
 

Introduction, Definition of Multimedia, Digitization principles- Analog signals, Encoder design, Decoder design, Text-Unformatted text, Formatted text, Hypertext, Images- Graphics, digitized documents, digitized pictures, Audio – PCM speech , CD quality audio, Synthesized audio. Video- Broadcast television, digital video, PC video, Video Content.

Unit-2
Teaching Hours:13
Text and Image Compression
 

Introduction, compression principles- Source encoders and destination decoders, Lossless and lossy compression, entropy encoding, source encoding. Text compression- static and dynamic Huffman coding, Arithmetic coding, Image compression-Graphics interchange format, Tagged image file format, digitized documents, digitized pictures, JPEG.

Unit-3
Teaching Hours:13
Audio and Video Compression
 

Introduction, Audio compression, Frequency, amplitude, sample rate, Differential pulse code modulation, Adaptive differential PCM, Adaptive predictive coding, Linear predictive coding, code-excited LPC, perceptual coding, MPEG-MP3 audio coders, Dolby audio coders. Video compression principles, video Standards: NTSC, PAL, SECAM, Inter-frame, Intra- frame, video encoding, algorithms H.261, H.263, MPEG, MPEG1, MPEG2, MPEG4, Video for WEB.

Unit-4
Teaching Hours:12
Standards for Multimedia communications
 

Reference models-TCP/IP, Protocol basics, standards relating to interpersonal communications, Circuit mode networks, Packet-switched networks, Electronic mail, standards relating to interactive applications over the Internet, information browsing, Electronic commerce, intermediate systems, Java and Java Script, Standards for entertainment applications, Movie/Video on demand, Interactive television.

Unit-5
Teaching Hours:10
Multimedia Applications
 

Understanding Designing and implementations of interactive applications, entertainment applications, Multimedia in internet and Web, Video Emails, video conferencing, Web casting, Software for image editing and Compression, Audio editing and compression, Video editing and compression, Voice recognition applications, Gesture based applications, interactive games design and implementation.

Text Books And Reference Books:

[1]. Fred Halshall, “Multimedia communication-application, network, protocol and standards”,1st Edition, Pearson Education Ltd, 2009

Essential Reading / Recommended Reading

[1]. Ralf Steinmetz andKlaraNahrstedt, “Media Coding and Content Processing”, Volume I, PHI, 2011.

 

Evaluation Pattern

CIA (Weightage):   60%

ESE (Weightage):  40%

MSP341C - SOFTWARE PROJECT MANAGEMENT (2018 Batch)

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

Course Objectives/Course Description

 

Software Project Management provides insight to the importance of careful project management. Topics are presented in the same order that they appear in the progression of actual projects 

Learning Outcome

Upon completion of the course the student will be able to

CO1: Practice project planning activities that accurately forecast project costs, timelines and quality

CO2: Value software projects effectively

CO3: Assess real world projects with a strong knowledge on basic measurements of monitoring and controlling

Unit-1
Teaching Hours:14
Introduction
 

Introduction to software project management and control whether software projects are different from other types of projects. Scope of project management.Management of project life cycle. Defining effective project objectives where there are multiple stakeholders. Software tools for project management. Project Planning: Creation of a project plan -step by step approach, The analysis of project characteristics in order to select the best general approach, Plan Execution, Scope Management, Use of Software (Microsoft Project) to Assist in Project Planning Activities.

Unit-2
Teaching Hours:12
Project Scheduling
 

Time Management, Project Network Diagram, Critical path Analysis, PERT, Use of Software (Microsoft Project) to Assist in Project Scheduling. Project Cost Management: Resource planning, Cost Estimation (Types, Expert Judgment, Estimation by Analogy, COCOMO).

Unit-3
Teaching Hours:10
Project Quality Management
 

Stages, Quality Planning, Quality Assurance, Quality Control, Quality Standards, Tools and Techniques for Quality Control.

Unit-4
Teaching Hours:12
Project Human Resource Management
 

Definition, Key to managing People, Organization Planning, Issues in Project Staff Acquisition and Team Development, Using Software to Assist in Human Resource Management, Communication Planning, Information Distribution, Performance Reporting.

Unit-5
Teaching Hours:12
Project Risk Management
 

Common Sources of Risk in IT projects, Risk Identification, Risk Quantification, Risk Response Development and Control. Project Procurement Management: Procurement Planning, Solicitation, Source Selection, Contract Administration.

Text Books And Reference Books:

[1]. Bob Hughes, Mike Cotterell, “Software Project Management”, Tata McGraw-Hill, 3rd Ed., 2009.

[2]. PankajJalote, “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., 2003.

 

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, 2012.

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

MSP341D - DISTRIBUTED SYSTEMS (2018 Batch)

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

Course Objectives/Course Description

 

To present the main characteristics of distributed systems, as well as the related problems and the most common solutions. Student can implement a simple distributed application using a message based protocol.

Learning Outcome

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

CO1: Understand basic structures and the existing middleware frameworks. 

CO2: Implement a simple distributed software laboratory work with socket and RMI interfaces. 

CO3: Relate the existing libraries and algorithmic solutions for the problems of distribution.

CO4: Judge the problems that will arise if atomicity and timing issues are not handled in a distributed application.

Unit-1
Teaching Hours:12
Introduction
 

Distributed System, Examples of Distributed Systems, Important Issues in Distributed Systems, Implementing a Distributed System, Parallel versus Distributed Systems.

Unit-1
Teaching Hours:12
Inter process Communication
 

Introduction, Processes and Threads, Client–Server Model, Middleware, Network Protocols, Ethernet, Wireless Networks, OSI Model, IP, Transport Layer Protocols, Inter process Communication Using Sockets.

Unit-2
Teaching Hours:12
Models for Communication
 

Need for a Model, Message-Passing Model for Inter process Communication, Process Actions, and Synchronous versus Asynchronous Systems.

Unit-2
Teaching Hours:12
Communication in Distributed Systems
 

The Client-Server Model – (Client and Servers, Addressing, Block versus Nonblocking Primitives, Buffered versus Unreliable Primitives) – Remote Procedure Call – (Basic RPC Operation, Parameter Passing, Dynamic Binding, RPC Semantics in the Presence of Failures) Distributed objects and remote invocation

Unit-3
Teaching Hours:12
Synchronization in a Distributed System
 

Introduction -- Clock Synchronization – (Logical Clocks, Physical Clocks, Clock Synchronization Algorithms) – Election Algorithms – (The Bully Algorithm, A Ring Algorithm) – Atomic Transactions – (Introduction to Atomic Transactions, The Transaction Model. Implementation, Concurrency Control) – Deadlocks in Distributed Systems – (Distributed Deadlock Detection & Prevention).Resource Deadlock and Communication Deadlock, Detection of Resource Deadlock, Detection of Communication Deadlock.

Unit-4
Teaching Hours:12
Distributed Snapshot
 

Introduction, Properties of Consistent Snapshots, Cuts and Consistent Cuts, Chandy–Lamport Algorithm.

Unit-4
Teaching Hours:12
Mutual Exclusion
 

Introduction, Solutions on Message-Passing Systems, Lamport’s Solution, Ricart–Agrawala’s Solution, Maekawa’s Solution, Token-Passing Algorithms, Suzuki–Kasami Algorithm, Raymond’s Algorithm

Unit-4
Teaching Hours:12
Global State Collection
 

Introduction, Elementary Algorithm for All-to-All Broadcasting, Termination-Detection Algorithms, Dijkstra–Scholten Algorithm.

Unit-5
Teaching Hours:12
Distributed Shared Memory
 

Introduction, What is Shared Memory , Consistency Models, Page-Based Distributed Shared Memory.

Unit-5
Teaching Hours:12
Distributed File Systems
 

Introduction – Distributed File System Design – (The File Service Interface, The Directory Server Interface, Semantics of File Sharing) -- Distributed File System Implementation – (File Usage, System Structure, Caching, Replication, An Example: Sun’s Network File System).

Unit-5
Teaching Hours:12
Fault Tolerance and File Systems
 

Fault-Tolerant Systems: Introduction, Classification of Faults, Specification of Faults, FaultTolerant Systems, Masking Tolerance, Nonmasking Tolerance, Fail-Safe Tolerance, Graceful Degradation, Detection of Failures in Synchronous Systems, Tolerating Crash Failures.

Text Books And Reference Books:

[1] SukumarGhosh,Distributed Systems: An Algorithmic Approach, Second Edition, Chapman and Hall/CRC , 2014.

[2] Coulouris G., Dollimore J., Kindberg T., Blair G., Distributed Systems: Concepts and Design, Addison-Wesley, 5th Edition, 2011.

Essential Reading / Recommended Reading

[1] Tanenbaum S Andrew, Distributed Operating Systems, Pearson Eduction Asia, 2001.

[2] SinghalMukesh, Shivaratri G Niranjan, Advanced Concepts In Operating Systems Distributed Data Base, And Multiprocessor Operating Systems, McGraw-Hill, Inc., 2009.

Evaluation Pattern

CIA (Weightage):  60%

ESE (Weightage): 40%

 



MSP341E - NEURAL NETWORKS (2018 Batch)

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

Course Objectives/Course Description

 

This course has the content to understand the concept of Neural Networks, models of artificial neural networks and its applications.

Learning Outcome

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

      CO1: Understand the concepts of neural networks.

      CO2: Understand the concepts of feed forward and backward neural networks.

      CO3: Design basic neural networks

      CO4: Implement neural networks concepts as solutions to real-time problems

Unit-1
Teaching Hours:11
Introduction
 

Fundamental concepts and Model: Biological Neurons and their Artificial models, Models of Artificial Neural Networks, Neural processing, Learning and Adaptation, Neural network Learning rules- Hebbian rule, Perceptron rule, Delta rule.

Unit-2
Teaching Hours:12
Single layer Perceptron Model
 

Single layer perceptron classifiers: Classification model, Features and decision regions, Discriminant functions, Linear machine and Minimum distance classification, Non parametric training concept, Training and Classification using the Discrete perceptron: algorithm and example, Single layer continuous Perceptron networks for linearly separable classifications.

Unit-3
Teaching Hours:12
Multi Layer Feed Forward Networks
 

Multilayer feed forward Networks: Linearly separable Pattern classification, Delta learning rule for Multiperceptron model, Generalized Delta learning rule, Feed forward recall and error back propagation training.

Unit-4
Teaching Hours:13
Single Layer Feedback Networks
 

Single layer Feedback Networks: Basic concepts of dynamic systems, Mathematical foundations of Discrete-time Hopfield Networks, Mathematical foundations of Gradient type Hopfield networks, Associative memories: Basic concepts, Linear Associator.

Unit-5
Teaching Hours:12
Associative Memory
 

Bidirectional associative memory, associative memory for spatio-temporal patterns. Case study: Implementation of NN in any simulator.

Self Learning: Bidirectional Associative memory

Text Books And Reference Books:

[1] Jacek M. Zurada, Introduction to Artificial Neural networks Jaico Publishing, 2006.

Essential Reading / Recommended Reading

[1] Limin Fu, Neural Network in Computer Intelligence, TMH, 1994.

[2] Yegnanarayana, Artificial Neural Networks, PHI Learning, 2007.

Evaluation Pattern

60% CIA + 40% ESE

 

MSP341F - IOT AND WIRELESS SENSOR NETWORKS (2018 Batch)

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

Course Objectives/Course Description

 

The explosive growth of the “Internet of Things” is changing our world and the rapid growth of IoT components is allowing people to innovate new designs and products at home.  Wireless Sensor Networks form the basis of the Internet of Things. To latch on to the applications in the field of IoT of the recent times, this course provides a deeper understanding of the underlying concepts of IoT and Wireless Sensor Networks.

Learning Outcome

Upon completion of this course students will be able to

CO1: Identify different issues in wireless ad hoc and sensor networks

CO2: Develop an understanding of sensor network architectures from a design and performance perspective.

CO3: Understand the layered approach in sensor networks and WSN protocols.

CO4: Implement real time IoT applications to create an impact 

Unit-1
Teaching Hours:12
Introduction to IOT
 

Introduction to IoT - Definition and Characteristics, Physical Design Things- Protocols, Logical Design- Functional Blocks, Communication Models- Communication APIs- Introduction to measure the physical quantities, IoT Enabling Technologies - Wireless Sensor Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded System- IoT Levels and Deployment Templates.

Unit-2
Teaching Hours:12
IOT Programming
 

Introduction to Smart Systems using IoT - IoT Design Methodology- IoT Boards (Rasberry Pi, Arduino) and IDE - Case Study: Weather Monitoring- Logical Design using Python, Data types & Data Structures- Control Flow, Functions- Modules- Packages, File Handling - Date/Time Operations, Classes- Python Packages of Interest for IoT.

Unit-3
Teaching Hours:12
IOT Applications
 

Home Automation – Smart Cities- Environment, Energy- Retail, Logistics- Agriculture, Industry- Health and Lifestyle- IoT and M2M.

Unit-4
Teaching Hours:12
Motivation for A Network Of Wireless Sensor Nodes
 

Sensing and Sensors, Wireless Sensor Networks, Challenges and Constraints; Applications:  Structural Health Monitoring, Traffic Control, Health Care; Node Architecture, Operating system.

Unit-5
Teaching Hours:12
MAC, Routing and Transport Control in WSN
 

Introduction – Fundamentals of MAC Protocols – MAC protocols for WSN – Sensor MAC Case Study – Routing Challenges and Design Issues – Routing Strategies – Transport Control Protocols – Transport Protocol Design Issues – Performance of Transport Protocols

Text Books And Reference Books:

[1].      ArshdeepBahga,Vijay Madisetti, “Internet of Things: Hands-on Approach”, Hyderabad University Press, 2015.(Unit -I to III)

[2].      KazemSohraby, Daniel Minoli, TaiebZnati ,“Wireless Sensor Networks: Technology. Protocols and Application”, Wiley Publications, 2010 (Unit IV & V)

[3].      WaltenegusDargie, Christian Poellabauer, "Fundamentals of Wireless Sensor Networks: Theory and Practice", A John Wiley and Sons, Ltd., Publication, 2010.

Essential Reading / Recommended Reading

[1].      Edgar Callaway , “Wireless Sensor Networks: Architecture and Protocols” , Auerbach Publications, 2003

[2].      Michael Miller, “The Internet of Things” , Pearson Education, 2015

[3].      Holger Karl, Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, John Wiley & Sons, Inc., 2005.

[4].      ErdalÇayırcı ,ChunmingRong, “Security in Wireless Ad Hoc and Sensor Networks”, John Wiley and Sons, 2009.

[5].      Carlos De MoraisCordeiro, Dharma PrakashAgrawal, “Ad Hoc and Sensor Networks: Theory and Applications (2nd Edition)”, World Scientific Publishing, 2011.

[6].      WaltenegusDargie, Christian Poellabauer, “Fundamentals of Wireless Sensor Networks Theory and Practice”, John Wiley and Sons, 2010

[7].      Adrian Perrig, J. D. Tygar, "Secure Broadcast Communication: In Wired and Wireless Networks", Springer, 2006.

Evaluation Pattern

60% CIA + 40% ESE

MSP342A - HIGH PERFORMANCE COMPUTING (2018 Batch)

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

Course Objectives/Course Description

 

Understand the concept of high performance computing and its applications. The course includes the introduction to load sharing and balancing concepts. The course will discuss Grid computing and cloud computing in terms of their architecture and performance.

Learning Outcome

Upon successful completion of the course, the student will have acquired the following knowledge and skills:

CO1: Understand computing, the concepts of load sharing and balancing.

CO2: Identify secure and feasible environment for applications.

CO3: Demonstrate the knowledge of grid computing and cloud computing.

Unit-1
Teaching Hours:12
Cluster Computing
 

Introduction to Cluster Computing, Scalable Parallel Computer Architectures, Cluster Computer and its Architecture, Classifications, Components for Clusters, Cluster Middleware and Single System Image, Resource Management and Scheduling

Unit-2
Teaching Hours:12
Cluster Computing
 

Programming Environments and Tools, Applications, Representative Cluster Systems, Heterogeneous Clusters, Security, Resource Sharing, Locality, Dependability, Cluster Architectures, Detecting and Masking Faults, Recovering from Faults, Condor, Evolution of Metacomputing.

Unit-3
Teaching Hours:12
Load Sharing and Balancing
 

Load Sharing and Balancing: Evolution, Job and Resource Management Systems, State-of-the-Art in RMS and Job, Rigid Jobs with Process Migration, Communication-Based Scheduling, Batch Scheduling, Fault Tolerance, Scheduling Problem for Network Computing, Dynamic Load Balancing, Mapping and Scheduling, Task Granularity and Partitioning, Static and Dynamic Scheduling.

Unit-4
Teaching Hours:12
Grid Computing
 

Grid Computing: Introduction to Grid Computing, Virtual Organizations, Architecture, Applications, Computational, Data, Desktop and Enterprise Grids, Data-intensive Applications, High-Performance Commodity Computing, High-Performance Schedulers, 

Unit-4
Teaching Hours:12
Heterogeneity
 

 Consistent, Inconsistent and Partially-Consistent, QoS Guided Min-Min, Selective Algorithm, Grid Computing Security.

Unit-4
Teaching Hours:12
Grid Middleware
 

Connectivity, Resource and Collective Layer, Heterogeneous Computing Systems

Unit-4
Teaching Hours:12
Mapping Heuristics:
 

Immediate and Batch Mode

Unit-5
Teaching Hours:12
High-Throughput Computing
 

Task Computing and Task-based Application Models, Market-Based Management of Clouds, Energy-Efficient and Green Cloud Computing Architecture, Resource Allocation, Leases, 

Unit-5
Teaching Hours:12
Cloud Computing
 

Introduction to Cloud Computing, Types: Deployment and Service Models, Characteristics, Applications, Service-Level Agreement, Virtualization, 

Unit-5
Teaching Hours:12
Task Scheduling
 

RR, CLS and CMMS, Workflow Scheduling, Montage, Epigenomics, SIPHT, LIGO, CyberShake, Task Consolidation, Introduction to CloudSim, Cloudlet, VirtualMachine and its Provisioning, Time and Space-shared Provisioning.

Text Books And Reference Books:

[1].      R. Buyya, “High Performance Cluster Computing: Architectures and Systems”, Volume 1, Pearson Education, 2008.

[2].      (Edited By) I. Foster and C. Kesselman, “The Grid: Blueprint for a New Computing Infrastructure”, Morgan Kaufmann, Elsevier, 2004.

[3].      D. Janakiram, “Grid Computing”, Tata McGraw-Hill, 2005.

[4].      R. Buyya, C. Vecchiola and S. T. Selvi, “Mastering Cloud Computing Foundations and Applications Programming”, Morgan Kaufmann, Elsevier, 2013.

Essential Reading / Recommended Reading

[1].      A. Chakrabarti, “Grid Computing Security”, Springer, 2007.

[2].      B. Wilkinson, “Grid Computing: Techniques and Applications”, CRC Press, 2009.

[3].      C. S. R. Prabhu, “Grid and Cluster Computing”, PHI, 2008.

[4].      B. Sosinsky, “Cloud Computing Bible”, Wiley, 2011.

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

MSP342B - OOAD WITH UML (2018 Batch)

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

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 successful completion of the course the student will be able to

CO1: Understand the object oriented life cycle, Use-case design, Object Oriented Design process, software quality and usability.

CO2: Identify objects, relationships, services and attributes through UML.

CO3: Apply UI design concepts in real-time applications

Unit-1
Teaching Hours:12
Complexity, The Object Model
 

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.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
Classes and Objects, Classification
 

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

Unit-3
Teaching Hours:12
Notation
 

Notation: Basic BehaviouralModelling,  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].      Grady Booch, “Object-Oriented Analysis And Design With Applications”, Pearson Education, 3rd edition, 2009.

Essential Reading / Recommended Reading

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

Evaluation Pattern

CIA (Weightage):   60%

ESE (Weightage):  40%

MSP342C - PRINCIPLES OF USER INTERFACE DESIGN (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 this course: is for students to learn how to design, prototype and evaluate user interfaces to effective browse and search systems by examining what research has uncovered, what developers have produced, and how people perform information tasks.

Learning Outcome

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

CO1: Demonstrate main concepts in human computer interaction.

CO2: Understand the basic user interface principles, cognitive and perceptual abilities and constraints that impact information use.

CO3: Apply human information processing to the design of user interfaces.

CO4: Design and prototype user interfaces. 

CO5: Identify research issues in user interface design.

Unit-1
Teaching Hours:12
Goals of User-Interface Design
 

Human factors in user interface design, Theories, Principles, and Guidelines, Goals of Systems Engineering, Accommodation of Human Diversity, Goals for Our Profession, High Level Theories, Object-Action Interface model, Principle 1:Recognize the Diversity, Principle 2: Use the Eight Golden Rules of Interface Design, Principle 3: Prevent Errors, Guidelines for Data Display, Guidelines for Data Entry, Balance of automation and Human Control, Practitioner’s Summary, Researcher’s Agenda. 

Management Issues - Introduction, Organizational; Design to Support Usability, The three Pillars of Design, Development Methodologies, Ethnographic  Observation, Participatory Design, Scenario Development, Social Impact Statement for Early Design Review, Legal issues, Expert Reviews, Usability, testing and Laboratories, Surveys, Acceptance tests, Evaluation During Active Use, Controlled Psychologically Oriented Experiments, Practitioner’s Summary, Researcher’s agenda. 

Unit-2
Teaching Hours:12
Tools Environment, and Menus
 

Introduction, Specification Methods; Interface-Building Tools, Evaluation and critiquing Tools. Direct Manipulation and virtual Environments: Introduction, Examples of Direct manipulation systems, Explanations of Direct manipulation, Visual Thinking and Icons, Direct Manipulation Programming, Home Automation, Remote Direct manipulation, Virtual Environments Menus: Task-Related Organization, Item Presentation Sequence, Response Time and Display Rate.

Fasty Movement through Menus, Menu Layout, Form Filling, Dialog boxes, Command-Organization strategies, The Benefits of Structure, Naming and Abbreviations, Command Menus, Natural Language in Computing, Practitioners Summary, Researcher’s Agenda. 

Unit-3
Teaching Hours:12
Interaction Devices
 

Response Times, Styles and Manuals: Interaction Devices, Introduction, Keyboards and Function Keys, Pointing Devices, speech Recognition, Digitization, and Generation, Image and Video displays, Printers. Response Time and Display Rate: Theoretical; Foundations, Exceptions and attitudes, User Productivity, variability, Presentation Styles and Manuals: Introduction, Error messages, Nonanthopomorphic Design, Color of Manuals, Help: Reading From paper Versus from Displays, Preparation of Printed manuals,  Preparation of Online Facilities, Practitioner’s Summary, Researcher’s Agend.

Unit-4
Teaching Hours:12
Multiple-Windows
 

Computer-Supported Cooperative work, Information’s search and www  Multiple-Windows Strategies: Introduction, Individual-Window Design, Multiple-window Design, Coordination by Tightly-Coupled Windows, Image Browsing and Tightly-Coupled Windows, Personal Role Management and Elastic Windows Computer-Supported Cooperative Work; Introduction, Goals of Cooperation, Asynchronous Interactions: Different Time, Different Place, Synchronous Distributed: Different Place, Same Time, Face to Face: Same Place, Same Time, Applying CSCW to Education.

Unit-5
Teaching Hours:12
Information Search and Visualization
 

Introduction, Database Query And Phrase Search in Textual Documents, Multimedia Document Searches, Information Visualization, Advanced Filtering. Hypermedia and the World wide Web: Introduction, Hypertext and Hypermedia, World Wide Web, Genres and Goals and Designers, Users and Their Tasks, Object Action Interface Model for Web Site Design, Practitioner’s summary, Researcher’s Agenda.

Text Books And Reference Books:

[1].      Ben Shneiderman, Designing the User Interface, Pearson Education, 5th Edition, 2010

[2].      Wilber O Galitz, An Introduction to GUI Design Principles and Techniques, John- Wiley &Sons, 2007.

Essential Reading / Recommended Reading

[1].      Jeff Johnson, Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Rules , Morgan Kaufmann, 1st Edition, 2010.

[2].      Alan Dix, Human-Computer Interaction, Pearson,2009.

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

MSP342D - DATA MINING AND DATA WAREHOUSING (2018 Batch)

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

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 are expected to master both the theoretical and practical aspects of Data Warehousing and data mining. More specifically, 

 CO1: Demonstrating basic data mining algorithms, methods, and tools.

CO2: Design methods to mine data based on data mining principles and techniques. 

CO3: Building the Data warehouse.

CO4: Understanding the basic concepts of OLAP.

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. DataWarehouse 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 Warehouse Architecture
 

The value of architecture – An architectural framework and approach – Technical architecture overview – Back room data stores – Back room services. Back Room Services. Data Staging:Data staging overview – Plan effectively – Dimension Table staging – Fact Table loads and warehouse operations – Data quality and cleansing – issues.

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. 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
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.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-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. 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, “The Data Warehouse Lifecycle Toolkit”, John Wiley & Sons, 2006.       

[2].    Jiawei Han and MichelineKamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, San Francisco, USA, 2nd Edition, 2011.

Essential Reading / Recommended Reading

[1].      Inmon W H, “Building the Data Warehouse”, John Wiley & Sons, 3rd Edition, 2005.

[2].      Margaret H. Dunham, “Data mining-Introductory and Advanced topics”, Pearson       Education, 2003.

[3].      Witten and E. Frank, “Data Mining : Practical Machine Learning Tools and        Techniques”, Morgan Kaufmann Publishers, 2005.

[4].      K P Soman, Shyam Diwakar, V. Ajay,“Insight into Data Mining-Theory and  Practice”, 6thReprint, PHI, 2012.

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

 

MSP342E - 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

Upon completion of the course students will be able to:

CO1: Understand the basic principles of machine learning techniques.

CO2: Demonstrate supervised and unsupervised machine learning algorithms.

CO3: Apply appropriate techniques for real time problems.

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 
Service Learning: Introduction to machine learning applications developed for betterment of society through select case studies.

Text Books And Reference Books:
  1. E. 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 - 60%

ESE - 40%

MSP342F - RISK ANALYSIS (2018 Batch)

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

Course Objectives/Course Description

 

To provide fundamental concepts of risk analysis and relevant details related to types of risks, risk management strategies and tools used for risk analysis.

Learning Outcome

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

CO1: Identify the different risks involved in finance arena.

CO2: Analyze the legal issues affecting the business.

CO3: Categorize the various risks faced by an organization.

CO4: Explore the tools and practices needed to assess and evaluate financial risks.

Unit-1
Teaching Hours:12
Introduction to Risk Analysis
 

Definition -Understanding Risk- Nature of Risk, Source of Risk, Need for risk, management, Benefits of Risk Management, Risk Management approaches.

Unit-2
Teaching Hours:12
Risk Classification
 

Credit risk, market risk, operational risk and other risk, Risk Measurements -Measurement of Risk – credit risk measurement, market risk, measurement, interest rate risk measurement, Asset liability management, measurement of operational risk

Unit-3
Teaching Hours:12
Risk Management
 

Risk management- Managing credit risk, managing operational risk, managing market risk, insurance

Unit-4
Teaching Hours:12
Tools for risk management
 

Derivatives, combinations of derivative instruments, Neutral and volatile strategies, credit derivatives, credit ratings, swaps.

Unit-5
Teaching Hours:12
Regulation and Other Issues
 

Issues in risk management – Regulatory framework, Basel committee, legal issues, accounting issues, tax issues, MIS and reporting, integrated risk management

Text Books And Reference Books:

[1].      Dun, Bradstreet, “Financial Risk Management”, TMH, 2006.

Essential Reading / Recommended Reading

[1].      John C Hull, “Risk management and Financial Institutions”, Pearson, 2015.

[2].      AswathDamodharan,“Strategic Risk Taking”, Pearson, 2008.

Evaluation Pattern

60% CIA + 40% ESE

MSP351 - MINI PROJECT (2018 Batch)

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

Course Objectives/Course Description

 

 To complete  a project based on previous semester’s course the student has taken or students Research specialization. The project need to be implemented using hardware and software. 

Learning Outcome

CO1:To identify the problem and relevant modules for the selected problem

CO 2:To apply appropriate design/development methodology and Tools

CO3:Competence to work in a team

CO4:Ability to complete the solution as a product

Unit-1
Teaching Hours:60
Minor Project Lab
 

Project will be based on any specialization papers which students are opted for.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA 60%

ESE 40%

MSP371 - RESEARCH (RESEARCH PROBLEM IDENTIFICATION AND DATA COLLECTION) (2018 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:01

Course Objectives/Course Description

 

This research inclusive curriculum is designed with two main objectives:

1. Inculcating research culture among the post graduate students.

2.Enhancing employability skills of students by providing necessary research foundation

Learning Outcome

CO1 – Identification of Research problem statement and Literature survey of existing data sets or any primary data sets in the respective area.

CO2 - Gather the datasets from various sources (like visiting websites, universities, person, creating individually, etc.

CO3 – Steps in pre-processing / Proposed model design / framework design

Unit-1
Teaching Hours:30
Research Problem Identification and Data Collection
 

There is only CIA for this course. Students should carry out the following tasks:

Literature survey of existing data sets or any primary data sets in the respective area

·         Introduction to topic, existing scenario and applications

·         Literature review (Minimum 25 references)

·         Existing Model and Methodology

·         Concrete problem statement definition

Note: In case the research problem involves no dataset or minimal dataset, then the guide allots marks based on the research work that is done during the semester.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

Evaluation Rubrics

S.NoCriteria for Evaluation         Marks

1Submission of document        35 Marks

2Presentation                        10 Marks

3Attendance                         5 Marks

MSP431 - CLOUD COMPUTING (2018 Batch)

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

Course Objectives/Course Description

 

Cloud computing has become a great solution for providing a flexible, on-demand, and dynamically scalable computing infrastructure for many applications. Cloud computing presents a significant technology trend. The course aims at familiarizing with the basic concepts of cloud computing and its applications.

Learning Outcome

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

CO1: Understand the common terms and definitions of virtualization and cloud computing and be able to give examples.

CO2: Comprehend the technical capabilities and business benefits of virtualization and cloud computing.

CO3: Describe the landscape of different types of virtualization and understand the different types of clouds.

CO4: Illustrate how key application features can be delivered more easily on virtual infrastructures.

Unit-1
Teaching Hours:12
Cloud Computing Basics
 

cloud computing Overview – Cloud components, Infrastructure, Services -, Applications – Storage, Database services -, Intranets and the cloud – components, Hypervisor applications - First Movers in the CloudYour Organization and Cloud Computing –  When you can use Cloud computing, Benefits, Limitations, Security Concerns, Regulatory Issues.

Unit-2
Teaching Hours:12
The Business case for going to the Cloud
 

Cloud Computing with the Titans – Google,  EMC, NetApp, Microsoft, Amazon, Salesforce.com, IBM. The Business case for going to the Cloud - Cloud Computing services- Infrastructure as a Service, Platform as a Service, Software as a Service, Software plus services, How applications help your business, Deleting your data center.

Unit-3
Teaching Hours:12
Cloud Computing Technology :Hardware and Infrastructure
 

Developing Applications-Google, Microsoft, Intuit QuickBase, Cast Iron cloud, Bungee connect,  Development, Trouble shooting, Application ManagementLocal clouds and Thin ClientsVirtualization in your Organization- why virtualize, How to virtualize, concerns, security-, Server solutions- Microsoft Hyper-V,VMware, VMware Infrastructure, Containers: Using and Managing Containers – Container Basics, Docker and the Hub, Container for Science, Creating your own Container, Secure your VMs and Containers.

Unit-4
Teaching Hours:12
Cloud Storage
 

Overview-The Basics, storage as a service, Providers, security, Reliability, advantages, cautions, Outages, Theft-,  Cloud storage providers, Standards- Application – Communication, Security -, Client – HTML, Dynamic HTML, JavaScript -, Infrastructure – Virtualization, OVF -, Service – Data, Web service

Unit-5
Teaching Hours:12
Developing Applications
 

Developing Applications-Google, Microsoft, Intuit QuickBase, Cast Iron cloud, Bungee connect,  Development, Trouble shooting, Application ManagementLocal clouds and Thin ClientsVirtualization in your Organization- why virtualize, How to virtualize, concerns, security-, Server solutions- Microsoft Hyper-V,VMware, VMware Infrastructure, Containers: Using and Managing Containers – Container Basics, Docker and the Hub, Container for Science, Creating your own Container, Secure your VMs and Containers.

Text Books And Reference Books:

[1].      Anthony TVelte, Toby JVelte and Robert Elsenpeter,” Cloud Computing –A Practical   Approach”, Tata McGraw Hill Education Pvt Ltd, 2010

[2].      Ian Foster and Dennis B. Gannon, “Cloud Computing for Science and Engineering”, MIT Press, 2017. 

Essential Reading / Recommended Reading

[1].      Syed A.AhsonandMohammedIlyas, “Cloud Computing and Software Services : Theory and Techniques”, CRC Press, Taylor and Francis Group, 2010

[2].      Judith Hurwitz, Robin Bloor, Marcia Kaufman and Fern Halper, “Cloud Computing for Dummies”. Wiley- India edition,2010

[3].      Ronald L. Krutz and Russell Dean Vines, “Cloud Security: A Comprehensive Guide to Secure Cloud Computing”. Wiley Publishing, Inc.,2012

[4].      Barrie Sosinky, “Cloud Computing : Bible”, 1st edition, Wiley Publishing, Inc.,2011

Evaluation Pattern

CIA (Weightage): 60%

ESE (Weightage): 40%

MSP441A - KNOWLEDGE MANAGEMENT (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 helps understand the concept of knowledge and knowledge-centric organization. It covers knowledge management life cycle, knowledge management tools and portals. The ethical, legal, and managerial issues in knowledge management are also covered in this course.

Learning Outcome

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

CO1.  Build knowledge management solutions and the knowledge management cycle.

CO2.  Design solutions based on the knowledge

CO3.  Implement tools based on resulting knowledge.

Unit-1
Teaching Hours:12
Knowledge Management
 

Basics - What is Knowledge Management - Key Challenges - KM Life Cycle - Understanding Knowledge - Definitions - Cognition and Knowledge Management - Data, Information, and Knowledge - Types of Knowledge - Expert Knowledge

Unit-2
Teaching Hours:12
Knowledge Management System Life Cycle
 

Knowledge Management System Life Cycle - Challenges in Building KM Systems - Conventional Versus KM System Life Cycle - KM System Life Cycle - System Justification - Role of Rapid Prototyping - Role of Knowledge Developer - User Training

Unit-3
Teaching Hours:12
Knowledge Creation
 

Knowledge Creation - Nonaka’s Model of Knowledge Creation and Transformation - Knowledge Architecture - Capturing Tacit Knowledge - Evaluating the Expert - Developing a relationship with Expert - Fuzzy Reasoning and the Quality of Knowledge Capture - Interview as a tool - Brainstorming - Repertory Grid - Nominal Group Techniques(NGT) - Delphi method - Concept mapping

Unit-4
Teaching Hours:12
Knowledge Codification
 

Knowledge Codification - Codification Tools and Procedures – Knowledge Developers Skill Set - Knowledge Transfer - Transfer Methods - Role of the Internet in Knowledge Transfer - Knowledge Transfer in the E-World - E-Business

Unit-5
Teaching Hours:12
KM Tools and Knowledge Portals
 

Knowledge Management Tools and Knowledge Portals - Portals Basics - Business Challenge - Knowledge Portal Technologies - Ethical and Legal Issues - Knowledge Owners - Legal Issues - Ethical Factors

Text Books And Reference Books:

[1].      Elias M.Awad, Hassan M.Ghaziri, Knowledge Management, Pearson Education, 2010.

Essential Reading / Recommended Reading

[1].      Awad Elias M, "Knowledge Management", Prentice Hall India Learning Private Limited, 2 edition, 2011.

Evaluation Pattern

60% CIA + 40% ESE

MSP441B - WIRELESS AND MOBILE NETWORKS (2018 Batch)

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

Course Objectives/Course Description

 

The goal is to make students familiar with the basic concepts and structure of modern wireless and mobile communication networks. 

Learning Outcome

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

CO1: Analyze the trends,strengths,problems and limitations of current wireless networking mechanisms for mobile communication.

CO2: Understand and identify the GSM,GPRS,CDMA,LTE,Bluetooth software model for mobile computing.

CO3: Investigate the characteristics and limitations of mobile hardware devices including their user-interface modalities.

CO4: Evaluate the performance of different  networks and algorithms for mobile communication.

 

Unit-1
Teaching Hours:12
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.

 

Unit-1
Teaching Hours:12
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
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.

 

Unit-2
Teaching Hours:12
Wireless Network Architecture and Operation
 

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

Unit-3
Teaching Hours:13
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
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-4
Teaching Hours:12
CDMA Technology
 

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

Unit-5
Teaching Hours:11
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] Gary J Mullett. Wireless Telecommunications Systems and Networks, Clifton Park (N.Y.) : Thomson Delmar Learning, cop.2008 

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

Evaluation Pattern: 60% CIA + 40% ESE

 

MSP441C - SOFTWARE QUALITY AND TESTING (2018 Batch)

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

Course Objectives/Course Description

 

To understand the need for Software Quality, Tools Used and Metrics involved. To appreciate software testing principles and methods to detect in the ever changing software technological changes 

Learning Outcome

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

CO1: Demonstrate the concepts of Software Quality and Testing

CO2: Apply the consepts to Test the codes, artifacts better and different Quality Tools

CO3: Understand the advantages of Extreme Testing and High Order Testing

CO4: Create effective test plan and test cases

CO5: Identify the need for Software Quality Metrics and Assessments

Unit-1
Teaching Hours:12
Introduction to Software Quality
 

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
 

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
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:12
Fundamentals of Software
 

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

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

Text Books And Reference Books:

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

Essential Reading / Recommended Reading

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

Evaluation Pattern

60% CIA + 40% ESE

MSP441D - STORAGE AREA NETWORK (2018 Batch)

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

Course Objectives/Course Description

 

This course provides a broad and in-depth knowledge of Storage and Storage networking concepts, applications, and technologies. Storage Fundamentals including storage attachment architectures, the SCSI protocol, disk and tape drive concepts, RAID and JBOD, IP-based SANs, and Storage Networking Issues. 

Learning Outcome

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

  • CO1: Explain Storage Fundamentals
  • CO2: Compare Direct Attach Storage (DAS) to Network Attach Storage (NAS)
  • CO3: Identify the components and uses of a Storage Area Networks (SAN)
  • CO4: Examine Fibre Channel and iSCSI

Unit-1
Teaching Hours:12
Introduction to Information Storage and Management, Storage System Environment
 

Information Storage, Evolution of Storage Technology and Architecture, Data Center Infrastructure, Key Challenges in Managing Information, Information Lifecycle Components of Storage System Environment, Disk Drive Components, Disk Drive Performance, Fundamental Laws Governing Disk Performance, Logical Components of the Host, Application Requirements and Disk Performance. 

Unit-2
Teaching Hours:10
Data Protection, Intelligent Storage system
 

Implementation of RAID, RAID Array Components, RAID Levels, RAID Comparison, RAID Impact on Disk Performance, Hot Spares Components of an Intelligent Storage System, Intelligent Storage Array 

Unit-3
Teaching Hours:10
Direct-Attached Storage, SCSI, and Storage Area Networks
 

Types of DAS, DAS Benefits and Limitations, Disk Drive Interfaces, Introduction to Parallel SCSI, Overview of Fibre Channel, The SAN and Its Evolution, Components of SAN, FC Connectivity, Fibre Channel Ports, Fibre Channel Architecture, Zoning, Fibre Channel Login Types, FC Topologies. 

Unit-4
Teaching Hours:14
Content-Addressed Storage, Storage Virtualization
 

Fixed Content and Archives, Types of Archive, Features and Benefits of CAS, CAS Architecture, Object Storage and Retrieval in CAS, CAS Examples. Forms of Virtualization, SNIA Storage Virtualization Taxonomy, Storage Virtualizations Configurations, Storage Virtualization Challenges, Types of Storage Virtualization,

Unit-4
Teaching Hours:14
NAS, IP SAN
 

General – Purpose Service vs. NAS Devices, Benefits of NAS, NAS File I / O, Components of NAS, NAS Implementations, NAS File-Sharing Protocols, NAS I/O Operations, Factors Affecting NAS Performance and Availability.iSCSI, FCIP.

Unit-5
Teaching Hours:14
Business Continuity, Backup and Recovery
 

Information Availability, BC Terminology, BC Planning Lifecycle, Failure Analysis, Business Impact Analysis, BC Technology Solutions. Backup Purpose, Backup Considerations, Backup Granularity, Recovery Considerations, Backup Methods, Backup Process, Backup and restore Operations, Backup Topologies, Backup in NAS Environments, Backup Technologies. 

Unit-5
Teaching Hours:14
Securing the Storage Infrastructure, Managing the Storage Infrastructure
 

Storage Security Framework, Risk Triad, Storage Security Domains, Security Implementations in Storage Networking Monitoring the Storage Infrastructure, Storage Management Activities, Storage Infrastructure Management Challenges, Developing an Ideal Solution. 

Text Books And Reference Books:

[1] G. Somasundaram, AlokShrivastava (Editors): Information Storage and Management: Storing, Managing & Protecting Digital Information in Classic, Visualized and Cloud Environments, 2nd edition, EMC Education Services, Wiley India, 2009. ISBN 978-1-1180-9483-9 

Essential Reading / Recommended Reading

[1] Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, Wiley India, 2003.

[2] Rebert Spalding: Storage Networks, The Complete Reference, Tata McGraw Hill, 2003.

[3] Richard Barker and Paul Massiglia: Storage Area Networks Essentials A Complete Guide to Understanding and Implementing SANs, Wiley India, 2002.

Evaluation Pattern

Evaluation Pattern: 60% CIA + 40% ESE

 

MSP441E - COMPUTER VISION (2018 Batch)

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

Course Objectives/Course Description

 

The goal of computer vision is to develop the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analyzed from a single image or a set of images. This course will cover the fundamentals of ComputerVision.

Learning Outcome

Student will be able to

CO1: Understand the Concepts of Computer Vision , Image Formation and Representation

CO2: Identify different image processing methods like Image Filtering (spatial domain), Mask-based (e.g., correlation, convolution), Smoothing (e.g., Gaussian), Sharpening (e.g., gradient), Edge Detection (e.g., Canny, Laplacian of Gaussian), Interest Point Detection (e.g., Moravec, Harris), Shape representation and Segmentation

CO3: Implement appropriate approach in real-time applications.

CO4: Design tools to process real-time graphic data for research.

Unit-1
Teaching Hours:12
Image Processing and Feature Extraction
 

Image representations (continuous and discrete)- Edge detection

Unit-1
Teaching Hours:12
Image Formation Models
 

Monocular imaging system, Orthographic & Perspective Projection, Camera model and Camera calibration, Binocular imaging systems.

Unit-2
Teaching Hours:12
Motion Estimation
 

Regularization theory, Optical computation, Stereo Vision, Motion estimation, Structure from motion.

Unit-3
Teaching Hours:12
Shape Representation and Segmentation
 

Deformable curves and surfaces, Snakes and active contours , Level set representations, Fourier and wavelet descriptors, Medial representations,• Multi-resolution analysis.

Unit-4
Teaching Hours:12
Object recognition
 

Hough transforms and other simple object recognition methods, Shape correspondence and shape matching, Principal Component analysis, Shape priors for recognition.

Unit-5
Teaching Hours:12
Applications
 

Application: finding in digital Libraries, organizing collections of images, including what do users want, how well does the system work, Representations of parts of the picture, including segmentation, template matching, shape and correspondence, and clustering and organizing collections, searching and browsing, Images based rendering, Constructing 3D models from image sequences, including scene modeling from registered and unregistered images.

Text Books And Reference Books:

[1].      Forsyth , Ponce, “Computer Vision – A Modern approach” , 2ndEdition, Pearson Education, 2003.

Essential Reading / Recommended Reading

[1].      Milan Sonka,‎ Vaclav Hlavac and  Roger Boyle,“Digital Image Processing and Computer Vision”, Thomson South-Western, Canada, 2008.

[2].      Richard Szeliski, “Computer Vision and Applications”, New Age Internations (P) Ltd., New Delhi, 2005.

[3].      S. Nagabhushana,“Computer Vision and Image Processing”, New Age Internations (P) Ltd., New Delhi, 2005.

Evaluation Pattern

60% CIA + 40% ESE

MSP442A - INFORMATION RETRIEVAL AND WEB MINING (2018 Batch)

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

Course Objectives/Course Description

 

The main objective of the courseis aimed at an entry level study of information retrieval and web mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and web mining are covered, the Course Description: is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations.

Learning Outcome

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

CO1: Understand the basic concepts and processes of information retrieval systems and data mining techniques. 

CO2: Demonstrate common algorithms and techniques for information retrieval (document indexing and retrieval, query processing, etc).

CO3: Analyze the quantitative evaluation methods for the IR systems and web mining techniques. 

CO4: Implement the popular probabilistic retrieval methods and ranking principle. 

Unit-1
Teaching Hours:12
Introduction
 

Introduction to Data mining. Relationship to machine learning. Summarization and feature extraction.Data Preprocessing: Introduction to  preprocessing. Data summarization. Date cleaning. Data integration, Data transformation. Data cube aggregation, attribute subset selection, Dimensionality reduction, Numerosity reduction. Data Discretization, Concept Hierarchy generation.

Unit-2
Teaching Hours:12
Introduction to Information Retrieval
 

Inverted indices and Boolean queries. Query optimization. The nature of unstructured and semi-structured text.The term vocabulary and posting lists. Text encoding: tokenization, stemming, lemmatization, stop words, phrases. Optimizing indices with skip lists. Proximity and phrase queries.Positionalindices.Dictionaries and tolerant retrieval.Dictionary data structures. Wild-card queries, permuterm indices, n-gram indices. Spelling correction and synonyms: edit distance, soundex, language detection.

Unit-2
Teaching Hours:12
Index construction.
 

Postings size estimation, sort-based indexing, dynamic indexing, positional indexes, n-gram indexes, distributed indexing

Unit-3
Teaching Hours:12
Scoring
 

Term weighting, and the vector space model. Parametric or fielded search.Documentzones.The vector space retrieval model.tf.idf weighting. The cosienmeasure.Scoringdocuments.  Map Reduce: Distributed file systems, Map and reduce tasks. Algorithms that use map-reduce: Matrix vector multiplication, Relational algebra operations. Mining Frequent Patterns and Associations: Near-neighbor search, Collaborative filtering, Shingling. Min-hashing and locality  sensitive hashing.

Unit-4
Teaching Hours:12
The stream data model
 

The stream data model, examples of stream sources and queries, sampling data in a stream. Filtering streams, bloom filters, counting distinct elements in a stream. Market-Basket model, Association rules. A-priori algorithm.Classification: Introduction to text classification. Naïve Baye’s models. Spam filtering. K nearest neighbors, Decision boundaries, vector space classification using centroids.Comparative results. Support vector machine classifiers. Kernel function.Evaluation of classification.Micro-and macro-averaging.Learning rankings.

Unit-5
Teaching Hours:12
Clustering
 

Introduction to the problem.Partitioning methods: K-means clustering; Hierarchical clustering.Latent semantic indexing (LSI).Applications to clustering and to information retrieval.Web Mining: Introduction to  web . Web search overview, web structure, the user, paid placement, search engine optimization/spam. Web measurement.Crawling and web indexes.Near-duplicate detection.Linkanalysis.Web as a graph.PageRank.Machine learning techniques for ranking.

Text Books And Reference Books:

[1].      C. Manning, P. Raghavan, and H. Schütze, “Introduction to Information Retrieval”,Cambridge University Press, 2008.

[2].      AnandRajaraman and Jeffery D.ullman, “Mining the Massive”,Cambridge University Press, 2008.

Essential Reading / Recommended Reading

[1].      Data,Bing Liu, “Web Data Minig,ExploringHyperlinks,contents and usage”,2nd        Edition, July 2011,Springer. 

[2].      K.P Soman, Shyamdiwakar and VAjay, “Insight into Data Mining – Theory and Practice”, 6th Ed  print, PHI India, 2012.

[3].      Jiawei Han and MichelineKamber, “Data Mining: Concepts and Techniques”, 2nd Edition,      2006, Morgan Kaufmann Publishers, San Francisco, USA.

Evaluation Pattern

60% CIA + 40% ESE

MSP442B - DATABASE ADMINISTRATION (2018 Batch)

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

Course Objectives/Course Description

 

This course provides insight on the administrative tasks, their implementation and effective usage of tools.

Learning Outcome

Upon successful completion of the course students will be able to

CO1:  Demonstrate sound knowledge of the administrative tasks

CO2:  Device configuration and troubleshoting for oracle installation

CO3:  Build Database with connectivity and user management

Unit-1
Teaching Hours:12
Introduction and Oracle 11g Architecture
 

Introduction: General Definition of DBA and Security, System Management & Database Design Roles of DBA – DBA Job Classification. Types of Databases: Online Transaction Processing System and Decision Support System Databases, Development, Test & Production Databases. Daily Routine of a DBA.Architecture: Database Structures- Logical & Physical, Trace Files, Data Files &Tablespaces, Oracle Managed Files. Processes- Interaction between User & Oracle Processes, The Server Process, Background Processes. Memory Structures- SGA, PGA. Oracle Transactions- Anatomy of SQL Transactions. Data Consistency & Concurrency- Database Writer & Write Ahead Protocol, The System Change Number, Undo Management. Backup and Recovery Architecture-User managed, RMAN, Flashback Techniques. Data Dictionary and Dynamic Performance Views- Data Dictionary, V$ views.

Unit-2
Teaching Hours:12
Database Installation and Creation
 

Installing Oracle 11g: Following OFA, System and Owners Pre-Installation Tasks, Installing Software, System Administrator and Oracle Owner’s Post-Installation Tasks, Uninstalling Oracle 11g.Database Creation: Creating SPFILE and pfile, Initialization Parameters, Creating a new Database, Using SPFILE, Starting up and Shutting Down Database

Unit-3
Teaching Hours:12
Database Connectivity and Networking and Security
 

Database Connectivity and Networking: Working of Oracle Network – instance names, global database names, connect descriptors, identifiers and strings, Establishing Connectivity, Oracle Client, Installing the Client, Naming and Connectivity – Local, Easy connect, External and Directory naming methods. 

Managing Users: Creating, altering and dropping users, Creating user Profiles & Resources, Database Resource Manager, Controlling Access to Data – Roles, Privileges and using Views, Stored Procedures to Manage Privileges, Auditing Database – Standard Auditing, Authentication – Database, External, Centralized user and Proxy Authentication. Database Security Do’s & Don’ts-User Accounts, Passwords, OS authentication, Auditing Database, Granting Appropriate Privileges, Permissions, Application Security.

Unit-4
Teaching Hours:12
Data Loading
 

Loading and Transforming Data: Overview of extraction loading and Transformation, Loading Data-Using the SQL Loader Utility, Using External Tables to Load Data. Overview of Common Techniques used for Transforming Data.Data Pump Technology: Introduction, Benefits, Uses and Components of Data Pump.Access method, Data Pump Files, Privileges, Mechanics of Data Pump Job. 

Unit-5
Teaching Hours:12
Backup, Recovery & Database Performance Tuning
 

Backing Up Oracle Databases: Backup Terms, Guidelines, Strategies, Examining Flash Recovery Area – benefits of Flash recovery Area, Looking into Flash Recovery Area, Setting size of Flash Recovery Area Creating Flash Recovery Area, Backing up Flash Recovery Area, RMAN – Benefits, Architecture, Connecting to RMAN. SQL Query Optimization:Approach to Performance Tuning, Optimizing Oracle Query Processing, Cost-based Optimizer, Drawbacks of CBO. SQL Performance Tuning Tools – EXPLAIN PLAN, Auto trace, SQL Trace and TKPROF. Tuning the instance:  Introduction, Automatic Tuning vs. Dynamic Views. Tuning Oracle Memory:

Self Learning:

Tuning Shared Pool – Library Cache, Dictionary Cache, Hard vs. Soft Parsing, Sizing Shared Pool, Tuning Buffer Cache – Sizing buffer Cache, Multiple pools for Buffer Cache.

Text Books And Reference Books:

[1].      Alapati, Sam R., “Expert Oracle Database 11g Administration”, Springer India Pvt. Ltd., 2009.

Essential Reading / Recommended Reading

[1].      Alapati, Sam R., “Expert Oracle Database 10g Administration”, Springer India Pvt. Ltd., 2008. 

[2].      Kyte and Thomas, “Expert Oracle”, Oracle Press Publication, Signature Edition, 2005.

Evaluation Pattern

ESE 40%

CIA 60% 

MSP442C - BIOINFORMATICS (2018 Batch)

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

Course Objectives/Course Description

 

To develop the skills of the students in Bioinformatics, develop knowledge on DNA mapping and sequencing, acquire knowledge on Database Algorithm and alignment Techniques, understand the nature of biological data and need for Biological databases, understand and explore the major bio molecular sequence databases (organization and contents) and their respective search engines and database searches, know the application of software analysis tools to sequence tools.

Learning Outcome

At the end of this course the students shall be able to

CO1: Apply knowledge of biology, computer science and mathematics

CO2: Solve problems by develooping new Bioinformatics algorithms

CO3: Undertake projects in the modern biology 

Unit-1
Teaching Hours:11
Bioinformatics,Information search and retrieval
 

An Introduction, Definition, Applications, Major Databases in Bioinformatics, Data Management and Analysis, Central Dogma of Molecular Biology. Information search and retrieval: Introduction, tools for web search , Data retrieval tools, Data mining and biological databases.

Unit-2
Teaching Hours:13
Genome Analysis and Gene Mapping , Alignment of pairs of sequences
 

Introduction, Genome Analysis, Gene Mapping, Sequence Assembly Problem, Genetic Mapping and Linkage Analysis, Physical Maps, Genome sequencing, Application of Genetic maps, Human Genome project(HGP). Alignment of pairs of sequences: Introduction, Biological motivation of Alignment problems, Methods of sequence Alignments, using scoring matrices, measuring sequence detection efficiency.

Unit-3
Teaching Hours:12
Alignment of multiple sequences
 

Methods of multiple sequence alignment, Evaluation of multiple alignments, Applications of multiple alignments. Tools for similarity search and sequence alignment: Introduction , FASTA, BLAST, Filtering and Gapped BLAST, FASTA and BLAST Algorithms comparison.

Unit-4
Teaching Hours:13
Perl for Bioinformatics: Sequences and strings
 

Representing Sequence data, Program to store DNA Sequence, Concatenating DNA fragments, Transcription : DNA to RNA, Perl documentation, Calculating the Reverse Complement in Perl, Proteins, Files and Arrays, Reading proteins in files, Arrays, Scalar and List Context. Motifs and loops – Flow control, code layout, Finding motifs, Counting nucleotides, Exploding strings into arrays, Operation on strings, Writing to files -  Subroutines and Bugs – Introduction, Scoping, Command Line Arguments, Passing data to subroutines, Modules and Libraries of Subroutines, Fixing bugs in the code.

Unit-5
Teaching Hours:11
Genetic code,GenBank,Protein data bank
 

Hashes, Data structures and Algorithms for Biology, Translating DNA into proteins, Reading DNA from files in FASTA Format, Reading Frames. GenBank: GenBank files, GenBank: libraries, Separating Sequence and annotation, Parsing annotations, Indexing GenBank with DBM. Protein data bank: Files and folders, PDB files, Parsing PDB files, Controlling other programs. 

Text Books And Reference Books:

1. S. C. Rastogi, NamitaMendirata andParagRastogi, “Bioinformatics: Methods and Applications”, 3rd Edition, PHI,2011.

2. Tisdall James, “Beginning Perl for Bioinformatics”,First edition, Shroff Publishers (O’Reilly), 2005

Essential Reading / Recommended Reading

[1].      T.K.Attwood and D.J.Perry Smith., “Introduction to Bioinformatics”, Pearson,2011

[2].      Arthur M. Lesk, Introduction to Bioinformatics, Oxford University Press, 2003

[3].      Irfan Ali Khan andAdityaKhanum, “ Fundamentals of Bioinformatics”,Ukaaz  publications , 2003

[4].    Harshawardhan P Bal, Perl Programming for Bioinformatics, Tata McGraw-Hill  Education private limited, 2009

Evaluation Pattern

CIA - 60%

ESE - 40%

MSP442D - SOFTWARE ARCHITECTURE (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 a sound technical exposure to the concepts, principles, methods, and best practices in software architecture and software design.

Learning Outcome

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

CO1: Demonstrate technological aspects of architecture.

CO2: have competence in building robust, scalable, and reliable software intensive systems in an extremely .

CO3: Implement appropriate software architecture for any project implementation.

Unit-1
Teaching Hours:11
Architecture Business Cycle
 

Origin of an Architecture , Software Processes and Architectural Business Cycle, A good architecture, Software Architecture, What is & what it is not the software Architecture is, Other points of view, Architectural Pattern, Reference Models and Reference Architectures, The Importance of Software Architecture, Architectural structures & views, Case study in utilizing Architectural Structures.

Unit-2
Teaching Hours:12
Creating An Architecture - Understanding the quality attributes
 

Functionality and Architecture, Architecture and Quality Attributes, System Quality Attributes, Quality Attributes Scenarios in practice, Other System Quality Attributes, Business Qualities, Architecture Qualities.

Achieving Qualities Introducing Tactics – Availability, Modifiability, Performance, Security, Testability, Usability, Relationships of Tactics to Architectural Patterns, Architectural Patterns and Style.

Unit-3
Teaching Hours:11
Design and Documentation
 

Designing the Architecture: Architecture in the life cycle, Designing the Architecture, Forming the Team Structure, Creating the Skeletal System. Documenting Software Architectures, Uses of Architectural Documentation, Views, Choosing the relevant views, Documenting a view, Documentation across views.

Unit-4
Teaching Hours:13
Analyzing Architecture
 

ATAM (Architecture Tradeoff Analysis Method): A comprehensive method for architecture evaluation, participants, outputs, phases of the ATAM, The Nightingale system - A case study in applying the ATAM.

CBAM (Cost Benefit Analysis Method): A quantitative approach to architecture design decision making: Decision making context, basis for CBAM, Implementing CBAM.Architecture of ORACLE 12c, Java.

Unit-5
Teaching Hours:13
Software Product Lines
 

Reusing Architectural Assets Overview – Successful working, Scope, Architectures and Difficulties in software product lines. Unix Architecture, Layered & State Diagram; Building systems from off-the-shelf components; Impact of components on Architecture, Architectural mismatch, Component-based design as search, ASEILM example.

Text Books And Reference Books:

[1] Len Bass, Paul Clements, Rick Kazman, Software Architecture In Practice, Pearson Education Asia , 3rd Edition, 2012.

Essential Reading / Recommended Reading

[1] Sommerville, Ian, Software Engineering, Addison Wesley, 9th Edition, 2010

[2] Jeff Garland,Richard Anthony, Large-Scale Software Architecture – A Practical Guide Using UML, Wiley –dreamtech India Pvt.,Ltd., 1st Edition,2002.

[3] Pressman S Roger, Software Engineering, McGraw Hill International Editions, 7th Edition, 2009.

[4] Rumbaugh, James, Object Oriented Modeling and design, Pearson Education, New Delhi, 2005.

Evaluation Pattern

CIA 60%

ESE 40%

MSP442E - AGENT BASED COMPUTING (2018 Batch)

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

Course Objectives/Course Description

 

On completion of the course students should have understood Software Agents and its Applications, Intelligent learning Methods.

Learning Outcome

On completion of the course students will be able to 

CO1: Understand Software Agents and its Applications

CO2: Apply Intelligent learning Methods

CO3: Analyze Rule learning with case studies

Unit-1
Teaching Hours:12
SOFTWARE AGENTS
 

Introduction to Software Agents: What is a software agent? - Why software agents? - Applications of Intelligent software agents-Practical design of intelligent agent systems.

Unit-2
Teaching Hours:12
INTELLIGENT AGENTS
 

Intelligent Agent Learning- Approaches to Knowledge base development-Disciple approach for building Intelligent agents- Knowledge representation-Generalization- Problem solving methods-Knowledge elicitation.

Unit-3
Teaching Hours:12
RULE LEARNING
 

Rule learning problem- Rule learning method- Learned rule characterization. Rule refinement: Rule refinement problem- Rule refinement method- Rule experimentation and verification-Refined rule characterization-Agent interactions.

Unit-4
Teaching Hours:12
BUILDING INTELLIGENT AGENTS
 

Disciple shell: Architecture of Disciple shell- Methodology for building Intelligent Agents- Expert-Agent interactions during knowledge elicitation process- Expert-Agent interactions during rule learning process- Expert-Agent interactions during rule refinement process.

Unit-5
Teaching Hours:12
CASE STUDIES
 

Case studies in building intelligent agents: Intelligent Agents in portfolio management- Intelligent Agents in financial services- Statistical Analysis assessment and support agent- Design assistant for configuring computer systems.

Text Books And Reference Books:

[1] Nicholas R Jennings, Michael J Wooldridge (Eds.), “Agent Technology – Foundations, Applications and Markets”, Springer, 1997.

Essential Reading / Recommended Reading

[1] Jeffrey M Bradshaw, “Software Agents”, AAAI Press/ the MIT Press, Standard Edition, 1997.
[2] Gheorghe Tecuci et al., “Building Intelligent Agents”, Academic Press, 2003.
[3] Eduardo Alanso, Daniel Kudenko, Dimitar Kazakov (Eds.) “Adaptive Agents and Multi-Agent Systems”, Springer Publications, 2003.

Evaluation Pattern

CIA Weight

ESE Weight

60%

40%

MSP451 - MAIN PROJECT (2018 Batch)

Total Teaching Hours for Semester:300
No of Lecture Hours/Week:20
Max Marks:200
Credits:04

Course Objectives/Course Description

 

The course is designed to provide a real-world project development and deployment environment for the students.

Learning Outcome

CO1:To identify the problem and relevant modules for the selected problem

CO 2:To apply appropriate design/development methodology and Tools

CO3:Competence to work in a team

CO4:Ability to complete the solution as a product

Unit-1
Teaching Hours:300
Main Project
 

It is a full time project to be taken up either in the industry or in an R&D organization.

Student can do project based on the specialization papers which students have opted for. 

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

60% CIA + 40% ESE

MSP471 - RESEARCH (IMPLEMENTATION AND PUBLICATION) (2018 Batch)

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

Course Objectives/Course Description

 

Research in Computer Science : Research inclusive curriculum is an initiative by the Department of Computer Science through which regular curriculum of Post Graduate Computer Science courses is augmented with formal research.

(a) Inculcating research culture among the post graduate students

(b) Enhancing employability skills of students by providing necessary research foundation

Learning Outcome

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

CO1: Demonstrate their understanding of Research article publication process – correcting review comments.

CO2: Able to produce commercially valuable intellectual property. 

CO3: Able to produce new products/processes/methods/model/Framework.

Unit-1
Teaching Hours:30
Research Modelling and Implementation
 

There is only CIA for this paper. Research work carried out in this semester is divided in two parts.

Modelling and implementation of their research work. Students should perform the following tasks:

 ·         Methodology

 ·         Evaluation and Discussion of Results

 ·         Limitations, Conclusions and Scope for future enhancements

 ·         Plagiarism report

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

 

Presentation

01

CIA -I

Presentation

02

CIA - II

Presentation

03

CIA - III

Report

Submission

Attendance

20%

20%

20%

15%

05%

 

Evaluation rubric for Presentation :

Evaluation Rubrics for Research Publication (Weightage – 30 Marks)

S.No

Type of publication

Range of marks

1

National Journal

16 – 20

2

International Journal

21 – 25

3

Scopus/SCI Journal

Above 25