CHRIST (Deemed to University), Bangalore

DEPARTMENT OF computer-science-and-engineering

school-of-engineering-and-technology

Syllabus for
Bachelor of Technology (Information Technology)
Academic Year  (2020)

 
3 Semester - 2019 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
CS331P DATABASE MANAGEMENT SYSTEMS Core Courses 5 4 100
CS332P DATA STRUCTURES AND ALGORITHMS Core Courses 5 4 100
CS333 SOFTWARE ENGINEERING Core Courses 3 3 100
EC337 DIGITAL SYSTEMS Core Courses 4 3 100
HS311 TECHNICAL WRITING Core Courses 2 2 50
MA334 DISCRETE MATHEMATICS Core Courses 3 3 100
MC321 CYBER SECURITY Skill Enhancement Courses 2 0 50
MIMBA331 PRINCIPLES OF MANAGEMENT Minors and Honours 6 4 100
MIPSY331 UNDERSTANDING HUMAN BEHAVIOR Minors and Honours 4 4 100
4 Semester - 2019 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
BS451 BIO SCIENCE LABORATORY Core Courses 2 1 50
CS431 PROBABILITY AND QUEUING THEORY Core Courses 3 3 100
CS432P OPERATING SYSTEMS Core Courses 5 4 100
CS433P PROGRAMMING PARADIGM Core Courses 5 4 100
CS434 FORMAL LANGUAGE AND AUTOMATA THEORY Core Courses 3 3 100
CS435P COMPUTER ORGANIZATION AND ARCHITECTURE Core Courses 5 4 100
CS436 PROFESSIONAL ETHICS Core Courses 3 3 100
MIMBA431 ORGANISATIONAL BEHAVIOUR - 6 4 100
MIPSY431 PEOPLE THOUGHTS AND SITUATIONS - 4 4 100
5 Semester - 2018 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
CS531 COMPUTER ORIENTED NUMERICAL ANALYSIS - 3 3 100
CS532E01 FORMAL LANGUAGE AND AUTOMATA THEORY - 3 3 100
CS532E02 COMPILER DESIGN - 3 3 100
CS532E03 FUZZY LOGIC - 3 3 100
CS533P INTERNET OF THINGS - 5 4 100
CS534 DESIGN AND ANALYSIS OF ALGORITHMS - 4 4 100
CS535 SOFTWARE ENGINEERING - 3 3 100
CS536P INTERNET AND WEB PROGRAMMING - 5 4 100
CSHO531AIP STATISTICAL FOUNDATION FOR ARTIFICIAL INTELLIGENCE Minors and Honours 4 4 100
CSHO531CSP PROBABILITY AND RANDOM PROCESS Minors and Honours 5 4 100
CSHO531DAP STATISTICAL FOUNDATION FOR DATA ANALYTICS Minors and Honours 5 4 50
6 Semester - 2018 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
CE636OE1 SOLID WASTE MANAGEMENT - 3 3 100
CE636OE2 ENVIRONMENTAL IMPACT ASSESSMENT - 3 3 100
CE636OE4 DISASTER MANAGEMENT - 4 3 100
CS631 CRYPTOGRAPHY AND NETWORK SECURITY - 3 3 100
CS632P OBJECT ORIENTED ANALYSIS AND DESIGN - 5 4 100
CS633P SYSTEM SOFTWARE - 5 4 100
CSHO631AI ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Minors and Honours 5 4 100
CSHO631AIP ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Minors and Honours 5 4 100
CSHO631CS MOBILE AND NETWORK-BASED ETHICAL HACKING Minors and Honours 5 4 100
CSHO631CSP MOBILE AND NETWORK BASED ETHICAL HACKING Minors and Honours 5 4 100
CSHO631DA BIG DATA ANALYTICS Minors and Honours 5 4 100
CSHO631DAP BIG DATA ANALYTICS Minors and Honours 5 4 100
CSHO632AI ROBOTICS AND PROCESS AUTOMATION Minors and Honours 5 4 100
CSHO632AIP ROBOTICS AND PROCESS AUTOMATION Minors and Honours 5 4 100
CSHO632CS CYBER FORENSICS AND MALWARE DETECTION Minors and Honours 5 4 100
CSHO632CSP CYBER FORENSICS AND MALWARE DETECTION Minors and Honours 5 4 100
CSHO632DA BIG DATA SECURITY ANALYTICS Minors and Honours 5 4 100
CSHO632DAP BIG DATA SECURITY ANALYTICS Minors and Honours 5 4 100
EC636OE1 EMBEDDED BOARDS FOR IOT APPLICATIONS - 3 34 100
EC636OE4 FUNDAMENTALS OF IMAGE PROCESSING - 3 3 100
EC636OE7 E-WASTE MANAGEMENT AND RADIATION EFFECT - 3 3 100
EE636OE2 NONCONVENTIONAL ENERGY SOURCES - 4 3 100
EE636OE3 INTRODUCTION OF HYBRID ELECTRIC VEHICLES - 4 3 100
EE636OE6 ROBOTICS AND AUTOMATION - 4 3 100
IT634P DATAWAREHOUSING AND DATAMINING - 5 4 100
IT635 SOFTWARE TESTING - 3 3 100
MA636OE3 NUMERICAL SOLUTION OF DIFFERENTIAL EQUATIONS - 3 3 100
ME636OE3 BASIC AUTOMOBILE ENGINEERING - 3 3 100
ME636OE4 PROJECT MANAGEMENT - 3 3 100
ME636OE5 BASIC AEROSPACE ENGINEERING - 3 3 100
7 Semester - 2017 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
BTGE 732 ACTING COURSE - 2 2 100
BTGE 734 DIGITAL WRITING - 2 2 100
BTGE 737 PROFESSIONAL PSYCHOLOGY - 2 2 100
BTGE 744 DIGITAL MARKETING - 2 2 100
BTGE 745 DATA ANALYTICS THROUGH SPSS - 2 2 100
BTGE735 DIGITAL MEDIA - 2 2 100
BTGE736 INTELLECTUAL PROPERTY RIGHTS - 2 2 100
BTGE738 CORPORATE SOCIAL RESPONSIBILITY - 2 2 100
BTGE739 CREATIVITY AND INNOVATION - 2 2 100
BTGE741 GERMAN - 2 2 100
BTGE749 PAINTING AND SKETCHING - 2 2 100
BTGE750 PHOTOGRAPHY - 2 2 100
BTGE754 FUNCTIONAL ENGLISH - 4 2 100
CS731 ARTIFICIAL INTELLIGENCE - 4 4 100
CS732 CLOUD COMPUTING - 3 3 100
CS733P MOBILE APPLICATION DEVELOPMENT - 5 4 100
CS735E01 NATURAL LANGUAGE PROCESSING - 3 3 100
CS735E02 RESEARCH METHODOLOGY - 3 3 100
CS736E01 GRAPH THEORY - 3 3 100
CS736E03 WIRELESS NETWORKS - 3 3 100
CS771 INTERNSHIP - 2 2 50
CS772 SERVICE LEARNING - 2 2 50
IT735E01 INFORMATION SECURITY - 3 3 100
IT736E01 SIMULATION AND MODELING - 3 3 100
IT736E03 ADVANCED DATABASES - 3 3 100
IT736E04 NETWORK ADMINISTRATION - 3 3 100
8 Semester - 2017 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
BTCY01 CYBER SECURITY - 2 2 50
CS831E01 QUANTUM COMPUTING - 3 3 100
CS831E02 GRID COMPUTING - 3 3 100
CS831E03 MOBILE COMPUTING - 3 3 100
CS832E01 SOFTWARE TESTING - 3 3 100
CS832E02 SOFTWARE PROCESS AND PROJECT MANAGEMENT - 3 3 100
CS833E02 INTRODUCTION TO DATA SCIENCE - 3 3 100
CS833E03 SOFT COMPUTING - 3 3 100
CS833E04 DIGITAL IMAGE PROCESSING - 3 3 100
CS871 PROJECT WORK - 12 6 200
CS872 COMPREHENSION - 4 2 50
IT832E02 WEB SERVICES AND SERVICE ORIENTED ARCHITECTURE - 3 3 100
IT833E04 PROFESSIONAL ETHICS AND HUMAN VALUES - 3 3 100
    

    

Introduction to Program:
The Undergraduate program in Information Technology is aimed at creating computer science engineers by providing the fundamentals of engineering and basic skills in computing. The special focus on employability is clear from the inclusion of subjects based on demand of industry and mandatory internships. A well-chosen elective basket gives the ward an opportunity to widen their knowledge in any specific domain.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO1: Apply Engineering knowledge of computing, mathematics, science, and computer science & engineering fundamentals for Problem solving.

PO2: Think critically to identify, formulate, and solve complex computer science & engineering problems by developing models, evaluating validity and accuracy of solutions in terms of computer science and engineering validity measures.

PO3: Analyse, design of complex problems, implement, and evaluate a computer-based system, to meet expected needs with appropriate considerations such as economic / environmental/societal.

PO4: Conduct experiments to investigate problems based on changing requirements, analyze and interpret results.

PO5: Create, select, adapt appropriate techniques and use of the modern computational tools, techniques and skills, and best of engineering practices.

PO6: Understand the impact of contextual knowledge on social aspects and cultural issues.

PO7: Understand contemporary issues related to social & environmental context for sustainable development of engineering solutions.

PO8: Understand professional & ethical responsibility to contribute for societal and national needs.

PO9: Function and coordinate effectively as an individual, as a member or leader in diverse, multicultural& multidisciplinary teams

PO10: Communicate effectively.

PO11: Demonstrate an understanding of computer science and engineering & management principles to manage software projects.

PO12: Demonstrate a recognition and realization of the need for, and an ability to engage in lifelong learning.

Assesment Pattern

 

 

Category 

Weightage for CIA

Weightage for ESE

1

Courses with theory and practical

70

30

2

Courses with only theory 

50

50

3

Courses with only Practical

50

50

 

COURSES WITH THEORY AND PRACTICAL

 

Component

Assessed for

Minimum marks

 to pass 

Maximum 

marks 

1

Theory CIA

30

-

30

2

Theory ESE

30

12

30

3

Practical CIA

35

14

35

4

Attendance

05

-

05

4

Aggregate 

100

40

100

 

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY 

PRACTICAL 

 

Component

Assessed for

Scaled down to

Minimum marks to pass 

Maximum marks 

Component

Assessed for

Scaled down to

Minimum marks to pass 

Maximum marks 

1

CIA-1 

20 

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50 

10

-

10




3

CIA-3

20 

10

-

10




4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100 

30

12

30

ESE

NA

NA

-

-

   

TOTAL 

65

-

65

TOTAL

 

35

14

35

 

  • Minimum marks required to pass in practical component is 40%. 

  • Pass in practical component is eligibility criteria to attend Theory End semester examination for the same course.

  • A minimum of 40 % required to pass in ESE -Theory component of a course.

  • Overall 40 % aggregate marks in Theory & practical component, is required to pass a course.

  • There is no minimum pass marks for the Theory - CIA component.

  • Less than 40% in practical component is refereed as FAIL.

  • Less than 40% in Theory ESE is declared as fail in the theory component.

  • Students who failed in theory ESE have to attend only theory ESE to pass in the course

 

II. ASSESSMENT - ONLY FOR THEORY COURSE (without practical component)

  • Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

  • End Semester Examination(ESE)   : 50% (50 marks out of 100 marks)

 

Components of the CIA 

CIA I:  Subject Assignments / Online Tests: 10 marks 

CIA II:   Mid Semester Examination (Theory): 25 marks

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications: 10 marks

Attendance: 05 marks

Total: 50 marks

 

Mid Semester Examination (MSE) : Theory Papers: 

  • The MSE is conducted for 50 marks of 2 hours duration. 

  • Question paper pattern; Five out of Six questions have to be answered. Each  question carries 10 marks

End Semester Examination (ESE): 

The ESE is conducted for 100 marks of 3 hours duration. 

The syllabus for the theory papers are divided into FIVE units and each unit carries equal Weightage in terms of marks distribution. 

Question paper pattern is as follows. 

Two full questions with either or choices will be outlined from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a particular question. The objective of the question paper is to test the application and analytical skill of the student. The major purpose of the question paper is to bring clarity about the process of associating questions to their respective performance indicators and hence to improve the ratings in course outcomes. Further, these question papers demonstrate how bloom’s taxonomy can be used to map the quality of question papers along with their effectiveness in the assessment pattern.

 

III. ASSESSMENT OF COMPREHENSION, INTERNSHIP and SERVICE LEARNING

COMPREHENSION

Maximum Marks = 50

Passing marks 40% min

Do not have ESE and completely evaluated through continuous assessment only, 

 

The evaluation (minimum 2 presentations) shall be based on the 

  • Topic / report :40% 

  • Presentation: 40% 

  • Response to the questions asked during presentation : 20%.

INTERNSHIP

 

Maximum Marks = 50(Only credit will be displayed in the score card)

Passing marks 40% min

Do not have ESE and completely evaluated through continuous assessment only

Continuous Internal Assessment is based upon

  • No of Internship Days: 20 marks

  • Report on Internship: 15 marks

  • Presentation on Internship: 15 marks

 

SERVICE LEARNING

Maximum Marks = 50

Passing marks 40% min

Do not have ESE and completely evaluated through continuous assessment only, 

Comprising

  • Internal Assessment with components like tests/quiz/written assignments: 25 marks 

  • Field Work or equivalent assignment as approved by the department panel: 25 marks  

 

V. ASSESSMENT OF PROJECT WORK

       Project Phase-I  

Project work may be assigned to a single student (with due approval from department) or to a group of students not exceeding 4 per group.

Maximum Marks = 100

  • Continuous Assessment :50 marks.

  • End Semester Examination (project report evaluation and viva-voce) : 50 marks.

  • The continuous assessment and End Semester Examinations marks for Project Work and the Viva-Voce Examination will be distributed as indicated below.

 


CIA 50 MARKS


ESE 50 MARKS

REVIEW 1

REVIEW 2

REVIEW 3

 

REVIEW COMMITTEE

GUIDE

REVIEW COMMITTEE

GUIDE

REVIEW COMMITTEE

GUIDE

EXAMINERES

10

05

10

05

10

10

50

TOTAL

15

TOTAL

15

TOTAL

20

 

  • There shall be 3 reviews and the student shall make presentation on the progress made before the committee constituted by the Department

  • The total marks obtained in the 3 reviews shall be 100 marks.

ESE 100 MARKS IS EVALUATED AS

  • Initial Write Up    : 10marks

  • Viva Voce: 25 marks

  • Demonstration: 40 marks

  • Project Report: 25 marks

 

Holistic Education:

   End Semester Examination          :25 Marks

   Participation:25 Marks

 

   Total :50 Marks

 

Examination And Assesments

Continuous internal Assesment-CIA-I,CIA-II,CIA-III

End Semester Examination

CS331P - DATABASE MANAGEMENT SYSTEMS (2019 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

·       To learn the fundamentals of data models and to conceptualize and depict a database system using ER diagram.

·       To make a study of SQL and relational database design.

·       To understand the internal storage structures using different file and indexing techniques which will help in physical DB design.

·       To know the fundamental concepts of transaction processing- concurrency control techniques and recovery procedure.

·       To have an introductory knowledge about the emerging trends in the area of distributed DB- OO DB- Data mining and Data Warehousing and XML.

·       To implement the design of the tables in DBMS.

·       To write queries to get optimized outputs. 

·       To store, retrieve and view the contents. To generate report based on customized need.

 

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1

Summarize the fundamental concepts of databases and Entity-Relationship (E-R) model.

L2

2

Apply E-R Model and Normalization principles to create relational databases for the given problems.

L3

3

Compare and contrast different file organization concepts for data storage in Relational databases

L4

4

Apply the transaction management principles on relational databases

L3

5

Demonstrate the current trends such as object oriented databases, distributed data storage in database technology

L2

Unit-1
Teaching Hours:15
INTRODUCTION AND CONCEPTUAL MODELING
 

Introduction to File and Database systems- Database system structure – Data Models – Introduction to Network and Hierarchical Models – ER model – Relational Model – Relational Algebra and Calculus.

Lab Programs

1. Data Definition Language (DDL) commands in RDBMS

2. Data Manipulation Language (DML) and Data Control Language (DCL) commands in   RDBMS.

Unit-2
Teaching Hours:15
RELATIONAL MODEL
 

SQL – Data definition- Queries in SQL- Updates- Views – Integrity and Security – Relational Database design – Functional dependences and Normalization for Relational Databases (up to BCNF).

Lab programs

3. High-level language extension with Cursors.

4.High level language extension with Triggers

Unit-3
Teaching Hours:15
DATA STORAGE AND QUERY PROCESSING
 

Record storage and Primary file organization- Secondary storage Devices- Operations on Files- Heap File- Sorted Files- Hashing Techniques – Index Structure for files –Different types of Indexes- B-Tree - B+ Tree – Query Processing.

Lab Programs 

5. Procedures and Functions.

6. Embedded SQL.

Unit-4
Teaching Hours:15
TRANSACTION MANAGEMENT
 

Transaction Processing – Introduction- Need for Concurrency control- Desirable properties of Transaction- Schedule and Recoverability- Serializability and Schedules – Concurrency Control – Types of Locks- Two Phases locking- Deadlock- Time stamp based concurrency control – Recovery Techniques – Concepts- Immediate Update- Deferred Update - Shadow Paging.

Lab Programs

7. Database design using E-R model and Normalization.

8. Design and implementation of Payroll Processing System.

Unit-5
Teaching Hours:15
CURRENT TRENDS
 

Object Oriented Databases – Need for Complex Data types- OO data Model- Nested relations- Complex Types- Inheritance Reference Types - Distributed databases- Homogenous and Heterogenous- Distributed data Storage – XML – Structure of XML- Data- XML Document- Schema- Querying and Transformation. – Data Mining and Data Warehousing.

Lab Programs:

9. Design and implementation of Banking System

10.Design and implementation of Library Information System

 

Text Books And Reference Books:

 

1.     Abraham Silberschatz, Henry F. Korth and S. Sudarshan- “Database System Concepts”, Sixth Edition, McGraw-Hill, 2010.

 

Essential Reading / Recommended Reading

REFERENCE BOOKS

1.     RamezElmasri and Shamkant B. Navathe, “Fundamental Database Systems”, Third Edition, Pearson Education, 2008.

2.     Raghu Ramakrishnan, “Database Management System”, Tata McGraw-Hill Publishing Company, 2003

 

Evaluation Pattern

 Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks) ·

 CIA 1 : Assignment and Quiz   10 marks

 CIA2  : Mid Semester Examination  10 marks

 CIA 3 : Practical Component and Mini Project  10 marks

Lab    :  Laboratory program  35 marks

Attendance -05 marks

End Semester Examination(ESE) : 30% (30 marks out of 100 marks)

CS332P - DATA STRUCTURES AND ALGORITHMS (2019 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 
  • To learn the systematic way of solving problems.
  • To understand the different methods of organizing large amounts of data.
  • To efficiently implement the different data structures.
  • To efficiently implement solutions for specific problems.

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Explain the basic concepts of data structures and solve the time complexity of the algorithm

L3

2.

Experiment with various operations on Linear Data structures

L3

3.

Examine the Structures and Operations of Trees and Heaps Data Structures

L4

4

Compare various given sorting techniques with respect to time complexity

L4

5

 Choose various shortest path algorithms to determine the minimum spanning path for the given graphs

L5

Unit-1
Teaching Hours:14
INTRODUCTION
 

Definition- Classification of data structures: primitive and non-primitive- Operations on data structures- Algorithm Analysis.

LAB Programs:

1a. Sample C Programs 1b. To determine the time complexity of a given logic. 

Unit-2
Teaching Hours:17
LISTS, STACKS AND QUEUES
 

Abstract Data Type (ADT) – The List ADT – The Stack ADT: Definition,Array representation of stack, Operations on stack: Infix, prefix and postfix notations Conversion of an arithmetic Expression from Infix to postfix. Applications of stacks. 

LAB Programs:

2. Implement the applications Stack ADT.

3. Implement the applications for Queue ADT.

4.Operations on stack[e.g.: infix to postfix, evaluation of postfix]

Unit-3
Teaching Hours:16
TREES
 

Preliminaries – Binary Trees – The Search Tree ADT – Binary Search Trees – AVL Trees – Tree Traversals – Hashing – General Idea – Hash Function – Separate Chaining – Open Addressing –Linear Probing – Priority Queues (Heaps) – Model – Simple implementations – Binary Heap.

LAB PROGRAMS:

5. Search Tree ADT - Binary Search Tree

Unit-4
Teaching Hours:14
SORTING
 

Preliminaries – Insertion Sort – Shell sort – Heap sort – Merge sort – Quicksort – External Sorting.

LAB PROGRAMS

6. Heap Sort.

7. Quick Sort.

8.Applications of Probability and Queuing Theory Problems to be implemented using data structures. 

Unit-5
Teaching Hours:14
GRAPHS
 

Definitions – Topological Sort – Shortest-Path Algorithms – Unweighted Shortest Paths – Dijkstra‘s Algorithm – Minimum Spanning Tree – Prim‘s Algorithm – Applications of Depth- First Search – Undirected Graphs – Bi-connectivity – Introduction to NP-Completeness-case study

LAB PROGRAMS

9. Implementing a Hash function/Hashing Mechanism.

10. Implementing any of the shortest path algorithms. 

 

Text Books And Reference Books:

TEXT BOOK

1.Mark Allen Weiss , “Data Structures and Algorithm Analysis in C”, 2nd  Edition, Addison-Wesley, 1997

Essential Reading / Recommended Reading

1. Michael T. Goodrich, Roberto Tamassia and Michael H. Goldwasser , ―Data Structures and Algorithms in Python  ‖, First  Edition, John Wiley & Sons, Incorporated, 2013.ISBN1118476735, 9781118476734

Evaluation Pattern

Components of the CIA

CIA I : Assignment  and Continuous Assessment : 10 marks

CIA II : Mid Semester Examination (Theory) : 10 marks

CIA III : Closed Book Test and Continuous Assessment: 10 marks

Lab marks :35 marks

Attendance : 05 marks

End Semester Examination(ESE) : 30% (30 marks out of 100 marks)

Total: 100 marks

CS333 - SOFTWARE ENGINEERING (2019 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

·         Learn different life cycle models and requirement elicitation process.

·         Understand various analysis modeling and specification, Architectural and detailed design methods.

·         Practice implementation methodologies and various testing strategies.

·         Analyze project planning and management concepts using various models and appropriate CASE tools.

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

CO1

Explain the fundamental of Software development Life cycle and different software development process models.

L2

CO2

Apply various requirement elicitation methods in software development process

L3

CO3

Develop the software processes using various design techniques.

L3

CO4

Analyze different testing techniques and maintenance principles in software development process.

L4

CO5

Formulate the cost estimation techniques and project scheduling methods in software development process.

L6

Unit-1
Teaching Hours:9
SOFTWARE PROCESS
 

Introduction –S/W Engineering Paradigm  – life cycle models (water fall, incremental, spiral, WINWIN spiral, evolutionary, prototyping, object oriented) - system engineering – computer based system  – verification – validation – life cycle process – development process –system engineering hierarchy.

Unit-2
Teaching Hours:9
SOFTWARE REQUIREMENTS
 

Functional and non-functional - user – system –requirement engineering process – feasibility studies – requirements – elicitation – validation and management – software prototyping – prototyping in the software process – rapid prototyping techniques – user interface prototyping -S/W document. Agile methods, Extreme Programming, SCRUM

Unit-3
Teaching Hours:9
DESIGN CONCEPTS AND PRINCIPLES
 

Design process and concepts – modular design – design heuristic – design model and document. Architectural design – software architecture – data design – architectural design – transform and transaction mapping – user interface design – user interface design principles. Real time systems - Real time software design – system design – real time executives – data acquisition system - monitoring and control system. SCM – Need for SCM – Version control – Introduction to SCM process – Software configuration items.

Unit-4
Teaching Hours:9
TESTING
 

Taxonomy of software testing – levels – test activities – types of s/w test – black box testing – testing boundary conditions – structural testing – test coverage criteria based on data flow mechanisms – regression testing – testing in the large. S/W testing strategies – strategic approach and issues - unit testing – integration testing – validation testing – system testing and debugging.

Unit-5
Teaching Hours:9
SOFTWARE PROJECT MANAGEMENT
 

Measures and measurements – S/W complexity and science measure – size measure – data and logic structure measure – information flow measure. Software cost estimation – function point models – COCOMO model- Delphi method.- Defining a Task Network – Scheduling – Earned Value Analysis – Error Tracking - Software changes – program evolution dynamics – software maintenance – Architectural evolution. Taxonomy of CASE tools – Case Study.

Text Books And Reference Books:

 Roger S. Pressman, Software engineering- A Practitioner’s Approach, McGraw-Hill International Edition, 6th Edition 2014 

Essential Reading / Recommended Reading

Anirban Basu, “Software Quality Assurance, Testing and Metrics”, First Edition, PHI Learning, 2015.

Ian Sommerville, “Software engineering,” Pearson education Asia, 9th Edition, 2013.

Pankaj Jalote- “An Integrated Approach to Software Engineering,” Narosa Publishing house, 2011.

James F Peters and Witold Pedryez, “Software Engineering – An Engineering Approach”, John Wiley and Sons, New Delhi, 2010.

Ali Behforooz and Frederick J Hudson, “Software Engineering Fundamentals”, OUP India 2012.

 

Evaluation Pattern

Continuos Internal Assessment CIA Marks 50

End Semester Exams ESE 50

EC337 - DIGITAL SYSTEMS (2019 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

·         This course is an introduction to the VHDL language. The emphasis is on writing synthesizable code and enough simulation code to write a viable test-bench.

·         The information gained can be applied to any digital design by using a top-down synthesis design approach.

Course Outcome

At the end of the course, the student will be able to do:

·         Implement the VHDL portion of coding for synthesis.

·         Identify the differences between behavioral and structural coding styles.

·         Understand the basic principle of circuit design and analysis.

Unit-1
Teaching Hours:9
INTRODUCTION AND METHODOLOGY
 

Digital Systems and Embedded Systems, Boolean Functions and Boolean algebra, Binary Coding, Combinational Components and Circuits, Verification of Combinational Circuits. Number Basics: Unsigned and Signed Integers, Fixed and Floating-point Numbers, Binary representation and Circuit Elements, Real-World Circuits, Models, Design Methodology.

Unit-2
Teaching Hours:9
SEQUENTIAL BASICS & MEMORIES
 

Storage elements, Counters, Sequential Data paths and Control, Clocked Synchronous Timing Methodology. Memories: Concepts, Memory Types, Error Detection and Correction.

Unit-3
Teaching Hours:9
IMPLEMENTATION FABRICS & PROCESSOR BASICS
 

ICs, PLDs, Packaging and Circuit Boards, Interconnection and Signal Integrity. Processor Basics: Embedded Computer Organization, Instruction and Data, Interfacing with memory.

Unit-4
Teaching Hours:9
I/O INTERFACING, ACCELERATORS & DESIGN METHODOLOGY
 

I/O devices, I/O controllers, Parallel Buses, Serial Transmission, I/O software. Accelerators: Concepts, case study, Verification of accelerators. Design Methodology: Design flow, Design optimization, Design for test.

Unit-5
Teaching Hours:9
SIMPLE SINGLE CYCLE AND MULTI CYCLE PROCESSOR DESIGN
 

Introduction of Simple Single Cycle and Multi Cycle Processor Design.

Text Books And Reference Books:

1.      C. H. Roth, Digital Systems Design Using VHDL, Thomson Publications, Fourth Edition, 2002.

V. A. Pedroni, Circuit Design with VHDL, MIT Press/PHI, 2004. 

Essential Reading / Recommended Reading

1.      Parhami, Behrooz, Computer Arithmetic: Algorithms and Hardware Designs, Oxford University Press, 2009.

2.      Z. Navabi, Verilog Digital System Design, Second Edition, Tata McGrawHill, 2008.

3.      R. C. Cofer and B. F. Harding, Rapid System Prototyping with FPGAs: Accelerating the Design Process, Elsevier/Newness, 2005.

Peter J. Ashenden, “Digital Design: An Embedded Systems Approach Using VERILOG”, Elesvier, 2010.

Evaluation Pattern

Assessment is based on the performance of the student throughout the semester.

Assessment of each paper

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

Attendance                                                                             : 05 marks

            Total                                                                                         : 50 marks

For subjects having practical as part of the subject

 

Assessment of Practical paper

Conduct of experiments                                                       : 25 marks

Observations/Lab Record                                                   : 15 marks

Viva voce                                                                                : 10 marks

Total                                                                                        : 50 marks

(All the above assessments are carried for each experiment during regular lab classes and averaged to max 50 marks at the end of the semester)

HS311 - TECHNICAL WRITING (2019 Batch)

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

Course Objectives/Course Description

 

The goal of this course is to prepare engineering students with individual and collaborative technical writing skills that are necessary to be effective technical communicators in academic and professional environments.

Course Outcome

CO1.  Understand the basics of technical communication and the use of formal elements of specific genres of documentation.

CO2.  Demonstrate the nuances of technical writing, with reference to english grammar and vocabulary.

CO3.  Recognize the importance of soft skills and personality development for effective  communication.

CO4.  Understand the various techniques involved in oral communication and its application.

CO5.  Realize the importance of having ethical work habits and professional etiquettes.

Unit-1
Teaching Hours:6
Design and Development
 

Different kinds of technical documents, Information development life cycle, Organization structures, factors affecting information and document design, Strategies for organization, Information design and writing for print and for online media.

Unit-2
Teaching Hours:6
Grammar and Editing
 

Technical writing process, writing drafts and revising,   technical writing style and language. Basics of grammar, study of advanced grammar, editing strategies to achieve appropriate technical style. Introduction to advanced technical communication

Unit-3
Teaching Hours:6
Self Development and Assessment
 

Self-assessment, Perception and Attitudes, Values and belief, Personal goal setting, career planning, Self- esteem. Managing Time; Personal memory, Rapid reading, Taking notes; Complex problem solving; Creativity

Unit-4
Teaching Hours:6
Communication and Writing
 

Public speaking, Group discussion, Oral presentation, Interviews, Presentation aids, Personality Development.  project proposals, brochures, newsletters, technical articles, manuals,  business letters, memos

Unit-5
Teaching Hours:6
Business Etiquettes
 

Etiquettes in social and office settings, Email etiquettes, Telephone Etiquettes, Engineering ethics, Time Management, Role and responsibility of engineer, Work culture in jobs

Text Books And Reference Books:

T1 : David F. Beer and David McMurrey, Guide to writing as an Engineer, John Willey. New    York, 2004 

       T2: Diane Hacker, Pocket Style Manual, Bedford Publication, New York, 2003. (ISBN 0312406843)

T3: Raman Sharma, Technical Communications, Oxford Publication, London, 2004

Essential Reading / Recommended Reading

R1.Dale Jungk, Applied Writing for Technicians, McGraw Hill, New York, 2004. (ISBN: 07828357-4)

R2. Sharma, R. and Mohan, K. Business Correspondence and Report Writing, TMH New Delhi 2002.

R3. Xebec, Presentation Book, TMH New Delhi, 2000. (ISBN 0402213)

Evaluation Pattern

CIA 1 - 10 Marks

Mid Semester Exams - 25 Marks

CIA 2 - 10 Marks

End Semester Exams - 50 Marks

Attendance - 5 marks

MA334 - DISCRETE MATHEMATICS (2019 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To extend student’s mathematical maturity and ability to deal with abstraction and to introduce most of the basic terminologies used in computer science courses and application of ideas to solve practical problems.

The objective of the paper is to develop:

·  The knowledge of the concepts needed to test the logic of a program.

·    Knowledge which has application in expert system, in data base and a basic for the programing language.

·    An understanding in identifying patterns on many levels.

·    Awareness about a class of functions which transform a finite set into another finite set that relates to input output functions in computer science.

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1

Checking  the  consistency of system of linear equations and solving by Gauss Jordan and Gauss Elimination methods, finding the spectral matrix with the aid of eigen values and eigen vector

L3

2

Finding the differentiation of multivariable functions using the concept of total derivatives, Jacobian,  evaluating definite integrals by Leibnitz rule of differentiation under integral sign

L4

3

Evaluating the definite integrals as surface area and volume of solid of revolution by   tracing the curves and using reduction formulae

L4

4

Solving first order nonlinear differential equations by reducing into homogenous, linear and exact forms

L3

5

Finding the velocity and acceleration of a moving particle, vector potential, scalar potential

L3

Unit-1
Teaching Hours:9
Propositional Calculus:
 

Propositions Logical connectives Compound propositions Conditional and bi conditional propositions Truth tables Tautologies and contradictions Contrapositive Logical equivalences and implications De Morgans Laws - Normal forms Principal conjunctive and disjunctive normal forms Rules of inference Arguments - Validity of arguments.

Unit-2
Teaching Hours:9
Predicate Calculus:
 

Predicates Statement Function Variables Free and bound variables Quantifiers Universe of discourse Logical equivalences and implications for quantified statements Theory of inference The rules of universal specification and generalization Validity of arguments

Unit-3
Teaching Hours:9
Set Theory
 

Basic concepts Notations Subset Algebra of sets The power set Ordered pairs and Cartesian product Relations on sets Types of relations and their properties Matrix and Graph representation of a relation Partial ordering Poset Hasse diagram Lattices and their properties Sublattices Boolean algebra.

Unit-4
Teaching Hours:9
Functions:
 

Definitions of functions Classification of functions Types of functions - Examples Composition of functions Inverse functions Characteristic function of a set Hashing functions Permutation functions.

Unit-5
Teaching Hours:9
Groups:
 

Groups - Properties Subgroups - Cosets and Lagranges theorem Normal subgroups Algebraic system with two binary operations Preliminaries of Coding - Hamming Metric - group codes: Basic notions of error correction - Error recovery in group codes.

Text Books And Reference Books:

Text Books

T1. Trembly J.P and Manohar R, Discrete Mathematical Structures with Applications to Computer Science, Tata McGrawHill Pub.Co. Ltd, New Delhi, 2003. 

T2. Ralph. P. Grimaldi, Discrete and Combinatorial Mathematics: An Applied Introduction, Fifth Edition, Pearson Education Asia,Delhi, 2009.

Essential Reading / Recommended Reading

Reference Books 

1.      R1. Bernard Kolman, Robert C. Busby, Sharan Cutler Ross, Discrete Mathematical Structures,  Fourth Indian reprint, Pearson Education Pvt Ltd., New Delhi, 2003.

2.      R2. Kenneth H. Rosen, Discrete Mathematics and its Applications, Fifth Edition, Tata McGraw Hill Pub. Co. Ltd., New Delhi, 2003.

3.      R3. .Richard Johnsonbaugh, Discrete Mathematics, Fifth Edition,Pearson Education Asia, New Delhi, 2002.

4.      R4. Dr K.S.C , Discrete Mathematical Structures, 5th Edition, Prism Engineering Education Series2018.

5.      R5. S Santha, Discrete Mathematics with Combinatorics and Graph Theory  Cengage, 1st Edition, 2019

Evaluation Pattern

Continuous Internal Assessment (CIA): 50% (50 marks out of 100 marks)

End Semester Examination(ESE)      : 50% (50 marks out of 100 marks) 

 

 

S.No

Assessment

Marks

Weightagemarks

1

CIA I

20

10

2

CIA II

     (MSE: Mid Semester Examination)

50

25

3

CIA III

20

10

4

Attendance

10

05

5

ESE

(End Semester Examination)

100

50

Total

100

Components of the CIA

CIA I  :  Subject Assignments / Online Tests                  : 10 marks

CIA II :   Mid Semester Examination (Theory)                : 25 marks            

CIAIII:Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications : 10 marks

Attendance                                                                           : 05 marks

            Total                                                                                       : 50 marks

Mid Semester Examination (MSE) : Theory Papers:

  • The MSE is conducted for 50 marks of 2 hours duration.
  • Question paper pattern; Five out of Six questions have to be answered. Each  question carries 10 marks

End Semester Examination (ESE):

The ESE is conducted for 100 marks of 3 hours duration.

The syllabus for the theory papers are divided into FIVE units and each unit carries equal Weightage in terms of marks distribution.

Question paper pattern is as follows.

Two full questions with either or choice will be drawn from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a question. The emphasis on the questions is to test the objectiveness, analytical skill and application skill of the concept, from a question bank which reviewed and updated every year

The criteria for drawing the questions from the Question Bank are as follows

50 % - Medium Level questions

25 % - Simple level questions

25 % - Complex level questions

MC321 - CYBER SECURITY (2019 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:0

Course Objectives/Course Description

 

This course is aimed at providing a comprehensive overview of the different facets of Cyber Security. In addition, the course will detail into specifics of Cyber Security with Cyber Laws both in Global and Indian Legal environments

Course Outcome

 

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Summarize the network security concepts and cyber laws

L2

2.

Explain different cyber attacks with relevant examples

L2

3.

Illustrate risk management process handled in the organization with business continuity planning

L2

4.

Outline the vulnerabilities that affect the organizational network

L2

5.

Identify cryptography algorithms for authentication purposes in the organizational network

L3

Unit-1
Teaching Hours:6
Security Fundamentals
 

Security Fundamentals-4 As Architecture Authentication Authorization Accountability, Social Media, Social Networking and Cyber Security.

Cyber Laws, IT Act 2000-IT Act 2008-Laws for Cyber-Security, Comprehensive National Cyber- Security Initiative CNCI – Legalities.

Unit-2
Teaching Hours:6
Cyber Attack and Cyber Services
 

 Cyber Attack and Cyber Services:  Computer Virus – Computer Worms – Trojan horse. Vulnerabilities - Phishing - Online Attacks – Pharming - Phoarging – Cyber Attacks – Cyber Threats - Zombie- stuxnet - Denial of Service Vulnerabilities - Server Hardening-TCP/IP attack- SYN Flood. 

Unit-3
Teaching Hours:6
Cyber Security Management
 

Cyber Security Management : Risk Management and Assessment - Risk Management Process - Threat Determination Process -Risk Assessment - Risk Management Lifecycle. Security Policy Management - Security Policies - Coverage Matrix. Business Continuity Planning - Disaster Types - Disaster Recovery Plan - Business Continuity Planning Process.

Unit-4
Teaching Hours:6
Vulnerability - Assessment and Tools
 

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Black box- white box Architectural Integration: Security Zones - Devices viz Routers, Firewalls, DMZ. Configuration Management - Certification and Accreditation for Cyber-Security

Unit-5
Teaching Hours:6
Authentication and Cryptography
 

Authentication and Cryptography: Authentication -Cryptosystems - Certificate Services .Securing Communications: Securing Services - Transport – Wireless - Steganography and NTFS Data Streams. Intrusion Detection and Prevention Systems: Intrusion - Defense in Depth - IDS/IPS  - IDS/IPS Weakness and Forensic Analysis. Cyber Evolution: Cyber Organization - Cyber Future 

Text Books And Reference Books:

  

1.      Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, Cyber Security Policy Guidebook, Wiley; 1 edition , 2012,    ISBN-10: 1118027809 

2.      Dan Shoemaker and Wm. Arthur Conklin, Cybersecurity: The Essential Body Of Knowledge,   Delmar Cengage Learning; 1 edition (May 17, 2011) ,ISBN-10: 1435481690

Essential Reading / Recommended Reading

     

1.      Matt Bishop, “Computer Security Art and Science”, Pearson/PHI, 2009.

2.      Stuart Mc Clure, Joel Scrambray, George Kurtz, “Hacking Exposed”, 7th Edition Tata McGraw-Hill, 2012.

3.      Jason Andress, The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice, Syngress; 1 edition (June 24, 2011) ,  ISBN-10: 1597496537

4.      Stallings, “Cryptography & Network Security - Principles & Practice”, Prentice Hall, 3rd Edition 2002.

5.      Bruce, Schneier, “Applied Cryptography”, 2nd Edition, Toha Wiley & Sons, 2007.

6.      Man Young Rhee, “Internet Security”, Wiley, 2003.

7.      Pfleeger & Pfleeger, “Security in Computing”, Pearson Education, 3rd Edition, 2003.   

Evaluation Pattern

 Continuous Internal Assessment (CIA) for theory papers : 100% (50 marks out of 50 marks)

 

MIMBA331 - PRINCIPLES OF MANAGEMENT (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:6
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course Description: This is offered as a core course in first trimester. This course will provide a general introduction to management principles and theories, and a brief outline on history and development of management thought. 

Course Objectives : This course describes the steps necessary to understand an organisation that are aligned with business objectives and provides an insight to address a range of challenges that every manager encounters. It aims to prepare students for an exciting challenging and rewarding managerial career through case studies on Global Perspective.

Course Outcome

Course Learning Outcomes: On having completed this course student should be able to:

CLO1   Understand different management approaches 

CLO2   Demonstrate planning techniques

CLO3   Able to work in dynamic teams within organizations 

CLO4   Analyze different processes in staffing and controlling

Unit-1
Teaching Hours:12
Unit 1. Nature, Purpose and Evolution of Management Thought
 

Meaning; Scope; Managerial levels and skills; Managerial Roles; Management: Science, Art or Profession; Universality of Management.

Ancient roots of management theory; Classical schools of management thought; Behavioral School, Quantitative School; Systems Approach, Contingency Approach; Contemporary Management thinkers & their contribution. Ancient Indian Management systems & practices. Comparative study of global management systems & practices. 

 

Evolution of Management: Teaching management through Indian Mythology (Videos of Devdutt Pattanaik, Self-learning mode)

Unit-2
Teaching Hours:12
Unit 2. Planning
 

Types of Plans; Steps in Planning Process; Strategies, level of Strategies, Policies and Planning; Decision making, Process of Decision Making, Techniques in Decision Making, Forecasting & Management by Objectives (MBO).

 

Planning: HBS Case and Projects of Events

Unit-3
Teaching Hours:12
UnitUnit 3. Organizing
 

Organizational structure and design; types of organizational structures; Span of control, authority, delegation, decentralization and reengineering. Social responsibility of managers, Managerial Ethics.

 

Organizing: Holacracy form of organization structure, HBS Case

Unit-4
Teaching Hours:12
Unit 4. Staffing
 

Human resource planning, Recruitment, selection, training & development, performance appraisal, managing change, compensation and employee welfare.

Motivation: Concept, Forms of employee motivation, Need for motivation, Theories of motivation, Stress Management

Staffing: Stress Management & Career path, HBS Case

Unit-5
Teaching Hours:12
Unit 5. Leading and Controlling
 

Leadership concept, leadership Styles, leadership theories, leadership communication.

Nature of organizational control; control process; Methods and techniques of control; Designing control systems, Quality Management

 Leading: Article on Styles of leadership by Daniel Goleman

Controlling: HBS Case and Projects of Events

Text Books And Reference Books:

Text Books:

T1. Heinz Weihrich, Mark V Cannice & Harold Koontz (2019). Management (15th Edition). McGraw Hill Publications.

Essential Reading / Recommended Reading

Reference Books:

R1. Daft, R. L. (2016). The new era of management (11th Edition). Cengage Publications.

R2. Prasad, L.M., Principles and practices of management. New Delhi: Sultan Chand & Sons.   

Evaluation Pattern

Test & Exam

 

Max Marks

Weightage

Total

CIA – I

20

10

20%

10

CIA – II

50

25

25%

25

CIA – III

20

10

10%

10

Attendance

5

5%

5

CIA – I, II, and III

 

50

50%

50

End – term

100

50

50%

50

Total

100

MIPSY331 - UNDERSTANDING HUMAN BEHAVIOR (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 focuses on the fundamentals of psychology. It is an introductory paper that gives an overall understanding about the human behavior. It will provide students with an introduction to the key concepts, perspectives, theories, and sub-fields on various basic processes underlying human behavior.

 Objectives

  1. To understand the fundamental processes underlying human behavior
  2. To become aware of one’s idiosyncrasies and predispositions
  3. To apply the understanding of concepts in day-to-day activities

Course Outcome

After the completion of this course students will be able to:

  1. Explain human behaviors using theoretical underpinnings
  2. Understand oneself and others, respecting the differences
  3. Demonstrate their understanding of psychological processes in daily activities. 

Unit-1
Teaching Hours:12
SENSATION
 

Definition, Characteristics of Sensory modalities: Absolute and difference threshold; Signal detection theory; sensory coding; Vision, Audition, Other Senses. Assessment of Perception and Sensation

Unit-2
Teaching Hours:12
PERCEPTION
 

Definition, Understanding perception, Gestalt laws of organization, Illusions and Perceptual constancy; Various sensory modalities; Extrasensory perception.

Practicum:  Muller-Lyer Illusion

Unit-3
Teaching Hours:12
LEARNING
 

Learning:Definition, Classical conditioning, Instrumental conditioning, learning and cognition; 

Unit-3
Teaching Hours:12
MEMORY
 

Types of Memory; Sensory memory, working memory, Long term memory, implicit memory, Constructive memory, improving memory; Assessment of memory.

Practicum: Memory drum

Unit-4
Teaching Hours:12
INDIVIDUAL DIFFERENCES
 

Concepts and nature of Individual differences; Nature vs. nurture; Gender difference in cognitive processes and social behavior; 

Unit-4
Teaching Hours:12
INTELLIGENCE
 

Definition, Contemporary theories of intelligence; Tests of intelligence; Emotional, Social and Spiritual intelligence.

Practicum: Bhatia’s Battery of Performance

Unit-5
Teaching Hours:12
PERSONALITY
 

Definition, Type and trait theories of personality, Type A, B & C. Psychoanalytic -  Freudian perspective; Types of personality assessment.

Practicum: NEO-FFI 3

Text Books And Reference Books:

Baron, R. A. (2001). Psychology. New Delhi: Pearson Education India.

Rathus, S. A. (2017). Introductory Psychology, 5thEd. Belmont, CA: Wadsworth.

Nolen-Hoeksema, S., Fredrickson, B.L. & Loftus, G.R. (2014). Atkinson & Hilgard'sIntroduction to Psychology.16th Ed. United Kingdom: Cengage Learning.

Essential Reading / Recommended Reading

Feldman, R. S. (2011). Understanding Psychology. New Delhi: Tata McGraw Hill.

Morgan, C. T., King, R. A., & Schopler, J. (2004). Introduction to Psychology. New Delhi: Tata     McGraw Hill.

Kalat, J. W. (2016). Understanding Psychology. New York: Cengage Learning.

Evaluation Pattern

Group Assignment

Individual Assignment

Mid semester

20

20

25

 

Mid Semester Examination

Section A

(Definition)

Section B

(Short note)

Section C

(Essay)

Section D

(Case Question)

Total

5×2=10

4×5=20

1×10=10

1×10=10

50

 

End Semester Examination

Section A

(Definition)

Section B

(Short note)

Section C

(Essay)

Section D

(Case Question)

Total

5×2=10

4×5=20

1×10=10

1×10=10

50

 

BS451 - BIO SCIENCE LABORATORY (2019 Batch)

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

Course Objectives/Course Description

 

 

 

 

 

Course Outcome

Course outcomes:

At the end of the course, the student will be able to do:

● Examine the applications of bioengineering and using common tool boxes for

analysing medical information.

Unit-1
Teaching Hours:30
list of experiments
 

1. Blood Pressure Measurement using Arduino 

2. To determine the R peaks in given ECG and to find HRV using Matlab.

3. To familiarize with the fundamentals of image processing in Matlab using simple tools and functions.

4. To determine the presence of fractures in the given X-ray file using simple Matlab image processing

5. To determine the presence of fractures in the given X-ray file using simple Matlab image processing toolbox.

6. Introduction to Tinkercad and using the various tools available for running a simple program of

lighting a LED bulb using Arduino (digital).

7. To design a temperature sensor in Tinkercad using Arduino and TMP36..

8. To design and simulate muscle contraction using potentiometers, Arduino and servo motors.

9. To design and simulate measuring pulse sensors using photodiodes, IR LED and Arduino.

10. Preparation of biopolymers (polylactic acid) at home using home-based ingredients.

Text Books And Reference Books:

NA

Essential Reading / Recommended Reading

NA

Evaluation Pattern

As per the university criteria

CS431 - PROBABILITY AND QUEUING THEORY (2019 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

At the end of the course, the students would have a fundamental knowledge of the basic probability concepts. Have a well – founded knowledge of standard distributions which can describe real life phenomena. Acquire skills in handling situations involving more than one random variable and functions of random variables. Understand and characterize phenomena which evolve with respect to time in a probabilistic manner. Be exposed to basic characteristic features of a queuing system and acquire skills in analyzing queuing models.

Course Outcome

CO1: Explain the basic perceptions of probability of an event and associated random variables.

CO2: Compare and contrast various standard distributions with suitable statistical analysis.

CO3: Apply and solve two dimensional random variable problems through joint distributions and central limit theorem.

CO4: Analyze probabilistic environment using random process and markov chain techniques.

CO5: Build and implement queuing model associated to stochastic process.

Unit-1
Teaching Hours:9
PROBABILITY AND RANDOM VARIABLE
 

Axioms of probability - Conditional probability - Total probability – Baye’s theorem Random variable - Probability mass function - Probability density function  - Properties – Moments - Moment generating functions and their properties.

Unit-2
Teaching Hours:9
STANDARD DISTRIBUTIONS
 

Binomial, Poisson, Geometric, Negative Binomial, Uniform, Exponential,Gamma,

Weibull and Normal distributions and their properties - Functions of a random variable.

Unit-3
Teaching Hours:9
TWO DIMENSIONAL RANDOM VARIABLES
 

Joint distributions - Marginal and conditional distributions – Covariance Correlation and regression - Transformation of random variables - Central limit theorem.

Unit-4
Teaching Hours:9
RANDOM PROCESSES AND MARKOV CHAINS
 

Classification - Stationary process - Markov process - Poisson process - Birth and death process - Markov chains - Transition probabilities - Limiting distributions. Transition Diagram.

Unit-5
Teaching Hours:9
QUEUING THEORY
 

Markovian models – M/M/1, M/M/C, finite and infinite capacity - M/M/∞ queues - Finite source model -  M/G/1 queue (steady state solutions only) – Pollaczek – Khintchine formula – Special cases.Single and Multiple Server System.

Text Books And Reference Books:

1.      Ross, S., “A first course in probability”,Sixth Edition, Pearson Education, Delhi, 2002.

2.      Medhi J., “Stochastic Processes”, New Age Publishers, New Delhi, 1994. (Chapters 2, 3,4)

3.      T.Veerarajan, “Probability, Statistics and Random process”, Second  Edition, Tata McGraw Hill, New Delhi,  2003

Essential Reading / Recommended Reading

1.      Allen A.O., “Probability, Statistics and Queuing Theory”, Academic press, New Delhi, 1981.

2.      Taha H. A., “Operations Research-An Introduction”,Seventh Edition, Pearson Education Edition Asia, Delhi, 2002.

3.      Gross, D. and Harris, C.M., “Fundamentals of Queuing theory”, John Wiley.

Evaluation Pattern
  • Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●   End Semester Examination(ESE)      : 50% (50 marks out of 100 marks) 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications                      : 10 marks

Attendance                                                                            : 05 marks

                      Total                                                                              : 50 marks

CS432P - OPERATING SYSTEMS (2019 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Objectives of this course is to have an overview of different types of operating systems. They also include an understanding of the components of an operating system; To develop knowledge of process management and have a thorough knowledge of storage management; To know the concepts of I/O and file systems.

 

Course Outcome

CO1: Demonstrate the Structure, Components and its basic functionalities of Operating System

CO2: Distinguish various process management principles for given problem using appropriate tool

CO3: Elucidate the process synchronization mechanisms, deadlock environment and its solutions in the given processes

CO4: Inspect various memory management strategies for the given problems in memory systems

CO5: Build file structure to distribute the same across the memory.

Unit-1
Teaching Hours:9
INTRODUCTION
 

Introduction : What operating systems do, Computer System Architecture, Operating System Structure, Operating System Operations, Process Management, Memory Management, Storage Management, Protection and Security; System Structures: Operating System Services, User Operating System Interface, System Calls, Types of System Calls

Unit-2
Teaching Hours:9
PROCESS MANAGEMENT
 

Process Management: Process Concept, Process Scheduling, Operations on Processes, Inter-process Communication; Threads: Overview, Multithreading Models, Thread Libraries; CPU Scheduling: Basic Concepts, Scheduling Criteria, Scheduling Algorithms, Multiple- Processor Scheduling

Unit-3
Teaching Hours:9
PROCESS SYNCHRONIZATION AND DEADLOCKS
 

Process Synchronization: Background, The Critical Section Problem, Petersons Solution, Synchronization Hardware, Semaphores, Classical Problems of Synchronization, Monitors, Synchronization Examples, Deadlocks 

Unit-4
Teaching Hours:9
MEMORY MANAGEMENT AND VIRTUAL MEMORY
 

Memory Management: Background, Swapping, Contiguous Memory Allocation, Paging,

Virtual Memory: Background, Demand Paging, Copy on Write, Page Replacement, Allocation of frames, Thrashing, Allocating Kernel Memory

 

Unit-5
Teaching Hours:9
FILE SYSTEM INTERFACE AND FILE SYSTEM IMPLEMENTATION & MASS STORAGE STRUCTURE
 

File System Interface: File System: File Concept, Access Methods, Directory Structure, File System Mounting, File Sharing, Protection;

File System Implementation & Mass Storage Structure: Implementing File Systems: File System Structure, File System Implementation, Directory Implementation, Allocation Methods, Free Space Management. Disk structure, Disk Attachment, Disk Scheduling Methods, Disk Management, Swap-Space Management

Text Books And Reference Books:

1.  Abraham Silberschatz, Peter Baer Galvin and Greg Gagne, “Operating System Concepts”, Ninth Edition, John Wiley & Sons (ASIA) Pvt. Ltd, 2013.

Essential Reading / Recommended Reading

1.      Harvey M. Deitel, “Operating Systems”, Third Edition, Pearson Education Pvt. Ltd, 2007.

2.      Andrew S. Tanenbaum, “Modern Operating Systems”, Prentice Hall of India Pvt. Ltd, 2009.

3.      William Stallings, “Operating System”, Pearson Education 2009

4.      Pramod Chandra P. Bhatt – “An Introduction to Operating Systems, Concepts and Practice”, PHI, 2010.

Evaluation Pattern

COURSES WITH THEORY AND PRACTICAL

 

Component

Assessed for

Minimum marks

 to pass

Maximum

marks

1

Theory CIA

30

-

30

2

Theory ESE

30

12

30

3

Practical CIA

35

14

35

4

Attendance

05

-

05

4

Aggregate

100

40

100

 

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

CS433P - PROGRAMMING PARADIGM (2019 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Software development in business environment has become more sophisticated, the software implementation is becoming increasingly complex and requires the best programming paradigm which helps to eliminate complexity of large projects. Object Oriented Programming (OOP) has become the predominant technique for writing software at present. Many other important software development techniques are based upon the fundamental ideas captured by object-oriented programming. The course also caters to the understanding of event driven programming, generic programming and concurrent programming. By the end of this COURSE, the student should acquire the basic knowledge and skills necessary to implement the concepts of various programming paradigms.

Course Outcome

CO1:   Demonstrate the fundamental concepts of Object Oriented Programming.(L2)

CO2:   Make use of the inheritance and interface concepts for effective code reuse.(L3)

CO3:   Inspect dynamic and interactive graphical applications using AWT and SWING.(L4)

CO4:   Build an application using generic programming and exception handling concepts.(L6)

CO5:   Assess and design concurrent and parallel applications using multithreaded concepts.(L5)

Unit-1
Teaching Hours:9
OBJECT-ORIENTED PROGRAMMING - FUNDAMENTALS
 

Review of OOP - Objects and classes in Java – defining classes – methods - access specifiers – static members – constructors – finalize method – Arrays – Strings - Packages – JavaDoc comments. 

Unit-2
Teaching Hours:9
OBJECT-ORIENTED PROGRAMMING - INHERITANCE
 

Inheritance – class hierarchy – polymorphism – dynamic binding – final keyword – abstract classes – the Object class – Reflection – interfaces – object cloning – inner classes.

Unit-3
Teaching Hours:9
EVENT-DRIVEN PROGRAMMING
 

Graphics programming – Frame – Components – working with 2D shapes – Using color, fonts, and images - Basics of event handling – event handlers – adapter classes – actions – mouse events – AWT event hierarchy – introduction to Swing – Model-View- Controller design pattern – buttons – layout management – Swing Components

Unit-4
Teaching Hours:9
GENERIC PROGRAMMING
 

Motivation for generic programming – generic classes – generic methods – generic code and virtual machine – inheritance and generics – reflection and generics – exceptions – exception hierarchy – throwing and catching exceptions.

Unit-5
Teaching Hours:9
CONCURRENT PROGRAMMING
 

Multi-threaded programming – interrupting threads – thread states – thread properties – thread synchronization – thread-safe Collections – Executors – synchronizers – threads and event-driven programming, Parallel programming –fork, join framework.

Text Books And Reference Books:

1.              Herbert Schildt, “Java The Complete Reference” , Ninth Edition, McGraw Hill Publishers 2014.

2.              Cay S. Horstmann and Gary Cornell, “Core Java: Volume I – undamentals”, Eighth Edition, Sun Microsystems Press, 2008.

Essential Reading / Recommended Reading

1.              Paul Deitel and Harvey Deitel , “Java How to program”, Tenth Edition, Deitel, 2016.

2.             Ivan BratikoPROLOG: Programming for Artificial Intelligence, Third Edition, Pearson Publisher, 2002.

3.            Bruce Eckel, “Thinking in Java”, 4th Edition, February 20, 2006.

4.          Doug Rosenberg, Matt Stephens, “Use Case Driven Object Modeling with UML: Theory and Practice (Expert's Voice in UML Modeling)”, January 16, 2013.

Evaluation Pattern

COURSES WITH THEORY AND PRACTICAL

 

Component

Assessed for

Minimum marks

 to pass

Maximum

marks

1

Theory CIA

30

-

30

2

Theory ESE

30

12

30

3

Practical CIA

35

14

35

4

Attendance

05

-

05

4

Aggregate

100

40

100

 

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

CS434 - FORMAL LANGUAGE AND AUTOMATA THEORY (2019 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To have an understanding of finite state and pushdown automata.

To have a knowledge of regular languages and context free languages.

To know the relation between regular language, context free language and corresponding recognizers.

To study the Turing machine and classes of problems.

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Design finite automata with conversion between types of finite automata.

L2

2.

Develop regular expression and minimize the given finite automata for any regular language.

L3

3.

Develop context free grammar, parse trees and pushdown automata for a given context free language.

L3

4.

Experiment with CFLs and design of Turing machine for a given language.

L3

5.

Explain decidable and undecidable problems, solvable and unsolvable problems with their complexity analysis.

L2

Unit-1
Teaching Hours:8
Automaton
 

Introduction to formal proof – Additional forms of proof – Inductive proofs –Finite Automata (FA) – Deterministic Finite Automata (DFA) – Non-deterministic Finite Automata (NFA) – Finite Automata with Epsilon transitions.

Unit-2
Teaching Hours:10
Regular Expressions and Languages
 

Regular Expression  – FA and Regular Expressions – Proving languages not to be regular – Closure properties of regular languages – Equivalence and minimization of Automata.

Unit-3
Teaching Hours:10
Context-Free Grammar and Languages
 

Context-Free Grammar (CFG) – Parse Trees – Ambiguity in grammars and languages – Definition of the Pushdown automata – Languages of a Pushdown Automata – Equivalence of Pushdown automata and CFG, Deterministic Pushdown Automata.

Unit-4
Teaching Hours:9
Properties of Context-Free Languages
 

Normal forms for CFG – Pumping Lemma for CFL - Closure Properties of CFL – Turing Machines – Programming Techniques for TM.

Unit-5
Teaching Hours:8
Undecidability
 

A language that is not Recursively Enumerable (RE) – An undecidable problem that is RE – Undecidable problems about Turing Machine – Post’s Correspondence Problem - The classes P and NP.

Text Books And Reference Books:

1. J.E.Hopcroft, R.Motwani and J.D Ullman, “Introduction to Automata Theory, Languages and Computations”, Pearson Education, 200

Essential Reading / Recommended Reading

R1. H.R. Lewis and C.H. Papadimitrou, “Elements of the Theory of Computation”, Second Edition, Pearson Education/PHI, 2003

R2. J.Martin, “Introduction to Languages and the Theory of Computation”, Third Edition, TMH,       2003.

R3. Michael Sipser, “Introduction of the Theory and Computation”, Thomson Brokecole, 1997. 

Evaluation Pattern

Assessment of each paper 

  • Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) 
  • End Semester Examination(ESE): 50% (50 marks out of 100 marks)

 Components of the CIA

  • CIA I: Quiz and Assignment                          : 10 marks
  • CIA II: Mid Semester Examination (Theory) : 25 marks
  • CIA III: Closed Book Test and  Assignment  : 10 marks
  • Attendance                                                           : 05 marks

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA 1 Assignment

1

CO 1

2

2

CIA 1 Test

2

CO 2

2

3

MSE

1,2,3

CO 1, CO 2, CO3

3

4

CIA 3 Test

4

CO 4

2

5

CIA 3 Assignment

5

CO 5

2

CS435P - COMPUTER ORGANIZATION AND ARCHITECTURE (2019 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course will help the students to learn about basic structure of computer system, design of arithmetic and logic unit with the implementation of fixed and floating point numbers. Further, it will give knowledge about design of control unit and pipelined processing concepts. It discusses about various parallel processing architectures, different memory systems and I/O Communication systems

Course Outcome

CO1: Demonstrate the functions of basic components of computer system and Instruction set Architecture

CO2: Identify suitable control unit design and pipelining principles in computer architecture design

CO3: Utilize appropriate instruction level parallelism concepts in multiprocessing environment

CO4: Select suitable arithmetic algorithm to solve given arithmetic and logical problems

CO5: Choose suitable memory and I/O system design

Unit-1
Teaching Hours:9
FUNDAMENTALS OF COMPUTER SYSTEM
 

Functional Units – Basic Operational Concepts – Performance – Instructions: Language of the Computer – Operations, Operands – Instruction representation – Logical operations – decision making – MIPS Addressing.

 

Unit-2
Teaching Hours:9
COMPUTER ARTHIMETIC
 

Addition and Subtraction – Multiplication – Division – Floating Point Representation – Floating Point Operations – Subword Parallelism

 

Unit-3
Teaching Hours:9
BASIC PROCESSING AND CONTROL UNIT
 

A Basic MIPS implementation – Building a Datapath – Control Implementation Scheme – Pipelining – Pipelined datapath and control – Handling Data Hazards & Control Hazards – Exceptions.

 

Unit-4
Teaching Hours:9
PARALLELISM
 

Parallel processing challenges – Flynn‘s classification – SISD, MIMD, SIMD, SPMD, and Vector Architectures - Hardware multithreading – Multi-core processors and other Shared Memory Multiprocessors - Introduction to Graphics Processing Units, Clusters, Warehouse Scale Computers and other Message-Passing Multiprocessors.

 

Unit-5
Teaching Hours:9
MEMORY AND I/O
 

Memory Hierarchy - memory technologies – cache memory – measuring and improving cache performance – virtual memory, TLB‘s – Accessing I/O Devices – Interrupts – Direct Memory Access – Bus structure – Bus operation – Arbitration – Interface circuits - USB.

 

Text Books And Reference Books:

T1. David A. Patterson and John L. Hennessy, “Computer Organization and Design: The Hardware/Software Interface”, Fifth Edition, Morgan Kaufmann / Elsevier, 2014.

T2. Carl Hamacher, ZvonkoVranesic, SafwatZaky and NaraigManjikian, “Computer Organization and Embedded Systems”, Sixth Edition, Tata McGraw Hill, 2012.

Essential Reading / Recommended Reading

R1. William Stallings, “Computer Organization and Architecture – Designing for Performance”, Eighth Edition, Pearson Education, 2010.

R2.  John L. Hennessey and David A. Patterson, “Computer Architecture – A Quantitative Approach”, Fifth Edition, Morgan Kaufmann / Elsevier Publishers, 2012.

Evaluation Pattern

COURSES WITH THEORY AND PRACTICAL

 

Component

Assessed for

Minimum marks

 to pass

Maximum

marks

1

Theory CIA

30

-

30

2

Theory ESE

30

12

30

3

Practical CIA

35

14

35

4

Attendance

05

-

05

4

Aggregate

100

40

100

 

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

CS436 - PROFESSIONAL ETHICS (2019 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Able to understand the importance of Values and Ethics in their personal and professional lives.

Able to learn the rights and responsibilities as an employee, team member and a global citizen.

Course Outcome

CO1:  Describe the values of Ethics in our lives.

CO2: Illustrate the professional Ethical theories

CO3 Discuss the ethics in working place.

CO4: Investigate the need of ethics as global issue.

CO5: Summarize professional ethics, professional rights , responsibilitiesof an engineer.

Unit-1
Teaching Hours:9
Introduction to Professional Ethics
 

Basic Concepts, Governing Ethics, Personal & Professional Ethics, Ethical Dilemmas, Life Skills, Emotional Intelligence, Thoughts of Ethics, Value Education, Dimensions of Ethics, Profession and professionalism, Professional Associations, Professional Risks, Professional Accountabilities, Professional Success, Ethics and Profession.

Unit-2
Teaching Hours:9
Ethical Theories
 

Ethical Theories: Basic Ethical Principles, Moral Developments, Deontology, Utilitarianism, Virtue Theory, Rights Theory, Casuist Theory, Moral Absolution, Moral Rationalism, Moral Pluralism, Ethical Egoism, Feminist Consequentialism, Moral Issues, Moral Dilemmas, Moral Autonomy.

Unit-3
Teaching Hours:9
Ethics in Engineering profession
 

Professions and Norms of Professional Conduct, Norms of Professional Conduct vs. Profession; Responsibilities, Obligations and Moral Values in Professional Ethics, Professional codes of ethics, the limits of predictability and responsibilities of the engineering profession. Central Responsibilities of Engineers – The Centrality of Responsibilities of Professional Ethics; lessons from 1979 American Airlines DC-10 Crash and Kansas City Hyatt Regency Walk away Collapse.

Unit-4
Teaching Hours:9
Work Place Rights & Responsibilities
 

Ethics in changing domains of Research, Engineers and Managers; Organizational Complaint Procedure, difference of Professional Judgment within the

Nuclear Regulatory Commission (NRC), the Hanford Nuclear Reservation. Ethics in changing domains of research – The US government wide definition of research misconduct, research misconduct distinguished from mistakes and errors, recent history of attention to research misconduct, the emerging emphasis on understanding and fostering responsible conduct, responsible authorship, reviewing & editing.

Unit-5
Teaching Hours:9
Global issues
 

Introduction – Current Scenario, Technology Globalization of MNCs, International Trade, World Summits, Issues, Business Ethics and Corporate Governance, Sustainable Development Ecosystem, Energy Concerns, Ozone Deflection, Pollution, Ethics in Manufacturing and Marketing, Media Ethics; War Ethics; Bio Ethics, Intellectual Property Rights.

Text Books And Reference Books:

T1.       Professional Ethics: R. Subramanian, Oxford University Press, 2015.

T2.       Ethics in Engineering Practice & Research, Caroline Whitbeck, 2e, Cambridge University Press 2015.

Essential Reading / Recommended Reading

R1.Engineering Ethics, Concepts Cases: Charles E Harris Jr., Michael S Pritchard, Michael J Rabins, 4e ,Cengage learning, 2015.

R2. Business Ethics concepts & Cases: Manuel G Velasquez, 6e, PHI, 2008

Evaluation Pattern

Assessment of each paper

Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks)

 End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                       : 25 marks                  

CIA II  :  Assignments                                                              : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

                       Attendance                                                             : 05 marks

                                   Total                                                                   : 50 marks

MIMBA431 - ORGANISATIONAL BEHAVIOUR (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:6
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course Description: The course is offered as a mandatory core course for all students in Trimester II.  The course introduces students to a comprehensive set of concepts and theories, facts about human behaviour and organizations that have been acquired over the years. The subject focuses on ways and means to improve productivity, minimize absenteeism, increase employee engagement and so on thus, contributing to the overall effectiveness. The basic discipline of the course is behavioral science, sociology, social psychology, anthropology and political science.

Course Objectives: To make sense of human behaviour, use of common sense and intuition is largely inadequate because human behaviour is seldom random. Every human action has an underlying purpose which was aimed at personal or societal interest. Moreover, the uniqueness of each individual provides enough challenges for the managers to predict their best behaviour at any point of time. A systematic study of human behaviour looks at the consistencies, patterns and cause effect relationships which will facilitate understanding it in a reasonable extent. Systematic study replaces the possible biases of intuition that can sabotage the employee morale in organizations.

Course Outcome

Course Learning Outcomes: On having completed this course student should be able to:

At the end of the course the student will be able to:

CLO1: Determine the individual and group behavior in the workplace. 

CLO2: Assess the concepts of personality, perception and learning in Organizations. 

CLO3: Analyze various job-related attitudes. 

CLO4: Design motivational techniques for job design, employee involvement, incentives, rewards & recognitions. 

CLO5: Manage effective groups and teams in organizations.

 

Unit-1
Teaching Hours:12
Unit-1: Introduction to Organizational Behaviour
 

Historical Development, Behavioural sciences and Organizational behaviour, Meaning, Importance, Basic concepts, methods and tools for understanding behaviour, Challenges and Opportunities, OB model, ethical issues in organizational Behaviour.

Cross-cultural management, managing multicultural teams, communicating across cultures, OB in the digital age.

Unit-2
Teaching Hours:12
Unit-2: Individual Behaviour ? Personality, Perception and Learning
 

Personality:  Foundations of individual behaviour, Personality, Meaning and Importance, Development of personality, Determinants of personality, Theories of personality, Relevance of personality to managers.

Perception: Nature, Importance and Definition of Perception, Factors involved in perception, The Perceptual Process, Perceptual Selectivity and Organization, Applications in Organizations.

Learning: Definition and Importance, Theories of learning, Principles of learning, Shaping as managerial tool.

Unit-3
Teaching Hours:12
Unit-3: Attitudes, Values & Job Satisfaction
 

Attitudes: Sources and types of attitudes, Attitude formation and change, Cognitive Dissonance Theory. Effects of employee attitude, Job related attitudes

Values: meaning, importance, source and types, and applications in organizations.

Job satisfaction: Measuring Job Satisfaction, Causes of Job Satisfaction, impact of satisfied and dissatisfied employees on the workplace.

Unit-4
Teaching Hours:12
Unit-4: Motivation
 

Meaning, process and significance of motivation, Early Theories of motivation: Hierarchy of Needs, Theory X Theory Y, Two Factor theory, McClelland Theory of Needs, Contemporary Theories of Motivation: Goal Setting theory, Self-Efficacy theory, Equity theory/Organizational justice, Expectancy theories, Motivation theories applied in organizations: Job design, employee involvement, rewards and global implications

Unit-5
Teaching Hours:12
Unit-5: Groups & Teams
 

Groups: Meaning, classification and nature of groups, Stages of group development, an alternative model for Temporary Groups with punctuated equilibrium model, Group properties: Roles, Norms, Status, Size and Cohesiveness, Group decision making.

Teams: Meaning of teams, Types of teams, Creating Effective teams, what makes individuals into effective team players, Team development, Team decision making. 

Text Books And Reference Books:

Core Text Books:

T1. Robbins, S P., Judge, T A and Vohra, N (2016).  Organizational Behavior. 16th Edition, Prentice Hall of India.

Essential Reading / Recommended Reading

Rao V S P & V Sudeep 2018, Managing Organisational Behavior, Trinity Press, 3rd edition, New Delhi.

Evaluation Pattern

Test & Exam

Exam conducted for

Marks conversion

Weightage

Total

CIA – I

20

10

20%

10

CIA – II

50

25

25%

25

CIA – III

20

10

10%

10

Attendance

5

5%

5

CIA – I, II, and III

 

50

50%

50

End – term

100

50

50%

50

Total

100

MIPSY431 - PEOPLE THOUGHTS AND SITUATIONS (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 is an exploration of the prevailing theories and empirical methods that explain about people’s thoughts, feelings and behaviors in a social context. This throws light on cognitive and social factors that influence human behavior, especially in situations populated by others. 

Objectives

  1. To understand different ways of thinking about people and the perception of self in social situations
  2. To comprehend factors of affect related to cognition in a social context
  3. To develop knowledge about the dynamics of person in different situation in a social living

Course Outcome

At the end of the course students will be able:

  1. To understand the thinking patterns of people and the perception of self in various cultural contexts
  2. To comprehend factors of affect related to cognition in a social context
  3. To inculcate dynamics of person in different situation
  4. To evaluate the person and situation by using psychometric tests

Unit-1
Teaching Hours:12
Introduction to Self
 

Definition, Person perception; Self-concept; Self-presentation; Self-esteem.

Unit-2
Teaching Hours:12
Affect and Cognition
 

Emotions - Positive and negative affect; Thoughts and expressions; Selective attention; Information processing; Memory; Cognitive appraisal; Judgment and Decision Making; Problem Solving.

Practicum: Decision making & Problem Solving scale

 

Unit-3
Teaching Hours:12
The Person in the Situation - I
 

Justifying our actions, Social Relations: Stereotypes; Prejudice: Definition and Types, Sources of Prejudice, Consequences of Prejudice; Strategies to reduce prejudice; Attribution, Attitude and Attitude Change.

Unit-4
Teaching Hours:12
The Person in the Situation - II
 

Aggression: Perspectives, Causes; Prevention and Control of Aggression; Pro-social Behavior.

Practicum: Pro-social behavior scale

Unit-5
Teaching Hours:12
Group Dynamics
 

Nature of Groups; Basic Processes, Group Performance, Group Decision Making; Group Interaction (Facilitation, Loafing)

Practicum: Sociometry

Text Books And Reference Books:

Myers, D.G (2002) Social Psychology,.New York: McGraw Hill Companies.

Baron, Robert A. and Byrne, D. (2001) .Social Psychology 8 th Edition (Reprint).New Delhi:Prentice-Hall of India Pvt Ltd.

Baumeister.R.F. and Bushman,B.J. (2008).Social Psychology and Human nature. Belmont,CA:Thomson Wadsworth

Essential Reading / Recommended Reading

Tuffin, K. (2005). Understanding critical social psychology. London: Sage Publications.

Brehm, S.S. and Kassin, SN. (1996) Social Psychology. Boston : Houghton Mifflin Company.

Crisp, R.J. and Turner, R.N. (2007), Essential Social Psychology. New Delhi: Sage Publications India Pvt., Ltd.

Taylor ,S .E, Peplau, L.A and Sears, D.O. (2006) Social Psychology. New Delhi: Pearson Prentice-Hall of India.

Misra, G., & Dalal, A. K. (2001). Social Psychology in India: Evolution and Emerging Trends. In K. A. Dala, & G. Misra, New Directions in Indian Psychology. New Delhi: Sage.

Evaluation Pattern

CIA Evaluation pattern

Group Assignment

Individual Assignment

Mid semester

20

20

25

 

Mid Semester Examination

Section A

(Definition)

Section B

(Short note)

Section C

(Essay)

Section D

(Case Question)

Total

5×2=10

4×5=20

1×10=10

1×10=10

50

 

End Semester Examination

Section A

(Definition)

Section B

(Short note)

Section C

(Essay)

Section D

(Case Question)

Total

5×2=10

4×5=20

1×10=10

1×10=10

50

CS531 - COMPUTER ORIENTED NUMERICAL ANALYSIS (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 
  • With the present development of computer technology, it is necessary to develop efficient algorithms for solving problems in science, engineering and technology.
  • This course gives a complete procedure for solving different kinds of problems occurring in engineering numerically.

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Illustrate the interpretation and convergence criteria of numerical errors in Linear Interpolation techniques

L2

2.

Solve a range of problems numerically based on Interpolation and Approximation.

L3

3.

Plan approximate solutions for Numerical Differentiation and Integration and to validate its effectiveness

L3

4.

Examine the accuracy of Initial Value problems for Ordinary Differential equations.

L4

5.

Evaluate mathematical models for Boundary value problems in ordinary and partial differential equations.

L5

Unit-1
Teaching Hours:9
SOLUTION OF EQUATIONS AND EIGENVALUE PROBLEMS
 

Introduction to Errors and their computations. Linear interpolation methods (method of false position) – Newton’s method, Solution of linear system by Gaussian elimination and Gauss-Jordon methods- Iterative methods: Gauss Jacobi and Gauss-Seidel methods- Inverse of a matrix by Gauss Jordon method – Eigenvalue of a matrix by power method

Unit-2
Teaching Hours:9
INTERPOLATION AND APPROXIMATION
 

Lagrangian Polynomials – Divided differences – Interpolating with a cubic spline – Newton’s forward and backward difference formulas.

Unit-3
Teaching Hours:9
NUMERICAL DIFFERENTIATION AND INTEGRATION
 

Derivatives from difference tables – Divided differences and finite differences –Numerical integration by trapezoidal and Simpson’s 1/3 and 3/8 rules – Romberg’s method – Two and Three point Gaussian quadrature formulas – Double integrals using trapezoidal and Simpson’s rules.    

Unit-4
Teaching Hours:9
INITIAL VALUE PROBLEMS FOR ORDINARY DIFFERENTIAL EQUATIONS
 

 Single step methods: Taylor series method – Euler and modified Euler methods – Fourth order Runge – Kutta method for solving first and second order equations – Multistep methods: Milne’s and Adam’s predictor and corrector methods. 

Unit-5
Teaching Hours:9
BOUNDARY VALUE PROBLEMS IN ORDINARY AND PARTIAL DIFFERENTIAL EQUATIONS
 

Finite difference solution of second order ordinary differential equation – Finite difference solution of one dimensional heat equation by explicit and implicit methods – One dimensional wave equation and two dimensional Laplace and Poisson equations- Case Studies - implement  concepts using python

Text Books And Reference Books:

1. JaanKiusalaas, “Numerical Methods in Engineering with Python”, Cambridge University Press; 3rd Edition, 2013. 

2. P. B. Patil, U. P. Verma , “Numerical Computational Methods” , Alpha Science Intl Ltd.,       Revised Edition  Reprint 2013.

3. J. N. Sharma , “Numerical Methods for Engineers and Scientists”, Alpha Science Intl Ltd., 2nd Edition Reprint 2008.

4. P. Dechaumphai, N. Wansophark , “Numerical Methods in Engineering Theories with MATLAB, Fortran, C and Pascal Programs”, Alpha Science Intl Ltd., 2015. 

5. E. Balagurusamy, “Numerical Methods”, Tata McGraw-Hill Pub. Co. Ltd, Reprint Edition, 2008.

 6. V. Rajaraman “Computer Oriented Numerical Methods”, PHI, 5th Edition.

Essential Reading / Recommended Reading

1. P. Kandasamy, K. Thilagavathy, and K. Gunavathy, “Numerical Methods”, S.Chand Co. Ltd., New Delhi, 2003.

 2. C.F Gerald, and P.O Wheatley, “Applied Numerical Analysis”, 6th Edition, Pearson Education Asia, New Delhi, 2002.

3. Burden, R.L and Faires, T.D., “Numerical Analysis”, 7th Edition, Thomson Asia Pvt. Ltd., Singapore, 2002.

Evaluation Pattern
  • Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

Components of the CIA:

CIA I : Assignment and Open Book Test: 10 marks

CIA II : Mid Semester Examination (Theory) : 25 marks

CIAIII: Case study and MCQ Quiz : 10 marks

● End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

  • Attendance : 05 marks

Total : 50 marks

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA – I – Component I

Assignment

UNIT – I, II

1, 2

3

 

 

CIA – I – Component II

Open Book Test

UNIT – I, II

1,2

2

2

CIA – II - MSE

UNIT -I, II & PART OF III

1 TO 3

3

3

CIA – III-Component I

Case Study Implementation

UNIT – REMAINING PART OF III, IV AND V

3, 4, 5

4 & 5

4

CIA III – Component II

MCQ Quiz

Unit IV and V

4,5

2

CS532E01 - FORMAL LANGUAGE AND AUTOMATA THEORY (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To have an understanding of finite state and pushdown automata.

To have a knowledge of regular languages and context free languages.

To know the relation between regular language, context free language and corresponding recognizers.

To study the Turing machine and classes of problems.

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Design finite automata with conversion between types of finite automata.

L2

2.

Develop regular expression and minimize the given finite automata for any regular language.

L3

3.

Develop context free grammar, parse trees and pushdown automata for a given context free language.

L3

4.

Experiment with CFLs and design of Turing machine for a given language.

L3

5.

Explain decidable and undecidable problems, solvable and unsolvable problems with their complexity analysis.

L2

Unit-1
Teaching Hours:8
Automaton
 

Introduction to formal proof – Additional forms of proof – Inductive proofs –Finite Automata (FA) – Deterministic Finite Automata (DFA) – Non-deterministic Finite Automata (NFA) – Finite Automata with Epsilon transitions.

Unit-2
Teaching Hours:10
Regular Expressions and Languages
 

Regular Expression  – FA and Regular Expressions – Proving languages not to be regular – Closure properties of regular languages – Equivalence and minimization of Automata.

Unit-3
Teaching Hours:10
Context-Free Grammar and Languages
 

Context-Free Grammar (CFG) – Parse Trees – Ambiguity in grammars and languages – Definition of the Pushdown automata – Languages of a Pushdown Automata – Equivalence of Pushdown automata and CFG, Deterministic Pushdown Automata.

Unit-4
Teaching Hours:9
Properties of Context-Free Languages
 

Normal forms for CFG – Pumping Lemma for CFL - Closure Properties of CFL – Turing Machines – Programming Techniques for TM.

Unit-5
Teaching Hours:8
Undecidability
 

A language that is not Recursively Enumerable (RE) – An undecidable problem that is RE – Undecidable problems about Turing Machine – Post’s Correspondence Problem - The classes P and NP.

Text Books And Reference Books:

1. J.E.Hopcroft, R.Motwani and J.D Ullman, “Introduction to Automata Theory, Languages and Computations”, Pearson Education, 200

Essential Reading / Recommended Reading

R1. H.R. Lewis and C.H. Papadimitrou, “Elements of the Theory of Computation”, Second Edition, Pearson Education/PHI, 2003

R2. J.Martin, “Introduction to Languages and the Theory of Computation”, Third Edition, TMH,       2003.

R3. Michael Sipser, “Introduction of the Theory and Computation”, Thomson Brokecole, 1997. 

Evaluation Pattern

Assessment of each paper 

  • Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) 
  • End Semester Examination(ESE): 50% (50 marks out of 100 marks)

 Components of the CIA

  • CIA I: Quiz and Assignment                          : 10 marks
  • CIA II: Mid Semester Examination (Theory) : 25 marks
  • CIA III: Closed Book Test and  Assignment  : 10 marks
  • Attendance                                                           : 05 marks

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA 1 Assignment

1

CO 1

2

2

CIA 1 Test

2

CO 2

2

3

MSE

1,2,3

CO 1, CO 2, CO3

3

4

CIA 3 Test

4

CO 4

2

5

CIA 3 Assignment

5

CO 5

2

CS532E02 - COMPILER DESIGN (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To have the better understanding of: 

  • Design principles of a Compiler. 
  • Various parsing techniques 
  • Different levels of translation
  • Optimization  and  generation of machine codes

 

 

Course Outcome

At end of the course, the students will able to 

CO1 - Explain the concepts and different phases of compilation with Compiler Construction Tools.

CO2 - Interpret language tokens using regular expressions and design lexical analyzer for a language.

CO3 - Build top down parsing, bottom up parsing and parse tree representation of the input.

CO4 - Outline intermediate code for the statements during the process of compilation.

CO5 - Experiment the optimization techniques to intermediate code and generate machine code for high level language program.

Unit-1
Teaching Hours:9
INTRODUCTION TO COMPILERS
 

 

Translators-Compilation and Interpretation-Language processors -The Phases of Compiler-Errors encountered in Different Phases-The Grouping of Phases-Compiler Construction Tools - Programming Language basics.

Unit-2
Teaching Hours:9
LEXICAL ANALYSIS
 

 

Need and Role of Lexical Analyzer-Lexical Errors-Expressing Tokens by Regular Expressions- Converting Regular Expression to DFA- Minimization of DFA-Language for Specifying Lexical Analyzers-LEX-Design of Lexical Analyzer for a sample Language.

Unit-3
Teaching Hours:9
SYNTAX ANALYSIS
 

Need and Role of the Parser-Context Free Grammars -Top Down Parsing -General Strategies- Recursive Descent Parser Predictive Parser-LL(1) Parser-Shift Reduce Parser-LR Parser-LR (0)Item- Construction of SLR Parsing Table -Introduction to LALR Parser - Error Handling and Recovery inSyntax Analyzer-YACC-Design of a syntax Analyzer for a Sample Language

Unit-4
Teaching Hours:9
SYNTAX DIRECTED TRANSLATION & RUN TIME ENVIRONMENT
 

Syntax directed Definitions-Construction of Syntax Tree-Bottom-up Evaluation of S-Attribute Definitions- Design of predictive translator - Type Systems-Specification of a simple type checker- Equivalence of Type Expressions-Type Conversions.

 

RUN-TIME ENVIRONMENT: Source Language Issues-Storage Organization-Storage Allocation- Parameter Passing-Symbol Tables-Dynamic Storage Allocation-Storage Allocation in FORTAN.

Unit-5
Teaching Hours:9
CODE OPTIMIZATION AND CODE GENERATION
 

 

Principal Sources of Optimization-DAG- Optimization of Basic Blocks-Global Data Flow Analysis- Efficient Data Flow Algorithms-Issues in Design of a Code Generator - A Simple Code Generator Algorithm.

Text Books And Reference Books:

 

  1. Alfred V Aho, Monica S. Lam, Ravi Sethi and Jeffrey D Ullman, “Compilers – Principles, Techniques and Tools”, 2nd Edition, Pearson Education, 2007

 

Essential Reading / Recommended Reading
  1. Randy Allen, Ken Kennedy, “Optimizing Compilers for Modern Architectures: A Dependence-based Approach”, Morgan Kaufmann Publishers, 2002.
  2. Steven S. Muchnick, “Advanced Compiler Design and Implementation, “Morgan Kaufmann Publishers - Elsevier Science, India, Indian Reprint 2003.
  3. Keith D Cooper and Linda Torczon, “Engineering a Compiler”, Morgan Kaufmann Publishers Elsevier Science, 2004.
Evaluation Pattern

Internal Assessment - 50 Marks

(CIA 1: 10 Marks, CIA 2: 25 Marks, CIA 3: 10 Marks, Attendance: 5 Marks)

End Semester Examination (ESE) - 50 Marks

Total = 100 Marks

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

Closed Book Test

Unit 1 and 2

1, 2

L2

2

Assignment on DFA

Unit 2

2

L2

3

Closed Book Test

Unit 4

4

L3

4

Assignment on Code Optimization

Unit 5

5

L3

CS532E03 - FUZZY LOGIC (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To understand the concepts of:

  • Fuzzy sets
  • Knowledge representation using fuzzy rules
  • Approximate reasoning
  • Fuzzy inference systems, and fuzzy logic control
  • Machine intelligence applications of fuzzy logic.

Course Outcome

  

  1.  

Demonstrate Fuzzy Set concepts.

L2

  1.  

Construct suitable membership functions for a given problem.

L3

  1.  

Comprehend Fuzzy Logic and Fuzzy Arithmetic concepts.

L2

  1.  

Implement Fuzzy Inference and Approximate Reasoning for real time projects.

L3

  1.  

Develop in a team, Fuzzy Classification and Clustering using any programming tool.

L6

 

Unit-1
Teaching Hours:9
INTRODUCTION
 

Classical Sets and Fuzzy Sets: Background, Uncertainty and Imprecision, Statistics and Random Processes, Uncertainty in Information, Fuzzy Sets and Membership, Chance versus Ambiguity. Classical Sets - Operations on Classical Sets, Properties of Classical (Crisp) Sets, Mapping of Classical Sets to Functions.Fuzzy Sets.

Unit-2
Teaching Hours:9
MEMBERSHIP FUNCTIONS
 

Features of the Membership Function, Standard Forms and Boundaries, Fuzzification, Membership Value Assignments – Intuition, Inference, Rank Ordering, Angular Fuzzy Sets, Neural Networks, Genetic Algorithms, Inductive Reasoning. Fuzzy- To- Crisp Conversions: Lambda- Cuts for Fuzzy Sets, Lambda- Cuts for Fuzzy Relations, Defuzzification Methods. Extension Principle - Crisp Functions, Mapping and Relations, Functions of fuzzy 

Unit-3
Teaching Hours:9
FUZZY ARITHMETIC
 

Fuzzy Numbers, Interval Analysis in Arithmetic, Approximate Methods of Extension - Vertex method, DSW Algorithm, Restricted DSW Algorithm, Comparisons. Fuzzy Vectors. Classical Logic And Fuzzy Logic: Classical Predicate Logic – Tautologies, Contradictions, Equivalence, Exclusive OR and Exclusive NOR.

Unit-4
Teaching Hours:9
FUZZY RULE
 

Based Systems: Natural Language, Linguistic Hedges, Rule- Based Systems - Canonical Rule Forms, Decomposition of Compound Rules, Likelihood and Truth Qualification, Aggregation of Fuzzy Rules, Graphical Techniques of Inference.Fuzzy Decision Making.

Unit-5
Teaching Hours:9
FUZZY CLASSIFICATION
 

Classification by Equivalence Relations - Crisp Relations, Fuzzy Relations. Cluster Analysis, Cluster Validity, c-Means Clustering- Hard c-Means (HCM), Fuzzy c-Means (FCM). Classification Metric, Hardening the Fuzzy c-Partition, Similarity Relations from Clustering.

 

Text Books And Reference Books:

Text Books: 

1. Fuzzy Logic with Engineering Applications by Timothy J. Ross, McGraw- Hill, III Edition

Essential Reading / Recommended Reading

Reference Books:

  1. Guanrong Chen & Trung Tat Pham Introduction to Fuzzy Systems, Chapman & hall /CRC, 2006
  2. Driankov D., Hellendoorn H., Reinfrank M, An Introduction to Fuzzy Control., Narosa Publications ,1993.
  3. Robert Babuska, Fuzzy Modeling for Control, International Series in Intelligent Technologies, Kluwer Academic Publications, 1998
  4.  Ronald R Yager and Dimitar P Filev, Essentials of Fuzzy Modelling &Control., John Wiley & Sons, Inc, 2002.
  5.  J.-S.R.Jang, C.-T.Sun,E.Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, 1997. 
  6.  B.Kosko, Fuzzy Engineering, Prentice Hall, 1997
Evaluation Pattern

Internal Assessment - 50 Marks

(CIA 1: 10 Marks, CIA 2: 25 Marks, CIA 3: 10 Marks, Attendance: 5 Marks)

End Semester Examination (ESE) - 50 Marks

Total = 100 Marks

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

Quiz

Unit 1 and 2

1, 2

L2

3

Problem Solving

Unit 3

3

L2

4

Literature Review

Applications of Fuzzy Rules

4

L3

5

Development  and Implementation

Unit 5

5

L3

CS533P - INTERNET OF THINGS (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course introduces

[1].  The basic concepts of IoT,

[2].  The functionalities of different types of sensors, actuators and micro controllers.

[3].  Covers the protocols used in different layers and gives insight on programming IoT for different domains.

Course Outcome

Sl.No

Description

Revised Blooms Taxonomy (RBT Level)

CO1

Explain the fundamental building blocks of an IoT environment from a logical and physical perspective.

L2

CO2

Summarize various IoT protocols in Application and Network layers by outlining their advantages and disadvantages

L2

CO3

Develop programming skills to design IoT solutions using Arduino and Raspberry Pi to solve real life problems

L3

CO4

Experiment with Arduino, CDAC, and Raspberry Pi to choose the appropriate hardware for different IoT projects

L3

CO5

Survey successful IoT products and solutions to analyze their architecture and technologies

L4

Unit-1
Teaching Hours:15
INTRODUCTION AND BACKGROUND
 

Definition and Characteristics of IoT, Physical Design of IoT: Things in IoT, Logical Design of IoT: IoT functional Blocks, IoT Communication Blocks, IoT communication APIs, IoT Enabling Technologies: WSN, Cloud Computing, Big Data Analysis, Communication Protocols, Embedded Systems.

 Labs:

[1].   Controlling LEDs blinking pattern through UART

[2].   On-chip Temperature measurement through ADC.

Unit-2
Teaching Hours:15
IOT HARDWARE, DEVICES AND PLATFORMS
 

Basics of Arduino: The Arduino Hardware, The Arduino IDE, Basic Arduino Programming, Basics of Raspberry pi: Introduction to Raspberry Pi, Programming with Raspberry Pi, CDAC IoT devices: Ubimote, Wi-Fi mote, BLE mote, WINGZ gateway, Introduction to IoT Platforms, IoT Sensors and actuators.

Labs:

[1].  Communication of two Motes over the radio frequency

[2].  Generation of alarm through Buzzer.

Unit-3
Teaching Hours:15
IOT ARCHITECTURE AND PROTOCOLS
 

IoT Architecture: Web of Things versus Internet of Things – Two Pillars of the Web – Unified Multitier WoT Architecture, Cloud Providers and Systems,The Cloud of Things Architecture. IoT Protocols: Application Protocols, Service Discovery Protocols, Infrastructure Protocols.

Labs:

[1].  Proximity detection with IR LED.

[2].  Demonstration of a Peer-to-Peer network topology using Coordinator and end device network device types

Unit-4
Teaching Hours:15
IOT PROGRAMMING
 

Arduino Programming: Serial Communications, Getting input from sensors, Visual, Physical and Audio Outputs, Remotely Controlling External Devices, Wireless Communication. Programming with Raspberry Pi: Basics of Python Programming, Python packages of IoT, IoT Programming with CDAC IoT devices.

Labs:

[1].  IP based sensor monitoring through Ubi-Sense

[2].  IP based lighting control through Data Acquisition Card

Unit-5
Teaching Hours:15
DOMAIN SPECIFIC IOT
 

Home automation, Smart cities,  Smart Environment, IoT in Energy, Logistics, Agriculture, Industry and Health & Life style secors. Case Studies: A Case study of Internet of Things Using Wireless Sensor Networks and Smartphones, Security Analysis of Internet-of-Things: A Case Study of August Smart Lock, OpenIoT platform.

Labs:

[1].  Transmitting the measured physical value from the UbiSense over the Air

[2].  Pushing data from device to cloud

Text Books And Reference Books:

[1].  Vijay Madisetti and ArshdeepBahga, “Internet of Things (A Hands-on-Approach)”, 1st Edition, VPT, 2014.

[2].  Margolis, Michael. “Arduino Cookbook: Recipes to Begin, Expand, and Enhance Your Projects. " O'Reilly Media, Inc.", 2011.

[3].  Monk, Simon. Raspberry Pi cookbook: Software and hardware problems and solutions. " O'Reilly Media, Inc.", 2016.

 

Essential Reading / Recommended Reading

[1].  The Internet of Things: Applications to the Smart Grid and Building Automation by – Olivier Hersent, Omar Elloumi and David Boswarthick – Wiley Publications -2012.

[2].  Honbo Zhou, “The Internet of Things in the Cloud: A Middleware Perspective”, CRC Press, 2012.

[3].  David Easley and Jon Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, Cambridge University Press, 2010.

[4].  Al-Fuqaha, Ala, et al. "Internet of things: A survey on enabling technologies, protocols, and applications." IEEE Communications Surveys & Tutorials 17.4 (2015): 2347-2376.

[5].  Tsitsigkos, Alkiviadis, et al. "A case study of internet of things using wireless sensor networks and smartphones." Proceedings of the Wireless World Research Forum (WWRF) Meeting: Technologies and Visions for a Sustainable Wireless Internet, Athens, Greece. Vol. 2325. 2012.

[6] Ye, Mengmei, et al. "Security Analysis of Internet-of-Things: A Case Study of August Smart Lock."

 

Evaluation Pattern

Evaluation Pattern:

  Assessment of each paper

Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks)

End Semester Examination (ESE): 30% (30 marks out of 100 marks)

  Marks

CIA I   : 10 Marks

CIA II  : 10 Marks

CIA III : 10 Marks

Lab      : 35 Marks

         Attendance : 05 Marks

      Total Marks:  70 Marks

           End Sem:  30 Marks

CS534 - DESIGN AND ANALYSIS OF ALGORITHMS (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 is designed to 

  • To introduce basic concepts of algorithms.

  • To introduce mathematical aspects and analysis of algorithms.

  • To introduce sorting and searching algorithms.

  • To introduce various algorithmic techniques.

  • To introduce algorithm design methods

Course Outcome

After completion of this course students are able to

 

SNO

DESCRIPTION

RBT LEVEL

1

Analyze a given software problem as an algorithm.

L4

2

Experiment whether the algorithm found is the most efficient.

L3

3

Formulate the time order analysis for an algorithm.

 L6

4

Formulate the space needs for the implementation of an algorithm.

L6

5

Prove the correctness of an algorithm.

L5

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern

Evaluation Pattern:

  Assessment of each paper

Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks)

End Semester Examination (ESE): 30% (30 marks out of 100 marks)

  Marks

CIA I   : 10 Marks

CIA II  : 10 Marks

CIA III : 10 Marks

Lab      : 35 Marks

         Attendance : 05 Marks

      Total Marks:  70 Marks

           End Sem:  30 Marks

CS535 - SOFTWARE ENGINEERING (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 
  • To be aware of different life cycle models and Requirement elicitation process
  • To understand various analysis modeling and specification, Architectural and detailed design methods
  • To learn Implementation methodologies and various testing strategies
  • To understand Project planning and management concepts using various models and appropriate CASE tools.

Course Outcome

Sl No

Description

Revised Bloom’s Taxonomy (RBT)Level

CO1

Explain the fundamental of Software development Life cycle and different software development process models.

L2

CO2

Build software requirements elicitation process and SRS document.

L6

CO3

Develop the software processes using various design techniques.

L3

CO4

Evaluate the different techniques of software testing and maintenance.

L5

CO5

Measure the cost estimation techniques and project scheduling methods in software development process.

L6

Unit-1
Teaching Hours:9
SOFTWARE PROCESS
 

Introduction –S/W Engineering Paradigm  – life cycle models (waterfall, incremental, spiral, WINWIN spiral, evolutionary, prototyping, object-oriented) - system engineering – computer-based system  – verification – validation – life cycle process – development process –system engineering hierarchy.

Unit-2
Teaching Hours:9
SOFTWARE REQUIREMENTS
 

Functional and non-functional - user – system –requirement engineering process – feasibility studies – requirements – elicitation – validation and management – software prototyping – prototyping in the software process – rapid prototyping techniques – user interface prototyping -S/W document. Agile methods, Extreme Programming, SCRUM.

Unit-3
Teaching Hours:9
DESIGN CONCEPTS AND PRINCIPLES
 

Design process and concepts – modular design – design heuristic – design model and document. Architectural design – software architecture – data design – architectural design – transform and transaction mapping – user interface design – user interface design principles. Real time systems - Real time software design – system design – real time executives – data acquisition system - monitoring and control system. SCM – Need for SCM – Version control – Introduction to SCM process – Software configuration items.

Unit-4
Teaching Hours:9
TESTING
 

Taxonomy of software testing – levels – test activities – types of s/w test – black box testing – testing boundary conditions – structural testing – test coverage criteria based on data flow mechanisms – regression testing – testing in the large. S/W testing strategies – strategic approach and issues - unit testing – integration testing – validation testing – system testing and debugging.

Unit-5
Teaching Hours:9
SOFTWARE PROJECT MANAGEMENT
 

Measures and measurements – S/W complexity and science measure – size measure – data and logic structure measure – information flow measure. Software cost estimation – function point models – COCOMO model- Delphi method.- Defining a Task Network – Scheduling – Earned Value Analysis – Error Tracking - Software changes – program evolution dynamics – software maintenance – Architectural evolution. Taxonomy of CASE tools – Case Study.

Text Books And Reference Books:
  1. Roger S. Pressman, Software engineering- A Practitioner’s Approach, McGraw-Hill International Edition, 6th Edition 2012.

Essential Reading / Recommended Reading
  1.  Anirban Basu, “Software Quality Assurance, Testing and Metrics”, First Edition, PHI Learning, 2015.
  2. Ian Sommerville, “Software engineering,” Pearson education Asia, 9th Edition, 2013.
  3. Pankaj Jalote- “An Integrated Approach to Software Engineering,” Narosa Publishing house, 2011.
  4. James F Peters and Witold Pedryez, “Software Engineering – An Engineering Approach”, John Wiley and Sons, New Delhi, 2010.
  5. Ali Behforooz and Frederick J Hudson, “Software Engineering Fundamentals”,  OUP India 2012.
Evaluation Pattern

Assessment of each paper 

  • Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) 
  • End Semester Examination(ESE): 50% (50 marks out of 100 marks)

 Components of the CIA

  • CIA I: Quiz and Assignment                          : 10 marks
  • CIA II: Mid Semester Examination (Theory) : 25 marks
  • CIA III: Closed Book Test and  Assignment  : 10 marks
  • Attendance                                                    : 05 marks

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA-1 Component- 1 Online Quiz 

UNIT-1

CO1

L2

2

CIA-1 Component-2 SRS Document Preparation

UNIT-2

CO2

L6

3

CIA-3 Component -1 Closed Book Test

UNIT-4

CO4

L3

4

CIA-3 Component-2 UML Based Assignment

UNIT-3

CO3,CO5

L3,L6

 

CS536P - INTERNET AND WEB PROGRAMMING (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

  

1.      To explain the use of HTML5 tags and web programming concepts.

2.      To illustrate the use of CSS3.

3.      To understand the basics of JavaScipt and implement Client-side scripts.

4.      To understand the use of MariaDB database and queries.

5.      To learn how to build server-side web applications using Node.js

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Build the basic web page using HTML concepts.

L5

2

Experiment with the concepts of CSS to build the web pages.

L4

3

Determine the usage of Java script scripts for making the effective web pages.

L5

4

Develop backend connection using MariaDB.

L3

5

Design web applications using platforms like Node.js

L5

Unit-1
Teaching Hours:15
HTML5
 

Contents:

Why HTML5 exists? Structuring a Web Page, Forms, Multimedia (video, audio) markup and APIs, Canvas, Data Storage, Drag & Drop, Messaging & Workers.

List of Experiments:

1.Creating static pages, forms [validating user i/p] using the features/attributes available in HTML5 and applying basic styling to the elements in an HTML5 page.

 

Unit-2
Teaching Hours:15
CSS3
 

Contents:

Understanding CSS and the Modern Web, Learning CSS Syntax and Adding Presentational Styles, Creating Styles Using Property Values, Adding Presentational Styles, Creating A Basic Page Structure, Understanding Display, Position, and Document Flow, Changing and styling fonts, Adding transitions and animations.

List of Experiments:

 1. Style text elements on an HTML5 page by using CSS3. Apply styling to block elements by using CSS3. Use CSS3  selectors to specify the elements to be styled in a Web application.

 

 

Unit-3
Teaching Hours:15
JAVASCRIPT
 

Contents:

Basic JavaScript Instructions, Functions, Methods & Objects, Decisions & Loops, Document Object Model, Events.

List of Experiments:

1. Implement graphical effects and transformations by using the new CSS3 properties.

 

Unit-4
Teaching Hours:15
NOSQL
 

Contents:

Installing MariaDB, Configuring MariaDB, MariaDB Security, MariaDB User Account Management, Using MariaDB.

List of Experiments:

1. Implementing JavaScript with HTML5. Writing Java Scripts to validate user inputs. Applying object-oriented programming concepts to Java script.

Unit-5
Teaching Hours:15
CASE STUDY ? Node.js
 

Contents:

The Node Module System, The Node Programming Model, Events and Timers, The Command Line Interface, The File System, Streams, Binary Data, Executing Code, Network Programming, HTTP.

List of Experiments:

  1. Connecting to Maria DB and doing CRUD operations.

Text Books And Reference Books:

TEXT BOOKS:

1.      Bruce Lawson, Remy Sharp, “Introducing HTML 5”, Pearson Education, 2011.

2.      Ian Lunn, “CSS3 Foundations”, Wiley Publishers, 2012.

3.      Jon Duckett, “JavaScript and JQuery: Interactive Front-End Web Development”, Wiley Publishers: 2014.

4.      Daniel Bartholomew, “Getting started with MariaDB”, 2013.

5.      Colin J. Ihrig, “Pro Node.js for Developers”, APRESS, 2013.

Essential Reading / Recommended Reading

REFERENCE BOOKS:

1.      Matt west, “HTML5 Foundations”, Wiley Publishers: 2012.

2.      Training Guide Programming in HTML5 with JavaScript and CSS3 (MCSD)   (Microsoft Press Training Guide), 2013.

3.      Elizabeth Castro, Bruce Hyslop, “HTML and CSS: Visual QuickStart Guide” 8th edition, 2013.

Evaluation Pattern

Assessment of each paper

Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks)

End Semester Examination (ESE): 30% (30 marks out of 100 marks)

 Marks

CIA I: 10 Marks

CIA II: 10 Marks

CIA III: 10 Marks

Lab: 35 Marks

Total Marks:  70 Marks

 End Sem:  30 Marks

CSHO531AIP - STATISTICAL FOUNDATION FOR ARTIFICIAL INTELLIGENCE (2018 Batch)

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

Course Objectives/Course Description

 

Course objectives:  

•Discuss the core concepts Statistical Analytics and Data manipulation

•Apply the basic principles, models, and algorithms supervised and unsupervised learning mechanisms.

•Analyse the structures and algorithms of regression methods

•Explain notions and theories associated to Convolutional Neural Networks

•Solve problems in High-Dimensional Regression

 

 

Course Outcome

•Understand and explain concepts associated to Statistical Analytics and Data manipulation L2

•Infer details of supervised and unsupervised learning mechanisms. L2

•Solve problems connected to regression methods. L3

•Analyse concepts of Convolutional Neural Networks. L4

• Appraise concepts of High-Dimensional Regression. L5

 

Unit-1
Teaching Hours:11
Statistical Analytics and Data manipulation
 

Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool.

Unit-2
Teaching Hours:11
Techniques for supervised and unsupervised learning
 

The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis.

Unit-3
Teaching Hours:11
Neural Networks
 

Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, 0 Computational Considerations.

Unit-4
Teaching Hours:11
Random Forests and Ensemble Learning
 

Definition of Random Forests, Details of Random Forests- Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.

Unit-5
Teaching Hours:11
High-Dimensional Problems: p ≫ N
 

Diagonal Linear Discriminant Analysis and Nearest Shrunken Centroids, Linear Classifiers with Quadratic Regularization, Linear Classifiers with L1 Regularization, Classification When Features are Unavailable, High-Dimensional Regression, Feature Assessment and the Multiple-Testing Problem

Text Books And Reference Books:

Text Books:

1.Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2017.

2.Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.

 

Essential Reading / Recommended Reading

Reference Books:

1.Ghahramani, Zoubin. "Probabilistic machine learning and artificial intelligence." Nature 521.7553 (2015): 452.

2.Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018.

3.Wu, James, and Stephen Coggeshall. Foundations of predictive analytics. Chapman and Hall/CRC, 2012.

4.Marcoulides, George A., and Scott L. Hershberger. Multivariate statistical methods: A first course. Psychology Press, 2014.

5.Morgan, George A., et al. IBM SPSS for introductory statistics: Use and interpretation. Routledge, 2012

 

Evaluation Pattern

Assessment of each paper

Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks)

End Semester Examination (ESE): 30% (30 marks out of 100 marks)

 Marks

CIA I: 10 Marks

CIA II: 10 Marks

CIA III: 10 Marks

Lab: 35 Marks

Total Marks:  70 Marks

 End Sem:  30 Marks

CSHO531CSP - PROBABILITY AND RANDOM PROCESS (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

After learning the course for a semester, the student will be aware of the important statistical information for addressing cryptography, error correction and coding, information theory and cryptanalysis. The student would also get a clear idea on some of the cases with their analytical studies in information coding and its related fields.

Course Outcome

CO 1: To define pattern searching algorithms for different applications 

CO 2: To classify vulnerability of subsystem based on the information gathered from different resources 

CO 3: To estimate different optimized process and models 

CO 4: To provide means to find the similarities between the applications and vulnerabilities of the sub-system/system

CO 5: To analyze about best possible patterns to cluster the possible solutions for different vulnerabilities

Unit-1
Teaching Hours:11
 

Probability Fundamentals, Bayes’ rule, Markov chains and application to pattern search algorithms, Classical statistical inference, Bayesian statistical inference, Regression techniques

Unit-2
Teaching Hours:11
 

Information coding, Pseudorandom number generators, discrete random variables, special distributions and mixed random variables, link and rank analysis , probability bounds, limiting theorem and convergence 

Unit-3
Teaching Hours:11
 

Risk M Basics of statistical learning: models, regression, curse of dimensionality, overfitting, etc. Optimization and convexity, Gradient descent, Newton’s method 

Unit-4
Teaching Hours:11
 

Classification and similarity analysis, linear discriminative analysis, regression analysis, iterative permutation analysis, Support vector machines, nearest neighbor and application of entropy

Unit-5
Teaching Hours:11
 

Clustering algorithms, graph analysis, pattern detection, Knowledge driven system design, learning with errors, Basics of neural networks

Text Books And Reference Books:

1.Gnedenko, Boris V. Theory of probability. Routledge, 2018.

2.Beichelt, Frank. Applied Probability and Stochastic Processes. Chapman and Hall/CRC, 2016.

3.Li, X. Rong. Probability, random signals, and statistics. CRC press, 2017

Essential Reading / Recommended Reading

1.Grimmett, Geoffrey, Geoffrey R. Grimmett, and David Stirzaker. Probability and random processes. Oxford university press, 2001.

2.Papoulis, Athanasios, and S. Unnikrishna Pillai. Probability, random variables, and stochastic processes. Tata McGraw-Hill Education, 2002.

3.Rozanov, Yu. Probability theory, random processes and mathematical statistics. Vol. 344. Springer Science & Business Media, 2012.

Evaluation Pattern

Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks) 

 

End Semester Examination(ESE) : 30% (30 marks out of 100 marks) 

 

Components of the CIA 

 

CIA I :Closed Book Test and Quiz: 10 marks 

 

CIA II :Mid Semester Examination (Theory): 10 marks 

 

CIA III :Closed Book Test and Quiz:10 marks 

 

Lab marks :35 marks

 

Attendance: 05 marks 

 

 

CSHO531DAP - STATISTICAL FOUNDATION FOR DATA ANALYTICS (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:50
Credits:4

Course Objectives/Course Description

 

•Discuss the core concepts Statistical Analytics and Data manipulation

•Apply the basic principles, models and algorithms supervised and unsupervised learning mechanisms.

•Analyse the structures and algorithms of regression methods

•Analyse the use of SVM in Data Science 

 •Explain notions and theories associated to Convolutional Neural Networks

 

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Understand and explain concepts associated to Statistical Analytics and Data manipulation

L2

2.

Infer details of supervised and unsupervised learning mechanisms.

L2

3.

Analyse concepts of Convolutional Neural Networks.

L4

4.

Appraise concepts of Support Vector Machine.

L5

5.

Solve problems using Random Forests and Ensemble Learning.

L3

Unit-1
Teaching Hours:9
Statistical Analytics and Data manipulation
 

Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool.

Statistical parameters (eg: Correlation analysis)

 

Unit-2
Teaching Hours:9
Techniques for supervised and unsupervised learning
 

The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis

Linear and polynomial Regression

Unit-3
Teaching Hours:9
Neural Networks
 

Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, Computational Considerations.

Prediction analysis (eg: Stocks)

 

Unit-4
Teaching Hours:9
Support Vector Machines and Flexible Discriminants
 

Introduction, The Support Vector Classifier, Support Vector Machines and Kernels, Generalizing Linear Discriminant Analysis, Flexible Discriminant Analysis, Penalized Discriminant Analysis, Mixture Discriminant Analysis

Time Series: predict web traffic

Unit-5
Teaching Hours:9
Random Forests and Ensemble Learning
 

Definition of Random Forests, Details of Random Forests- Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.

 

Convolutional Neural Network - Step by Step

Text Books And Reference Books:
  1. Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2017.
  2. Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,2016.

 

Essential Reading / Recommended Reading
  1. Ghahramani, Zoubin. "Probabilistic machine learning and artificial  intelligence."  Nature 521.7553 (2015): 452.
  2. Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018.
  3. Wu, James, and Stephen Coggeshall. Foundations of predictive analytics. Chapman and Hall/CRC, 2012.
  4. Marcoulides, George A., and Scott L. Hershberger. Multivariate statistical methods: A first course. Psychology Press, 2014.
  5. Morgan, George A., et al. IBM SPSS for introductory statistics: Use and interpretation. Routledge, 2012.

 

Evaluation Pattern

Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks)

End Semester Examination(ESE) : 30% (30 marks out of 100 marks)

Components of the CIA

CIA I :Closed Book Test and Quiz: 10 marks

CIA II :Mid Semester Examination (Theory): 10 marks

CIA III :Closed Book Test and Quiz:10 marks

Lab marks :35 marks

Attendance: 05 marks

 

 

 

1)      CIA ASSESSMENT DETAILS - THEORY

 

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA – 1

1.Closed Book Test

Unit 1.

CO1

L2

2

CIA -1

2.Quiz

Unit 1 and 2

CO1 and CO2

L2

3

CIA-3

1. Closed Book Test

Unit 4 and 5

CO4 and CO5

L3,L5

4

CIA-3

2.Quiz

Unit 4 and 5.

CO4 and CO5

L3,L5

 

2)      LAB ASSESSMENT DETAILS

 

Sl No

Lab Component

CO

RBT Level

1

Mid Semester Examination for Lab

CO1, CO2 and CO3

L2,L4

2.

End Semester Examination for Lab

CO1,CO2,CO3,CO4 and CO5

L2,L3,L4,L5

 

CE636OE1 - SOLID WASTE MANAGEMENT (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course give  introduction to solid waste management, collection and transportation, treatment/processing techniques, incineration , composting, sanitary land filling, disposal methods, recycle and reuse.

 Objective of this course is to provide insight to manage  solid waste. It is designed as a source of information on solid waste management , includiing the principles of solid waste management , processing and treatment, final disposal, recycle and reuse

 

 

Course Outcome

CO1Define and explain important concepts in the field of solid waste management, such as waste hierarchy, waste prevention, recirculation, municipal solid waste etc.

CO2 Suggest and describe suitable technical solutions for biological and thermal treatment.

CO3Suggest, motivate and describe a way to tackle the problem from a system analysis approach.

CO4 Describe the construction and operation of a modern landfill according to the demands

CO5 Discuss social aspects connected to handling and recirculation of solid waste from a local as well as global perspective.

Unit-1
Teaching Hours:9
Sources
 

Classification and characteristics – municipal, commercial & industrial. Methods of quantification

Unit-1
Teaching Hours:9
Introduction
 

Definition, Land Pollution – scope and importance of solid waste management, functional elements of solid waste management. 

Unit-2
Teaching Hours:9
Collection and Transportation
 

Systems of collection, collection equipment, garbage chutes, transfer stations – bailing and compacting, route optimization techniques and problems.                               

Unit-3
Teaching Hours:9
Treatment/Processing Techniques
 

Components separation, volume reduction, size reduction, chemical reduction and biological processing problems.                     

Unit-3
Teaching Hours:9
Incineration
 

Process – 3 T’s, factors affecting incineration process, incinerators – types, prevention of air pollution, pyrolsis, design criteria for incineration.                              

Unit-4
Teaching Hours:9
Composting
 

Aerobic and anaerobic composting, factors affecting composting, Indore and Bangalore processes, mechanical and semi mechanical composting processes. Vermi composting.

Unit-4
Teaching Hours:9
Sanitary land filling
 

Different types, trench area, Ramp and pit method, site selection, basic steps involved, cell design, prevention of site  pollution, leachate & gas collection and control methods, geo-synthetic fabricsin sanitary landfills.   

Unit-5
Teaching Hours:9
Recycle and Reuse
 

Material and energy recovery operations, reuse in other industries, plastic wastes, environmental significance and reuse.     

Unit-5
Teaching Hours:9
Disposal Methods
 

Open dumping – selection of site, ocean disposal, feeding to hogs, incineration, pyrolsis, composting, sanitary land filling,  merits and demerits, biomedical wastes and disposal.

Text Books And Reference Books:

T1 Bhide and Sunderashan “Solid Waste Management in developing countries”,

T2 Tchobanoglous “Integrated Solid Waste Management”, Mc Graw Hill.

Essential Reading / Recommended Reading

R1. Peavy and Tchobanoglous “Environmental Engineering”,

R2. Garg S K “Environmental Engineering”, Vol II

R3. “Biomedical waste handling rules – 2000”.

R4. Pavoni J.L. “Hand book on Solid Waste Disposal”

Evaluation Pattern

Sl No.

Evaluation Component

Module

Duration

(min)

Nature of Component

Validation

1

CIA I

Quiz, assignment, & test

------

Closed Book/ Open book

Written test

2

CIA II

MSE

120

Closed Book

MSE

3

CIA  III

Seminar/assignment, Test

-----

Closed/Open Book

Seminar and test

4

Semester Exam

ESE

180

Closed Book

ESE

CE636OE2 - ENVIRONMENTAL IMPACT ASSESSMENT (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Over the past three decades, environmental impact assessment has been an important foundation for public and private development and planning decisions. In development disputes, the interaction between communities and government and special interests and the private sector shape the fabric of neighborhoods, cities and regions around the world. The objective of this paper is to create awareness about the environmental impact on the earth and for assessment among the student's community this paper has been introduced as elective.

Course Objectives:

1.     1. To study and understand the basics of EIA and the need for it and explain the step-by-step procedure for conducting EIA.

2.     2. To plan the framework of Impact Assessment with methodologies and techniques of EIA

3.     3. To explain the impact of activities on different environmental elements

4.     4. To study and develop the guideline for the projects and public participation in the decision-making process

5.     5. To understand the salient feature of the project activity and categorize various developmental activities.

Course Outcome

Course Outcomes:

Upon completion of Course the student would be able to

1.Outline need for EIA studies, Baseline information and Explain step-by-step procedure for conducting EIA

2. Plan the framework of Impact Assessment with methodologies and techniques of EIA

3. Assess the impact of activities on different elements of Environment.

4. Develop guidelines for projects and public participation in the decision-making process

5. Categorize various developmental activities and list salient features of the project activity

Unit-1
Teaching Hours:9
UNIT 1
 

Development Activity and Ecological Factors EIA, EIS, FONSI. Need for EIA Studies, Baseline Information, Step-by-step procedures for conducting EIA, Limitations of EIA

Unit-2
Teaching Hours:9
UNIT 2
 

Frame work of Impact Assessment. Development Projects-Environmental Setting, Objectives and Scope, Contents of EIA, Methodologies, Techniques of EIA.

Unit-3
Teaching Hours:9
UNIT 3
 

Assessment and Prediction of Impacts on Attributes Air, Water, Noise, Land Ecology, Soil, Cultural and Socio-economic Environment. EIA guidelines for Development Projects, Rapid and Comprehensive EIA

Unit-4
Teaching Hours:9
UNIT 4
 

EIA guidelines for Development Projects, Rapid and Comprehensive EIA. Public Participation in Environmental Decision making. Practical Considerations in preparing Environmental Impact Assessment and Statements

Unit-5
Teaching Hours:9
UNIT 5
 

Salient Features of the Project Activity-Environmental Parameter Activity Relationships- Matrices. EIA for Water resource developmental projects, Highway projects: Nuclear-Power plant projects, mining project (Coal, Iron ore).

Text Books And Reference Books:

  1. Anjaneyalu. Y“Environment Impact Assessment”,
  2. Jain R.K“Environmental Impact Analysis”, Van Nostrand Reinhold Co.
  3. “Guidelines for EIA of developmental Projects Ministry of Environment and Forests, GOI”,
  4. Larry W. Canter “Environment Impact Assessment”,Mc Graw Hill Publication.
Essential Reading / Recommended Reading

NEPA - National Environmental Protection agency reports on Various projects

Evaluation Pattern

·        

Assessment is based on the performance of the student throughout the semester.

Assessment of each paper

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I:  Assignments                                                     : 10 marks

CIA II:   Mid Semester Examination (Theory)          : 25 marks                              

CIA III: Quizzes/Seminar/Case Studies/Project Work : 10 marks

Attendance                                                                    : 05 marks

            Total                                                                                       : 50 marks

 

CE636OE4 - DISASTER MANAGEMENT (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

 

To study the emerging approaches in Disaster Reduction & Management. The emphasis will be on programmes of National & International organizations for Disaster preparedness, Mitigation and awareness to prevent or reduce losses that occur due to hazards, disaster and emergencies.

 

Course Outcome

CO1 : Explain Hazards and Disasters (L1, L2)

CO2 : Outline the managerial aspects of Disaster Management,  plan and explain risk analysis (L2,L3,L4,L5)

CO3 : Relate Disasters and Development (L3,L4)

CO4 : Classify climate change impacts and develop scenarios (L3,L4)

CO5: Categorize policies and institutional mechanisms in Disaster Management and the impacts on society (L3,L4,L5)

 

Unit-1
Teaching Hours:9
Types of Global Disasters
 

 

Principles of Disaster Management, Natural Disasters such as Earthquake, Floods, Fire, Landslides, Tornado, Cyclones, Tsunamis, Nuclear, Chemical, Terrorism, Extra Terrestrial and other natural calamities. Hazards, Risks and Vulnerabilities. Assessment of Disaster Vulnerability of a location and vulnerable groups, National policy on disaster Management,

 

Unit-2
Teaching Hours:10
Disaster Mitigation
 

 

Prevention, Preparedness and Mitigation measures for various Disasters, Post Disaster Relief & Logistics Management, Emergency Support Functions and their coordination mechanism, Resource & Material Management, Management of Relief Camp, Information systems & decision making tools, Voluntary Agencies & Community Participation at various stages of disaster management, Integration of Rural Development Programmes with disaster reduction and mitigation activities.

 

Unit-3
Teaching Hours:9
Renewable and Non-Renewable resources
 

 

Renewable and non-renewable resources, Role of individual in conservation of natural resources for sustainable life styles. Use and over exploitation of Forest resources, Deforestation, Timber extraction, Mining, Dams and their effects on forest and tribal people. Use and over exploitation of surface and ground water resources, Floods, Drought, Conflicts over water, Dams- benefits and problems. Causes, effects and control measures of Air pollution, Water pollution, soil pollution, Noise pollution, Thermal pollution, Nuclear hazards.

 

Unit-4
Teaching Hours:8
Global Environmental Issues
 

 

Global Environmental crisis, Current global environment issues, Global Warming, Greenhouse Effect, role of Carbon Dioxide and Methane, Ozone Problem, CFCs and Alternatives, Causes of Climate Change Energy Use: past, present and future, Role of Engineers.

 

Unit-5
Teaching Hours:9
Disaster Management organisations and Media
 

 

Mitigation- Institutions- the work of-. Meteorological observatory – Seismological observatory - Volcano logy institution - Hydrology Laboratory - Industrial Safety inspectorate - Institution of urban & regional planners -. Chambers of Architects. Engineering Council-. National Standards Committee

Integration of public policy: Planning and design of infrastructure for disaster management, Community based approach in disaster management, methods for effective dissemination of information, ecological and sustainable development models for disaster management.

Role of Media Monitoring Management- programme of disaster research &mitigation ofdisaster of following organizations. International Council for Scientific Unions (ICSU)- Scientific committee on problems of the Environment (SCOPE), International Geosphere-Biosphere programme (IGBP) – World federation of Engineering Organizations(WFED)-National Academy of Sciences-World Meteorological organizations(WMO)-Geographical Information System(GIS)- International Association of Seismology & Physics of Earth’s Interior(IASPEI)-Various U.N agencies like UNCRD, IDNDR, WHO, UNESCO, UNICEF,UNEP.

 

Text Books And Reference Books:

 

1.      Sharma, Dutt Varun; Pandey, S K; Sharma, Vimal Kumar “Environmental Education and Disaster ManagementCBS Publishers and Distributors, New Delhi, 2008.

2.      Shaw, Rajib; Krishnamurthy, R R. “Disaster Management: Global Challenges and Local SolutionsUniversities Press, Hyderabad, 2009.

3.      Yadav, Rajesh K; Singh, Rajbir. “Recent Approaches in Disaster ManagementOxford Book Company, Jaipur, 2013.

4.      Sharma, Sanjay K. “Environment Engineering and Disaster ManagementUniversity Science Press, New Delhi, 2014.

5.      Singh, Jagbir. “DisasterManagement: Future Challenges and OpportunitiesI K International Publishing, New Delhi, 2007.

 

Essential Reading / Recommended Reading

 

1. Rajat, B C Bose ”Modern Encyclopaedia of Disaster and Hazard Management “.

2. Singh R.B “Disaster Management” Rawat Publications.

3. Narayan “Disaster Management” B A.P.H. Publishing Corporation.

Case Studies on Global disasters.

Evaluation Pattern

 

Ser No

Evaluation Component

Module

Duration (Mins)

Nature Of Component

Weightage Of Module

Validation

1

CIA I

 Test 1

 30

CLOSED BOOK

  Test 100%

Written Test

2

CIA II

MSE

120

CLOSED BOOK

 

Written Test

3

CIA III

Test 2

 30

CLOSED BOOK

Test  100%

Written Test

4

SEMESTER EXAM

ESE

180

CLOSED BOOK

 

Written Test

 

CS631 - CRYPTOGRAPHY AND NETWORK SECURITY (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To understand the principles of encryption algorithms; conventional and public key cryptography. To have a detailed knowledge about authentication, hash functions and application level security mechanisms.

 

Course Outcome

    CO1: Explain various features of Security mechanisms and services to study Standard Block Ciphers along with their design principles

 

CO2: Utlize the basic concepts and algorithms of Public key encryption mechanism for secure data transmission.

 

CO3: Compare various Cryptographic authentications protocols, Hash Functions, Algorithms and Standards.

 

CO4: Identify Various Protocols and Standards in Network Security

CO5: Make use of various applications at system level security

Unit-1
Teaching Hours:9
INTRODUCTION
 

OSI Security Architecture - Classical Encryption techniques – Cipher Principles – Data Encryption Standard – Block Cipher Design Principles and Modes of Operation - Evaluation criteria for AES – AES Cipher – Triple DES – Placement of Encryption Function – Traffic Confidentiality

Unit-2
Teaching Hours:9
PUBLIC KEY CRYPTOGRAPHY
 

Key Management - Diffie-Hellman key Exchange – Elliptic Curve Architecture and Cryptography - Introduction to Number Theory – Confidentiality using Symmetric Encryption – Public Key Cryptography and RSA

Unit-3
Teaching Hours:9
AUTHENTICATION AND HASH FUNCTION
 

Authentication requirements – Authentication functions – Message Authentication Codes – Hash Functions – Security of Hash Functions and MACs – MD5 message Digest algorithm - Secure Hash Algorithm – RIPEMD – HMAC Digital Signatures – Authentication Protocols – Digital Signature Standard

Unit-4
Teaching Hours:9
NETWORK SECURITY
 

Authentication Applications: Kerberos – X.509 Authentication Service – Electronic Mail Security – PGP – S/MIME - IP Security – Web Security.

Unit-5
Teaching Hours:9
Application Security
 

Intrusion detection – password management – Viruses and related Threats – Virus Counter measures – Firewall Design Principles – Trusted Systems, CASE-Study.

Text Books And Reference Books:

TEXT BOOKS

1.                  William Stallings, “Cryptography And Network Security – Principles and Practices”, Pearson Education, 2013

Essential Reading / Recommended Reading

REFERENCE BOOKS

1.                  Atul Kahate, “Cryptography and Network Security”, Tata McGraw-Hill, 2011.

2.                  Bruce Schneier, “Applied Cryptography”, John Wiley & Sons Inc, Reprint 2001.

3.                  Charles B. Pfleeger, Shari Lawrence Pfleeger, “Security in Computing”, 5th Edition, Pearson Education, 2015.

Evaluation Pattern

Internal Assessment - 50 Marks

(CIA 1: 10 Marks, CIA 2: 25 Marks, CIA 3: 10 Marks, Attendance: 5 Marks)

End Semester Examination (ESE) - 50 Marks

Total = 100 Marks

CS632P - OBJECT ORIENTED ANALYSIS AND DESIGN (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To understand the object oriented life cycle; To know how to identify objects, relationships, services and attributes through UML; To understand the use-case diagrams; To know the Object Oriented Design process; To know about software quality and usability.

Course Outcome

CO1: Explain the basic concepts and the lifecycle of Object -Oriented System Development

CO2: Develop UML diagrams based on Unified Approach

CO3: Draw Class diagrams for Real Time Systems

CO4: Demonstrate the concept of design axioms and object interoperability in Modeling Language.

CO5: Validate UML frameworks for real world systems with assured quality and user satisfaction

Unit-1
Teaching Hours:9
INTRODUCTION
 

An Overview of Object Oriented Systems Development - Object and Classes Basics – Object Oriented Systems Development Life Cycle.

Unit-2
Teaching Hours:9
OBJECT ORIENTED METHODOLOGIES
 

Rumbaugh Methodology - Booch Methodology - Jacobson Methodology - Patterns – Frameworks – Unified Approach – Unified Process Model – Unified Modeling Language – Use case - class diagram - Interactive Diagram - Package Diagram - Collaboration Diagram - State Diagram - Activity Diagram

Unit-3
Teaching Hours:9
OBJECT ORIENTED ANALYSIS
 

Identifying use cases - Object Analysis - Classification – Identifying Object relationships - Attributes and Methods.

Unit-4
Teaching Hours:9
OBJECT ORIENTED DESIGN
 

Design axioms - Designing Classes – Access Layer - Object Storage - Object Interoperability

Unit-5
Teaching Hours:9
SOFTWARE QUALITY AND USABILITY
 

Designing Interface Objects – Software Quality Assurance – System Usability - Measuring User Satisfaction – Case Study

Text Books And Reference Books:

TEXT BOOKS

1.      Ali Bahrami, “Object Oriented Systems Development”, Tata McGraw-Hill, 2008 (Unit I, III, IV, V).

2.      Martin Fowler, “UML Distilled”, Third Edition, PHI/Pearson Education, 2011 Edition. (UNIT II).

Essential Reading / Recommended Reading

 REFERENCE BOOKS

1.         Robert A. Maksimchuk , Bobbi J. Young ,Grady Booch , Jim Conallen , Michael W. Engel , Kelli A. Houston, “Object-Oriented Analysis and Design with Applications”, Pearson India, 3rd Edition 2009.

2.         James Rumbaugh, Ivar Jacobson, Grady Booch “The Unified Modeling Language User Guide”, Pearson  Education, 2nd Edition, 2007.

3.         Hans-Erik Eriksson, Magnus Penker, Brain Lyons, David Fado, “UML2 Toolkit”, OMG Press Wiley Publishing Inc., 1st Edition 2011.

Evaluation Pattern

 

Component

Assessed for

Minimum marks

 to pass

Maximum

marks

1

Theory CIA

30

-

30

2

Theory ESE

30

12

30

3

Practical CIA

35

14

35

4

Attendance

05

-

05

4

Aggregate

100

40

100

CS633P - SYSTEM SOFTWARE (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To understand the relationship between system software and machine architecture; To know the design and implementation of assemblers; To know the design and implementation of linkers and loaders; To have an understanding of macro processors; To have an understanding of system software tools.

 

Course Outcome

CO1: Summarize the basic concepts of SIC and SIC/XE architecture.

CO2: Make Use of the  concept of assembler according to  SIC and SIC/XE architecture with real world cases.

CO3: Utilize the detailed working of linker and loader with respect to  SIC and SIC/XE architecture for real world cases .

CO4: Make use of Microprocessor functionalities according to SIC and SIC/XE architecture with real world cases

CO5: Examine the role of compiler in programming environment.

Unit-1
Teaching Hours:9
MACHINE STRUCTURE AND EVOLUTION OF A PROGRAMMING SYSTEM
 

Introduction to System Software, Components of System Software, Evolution of System Software, Assembler, Loader, Macros, Compilers, Simplified Instructional Computer: SIC machine architecture, SIC/XE machine architecture, SIC programming examples.

Unit-2
Teaching Hours:9
ASSEMBLER
 

Basic assembler functions (SIC assembler, algorithm and data structure), Machine dependent assembler features (Instruction formats and addressing modes, program relocation), Machine independent assembly features (Literals, Symbol defining statements, expressions, program blocks, control sections and program linking), Assembler design options (One pass assembler, multi pass assembler)

 

Unit-3
Teaching Hours:9
LOADERS AND LINKERS
 

Basic loader functions (Design of an absolute loader, simple bootstrap loader), Machine dependent loader features (Relocation, program linking, algorithm and data structures for a linking loader), Machine independent loader features (Automatic library search, loader options), Loader design options (Linkage editor, dynamic linking, bootstrap loaders).

Unit-4
Teaching Hours:9
MACRO PROCESSOR
 

Macro Instructions, Features of a macro facility (Macro instruction arguments, Conditional macro expansion, Macro calls within macro, Macro instructions defining macros), Implementation (Two pass algorithm, Single pass algorithm).

Unit-5
Teaching Hours:9
COMPILERS
 

Part1: Basic elements, Syntactic units and interpreting meaning, Intermediate form (Arithmetic statements, Non-arithmetic statements, Non-executable statements), Storage allocation, Code generation, Optimization (Machine independent, Machine dependent, Assembly phase).

Part2: Phases of the compiler (Lexical phase, Syntax phase, Interpretation phase, Optimization, Storage assignment, Code generation, Assembly phase), Passes of a compiler. Case study.

Text Books And Reference Books:

TEXT BOOKS

  1. Donovan, “John, System programming”, Tata McGraw-Hill, Reprint 2008
  2. Beck, Leland, “System Software An Introduction to System Programming”, Addison-Wesley, 3rd Edition, Reprint 2009

 

Essential Reading / Recommended Reading

REFERENCE BOOKS

  1. Dhamdhere D M, “Systems programming and operating systems”, Tata McGraw-Hill, Reprint 2006.
Evaluation Pattern

 

 

Component

Assessed for

Minimum marks

 to pass

Maximum

marks

1

Theory CIA

30

-

30

2

Theory ESE

30

12

30

3

Practical CIA

35

14

35

4

Attendance

05

-

05

4

Aggregate

100

40

100

CSHO631AI - ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

•Discuss the core concepts Statistical Analytics and Regression

•Apply the basic principles, models, and algorithms for Multiple and Non Linear Regression

•Analyse the structures and algorithms of Convolutional Neural Networks

•Explain notions and theories associated to Convolutional Neural Networks

•Solve problems in Deep Unsupervised Learning

Course Outcome

CO1: Understand and explain concepts associated Statistical Analytics and Regression

CO2: Infer details of Multiple and Non Linear Regression mechanisms.

CO3: Solve problems connected to Convolutional Neural Networks.

CO4: Analyse concepts of Convolutional Neural Networks.

CO5: Appraise concepts of Deep Unsupervised Learning. 

Unit-1
Teaching Hours:9
Regression
 

Relationship between attributes using Covariance and Correlation, Relationship between multiple variables: Regression (Linear, Multivariate) in prediction. Residual Analysis, Identifying significant features, feature reduction using AIC, multi-collinearity, Non-normality and Heteroscedasticity

Unit-2
Teaching Hours:9
Multiple and Non Linear Regression
 

Polynomial Regression, Regularization methods, Lasso, Ridge and Elastic nets, Categorical Variables in Regression, Logit function and interpretation, Types of error measures (ROCR), Logistic Regression in classification

Unit-3
Teaching Hours:9
Convolutional Neural Networks I
 

Invariance, stability. Variability models (deformation model, stochastic model). Scattering networks, Group Formalism, Supervised Learning: classification. Properties of CNN representations: invertibility, stability, invariance. Covariance/invariance: capsules and related models.

Unit-4
Teaching Hours:9
Convolutional Neural Networks II
 

Connections with other models: dictionary learning, LISTA. Other tasks: localization, regression. Embeddings (DrLim), inverse problems, Extensions to non-euclidean domains Dynamical systems: RNNs.

Unit-5
Teaching Hours:9
Deep Unsupervised Learning
 

Autoencoders (standard, denoising, contractive, Variational Autoencoders Adversarial Generative Networks, Maximum Entropy Distributions

Text Books And Reference Books:

T1.Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018.

T2.Sebastian Raschka and Vahid MirjaliliPython Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition, Packt, 2019

Essential Reading / Recommended Reading

R1. Seber, Linear Regression Analysis 2ed,Wiley India Exclusive (Cbs), 2018

R2. Jeremy Arkes, Regression Analysis: A Practical Introduction, Routledge, 2019

R3. Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras and Tensor Flow: Concepts, Tools and Techniques to Build Intelligent Systems, Shroff/O'Reilly, 2019

R4. Andreas Muller, Introduction to Machine Learning with Python: A Guide for Data Scientists, Shroff/O'Reilly, 2016

R5. François Chollet, Deep Learning with Python, Manning Publications, 2017

Evaluation Pattern

CIA-70 Marks

ESE-30 Marks

CSHO631AIP - ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Discuss the core concepts Statistical Analytics and Regression

Apply the basic principles, models, and algorithms for Multiple and Non Linear Regression

Analyse the structures and algorithms of Convolutional Neural Networks

Explain notions and theories associated to Convolutional Neural Networks

Solve problems in Deep Unsupervised Learning

Course Outcome

·     Understand and explain concepts associated Statistical Analytics and Regression L2

Infer details of Multiple and Non Linear Regression mechanisms. L2

Solve problems connected to Convolutional Neural Networks. L3

Analyse concepts of Convolutional Neural Networks. L4

Appraise concepts of Deep Unsupervised Learning. L5

Unit-1
Teaching Hours:9
Regression
 

Relationship between attributes using Covariance and Correlation, Relationship between multiple variables: Regression (Linear, Multivariate) in prediction. Residual Analysis, Identifying significant features, feature reduction using AIC, multi-collinearity, Non-normality and Heteroscedasticity

Unit-2
Teaching Hours:9
Multiple and Non Linear Regression
 

Polynomial Regression, Regularization methods, Lasso, Ridge and Elastic nets, Categorical Variables in Regression, Logit function and interpretation, Types of error measures (ROCR), Logistic Regression in classification

Unit-3
Teaching Hours:9
Convolutional Neural Networks 1
 

Invariance, stability. Variability models (deformation model, stochastic model). Scattering networks, Group Formalism, Supervised Learning: classification. Properties of CNN representations: invertibility, stability, invariance. Covariance/invariance: capsules and related models

Unit-4
Teaching Hours:9
Convolutional Neural Network II
 

Connections with other models: dictionary learning, LISTA. Other tasks: localization, regression. Embeddings (DrLim), inverse problems, Extensions to non-euclidean domains Dynamical systems: RNNs

Unit-5
Teaching Hours:9
Deep Unsupervised Learning
 

Autoencoders (standard, denoising, contractive, Variational Autoencoders Adversarial Generative Networks, Maximum Entropy Distributions

Text Books And Reference Books:

Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018.

Sebastian Raschka and Vahid MirjaliliPython Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition, Packt, 2019

Essential Reading / Recommended Reading

1.      Seber, Linear Regression Analysis 2ed,Wiley India Exclusive (Cbs), 2018

Jeremy Arkes, Regression Analysis: A Practical Introduction,  Routledge, 2019

Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras and Tensor Flow: Concepts, Tools and Techniques to Build Intelligent Systems, Shroff/O'Reilly, 2019

Andreas Muller, Introduction to Machine Learning with Python: A Guide for Data Scientists, Shroff/O'Reilly, 2016

François Chollet, Deep Learning with Python, Manning Publications, 2017

Evaluation Pattern

 

 

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

             Minimum marks required to pass in practical component is 40%.

       Pass in practical component is eligibility criteria to attend Theory End semester examination for the same course.

       A minimum of 40 % required to pass in ESE -Theory component of a course.

       Overall 40 % aggregate marks in Theory & practical component, is required to pass a course.

       There is no minimum pass marks for the Theory - CIA component.

       Less than 40% in practical component is refereed as FAIL.

       Less than 40% in Theory ESE is declared as fail in the theory component.

       Students who failed in theory ESE have to attend only theory ESE to pass in the course

 

 

CSHO631CS - MOBILE AND NETWORK-BASED ETHICAL HACKING (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

After learning the course for a semester, the student will be aware of the hacking concepts in cyber security for addressing cryptography, data protection, information-network security and detection of attacks. The student would also get a clear idea on some of the cases with their analytical studies in cyber-attacks and hacking in the related fields.

Course Outcome

CO 1: to describe the vulnerability scanning for network   

CO 2: to identify the information gathering resources for any attack on the network 

CO 3: to evaluate different hacking process and corresponding attacks for mobile platforms 

CO 4: to provide means to evade fire-walls and other security parameter for ethical hacking 

CO 5: to analyze about best possible solutions for different vulnerabilities that are exploited for hacking

Unit-1
Teaching Hours:9
Unit-1
 

Introduction to ethical hacking, IP addressing, Network routing protocols, network security, network scanning, and vulnerability assessment OpenVAS, Nessus, etc. of computation device (mobile, pc, etc.) and network of the system

 

Unit-2
Teaching Hours:9
Unit-2
 

Computation system hacking, modes of gathering information, password cracking, penetration testing including backdoor issues, Malware threats and different cyber-related attacks

Unit-3
Teaching Hours:9
Unit-3
 

Introduction to Mobile Hacking, encryption types and attacks, different mobile platforms and corresponding vulnerabilities

Unit-4
Teaching Hours:9
Unit-4
 

Evading firewalls, standard detection systems and frameworks, and other possible ways of detecting attacks

Unit-5
Teaching Hours:9
Unit-5
 

Case studies: various hacking scenarios and their information gathering along with possible solutions.

Text Books And Reference Books:

T1. Thompsons, Josh. Hacking: Hacking For Beginners Guide On How To Hack, Computer Hacking, And The Basics Of Ethical Hacking (Hacking Books). CreateSpace Independent Publishing Platform, 2017.

T2. Weidman, Georgia. Penetration testing: a hands-on introduction to hacking. No Starch Press, 2014.

T3. Dwivedi, Himanshu. Mobile application security. Tata McGraw-Hill Education, 2010

Essential Reading / Recommended Reading

R1. Engebretson, Patrick. The basics of hacking and penetration testing: ethical hacking and penetration testing made easy. Elsevier, 2013.

R2. McNab, Chris. Network security assessment: know your network. " O'Reilly Media, Inc.", 2007.

R3. Simpson, Michael T., Kent Backman, and James Corley. Hands-on ethical hacking and network defense. Cengage Learning, 2010

Evaluation Pattern

CIA-70 Marks

ESE-30 Marks

CSHO631CSP - MOBILE AND NETWORK BASED ETHICAL HACKING (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

After learning the course for a semester, the student will be aware of the hacking concepts in cyber security for addressing cryptography, data protection, information-network security and detection of attacks. The student would also get a clear idea on some of the cases with their analytical studies in cyber-attacks and hacking in the related fields

Course Outcome

To describe the vulnerability scanning for network  

To identify the information gathering resources for any attack on the network

To evaluate different hacking process and corresponding attacks for mobile platforms

To provide means to evade fire-walls and other security parameter for ethical hacking

To analyze about best possible solutions for different vulnerabilities that are exploited for hacking

Unit-1
Teaching Hours:9
Introduction
 

Introduction to ethical hacking, IP addressing, Network routing protocols, network security, network scanning, and vulnerability assessment OpenVAS, Nessus, etc. of computation device (mobile, pc, etc.) and network of the system

Unit-2
Teaching Hours:9
Threat Analysis
 

Computation system hacking, modes of gathering information, password cracking, penetration testing including backdoor issues, Malware threats and different cyber-related attacks

Unit-3
Teaching Hours:9
Mobile Hacking
 

Introduction to Mobile Hacking, encryption types and attacks, different mobile platforms and corresponding vulnerabilities

Unit-4
Teaching Hours:9
Detection of Threats
 

Evading firewalls, standard detection systems and frameworks, and other possible ways of detecting attacks

Unit-5
Teaching Hours:9
Case Studies
 

Case studies: various hacking scenarios and their information gathering along with possible solutions

Text Books And Reference Books:

    Thompsons, Josh. Hacking: Hacking For Beginners Guide On How To Hack, Computer Hacking, And The Basics Of Ethical Hacking (Hacking Books). CreateSpace Independent Publishing Platform, 2017.

Weidman, Georgia. Penetration testing: a hands-on introduction to hacking. No Starch Press, 2014.

Dwivedi, Himanshu. Mobile application security. Tata McGraw-Hill Education, 2010

Essential Reading / Recommended Reading

 

Engebretson, Patrick. The basics of hacking and penetration testing: ethical hacking and penetration testing made easy. Elsevier, 2013.     

McNab, Chris. Network security assessment: know your network. " O'Reilly Media, Inc.", 2007.

Simpson, Michael T., Kent Backman, and James Corley. Hands-on ethical hacking and network defense. Cengage Learning, 2010

Evaluation Pattern

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

 

       Minimum marks required to pass in practical component is 40%.

       Pass in practical component is eligibility criteria to attend Theory End semester examination for the same course.

       A minimum of 40 % required to pass in ESE -Theory component of a course.

       Overall 40 % aggregate marks in Theory & practical component, is required to pass a course.

       There is no minimum pass marks for the Theory - CIA component.

       Less than 40% in practical component is refereed as FAIL.

       Less than 40% in Theory ESE is declared as fail in the theory component.

       Students who failed in theory ESE have to attend only theory ESE to pass in the course

CSHO631DA - BIG DATA ANALYTICS (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

•To Understand big data for business intelligence

•To Learn business case studies for big data analytics

•To Understand Nosql big data management

•To manage Big data without SQL

•To understanding map-reduce analytics using Hadoop and related tools

Course Outcome

CO1: Describe big data and use cases from selected business domains

CO2: Discuss open source technologies

CO3: Explain NoSQL big data management

CO4: Discuss basics of Hadoop and HDFS

CO5: Discuss map-reduce analytics using Hadoop along with as HBase, Cassandra, Pig, and Hive for big data Analytics

Unit-1
Teaching Hours:9
UNDERSTANDING BIG DATA
 

What is big data – why big data –.Data!, Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big data– big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics.

Unit-2
Teaching Hours:9
NOSQL DATA MANAGEMENT
 

Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships –graph databases – schema less databases – materialized views – distribution models – sharding –– version – Map reduce –partitioning and combining – composing map-reduce calculations

Unit-3
Teaching Hours:9
BASICS OF HADOOP
 

Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures

Unit-4
Teaching Hours:9
MAPREDUCE APPLICATIONS
 

MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution –MapReduce types – input formats – output formats

Unit-5
Teaching Hours:9
HADOOP RELATED TOOLS
 

Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model –cassandra examples – cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queries-case study.

Text Books And Reference Books:

T1. Tom White, "Hadoop: The Definitive Guide", 4th  Edition, O'Reilley, 2012.

T2. Eric Sammer, "Hadoop Operations",1st Edition, O'Reilley, 2012.

Essential Reading / Recommended Reading

R1. VigneshPrajapati, Big data analytics with R and Hadoop, SPD 2013.

R2. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.

R3. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.

R4. Alan Gates, "Programming Pig", O'Reilley, 2011.

Evaluation Pattern

CIA-70 Marks

ESE-30 Marks

CSHO631DAP - BIG DATA ANALYTICS (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To Understand big data for business intelligence

To Learn business case studies for big data analytics

To Understand Nosql big data management

To manage Big data without SQL

To understanding map-reduce analytics using Hadoop and related tools

Course Outcome

·       Describe big data and use cases from selected business domains

  Discuss open source technologies

  Explain NoSQL big data management

  Discuss basics of Hadoop and HDFS

  Discuss map-reduce analytics using Hadoop along with as HBase, Cassandra, Pig, and Hive for big data Analytics

Unit-1
Teaching Hours:9
Understanding Big Data
 

What is big data – why big data –.Data!, Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big data– big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics

Unit-2
Teaching Hours:9
NOSQL Data Management
 

Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships –graph databases – schema less databases – materialized views – distribution models – sharding –– version – Map reduce –partitioning and combining – composing map-reduce calculations

Unit-3
Teaching Hours:9
Basics of Hadoop
 

Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data Structures

Unit-4
Teaching Hours:9
MapReduce Applications
 

MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution –MapReduce types – input formats – output formats

Unit-5
Teaching Hours:9
Hadoop Related Tools
 

Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model –cassandra examples – cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queries-case study

Text Books And Reference Books:

1.      Tom White, "Hadoop: The Definitive Guide", 4th  Edition, O'Reilley, 2012.

Eric Sammer, "Hadoop Operations",1st Edition, O'Reilley, 2012.

Essential Reading / Recommended Reading

1.    VigneshPrajapati, Big data analytics with R and Hadoop, SPD 2013.

 E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.

 Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.

 Alan Gates, "Programming Pig", O'Reilley, 2011.

Evaluation Pattern

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

 

       Minimum marks required to pass in practical component is 40%.

       Pass in practical component is eligibility criteria to attend Theory End semester examination for the same course.

       A minimum of 40 % required to pass in ESE -Theory component of a course.

       Overall 40 % aggregate marks in Theory & practical component, is required to pass a course.

       There is no minimum pass marks for the Theory - CIA component.

       Less than 40% in practical component is refereed as FAIL.

       Less than 40% in Theory ESE is declared as fail in the theory component.

       Students who failed in theory ESE have to attend only theory ESE to pass in the course

CSHO632AI - ROBOTICS AND PROCESS AUTOMATION (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

•To understand about  RPA  and its concepts

•Apply RPA tools and functionalities 

•Understand the challenges and risks in RPA implementation

 

•Understand about bots and its usage in real time applications

Course Outcome

CO1: Illustrate the advantages, techniques of RPA

CO2: Experiment with data manipulation and sequencing 

CO3: Experiment with Conditional and Control activities

CO4: Implement event handling and launching the bots

CO5: Organize the project using project management tools

Unit-1
Teaching Hours:9
INTRODUCTION
 

Introduction to RPA, what should be automated, what can be automated, Techniques of automation, Benefits of RPA, Components of RPA, RPA Platforms, Record and Play.

Unit-2
Teaching Hours:9
CONTROL FLOW AND DATA MANIPULATION
 

Sequence, Activities, Control flow and Decision Making, Variables and Scope, Collections, Arguments-Purpose and use, Data Table, File Operation.

Unit-3
Teaching Hours:9
TAKING CONTROL OF THE CONTROLS
 

Finding and attaching windows, Finding the control, Techniques for waiting for a control, Act on controls – mouse and keyboard activities, Handling events

Unit-4
Teaching Hours:9
HANDLING USER EVENTS AND ASSISTANT BOTS
 

Assistant Bots, Monitoring system event triggers, Monitoring image and element triggers, Launching an assistant bot on a keyboard event, 

Exception handling.

Unit-5
Teaching Hours:9
MANAGING THE CODE AND MAINTAINING THE BOT
 

Project organization, Nesting workflows, Reusability of workflows, Overview of Orchestration Server, Using Orchestration Server to control bots, Using Orchestration Server to deploy bots.

Text Books And Reference Books:

T1. Tripathi Alok Mani,” Learning Robotic Process Automation”, Packt Publishing, March 2018.

 

Essential Reading / Recommended Reading

NIL

Evaluation Pattern

CIA-70 Marks

ESE-30 Marks

CSHO632AIP - ROBOTICS AND PROCESS AUTOMATION (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

 

 

To understand about RPA and its concepts

Apply RPA tools and functionalities

Understand the challenges and risks in RPA implementation

Understand about bots and its usage in real time applications

Course Outcome

Illustrate the advantages, techniques of RPA

Experiment with data manipulation and sequencing

Experiment with Conditional and Control activities

Implement event handling and launching the bots

Organize the project using project management tools

Unit-1
Teaching Hours:9
Introduction
 

Introduction to RPA, what should be automated, what can be automated, Techniques of automation, Benefits of RPA, Components of RPA, RPA Platforms, Record and Play

Unit-2
Teaching Hours:9
Sequencing and Data Manipulation
 

Sequence, Activities, Control flow and Decision Making, Variables and Scope, Collections, Arguments-Purpose and use, Data Table, File Operation

Unit-3
Teaching Hours:9
Taking Control of Controls
 

Finding and attaching windows, Finding the control, Techniques for waiting for a control, Act on controls – mouse and keyboard activities, Handling events

Unit-4
Teaching Hours:9
Handling User Events and Assistant Bots
 

Assistant Bots, Monitoring system event triggers, Monitoring image and element triggers, Launching an assistant bot on a key board event, Exception handling 

Unit-5
Teaching Hours:9
Managing the code and Maintaining the Bot
 

Project organization, Nesting workflows, Reusability of workflows, Overview of Orchestration Server, Using Orchestration Server to control bots, Using Orchestration Server to deploy bots

Text Books And Reference Books:

Tripathi Alok Mani,” Learning Robotic Process Automation”, Packt Publishing, March 2018.

Essential Reading / Recommended Reading

None

Evaluation Pattern

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

 

       Minimum marks required to pass in practical component is 40%.

       Pass in practical component is eligibility criteria to attend Theory End semester examination for the same course.

       A minimum of 40 % required to pass in ESE -Theory component of a course.

       Overall 40 % aggregate marks in Theory & practical component, is required to pass a course.

       There is no minimum pass marks for the Theory - CIA component.

       Less than 40% in practical component is refereed as FAIL.

       Less than 40% in Theory ESE is declared as fail in the theory component.

       Students who failed in theory ESE have to attend only theory ESE to pass in the course

CSHO632CS - CYBER FORENSICS AND MALWARE DETECTION (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course is designed to explore fundamental concepts of cyber forensic, cyber laws and Data recovery and its analysis. This course covers the topics of malware detection, classification, tools & methodology applied to analysis and protection from malware.

Course Outcome

CO1: To understand the fundamentals of Cyber forensic over different platforms.

CO2: To understand concepts of Malware Forensics; Web Attack Forensics; Bitcoin Forensics; Cyber Laws and Data Recovery & Analysis

CO3: To understand the nature of malware, its capabilities, and how it is combated through detection and classification 

CO4: To apply the tools and methodologies used to perform static and dynamic analysis on unknown executables.

CO5: To understand the malware functionality and malware detection techniques

Unit-1
Teaching Hours:9
Unit-1
 

Introduction to Cyber Forensics; Windows Forensics; Linux Forensics, Mac OS Forensics; Anti-forensics; Network Forensics; Mobile Forensics; Cloud Forensics

 

Unit-2
Teaching Hours:9
Unit-2
 

Malware Forensics; Web Attack Forensics; Emails and Email Crime, Bitcoin Forensics; Cyber Law and Cyberwarfare; Data Recovery & Data Analysis

Unit-3
Teaching Hours:9
Unit-3
 

Introduction to malware, OS security concepts, malware threats, evolution of malware, malware types- viruses, worms, rootkits, Trojans, bots, spyware, adware, logic bombs, malware analysis, static malware analysis, dynamic malware analysis

Unit-4
Teaching Hours:9
Unit-4
 

STATIC ANALYSIS:

Analyzing Windows programs, Anti-static analysis techniques- obfuscation, packing, metamorphism, polymorphism

DYNAMIC ANALYSIS:

Live malware analysis, dead malware analysis, analyzing traces of malware- system-calls, api-calls, registries, network activities. Anti-dynamic analysis techniques- anti-vm, runtime-evasion techniques, Malware Sandbox, Monitoring with Process Monitor, Packet Sniffing with Wireshark, Kernel vs. User-Mode Debugging, OllyDbg, Breakpoints, Tracing, Exception Handling, Patching

Unit-5
Teaching Hours:9
Unit-5
 

Malware Functionality: Downloader, Backdoors, Credential Stealers, Persistence Mechanisms, Privilege Escalation, Covert malware launching- Launchers, Process Injection, Process Replacement, Hook Injection, Detours, APC injection

Malware Detection Techniques: Signature-based techniques: malware signatures, packed malware signature, metamorphic and polymorphic malware signature Non-signature based techniques: similarity-based techniques, machine-learning methods, invariant inferences

Text Books And Reference Books:

T1. Practical Cyber Forensics: An Incident-Based Approach to Forensic Investigations: Reddy, Niranjan, Published by Apress, Berkeley, CA, DOIhttps://doi.org/10.1007/978-1-4842-4460-9, Print ISBN 978-1-4842-4459-3, 2019

T2. Practical malware analysis The Hands-On Guide to Dissecting Malicious Software by Michael Sikorski and Andrew Honig ISBN-10: 159327-290-1, ISBN-13: 978-1-59327-290-6, 2012

Essential Reading / Recommended Reading

R1. Malware Detection A Complete Guide - 2019 Edition,  Gerardus Blokdyk, Published by 5STARCooks, 2019, ISBN: 0655900845, 9780655900849

Evaluation Pattern

CIA-70 Marks

ESE-30 Marks

CSHO632CSP - CYBER FORENSICS AND MALWARE DETECTION (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course is designed to explore fundamental concepts of cyber forensic, cyber laws and Data recovery and its analysis. This course covers the topics of malware detection, classification, tools & methodology applied to analysis and protection from malware.

Course Outcome

        Students will be able to understand the fundamentals of Cyber forensic over different flatforms.

 Students are able understand concepts of Malware Forensics; Web Attack Forensics; Bitcoin Forensics; Cyber Laws and Data Recovery & Analysis

 Students will be able to understand the nature of malware, its capabilities, and how it is combated through detection and classification

 Students will be able to apply the tools and methodologies used to perform static and dynamic analysis on unknown executables.

 Students will be able to understand the malware functionality and malware detection techniques

Unit-1
Teaching Hours:9
Introduction
 

Introduction to Cyber Forensics; Windows Forensics; Linux Forensics, Mac OS Forensics; Anti-forensics; Network Forensics; Mobile Forensics; Cloud Forensics

Unit-2
Teaching Hours:9
Malware Forensics
 

Malware Forensics; Web Attack Forensics; Emails and Email Crime, Bitcoin Forensics; Cyber Law and Cyberwarfare; Data Recovery & Data Analysis

Unit-3
Teaching Hours:9
Malware types and analysis
 

Introduction to malware, OS security concepts, malware threats, evolution of malware, malware types- viruses, worms, rootkits, Trojans, bots, spyware, adware, logic bombs, malware analysis, static malware analysis, dynamic malware analysis

Unit-4
Teaching Hours:9
Static and Dynamic Analysis
 

STATIC ANALYSIS:  Analyzing Windows programs, Anti-static analysis techniques- obfuscation, packing, metamorphism, polymorphism

 

DYNAMIC ANALYSIS: Live malware analysis, dead malware analysis, analyzing traces of malware- system-calls, api-calls, registries, network activities. Anti-dynamic analysis techniques- anti-vm, runtime-evasion techniques, Malware Sandbox, Monitoring with Process Monitor, Packet Sniffing with Wireshark, Kernel vs. User-Mode Debugging, OllyDbg, Breakpoints, Tracing, Exception Handling, Patching

Unit-5
Teaching Hours:9
Malware Detection and Analysis
 

Malware Functionality: Downloader, Backdoors, Credential Stealers, Persistence Mechanisms, Privilege Escalation, Covert malware launching- Launchers, Process Injection, Process Replacement, Hook Injection, Detours, APC injection

Malware Detection Techniques: Signature-based techniques: malware signatures, packed malware signature, metamorphic and polymorphic malware signature Non-signature based techniques: similarity-based techniques, machine-learning methods, invariant inferences

Text Books And Reference Books:

1.      Practical Cyber Forensics: An Incident-Based Approach to Forensic Investigations: Reddy, Niranjan, Published by Apress, Berkeley, CA, DOIhttps://doi.org/10.1007/978-1-4842-4460-9, Print ISBN 978-1-4842-4459-3, 2019

Practical malware analysis The Hands-On Guide to Dissecting Malicious Software by Michael Sikorski and Andrew Honig ISBN-10: 159327-290-1, ISBN-13: 978-1-59327-290-6, 2012 2

Essential Reading / Recommended Reading

Malware Detection A Complete Guide - 2019 Edition,  Gerardus Blokdyk, Published by 5STARCooks, 2019, ISBN: 0655900845, 9780655900849

Evaluation Pattern

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

 

       Minimum marks required to pass in practical component is 40%.

       Pass in practical component is eligibility criteria to attend Theory End semester examination for the same course.

       A minimum of 40 % required to pass in ESE -Theory component of a course.

       Overall 40 % aggregate marks in Theory & practical component, is required to pass a course.

       There is no minimum pass marks for the Theory - CIA component.

       Less than 40% in practical component is refereed as FAIL.

       Less than 40% in Theory ESE is declared as fail in the theory component.

       Students who failed in theory ESE have to attend only theory ESE to pass in the course

CSHO632DA - BIG DATA SECURITY ANALYTICS (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To provide the fundamental techniques and principles of security model in Big Data

Course Outcome

CO1:  Explain the various security models and threats

CO2: Outline the need of security services, manage and address the challenges in web applications

CO3: Examine various network security models

CO4: Classify the various security threats, attacks and counter measures in securing data.

CO5: Conclude the attacks and counter measure tools using watermarking technique

Unit-1
Teaching Hours:9
SECURITY MODELS
 

Critical characteristics of Information - NSTISSC Security Model -Components of information System –SDLC – Information assurance - Security Threats and vulnerabilities - Overview of Security threats-– Security Standards.

Unit-2
Teaching Hours:9
WEB SECURITY
 

Introduction, Basic security for HTTP Applications and Services, Basic Security for SOAP Services, Identity Management and Web Services, Authorization Patterns, Security Considerations, Challenges.

Unit-3
Teaching Hours:9
NETWORK SECURITY
 

Network security - Intrusion Prevention, detection and Management - Firewall – Ecommerce Security - Computer Forensics - Security for VPN and Next Generation Networks.

Unit-4
Teaching Hours:9
ATTACKS & SECURITY MECHANISMS
 

Host and Application security -Control hijacking, Software architecture and a simple buffer overflow - Common exploitable application bugs, shellcode - Buffer Overflow - Side-channel attacks - Timing attacks, power analysis, cold-boot attacks, defenses – Malware - Viruses and worms, spyware, key loggers, and botnets; defenses auditing, policy - Defending weak applications - Isolation, sandboxing, virtual machines.

Unit-5
Teaching Hours:9
DIGITAL WATER MARKING
 

Introduction, Difference between Watermarking and Steganography, Types and techniques (Spatial-domain, Frequency-domain, and Vector quantization based watermarking), Attacks and Tools (Attacks by Filtering, Remodulation, Distortion, Geometric Compression, Linear Compression), Watermark security & authentication. 

Text Books And Reference Books:

T1. William Stallings, “Cryptography and Network Security: Principles and Practice”, 6th Edition,PHI, 2014. 

T2. Michael E. Whitman and Herbert J Mattord, "Principles of Information Security", 6th edition,Vikas Publishing House, 2017. 

T3. Peter Wayner, Disappearing Cryptography–Information Hiding: Steganography & Watermarking, Morgan Kaufmann Publishers, New York, 2002.

T4. Ingemar J. Cox, Matthew L. Miller, Jeffrey A. Bloom, Jessica Fridrich, TonKalker, Digital Watermarking and Steganography, Margan Kaufmann Publishers, New York, 2008. 

Essential Reading / Recommended Reading

R1. Bill Nelson, Amelia Phillips, F.Enfinger and Christopher Stuart, “Guide to Computer Forensics and Investigations, 4 th ed., Thomson Course Technology, 2010. 

R2. Matt Bishop, “Computer Security: Art and Science”, 1 st edition, Addison-Wesley Professional, 2015.

R3. Neil F. Johnson, Zoran Duric, Sushil Jajodia, Information Hiding: Steganography and Watermarking-Attacks and Countermeasures, Springer, 2012. 

R4. Stefan Katzenbeisser, Fabien A. P. Petitcolas, Information Hiding Techniques for Steganography and Digital Watermarking, Artech House Print on Demand, 1999.

Evaluation Pattern

CIA-70 Marks

ESE-30 Marks

CSHO632DAP - BIG DATA SECURITY ANALYTICS (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To provide the fundamental techniques and principles of security model in Big Data

Course Outcome

Explain the various security models and threats

Outline the need of security services, manage and address the challenges in web applications

Examine various network security models

Classify the various security threats, attacks and counter measures in securing data

Conclude the attacks and counter measure tools using watermarking technique

Unit-1
Teaching Hours:9
Security Models
 

Critical characteristics of Information - NSTISSC Security Model -Components of information System –SDLC – Information assurance - Security Threats and vulnerabilities - Overview of Security threats-– Security Standards 

Unit-2
Teaching Hours:9
Web Security
 

Introduction, Basic security for HTTP Applications and Services, Basic Security for SOAP Services, Identity Management and Web Services, Authorization Patterns, Security Considerations, Challenges

Unit-3
Teaching Hours:9
Network Security
 

Network security - Intrusion Prevention, detection and Management - Firewall – Ecommerce Security - Computer Forensics - Security for VPN and Next Generation Networks

Unit-4
Teaching Hours:9
Attacks and Security Mechanisms
 

Host and Application security -Control hijacking, Software architecture and a simple buffer overflow - Common exploitable application bugs, shellcode - Buffer Overflow - Side-channel attacks - Timing attacks, power analysis, cold-boot attacks, defenses – Malware - Viruses and worms, spyware, key loggers, and botnets; defenses auditing, policy - Defending weak applications - Isolation, sandboxing, virtual machines

Unit-5
Teaching Hours:9
Digital Watermarking
 

Introduction, Difference between Watermarking and Steganography, Types and techniques (Spatial-domain, Frequency-domain, and Vector quantization based watermarking), Attacks and Tools (Attacks by Filtering, Remodulation, Distortion, Geometric Compression, Linear Compression), Watermark security & authentication

Text Books And Reference Books:

William Stallings, “Cryptography and Network Security: Principles and Practice”, 6 th Edition,    PHI, 2014.

Michael E. Whitman and Herbert J Mattord, "Principles of Information Security", 6 th edition,  Vikas Publishing House, 2017.

Peter Wayner, Disappearing Cryptography–Information Hiding: Steganography & Watermarking, Morgan Kaufmann Publishers, New York, 2002.

Ingemar J. Cox, Matthew L. Miller, Jeffrey A. Bloom, Jessica Fridrich, TonKalker, Digital Watermarking and Steganography, Margan Kaufmann Publishers, New York, 2008.

Essential Reading / Recommended Reading

Bill Nelson, Amelia Phillips, F.Enfinger and Christopher Stuart, “Guide to Computer Forensics and Investigations, 4 th ed., Thomson Course Technology, 2010.

Matt Bishop, “Computer Security: Art and Science”, 1 st edition, Addison-Wesley Professional, 2015.

Neil F. Johnson, Zoran Duric, Sushil Jajodia, Information Hiding: Steganography and Watermarking-Attacks and Countermeasures, Springer, 2012.

Stefan Katzenbeisser, Fabien A. P. Petitcolas, Information Hiding Techniques for Steganography and Digital Watermarking, Artech House Print on Demand, 1999

Evaluation Pattern

DETAIL OF MARK FOR COURSES WITH THOERY AND PRACTICAL

THEORY

PRACTICAL

 

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

Component

Assessed for

Scaled down to

Minimum marks to pass

Maximum marks

1

CIA-1

20

10

-

10

Overall CIA

50

35

14

35

2

CIA-2

50

10

-

10

 

 

 

3

CIA-3

20

10

-

10

 

 

 

4

Attendance

05

05

-

05

Attendance

NA

NA

-

-

5

ESE

100

30

12

30

ESE

NA

NA

-

-

 

 

TOTAL

65

-

65

TOTAL

 

35

14

35

 

       Minimum marks required to pass in practical component is 40%.

       Pass in practical component is eligibility criteria to attend Theory End semester examination for the same course.

       A minimum of 40 % required to pass in ESE -Theory component of a course.

       Overall 40 % aggregate marks in Theory & practical component, is required to pass a course.

       There is no minimum pass marks for the Theory - CIA component.

       Less than 40% in practical component is refereed as FAIL.

       Less than 40% in Theory ESE is declared as fail in the theory component.

       Students who failed in theory ESE have to attend only theory ESE to pass in the course

EC636OE1 - EMBEDDED BOARDS FOR IOT APPLICATIONS (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:34

Course Objectives/Course Description

 

·      To introduce the architecture, programming and interfacing of peripheral devices with embedded boards for IOT applications.

·       To design IOT based smart applications.

 

Course Outcome

Understand the architecture and imporatnce of wireless sensor network

Understand the architecture, programming and interfacing principles of ATMEGA32 AVR microcontroller

Understand the architecture, programming and interfacing principles of Raspberry pi

Understand the applications of ATMEGA32 AVR microcontroller, Microprocessor and Rasberry Pi in IOT.

Analyze the design scheme for IOT using Microcontrollers.

Unit-1
Teaching Hours:9
NETWORKING SENSORS
 

Network Architecture - Sensor Network Scenarios- Optimization Goals and Figures of Merit- Physical Layer and Transceiver Design Considerations-MAC Protocols for Wireless Sensor Networks- Introduction of sensors and transducers.

Unit-2
Teaching Hours:9
ARDUINO BOARD AND its? INTERFACING
 

ATMEGA328 microcontroller - Architecture- memory organisation – Operating modes – On chip peripherals- Embedded communication interfaces-  Example programs using Arduino IDE- Integration of peripherals (Buttons & switches, digital inputs, Matrix keypad, Basic RGB color-mixing, electromechanical devices- Displays- sensors(Temperature, Pressure, Humidity, Water level etc.), camera, real time clock, relays, actuators, Bluetooth, Wi-fi).

 

Unit-3
Teaching Hours:9
IOT BASED SYSTEM DESIGN
 

Definition of IoT- Applications and Verticals- System Architecture-Typical Process Flows-Technological Enablers- Open Standard Reference Model- Design Constraints and Considerations- IoT Security-  Experiments using Arduino Platform (3 hours).

Unit-4
Teaching Hours:9
RASBERRY-PI
 

Introduction to Raspberry pi – configuration of Raspberry pi – programming raspberry pi - Implementation of IOT with Rasberry pi

Unit-5
Teaching Hours:9
Unit-5 {This unit is entirely practical based}
 

Implementation of a IOT based real time system. The concept of the specific embedded design has to be discussed.

Eg: Smart Irrigation using IOT/ IoT Based Biometrics Implementation on Raspberry Pi/ Automation etc.

Note: Unit – V will be based on a group project. Each group comprising of maximum 3 members. Any microcontroller can be used in Unit-V. 

Text Books And Reference Books:

 

1.     Slama, Dirak “Enterprise IOT : Strategies and Best Practices for Connected Products and services”, Shroff Publisher, 1st edition,2015.

2.      "Protocols and Architectures for Wireless Sensor Networks", John Wiley, 2007.

3.     Ali Mazidi, Sarmad Naimi, Sepehr Naimi “AVR Microcontroller and Embedded Systems: Using Assembly and C”, Pearson 2013.

4.      Wentk, “Richard Raspberry Pi”, John Wiley & Sons, 2014

Essential Reading / Recommended Reading

1.     A.K. Ray & K.M.Bhurchandi, “Advanced Microprocessors and peripherals- Architectures, Programming and Interfacing”, Tata McGraw Hill, 2002 reprint.

2.     Gibson, “Microprocessor and Interfacing” Tata McGraw Hill,II edition

Muhammad Ali Mazidi, Rolin D. Mckinlay, Danny Causey “8051 Microcontroller and

Evaluation Pattern

CIA marks=70

ESE marks= 30

EC636OE4 - FUNDAMENTALS OF IMAGE PROCESSING (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This Course is for third year students and is offered as interdisciplinary course.  Students will be understanding the concepts and apply in their respective domains

 

1)     COURSE OBJECTIVES

 

1.     Understand the basic principles of digital image processing.

 

2.     Analyze and apply the algorithms based on the applications given

 

3.     Implement the algorithms of a given application using MATLAB/Python

 

Course Outcome

 

At the end of the course, students will be able to 

CO1-Understand the basic principles of image processing

L2

 

CO2-Understand the tools used for image processing applications

L2

 

CO3-Analyze the methods used for image preprocessing

L4

 

CO4-Apply the compression techniques and analyze the results

L3

 

CO5-Develop an image processing system for a given application

L5

 

 

Unit-1
Teaching Hours:9
Digital Image Fundamntals
 

Concept of Digital Image, conversion of analog image to digital, General Applications of image processing, Fundamental Steps in Digital Image Processing. Components of an Image Processing System. Elements of Visual Perception. Light and the Electromagnetic Spectrum. Image Sensing and Acquisition. Image Sampling and Quantization

 

Unit-2
Teaching Hours:9
Introduction MATLAB and IP toolbox
 

Introduction to MATLAB, Introduction to IP Tool box, Exercises on image enhancement, image restoration, and image segmentation

Unit-3
Teaching Hours:9
IMAGE PROCESSING TECHNIQUES PART 1
 

Image Enhancement in the Spatial Domain: Some Basic Gray Level Transformations. Histogram Processing. Enhancement Using Arithmetic/Logic Operations. Basics of Spatial Filtering. Smoothing Spatial Filters. Sharpening Spatial Filters. Importance of Image Restoration, Model of the Image Degradation/Restoration Process. Noise Models. Filters for Image Restoration: Minimum Mean Square Error (Wiener) Filtering. Constrained Least Squares Filtering. Geometric Mean Filter

Unit-4
Teaching Hours:9
IMAGE PROCESSING TECHNIQUES PART 2
 

Image Compression: Fundamentals. Image Compression Models. Elements of Information Theory. Error-Free Compression. Lossy Compression. Image Compression Standards. Image Segmentation: Detection of Discontinuities. Edge Linking and Boundary Detection. Thresholding. Region-Based Segmentation. Segmentation by Morphological Watersheds

Unit-5
Teaching Hours:9
APPLICATION OF IMAGE PROCESSING
 

Applications of image processing in the field of Biomedical, Remote sensing, Machine vision, Pattern recognition, and Microscopic Imaging

Text Books And Reference Books:

T1. Rafael C. Gonzalez, Richard E.Woods, Digital Image Processing using MATLAB, PHI, 2005

 

 

Essential Reading / Recommended Reading

R1. Rafael C. Gonzalez, Richard E.Woods, Digital Image Processing‘, Pearson Education, Inc., Third Edition, 2016

R2. Anil K. Jain, Fundamentals of Digital Image Processing‘, Prentice Hall of India, 2002

Evaluation Pattern

 

CIA I (20)

CIA II (50)

CIA III (20)

ESE (100

Attendance

20

50

20

100

10

 

EC636OE7 - E-WASTE MANAGEMENT AND RADIATION EFFECT (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Describe E-waste disposal, collection, recycling, and materials recovery techniques and technologies and effect of mobile radiation on human health and life .

 

Course Outcome

Summarize the history of E-waste management including impacts from early human civilization to current day.

Describe the major categories of E-waste.

Assess the major regulatory developments surrounding E-waste management.

Characterize the components and chemical and physical properties

Summarize the impact of radiation, smartphones and mobile devices on human health and life

Unit-1
Teaching Hours:12
Magnitude of the Global E-Waste Problem
 

Scope Of The Problem, Refurbishing Discarded Electronic Devices , Recycling Of Devices Manufactured With Newer High Technology Alloy Nano-materials, Global Distribution Steams Of E-Waste— Where Does It Go?, Uptake Of Toxic Chemicals Originating From E-Waste Into Food , Biological Effects Of E-Waste Chemicals, Refurbishing Of Outdated Electronic Devices, Inter-country Variations In The Collection Of Electronic Devices For Recycling, Recycling Of Component Materials In Electronic Devices, Differences In E-Waste Handling Between Developed And Developing Countries, Child Labor, Occupational And Environmental Safety Issues, Landfill Operations, Waste Ponds, Incineration.  

Unit-2
Teaching Hours:8
Metals, Metallic Compounds, Organic Chemicals, E-Waste Chemical Mixtures and Toxicology of E-Waste
 

Metals And Metallic Compounds, Nano-materials, Representative Organic E-Waste Chemicals, ChemicalMixtures Exposures In E-Waste Recycling, Risk Assessment Approaches For E-Waste, Public Health Implications And Directions Forward, Toxic Metals/Metalloids, Organic Chemicals.

Unit-3
Teaching Hours:9
Risk Assessment/Risk Communication Approaches for E-Waste Sites
 

 In Utero Exposure To E-Waste Chemicals,  Children And Adult, Genetic Inheritance, Persons Of Poor Nutritional Status, Subsistence Farmers/Hunters And Fishers/ Low Socioeconomic Status, Contamination Of Local Food Supplies And House Dust,Individual Chemical Approaches,Mixture Approaches, Perceptions Of Risk At Toxic Waste Sites In Relation To Economic And Food Concerns: The Role Of Risk Communication.    

Unit-4
Teaching Hours:6
Translation of Risk Assessment Information Into Effective International Policies and Actions.
 

Communication Of Scientific Information In Practical Terminology, Information Mapping Technology Approaches, Collaborations Among Interested International Stakeholders/Government Agencies/Industrial Groups/Ngos, International Conferences And Diplomatic Interactions—Both Formal And Informal

 

Unit-5
Teaching Hours:9
The impact of radiation, smartphones and mobile devices on human health and life
 

Introduction, Effect of electromagnetic waves on human brains, Effect on human’s upper extremities, back and neck caused by handheld devices, Effect of smartphones on drivers, Advantages and disadvantages of using smartphones and HHDs, Can people live without cell phones, Cellular Tower Radiation effects, Solutions to mitigate impact of cell phones and mobile devices on human health and life. Harmful Effects of Radiation, Health Effects of Radiation, Threshold Effects of Radiation, Non-threshold Effects of Radiation,Sources of radiation to the human population,Doses and risks associated with diagnostic radiology, interventional radiology/cardiology, and nuclear medicine, cellular response to radiation, risk associated with diagnostic radiology, radiation sickness ,radiation therapy

 

Text Books And Reference Books:

1.

 

1.     Bruce A. Fowler., “ Electronic WasteToxicology and Public Health Issues”,Acadamic press

2.     Johri R., “E-waste: implications, regulations, and management in India and current global best

a.     practices”, TERI Press, New Delhi.

3.     R.E Hester and R.M Harrison., “E-waste Recycling”, RSC publication.

4.     Leonid Miakotko., “The impact of smartphones and mobile devices on human health and life”,          https://www.nyu.edu/classes/keefer/waoe/miakotkol.pdf.

 

Essential Reading / Recommended Reading

1.     Daniel Grosch., “Biological Effects of Radiations’’,  2nd Edition Academic Press

2.      Electronic Waste Management Rules 2016, Govt. of India, available online at CPCB website.

Evaluation Pattern

CIA=70

ESE=30

EE636OE2 - NONCONVENTIONAL ENERGY SOURCES (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

·         To recognize the need of renewable energy technologies and their role in the current scenario of energy crisis

·         .Distinguish between the sustainable energy sources and fossil energy sources

·         Describe the principles of renewable energy production from various renewable sources

Course Outcome

Upon completion of this course, the students will be able

·         Demonstrate an understanding of the scientific principles of methodology of Non-conventional energy.

·         Acquire working knowledge of different Renewable energy science-related topics

Unit-1
Teaching Hours:12
Introduction
 

Conventional energy resources-availabilty and sustainability issues-,Non conventional sources-advantages over conventional sources- Renewable Energy sources-Advantages and limitations

Unit-2
Teaching Hours:12
Solar energy
 

Solar energy – Introduction to solar energy: solar radiation, availability, measurement and estimation.

Solar Thermal systems- Solar collectors(fundamentals only)- Applications -Solar heating system, Air conditioning and Refrigeration system ,Pumping system, solar cooker, Solar Furnace, Solar Greenhouse -Design of solar water heater

Unit-3
Teaching Hours:12
Solar Photovoltaic Systems
 

Solar Photovoltaic Systems:- Photovoltaic conversion- Solar Cell, module, Panel and Array Solar cell- materials-characteristics- efficiency-Battery back up-Charge controller- MPPT-PV system classification- Design of stand-alone PV system.

Unit-4
Teaching Hours:12
Wind Power Systems
 

Wind source – wind statistics - energy in the wind –betz criterion-,mechanical components-aerodynamic force-angle of attack-pitch angle-yaw-rotor types, wind driven generators-fixed speed drives- variable speed drives- –-environmental aspects.

Unit-5
Teaching Hours:12
Ocean, Geothermal and other resources
 

OTEC systems-types, wave energy-types, tidal energy-different schemes, Renewable Hydro –Power -Small, Mini and Micro hydro power-Types of turbines and generators

Geothermal energy-geothermal resources, limitations and environmental aspects of each type

Fuel cells, MHD power generation, Biomass energy

Text Books And Reference Books:

1.      Non Conventional Energy Resources-B.H.Khan

2.      G.D.Rai ,Non Conventional Energy Sources, Khanna Publishers,4 th Edition,2009

3.      D.P.Kothari, K.C.Singal, Rakesh Ranjan, Renewable Energy Sources and Emerging Technologies, Prentice Hall of India, New Delhi, 2009

4.      Mukund R Patel “Wind and solar power systems Design ,Analysis and operation” Taylor and Francis publishers ,2nd edition,2006,ISBN978-0-8493-1570-1

Essential Reading / Recommended Reading

1.      A.K. Mukherjee, Nivedita Takur  Photovoltaic Systems –Analysis and Design(PHI-2011)

2.      Ahmed Hemami, Wind Turbine Technology, (Cengate Learning,2012,First India Edition)

3.      Wind energy Conversion Systems – Freris L.L. (Prentice Hall,1990)

4.      Wind Turbine Technology: Fundamental concepts of wind turbine technology Spera D.A. (ASME Press, NY, 1994)

Evaluation Pattern

CIA I - 20 marks

CIA II - midsem - 50 marks

CIA III - 20 marks

ESE - 100 marks

EE636OE3 - INTRODUCTION OF HYBRID ELECTRIC VEHICLES (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course introduces the fundamental concepts, principles, analysis and design of hybrid and electric vehicles.

Course Outcome

·         To understand concepts of hybrid and electric drive configuration, types of electric machines that can be used, suitable energy storage devices etc

·         To recognize the application of various drive components and selection of proper component for particular applications.

Unit-1
Teaching Hours:12
HYBRID VEHICLES
 

History and importance of hybrid and electric vehicles, impact of modern drive-trains on energy supplies. Basics of vehicle performance, vehicle power sources, transmission characteristics, and mathematical models to describe vehicle performance.

Unit-2
Teaching Hours:12
HYBRID TRACTION
 

Basic concept of hybrid traction, introduction to various hybrid drive-train topologies, power flow control in hybrid drive-train topologies, fuel efficiency analysis. Basic concepts of electric traction, introduction to various electric drive-train topologies, power flow control in hybrid drive-train topologies, fuel efficiency analysis.

Unit-3
Teaching Hours:12
MOTORS AND DRIVES
 

Introduction to electric components used in hybrid and electric vehicles, configuration and control of DC Motor drives, Configuration and control of Induction Motor drives, configuration and control of Permanent Magnet Motor drives, Configuration and control of Switch Reluctance Motor drives, drive system efficiency.

Unit-4
Teaching Hours:12
INTEGRATION OF SUBSYSTEMS
 

Matching the electric machine and the internal combustion engine (ICE), Sizing the propulsion motor, sizing the power electronics, selecting the energy storage technology, Communications, supporting subsystems

Unit-5
Teaching Hours:12
ENERGY MANAGEMENT STRATEGIES
 

Introduction to energy management strategies used in hybrid and electric vehicle, classification of different energy management strategies, comparison of different energy management strategies, implementation issues of energy strategies.

Text Books And Reference Books:

1.      BimalK. Bose, ‘Power Electronics and Motor drives’ , Elsevier, 2011

2.      IqbalHussain, ‘Electric and Hybrid Vehicles: Design Fundamentals’, 2nd edition, CRC Pr I Llc, 2010

Essential Reading / Recommended Reading

1.      Sira -Ramirez, R. Silva Ortigoza, ‘Control Design Techniques in Power Electronics Devices’, Springer, 2006

2.      Siew-Chong Tan, Yuk-Ming Lai, Chi Kong Tse, ‘Sliding mode control of switching Power Converters’, CRC Press, 2011

3.      Ion Boldea and S.A Nasar, ‘Electric drives’, CRC Press, 2005

Evaluation Pattern

CIA I - 20 marks

CIA II -midsem 50 marks

CIA III - 20 marks

ESE - 100 marks

EE636OE6 - ROBOTICS AND AUTOMATION (2018 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

·         To understand concepts in kinematics and dynamics of robotic system.

·         To introduce control strategies of simple robotic system.

·         To study the applications of computer based control to integrated automation systems.

Course Outcome

·         Understand the basic concept of robotics and automation.

·         Mechanical requirement and design of control system for robot.

·         Applications of robots in various domains.

Unit-1
Teaching Hours:12
Introduction
 

Robot definitions - Laws of robotics - Robot anatomy - History - Human systems and Robotics - Specifications of Robots - Flexible automation versus Robotic technology - Classification applications

Unit-2
Teaching Hours:12
Robotic systems
 

Basic structure of a robot – Robot end effectors - Manipulators - Classification of robots – Accuracy - Resolution and repeatability of a robot - Drives and control systems – Mechanical components of robots – Sensors and vision systems - Transducers and sensors - Tactile sensors – Proximity sensors and range sensors - Vision systems - RTOS - PLCs - Power electronics

Unit-3
Teaching Hours:12
Robot kinematics, dynamics and programming
 

Matrix representation - Forward and reverse kinematics of three degree of freedom – Robot Arm – Homogeneous transformations – Inverse kinematics of Robot – Robo Arm dynamics - D-H representation of forward kinematic equations of robots - Trajectory planning and avoidance of obstacles - Path planning - Skew motion - Joint integrated motion – Straight line motion - Robot languages- Computer control and Robot programming/software

Unit-4
Teaching Hours:12
Control system design
 

Open loop and feedback control - General approach to control system design - Symbols and drawings - Schematic layout - Travel step diagram, circuit and control modes - Program control - Sequence control - Cascade method - Karnaugh-Veitch mapping - Microcontrollers - Neural network - Artificial Intelligence - Adaptive Control – Hybrid control

Unit-5
Teaching Hours:12
Robot applications
 

Material handling - Machine loading, Assembly, inspection, processing operations and service robots - Mobile Robots - Robot cell layouts - Robot programming languages

Text Books And Reference Books:

1.      Nagrath and Mittal, “Robotics and Control”, Tata McGraw-Hill, 2003.

2.      Spong and Vidhyasagar, “Robot Dynamics and Control”, John Wiley and sons, 2008.

3.      S. R. Deb and S. Deb, ‘Robotics Technology and Flexible Automation’, Tata McGraw Hill Education Pvt. Ltd, 2010.

Essential Reading / Recommended Reading

1.      Saeed B. Niku, ‘Introduction to Robotics’,Prentice Hall of India, 2003.

2.      Mikell P. Grooveret. al., "Industrial Robots - Technology, Programming and Applications",     McGraw Hill, New York, 2008.

Evaluation Pattern

CIA I -20 marks

CIA II - midsem 50 marks

CIA III - 20 marks

ESE - 100 marks

IT634P - DATAWAREHOUSING AND DATAMINING (2018 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

        To serve as an introductory course to under graduate students with an emphasis on the design aspects of Data mining and Data Warehousing

        To introduce the concept of data mining with in detail coverage of basic tasks, metrics, issues, and implication. Core topics like classification, clustering and association rules are exhaustively dealt with.

        To introduce the concept of data warehousing with special emphasis on architecture and design.

        Practical exposure on implementation of well known data mining tasks.

        Exposure to real life data sets for analysis and prediction.

        Learning performance evaluation of data mining algorithms in a supervised and an unsupervised setting.

Course Outcome

CO1:   Identify the differences  between relational database and data warehouses, the need for data warehousing to formulate the decision support system an engineering specialization for the prediction and modeling to complex engineering activities.(L3)

CO2:   Summarize the dominant data warehousing architectures and analyze their implementation details to develop multidimensional data models to analyze complex engineering problems.(L2)

CO3:   Implement various data pre-processing techniques to design data warehouses that meet the specified needs of the society with appropriate environmental considerations.(L3)

CO4:   Analyze the various clustering and classification algorithm functionalities and evaluate their merits and demerits to acquire research based knowledge for the synthesis of the information to provide valid conclusions.(L4)

CO5:   Explain the advanced data mining concepts and outline their scope of providing IT solutions for different domains which helps in the betterment of life.(L5)


Unit-1
Teaching Hours:9
INTRODUCTION AND DATA WAREHOUSING
 

Introduction, Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Implementation, Further Development, Data Warehousing to Data Mining

Unit-2
Teaching Hours:9
DATA PREPROCESSING, LANGUAGE, ARCHITECTURES, CONCEPT DESCRIPTION
 

Why Preprocessing, Cleaning, Integration, Transformation, Reduction, Discretization, Concept Hierarchy Generation, Data Mining Primitives, Query Language, Graphical User Interfaces, Architectures, Concept Description, Data Generalization, Characterizations, Class Comparisons, Descriptive Statistical Measures.

Unit-3
Teaching Hours:9
ASSOCIATION RULES
 

Association Rule Mining, Single-Dimensional Boolean Association Rules from Transactional Databases, Multi-Level Association Rules from Transaction Databases

Unit-4
Teaching Hours:9
CLASSIFICATION AND CLUSTERING
 

Classification and Prediction, Issues, Decision Tree Induction, Bayesian Classification, Association Rule Based, Other Classification Methods, Prediction, Classifier Accuracy, Cluster Analysis, Types of data, Categorization of methods, Partitioning methods, Outlier Analysis

Unit-5
Teaching Hours:9
RECENT TREND
 

Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial Databases, Multimedia Databases, Time Series and Sequence Data, Text Databases, World Wide Web, Applications and Trends in Data Mining

 

Text Books And Reference Books:

1.      J. Han, M. Kamber, “Data Mining: Concepts and Techniques”, Harcourt India / Morgan Kauffman, 2011.

2.      Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson Education, 2012.

Essential Reading / Recommended Reading

1.      K.P.Soman, Shyam Diwakar, V.Ajay: Insight into Data Mining – Theory and   Practice, PHI, 2012.

2.      David Hand, Heikki Manila, Padhraic Symth, “Principles of Data Mining”, PHI 2012.

3.      W.H.Inmon, “Building the Data Warehouse”, 3rd Edition, Wiley, 2011.

4.      Alex Bezon, Stephen J.Smith, “Data Warehousing, Data Mining & OLAP”, MeGraw-Hill Edition, 2001

5.      Paulraj Ponniah, “Data Warehousing Fundamentals”, Wiley-Interscience Publication, 2003.

Evaluation Pattern

 

Component

Assessed for

Minimum marks

 to pass

Maximum

marks

1

Theory CIA

30

-

30

2

Theory ESE

30

12

30

3

Practical CIA

35

14

35

4

Attendance

05

-

05

4

Aggregate

100

40

100

IT635 - SOFTWARE TESTING (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To give an overview of the software testing techniques. To design and understand test cases, various levels of testing and related concepts.

Course Outcome

CO1:   Identify the reason for bugs and device mechanism for preventing /fixing bugs with respect to the principles in software testing . (L3)

CO2:   Interpret the existing procedures for software testing which would enhance the software quality. (L2)

CO3:   Construct a software test plan to validate the software with respect to defined test scenarios. (L3)

CO4:   Justify the test processes applied in the testing framework and incorporate the procedures as a formatted report.(L5)

CO5:   Analyze the available techniques in software testing which would validate any given software product in a commercial environment. (L4)

Unit-1
Teaching Hours:9
INTRODUCTION
 

Testing as an Engineering Activity – Role of Process in Software Quality – Testing as a Process – Basic Definitions – Software Testing Principles – The Tester’s Role in a Software Development Organization – Origins of Defects – Defect Classes – The Defect Repository and Test Design – Defect Examples – Developer/Tester Support for Developing a Defect Repository

Unit-2
Teaching Hours:9
TEST CASE DESIGN
 

Introduction to Testing Design Strategies – The Smarter Tester – Test Case Design Strategies – Using Black Box Approach to Test Case Design Random Testing – Requirements based testing – positive and negative testing – Boundary Value Analysis – decision tables - Equivalence Class Partitioning state-based testing – cause effect graphing – error guessing - compatibility testing – user documentation testing – domain testing Using White–Box Approach to Test design – Test Adequacy Criteria – static testing vs. structural testing – code functional testing - Coverage and Control Flow Graphs – Covering Code Logic – Paths – Their Role in White–box Based Test Design – code complexity testing – Evaluating Test Adequacy Criteria.

Unit-3
Teaching Hours:9
LEVELS OF TESTING
 

The Need for Levels of Testing – Unit Test – Unit Test Planning –Designing the Unit Tests. The Test Harness – Running the Unit tests and Recording results – Integration tests – Designing Integration Tests – Integration Test Planning – scenario testing – defect bash elimination -System Testing – types of system testing - Acceptance testing – performance testing - Regression Testing – internationalization testing – ad-hoc testing - Alpha – Beta Tests – testing OO systems – usability and accessibility testing

Unit-4
Teaching Hours:9
TEST MANAGEMENT
 

People and organizational issues in testing – organization structures for testing teams – testing services - Test Planning – Test Plan Components – Test Plan Attachments – Locating Test Items – test management – test process - Reporting Test Results – The role of three groups in Test Planning and Policy Development – Introducing the test specialist – Skills needed by a test specialist – Building a Testing Group.

Unit-5
Teaching Hours:9
CONTROLLING AND MONITORING
 

Software test automation – skills needed for automation – scope of automation – design and architecture for automation – requirements for a test tool – challenges in automation - Test metrics and measurements –project, progress and productivity metrics – Status Meetings – Reports and Control Issues – Criteria for Test Completion – SCM – Types of reviews – Developing a review program – Components of Review Plans– Reporting Review Results. – Evaluating software quality – defect prevention – testing maturity model – Case Studies.

Text Books And Reference Books:

TEXT BOOKS

1. Boris Beizer, “Software Testing Techniques”, Dreamtech. Second Edition, 2009

2. Srinivasan Desikan and Gopalaswamy Ramesh, “Software Testing – Principles and Practices”, Pearson education, 2008.

Essential Reading / Recommended Reading

REFERENCE BOOKS

1. Elfriede Dustin, “Effective Software Testing”, Pearson Education, First Edition, 2008.

2. Edward Kit, “Software Testing in the Real World”, Pearson Education, 2008.

3. Aditya P.Mathur, “Foundations of Software Testing”, Pearson Education, 2011.

 

Evaluation Pattern
 
 

Internal Assessment - 50 Marks

(CIA 1: 10 Marks, CIA 2: 25 Marks, CIA 3: 10 Marks, Attendance: 5 Marks)

End Semester Examination (ESE) - 50 Marks

Total = 100 Marks

MA636OE3 - NUMERICAL SOLUTION OF DIFFERENTIAL EQUATIONS (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Many physical laws are couched in terms of rate of change of one/two or more independent variables, most of the engineering problems are characterized in the form of either nonlinear ordinary differential equations or partial differential equations. The methods introduced in the solution of ordinary differential equations and partial differential equations will be useful in attempting any engineering problem.

Course Outcome

CO1: Operate multistep numerical techniques to solve first and second order ordinary differential equations. L3

CO2: Discuss finite difference approximations to solve boundary value problems. L4

CO3: Discuss finite difference schemes for Parabolic equation. L3

CO4: Operate finite difference method to solve boundary value problems of  hyperbolic and elliptic differential equations  L4

CO5: Construct  finite volume method to solve differential equations. L3

Unit-1
Teaching Hours:9
Ordinary Differential Equations
 

Multistep (explicit and implicit) methods for initial value problems

Unit-2
Teaching Hours:9
Finite Difference Methods
 

Finite difference approximations for derivatives, boundary value problems with explicit boundary conditions, implicit

boundary conditions, error analysis

Unit-3
Teaching Hours:9
Partial Differential Equations
 

Classification of partial differential equations, finite difference schemes for Parabolic equations, multilevel explicit

and implicit methods for one dimensional heat equation, iterative methods for one dimensional heat  equation

Unit-4
Teaching Hours:9
Hyperbolic And Elliptic Equations
 

Finite difference schemes for hyperbolic and elliptic equations, implicit method of solving one dimensional wave equation, iterative scheme of solving Laplace and Poisson equation, ADI method

Unit-5
Teaching Hours:9
Discretization
 

The Discretization Concept: Methods of deriving  the discretization equation: Taylor series formulation,  control –Volume Formation: Illustrative example: One dimensional heat conduction equation, Steady  one dimensional Convection and Diffusion Equation and its Physical Phenomena.

Text Books And Reference Books:

M.K. Jain, “Numerical Solution of Differential Equations”, Wiley Eastern, 1984.

Essential Reading / Recommended Reading

R1. G.D. Smith, “Numerical Solution of Partial Differential Equations”, Oxford Univ. Press, 2004.

R2. M.K.Jain, S.R.K. Iyengar and R.K. Jain, “Computational Methods for Partial Differential Equations”, Wiley Eastern, 2005.

R3. S. S. Sastry, “Numerical Analysis for Engineers”,  Tata Mcgraw Hill Edition.

Evaluation Pattern

Continuous Internal Assessment (CIA): 50% (50 marks out of 100 marks)

End Semester Examination(ESE): 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I  :  Subject Assignments / Online Tests                  : 10 marks

CIA II :   Mid Semester Examination (Theory)                : 25 marks                   

CIAIII:Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications: 10 marks

Attendance                                                                           : 05 marks

            Total                                                                              : 50 marks

 

Mid Semester Examination (MSE) : 

The MSE is conducted for 50 marks of 2 hours duration.

Question paper pattern; Five out of Six questions have to be answered. Each  question carries 10 marks

 

End Semester Examination (ESE):

The ESE is conducted for 100 marks of 3 hours duration.

The syllabus for the theory papers are divided into FIVE units and each unit carries equal Weightage in terms of marks distribution.

Question paper pattern is as follows:

Two full questions with either or choice will be drawn from each unit. Each question carries 20 marks. There could be a maximum of

three sub divisions in a question. The emphasis on the questions is to test the objectiveness, analytical skill and application skill of the

concept, from a question bank which reviewed and updated every year

The criteria for drawing the questions from the Question Bank are as follows

50 % - Medium Level questions

25 % - Simple level questions

25 % - Complex level questions

ME636OE3 - BASIC AUTOMOBILE ENGINEERING (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

 The objective of this course isto impact knowledge to students in various systems of Automobile Engineering and to learn the fundamental principles, construction and auxiliary systems of automotive engines

Course Outcome

Upon completion of this course, the students will be able to

 

CO1:  To describe chassis, body and engine components of automobile

 

CO2:  To demonstrate knowledge of transmission, cooling and lubrication systems

 

CO3:  To demonstrate knowledge of engine injection and ignition systems

 

CO4:  To demonstrate knowledge of steering, brakes and suspension systems

 

CO5:  To describe environmental impact of emissions from vehicles and methods for controlling it.

 

Unit-1
Teaching Hours:9
Engine
 

Engine classifications, number of strokes, cylinders, types of combustion chambers for petrol and diesel engines, valves, valve arrangements and operating mechanisms, piston, design basis, types, piston rings, firing order, fly wheel.

Unit-1
Teaching Hours:9
Introduction
 

Classification of vehicles, options of prime movers, transmission and arrangements.

Unit-2
Teaching Hours:9
Fuel Supply Systems
 

Petrol and diesel engines, fuel pumps, Mechanical and electrical diaphragm pumps, air and fuel filters.

Unit-2
Teaching Hours:9
Carburettors and Injection Systems
 

carburetors, fuel injection systems for diesel and petrol engines, electronic fuel injection, super chargers, muffers.

Unit-3
Teaching Hours:9
Electrical System
 

Ignition system, distributor, electronic ignition, magneto, dynamo, alternator, regulator, starting motor, introduction to various accessories, typical wiring diagram.

 

Unit-3
Teaching Hours:9
Cooling and Lubrication system for IC Engines
 

Necessity, methods of cooling, air cooling, water cooling, components of water cooling systems, Objective of lubrication, requirements of lubricant, types of lubricant, various systems of engine lubrication. 

Unit-4
Teaching Hours:9
Chassis
 

Introduction of chassis, classification, conventional construction, frameless construction, introduction to vehicle dimensions. 

Unit-4
Teaching Hours:9
Transmission System
 

Introduction to single plate clutch, wet and dry type, clutch actuating mechanisms, study of clutch components, fluid fly wheel. Gear box , Theory, four speed and five speed sliding mesh, constant mesh and synchromesh type, selector mechanism, automatic transmission, overdrive, transfer box four wheel drive, torque converter, propeller shaft. 

Unit-5
Teaching Hours:9
Suspension System
 

Systems, springs, shock absorbers, axles, front and rear, different methods of floating rear axle, front axle and wheel alignment, types of rims and tyres.

Unit-5
Teaching Hours:9
Steering System
 

Steering mechanisms, types of brakes and brake actuation mechanisms.

Text Books And Reference Books:

 1. Kripal Singh,“Automobile Engineering”, Vol.-1 & 2, Standard publisher distributors 2015.

 

2. Joseph Heitner,“Automotive Mechanics”, East-West student edition 2014.

Essential Reading / Recommended Reading

1. Crouse. W.H. and Angling, D.L “Automobile Mechanics”2009.

2. Judge, A.W ,“Automobile Electrical System”

 

3. K.k.Ramalingam,“Automobile engineering”, scitech publications 2001

Evaluation Pattern

CIA1-10MARKS

CIA2-25MARKS

CIA3-10MARKS

ATTENDANCE-5MARKS

ESE-50MARKS

ME636OE4 - PROJECT MANAGEMENT (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The course aims at the following learning targets

·        To understand the concepts of project definition, life cycle, and systems approach;

·        To develop competency in project scooping, work definition, and work breakdown structure (WBS)

·        To handle the complex tasks of time estimation and project scheduling, including PERT and CPM

  • To develop competencies in project costing, budgeting, and financial appraisal

Course Outcome

CO1: Apply the concept of project management in engineering field through project management life cycle.(L3)(PO11)

CO2: Analyze the quality management and project activity in engineering field through work breakdown structure. (L4)(PO10)

CO3: Analyze the fundamentals of project and network diagram in engineering and management domain through PDM techniques. (L4)(PO1)

CO4: Evaluate the concept of network analysis through PERT and CPM techniques. (L5)(PO2)

CO5: Apply the concept of scheduler based on resource availability in engineering and management field through project proposal. (L3)(PO11)

Unit-1
Teaching Hours:9
Project Management
 

Principles of Project Management: Defining, Planning, Executing, Controlling, Closing; Project Management Life Cycle: Phases of Project Management, Levels of Project Management

Unit-1
Teaching Hours:9
Introduction to Project
 

Definition of a Project, Sequence of Activities, Unique activities, Complex Activities, Connected Activities, One Goal, Specified Time, Within Budget, According to Specification. Defining a Program, Project parameters: Scope, Quality, Cost, Time, Resources; The scope triangle: Time, Cost, and Resource Availability, Project Classification 

Unit-2
Teaching Hours:9
Quality Management
 

Continuous Quality Management Model, Process Quality Management Model; Risk Management, Risk Analysis; Relationship between Project Management and other Methodologies

Unit-2
Teaching Hours:9
Project Activities
 

Work Breakdown Structure, Uses of WBS, Generating the WBS: Top-Down/ Bottom-Up Approach, WBS for Small Projects, Intermediate WBS for large projects; Criteria to Test for Completeness in the WBS: Measurable Status, Bounded, Deliverable, Cost/Time Estimate, Acceptable Duration Limits, Activity Independence; Approaches to Building the WBS: various approaches, Representing WBS

Unit-3
Teaching Hours:9
Activity Duration, Resource Requirements, & Cost
 

Duration: Resource Loading versus Activity Duration, Variation in Activity Duration, Methods for Estimating Activity Duration, Estimation Precision; Resources; Estimating Cost, JPP Session to Estimate Activity Duration & Resource Requirements, Determining Resource Requirements

Unit-3
Teaching Hours:9
Fundamentals of Project Network Diagram
 

Project Network Diagram, Benefits to Network- Based Scheduling, Building the Network Diagram Using the PDM, Analyzing the Initial Project Network Diagram. 

Unit-4
Teaching Hours:9
Network Analysis ? PERT
 

Introduction to Project Evaluation and Review Technique, Event, Activity, Dummy, Network rules, Graphical guidelines for network, Common partial situations in network, numbering the events, Cycles; Developing the Network, Planning for network construction, modes of network construction, steps in developing network, hierarchies; Time Estimates in PERT, Uncertainties and use of PERT, Time estimates, Frequency distribution, Mean, Variance & standard deviation, Probability distribution, Beta distribution, Expected time; Time Computations in PERT, Earliest expected time, Formulation for TE, Latest allowable occurrence time, Formulation for TL, Combined tabular computations for TE, TL; Slack, Critical Path, Probability of meeting schedule date.

Unit-4
Teaching Hours:9
Network Analysis- CPM
 

Introduction to Critical Path Method, Procedure, Networks, Activity time estimate, Earliest event time, Latest allowable occurrence time, Combined tabular computations for TE and TL, Start & Finish times of activity, Float, Critical activities & Critical path. Crashing of project network, Resource leveling and Resource allocation 

Unit-5
Teaching Hours:9
Schedules Based on Resource Availability
 

Resources, Leveling Resources, Acceptability Leveled Schedule, Resource Leveling Strategies, Work Packages: Purpose of a Work Package, Format of a Work Package

Unit-5
Teaching Hours:9
Joint Project Planning Session
 

Planning the Sessions, Attendees, Facilities, Equipments, Complete Planning Agenda, Deliverables, Project Proposal

Text Books And Reference Books:

TEXT BOOKS:

T1.“Effective Project Management”, Robert K. Wysocki, Robert Beck. Jr., and David B. Crane; - John Wiley & Sons 2003.

T2. Project Planning and Control with CPM and PERT” Dr. B.C. Punmia &      K.K.Khandelwal; - Laxmi Publications, New Delhi 2011.

 

Essential Reading / Recommended Reading

R1. “Project Management” S. Choudhury, - TMH Publishing Co. Ltd, New Delhi 1998.

R2. “Total Project Management- The Indian Context” P. K. Joy, - Macmillan India Ltd., Delhi 2017.

R3. “Project Management in Manufacturing and High Technology Operations” Adedeji Bodunde Badiru, - John Wiley and Sons 2008.

R4. “Course in PERT & CPM” R.C.Gupta, - DhanpatRai and Sons, New Delhi

R5. “Fundamentals of PERT/ CPM and Project Management” S.K. Bhattacharjee; - Khanna Publishers, New Delhi 2004.

Evaluation Pattern

CIA1-10Marks

CIA2-25Marks

CIA3-10Marks

ESE-50Marks

Attendance-5Marks

ME636OE5 - BASIC AEROSPACE ENGINEERING (2018 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

 Course Description: This first part of the course “Basic Aeronautical Engineering” presents an overall picture of the aeronautics domain. This overview involves a number of different perspectives on the aerospace domain, and shows some basic principles of the most important concepts for flight. Then the basic aerodynamics are covered, followed by flight mechanics

Course Objectives:

·        To familiarize with the basics of aerodynamics

·        To familiarize with the basics of aircraft structures, systems & instruments

·        To give exposure to the power plants cased in Aircraft

Course Outcome

 Upon completion of this course, the students will be able to

CO1: To explain flow regimes (viscous/non-viscous; compressible/incompressible aerodynamics) and to estimate viscous and thermal effects
CO2: To compute lift/drag of simple aero foil configurations
CO3: To describe reference frames and derive general equations of motion for flight and orbital mechanics
CO4: To apply equations of motion to determine aircraft performance in steady gliding, horizontal and climbing flight
CO5: To derive aircraft performance diagram and flight envelope, in relation to aircraft morphology, lift-drag polar and engine performance 

Unit-1
Teaching Hours:9
Introduction to Principles of Flight
 

Physical properties and structure of the atmosphere, Temperature, pressure and altituderelationships, Evolution of lift, drag and moment, different types of drag.

Unit-1
Teaching Hours:9
AIRCRAFT CONFIGURATION
 

Brief History- airplanes and Helicopters – Components of an airplane and their functions. Different types of flightvehicles, classifications, Basic instruments for flying

Unit-2
Teaching Hours:9
Introduction to Aerodynamics
 

Aerodynamic forces on aircraft,Basic characteristics of aerofoils, NACA nomenclature, Classification of NACA aerofoils, propagation of sound, Mach number, subsonic, transonic, supersonic, hypersonic flows.

Unit-2
Teaching Hours:9
Elements of Airplane Performance
 

Introduction, Equation of motion, Thrust required for level unaccelerated flight, Thrust available and maximum velocity, Power required for level unaccelerated flight, Power available and maximum velocity for reciprocating engine – propeller combination and jet engine, Altitude effect of power available and power required. Rate of climb, gliding flight, Absolute and Ceiling, Time of climb, Range & Endurance for propeller driven and jet air plane.

Unit-3
Teaching Hours:9
Aircraft Structures
 

 

General types of construction, Monocoque and Semi-monocoque - construction, Typical wing and fuselage Structures

 

Unit-3
Teaching Hours:9
Landing Gears
 

Introduction to Landing Gears, Types of Landing Gears

Unit-4
Teaching Hours:9
Aircraft Materials
 

 

Metallic and non-metallic materials, Use of aluminium alloy, titanium, stainless steel and composite materials

 

Unit-4
Teaching Hours:9
Systems and Instruments
 

Conventional control, Powered controls, Basic instruments for flying, typical systems for control actuation

Unit-5
Teaching Hours:9
Jet Propulsion
 

Basic ideas about piston, turboprop and jet engines – comparative merits, Propellers and Jet for thrust production.

Unit-5
Teaching Hours:9
Rocket Propulsion
 

Principle of operation of rocket, types of rocket and typical applications, Exploration into space, Use of multistage rockets

Text Books And Reference Books:

 1. Kermode,A.C., ‘Flight without Formulae’, Pearson,2004

2. Shevell,R.S., Fundamentals of flights, Pearson education 2004

Essential Reading / Recommended Reading

1. Anderson.J.D., Introduction to Flight, McGraw Hill,2010

2. McKinley.J.L. and R.D. Bent, Aircraft Power Plants, McGraw Hill1993

 3. Pallet.E.H.J. Aircraft Instruments & Principles, Pearson 2010

Evaluation Pattern

CIA1-10MARKS

CIA2-25MARKS

CIA3-10MARKS

ATTENDANCE-5MARKS

ESE-50MARKS

 

BTGE 732 - ACTING COURSE (2017 Batch)

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

Course Objectives/Course Description

 

In this course the students are introduced different aspects of the theatre such as, acting, direction, scenic design, costume, make-up. At the end of the course the learners will put up one-act plays. The course aims at the study and practice of Classical Acting. The development of individual imagination, insight, skills and disciplines in the presentation of drama to audience.

Course Outcome

*             To gain an understanding of acting principles and techniques

             Develop skills in the analysis and interpretation of dramatic texts for performance

             Explore basic voice and movement skills to create dramatic effect on stage

             Understand the basic production processes

             The ability and willingness to engage in a structured play in an ensemble as an actor

Unit-1
Teaching Hours:12
Method of acting from ?inside out? that?s introduced in the Stanislavsky?s System
 

The Keys of the “System”: Objective, Super-objective, given circumstances, emotional memory,

“Magic If”, subtext, method of physical action, through line.

Unit-2
Teaching Hours:10
The opposite method from ?outside in? by Jacques Lecoq
 

Energy centers, Sectors of gestures, Animals in characterization.

Unit-3
Teaching Hours:10
Adaptation of the ?system? by Lee Strasberg, Stella Adler
 

Adaptation of the “system” by Lee Strasberg, Stella Adler,                                                 

Sanford Meisner and putting it in their “methods”. 

Work with the senses.  Discovering the sensory base of the work: learning to memorize and recall sensations, often called “sense memory” and /or “affective memory”;

Unit-4
Teaching Hours:11
Technical aspects
 

The students are introduced to scenic design and costume.

Unit-5
Teaching Hours:17
Creating a scene
 

Analyzing, rehearsing and performing a short scene from any of famous classical plays by using rehearsal steps for active analysis through physical actions

Text Books And Reference Books:

Stanislavsky, Constantine. An Actor prepares. New Delhi: Research Press, 2006.Print

Essential Reading / Recommended Reading

Stanislavsky, Constantine. An Actor prepares. New Delhi: Research Press, 2006.Print

Evaluation Pattern

1. Monologue

2. Dialogue Delivery

3. Skit

4. Story Telling

BTGE 734 - DIGITAL WRITING (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

·         Planning the content for a website and writing it

·         Writing news reports, feature articles, listicles

·         Understanding the audience and developing audience personas

·         Content strategy and creating a content calendar

·         Executing a content calendar and writing for social media

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1

Planning the content for a website and writing it

Understanding, applying, analysing, evaluating, creating

2

Writing news reports, feature articles, listicles

Understanding, analysing, evaluating, creating

3

Understanding the audience and developing audience personas

Analysing, evaluating, creating

4

Content strategy and creating a content calendar

Analysing, evaluating, creating

5

Executing a content calendar and writing for social media

Remembering, understanding, applying, analysing, evaluating, creating

 

Unit-1
Teaching Hours:6
UNIT I Introduction
 

Introduction to Digital Writing: What is online writing? Narrative structure for online and digital stories, Writing for university publications, Writing for specific platforms (eg various social and new media platforms), The Progress from Blogging to Freelancing, Copyright, Ownership, and authorship, Theorizing online spaces

Unit-2
Teaching Hours:6
UNIT II Digital Writing Approaches
 

Approaches to Digital Writing: Approach to digital storytelling, Interactive narratives, Sourcing information, Exploring Trans media stories, data visualization, online identities and the self, alternate realities.

Unit-3
Teaching Hours:6
UNIT III Writing Techniques
 

Writing Techniques:  Online news writing, Headlines, Sentences, Links, Tables and Info graphics, Meaningful Linking, Effective Illustrations, Content Strategy, Message, Media, Style and Tone, Purposes, Personas and Scenarios

Unit-4
Teaching Hours:6
UNIT IV Publishing and Editing
 

Editing: What is Deep Editing, Organization of your write up, refining your writing through the stages of editing, Content editing as first step of Deep Editing, Structure Editing, Relooking at your piece: Style Edit, Final Stage of Editing: Presentation Editing Understanding Publishing, What is the difference between academic and op-ed publishing?, The role of insights in the publication process, Choosing the right platform for publishing, Marking your social presence

Unit-5
Teaching Hours:6
UNIT V Publications
 

Domains related to publishing: Understanding the role of Keywords, Examination of websites – Topics covered, Regions covered, Author Guidelines, ORCID &DOI, Predatory Publications: The Cons, What are the common features of Predatory Publications? Open and Close Access Journals

Text Books And Reference Books:

Carroll, Brian. Writing and Editing for Digital Media, 1st edition. ISBN 978-0-415-99201-5. Routledge.

Essential Reading / Recommended Reading

Peter Clark, Roy. How to Write Short: Word Craft for Fast Times. Little Brown and Company. ISBN 0316204323.

Online Journalism: Reporting, Writing and Editing for New Media, Richard Craig.

 Broadcast News Handbook: Writing, Reporting & Producing in a Converging Media World 2007, Third Edition, C.A. Tuggle,  Forrest Carr and Suzanne Huffman

Writing New Media Theory and Applications for Expanding the Teaching of Composition; Anne Frances Wysocki, Johndan Johnson-Eilola, Cynthia L. Selfe, & Geoffrey Sirc Publication Year: 2004.

Evaluation Pattern

CIA 50 Marks

ESE 50 Marks

BTGE 737 - PROFESSIONAL PSYCHOLOGY (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

To provide students with frameworks from psychology of human development

 To enhance their personal and professional development.

 To examine their behavioural and relational styles, develop skills of managing

work life interface issues and become more sensitive cultural differences and diversity

in groups.

Course Outcome

By the end of the course the learner will be able to:

1. Upon successful completion of the course and through participation in the class

room lectures and activities

2. Students will have greater awareness of their thinking styles, relational styles

and behavioural styles of functioning.

3. Students will develop interpersonal awareness and skills especially in the context

of diversity and difference.

4. Students will develop preparatory skills towards effective work – life balance.

5. Students will develop overall understanding of the psychosocial skills required

in professional world

Unit-1
Teaching Hours:6
Human Development and Growth- Introduction,
 

Psychosocial development (Erickson).

Development of Cognition (Piaget),

Moral Development (Kohlberg),

Faith Development (Fowler)

Unit-2
Teaching Hours:6
Self-Awareness
 

Thinking Styles (Cognitive distortions),

Interpersonal relationship styles (adult attachment theories),

personality styles (Jung type indicator or Myers Briggs Type Indicator),

Coping styles (Emotion focused and Problem focused)

Unit-3
Teaching Hours:5
Social Networks and self
 

Family Genogram (Bowen),

Community, Genogram (Ivey)

Unit-4
Teaching Hours:5
Work Life Balance
 

Work life balance and Emotion – decision link in Work life balance,

Connecting life goals with work goals,

Unit-5
Teaching Hours:8
Professional development and Diversity
 

Coaching skills,  Mentoring skills, Effective feedback, Developing a competency framework,

Self Determination Theory (Ryan and Deci),

Burke –Litwin change model.

Diversity and challenge Cross cultural communication, respecting diversity, Intercultural awareness, Multicultural awareness.

Text Books And Reference Books:

Mohan Krishnan, R. HR Strategy to optimize human capital: an integrated approach

through talent management.

Huselid, M.A., Becker, B.E., & Beatty, R.W. (2005). The

Workforce Scorecard: Managing Human Capital to execute strategy.

Harvard Business School Press.

Essential Reading / Recommended Reading

[1] Nelson Goud and Abe Arkoff, Psychology and Personal Growth, Edition, Allyn and

Bacon, 2005.

[2] Richard Nelson Jones, Human Relationship skills: Coaching and self coaching, 4th edition,

Routledge, 2006

Evaluation Pattern

CIA – 1 for 20 marks reduced to 10

CIA – 2 for 50 marks reduced to 25

CIA – 3 for 20 marks reduced to 10

Attendance is for 5 marks

End Semester Exam for 100 marks reduced to 50

Total marks = 100

BTGE 744 - DIGITAL MARKETING (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

Course Description: Developing a successful digital marketing strategy and implementation is both an art and science. It involves in-depth knowledge of dynamics of new media (Social Media, Mobile) and utilizing the right resources and marketing skills to design and launch successful customer engagement campaigns. Digital Marketing course has been designed to help students to understand both functional and management roles required to plan and execute effective Digital Marketing campaigns. The course also helps students gain an insight how to plan and implement Digital Marketing initiatives.

Course Objectives:This course attempts to help students to understand both functional and management roles required to plan and execute effective Digital Marketing campaigns.

Course Outcome

On having completed this course student should be able to:

CLO 1: Outline the basics of digital marketing and digital marketing plan

CLO 2: Utilize the concepts of display ads and e-mail marketing in digital campaigns

CLO 3: Choose the appropriate social media for achieving the objectives of the campaign

CLO 4: Appraise the SEO and SEM efforts of any business organization

CLO 5: Explain Mobile Marketing and Web Analytics pertaining to any business

CLO 6: Design and run a digital marketing campaign for a client 

Unit-1
Teaching Hours:5
Introduction to Digital Marketing
 

Digital Marketing: Origin of digital marketing; Traditional Vs Digital Marketing; Internet Users in India; Grehan’s 4Ps of digital marketing; The consumer decision journey; The P-O-E-M Framework; The digital landscape; Digital Marketing Plan.

Ethical Challenges: Frauds on the Web, Data and Identity Theft, Issue of Privacy. Information Technology Act, 2000.

Unit-2
Teaching Hours:6
Display Advertising and e-mail Marketing
 

Concept of Display Advertising; Types of display Ads; Buying Models; Display Plan; Targeting – Contextual targeting- Placement Targeting-Remarketing- Interest categories- Geographic Language Tagging; What makes a good Ad? Programmatic digital advertising; Analytics tools – viewability, on target reach, Ad fraud, Brand Health.

e-mail Marketing – Building a List- Content Strategies – e-mail newsletter – Automating e-mail marketing- Analytics

Unit-3
Teaching Hours:9
Social Media Marketing
 

How to build a successful social media strategy? Facebook Marketing- Facebook for Business-Anatomy of an Ad campaign – Adverts - Facebook Insights

Linkedin Marketing – Linkedin Strategy- Sales lead generation – Content Strategy – Linkedin Analytics – Targeting – Ad Campaign,Twitter Marketing – Getting started with Twitter – Building a content strategy – Twitter Ads – Twitter Analytics

Instagram Marketing – Objectives – Content Strategy – Style guidelines – Hashtags – Videos- Sponsored Ads – Apps – Generate leads

Unit-4
Teaching Hours:6
Search Engine Advertising and Search Engine Optimisation
 

Why pay for Search Advertising? Understanding Ad Placement; Understanding Ad ranks; Creating the first Ad campaign; Enhancing the Ad campaigns; Performance reports. Google Adsense.

Search Engine Optimisation – How search engine works? SEO Phases; On page Optimisation; Off-page Optimisation; Social Media Reach; Maintenance

Unit-5
Teaching Hours:4
Mobile Marketing and Web Analytics
 

Mobile Advertising – Mobile Marketing toolkit – Mobile Marketing Features – Mobile Analytics

Web Analytics – Key Metrics – Making web analytics actionable – Types of tracking codes

 

Text Books And Reference Books:

Seema Gupta. (2018). Digital Marketing (1st Ed). Tata Mc Graw Hill

Essential Reading / Recommended Reading

1)     Evans. D. & Bratton, S. (2008).  Social Media Marketing: An Hour a Day (2nded.). Wiley.

2)     Ryan, D. & Jones, C. (2012). Understanding digital marketing: Marketing strategies for engaging the digital generation. Kogan Page.

3)     Teixeira, J. (2010). Your Google Game Plan for Success: Increasing Your Web Presence with Google AdWords, Analytics and Website Optimizer. Wiley.

Evaluation Pattern

Phase 1: Digital Marketing Plan (10 Marks)

Assignment Description

  • Assignment Title: Digital marketing plan.
  • Individual or group work:  Group

Other instructions for the learners

·       Groups to identify a client (Business of any kind) and understand their digital marketing requirements. After approval from the faculty, groups have to draft a digital marketing plan. The template is annexed in the course pack.

Phase 2: Google Ads (10 Marks)

Assignment Description

  • Assignment Title: Google Ads.
  • Individual or group work:  Group

Other instructions for the learners

Google Ads campaign to be run by the groups as per client requirements. A real or dummy campaign would suffice.

Phase 3: Other DM campaigns (10 Marks)

Assignment Description

  • Assignment Title: Digital Marketing Campaigns
  • Individual or group work:  Group

Other instructions for the learners

Students should demonstrate the progress of the digital marketing campaigns mentioned in the course pack. The presentation should show case appropriate screen shots and social media pages

 

Phase 4: Digital Medium Analytics (25 Marks)

Assignment Description

  • Assignment Title: Digital Medium Analytics
  • Individual or group work:  Group

Other instructions for the learners

Groups must use appropriate analytical methods and tools to exhibit pre-campaign and post-campaign analysis

Digital Media Mix (20 Marks)

Assignment Description

  • Assignment Title: Digital Media Mix
  • Individual or group work:  Individual and Group

 

Other instructions for the learners

Individual – Students are required to choose an inventory of digital media from a given list for their respective clients.  (10 Marks)

Team – Students can deliberate the choice of inventory for their client. Participate in the bidding process to buy the digital inventory. Submit a brief on the choice of media and the rationale. (10 Marks)

 

Competitor Analysis (20 Marks)

Assignment Description

  • Assignment Title:  Competitor Analysis using digital tools
  • Individual or group work:  Individual

 

Other instructions for the learners

Use the tools such as similarweb.com, alexa, google trends, twitonomy.com, semrush.com to draw a competitive analysis of two different organizations which are trying to use the Internet to generate traffic. Make sure the organizations belong to the same industry vertical. Create a web visibility and competitive analysis chart for the same. Submit report with screen shots and your own analysis

BTGE 745 - DATA ANALYTICS THROUGH SPSS (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

.As an enormous amount of data gets generated, the need to extract useful insights is a must for a business enterprise. Data Analytics has a key role in improving any business.This course provides an overview of approaches facilitating data analytics on huge datasets.As the word suggests Data Analytics refers to the techniques to analyze data to enhance productivity and business gain.  Data is extracted from various sources and is cleaned and categorized to analyze different behavioral patterns.

1)COURSE OBJECTIVES

a)To make students understand the concepts used to analyse business data

b)To enable students to analyse data using softwares like SPSS

 

c)To enable students to understand how Analytics helps decision makers

 

 

Course Outcome

 

At the end of the course, students will be able to

Co 01: Understand the concepts involved for analyzing Business data

Co 02: Understand how to use software like SPSS to analyse data

CO 03: Appreciate the use of Data Analytics for business decision making

Unit-1
Teaching Hours:6
Introduction to Data Analytics
 

 

Introduction to Data Analytics, Steps involved in data Analysis, Types of Data, Data cleaning 

 

 

Unit-2
Teaching Hours:6
Introspection to SPSS
 

Understanding SPSS, Creating SPSS files, importing Data,SPSS Interface, Modules, Importing Data From excel, Creating a SPSS File

Unit-3
Teaching Hours:6
Data Types and manipulation
 

Entering Differing types of Data, Defining Variables, Data Manipulation in SPSS, Recoding Variables, Splitting File, Merging Files, Weight Cases,Saving File and Building Charts

Unit-4
Teaching Hours:6
Hypothesis Testing and Univariate Analysis
 

T Test, correlation and Regression, 1-Way and 2-Way ANOVA, Univariate Analysis,Chi Square Test

Unit-5
Teaching Hours:6
Multivariate analysis
 

2-Way ANOVA, Multiple Regression, Logistic Regression,2-Way ANOVA, Multiple Regression, Logistic Regression, Multiple Discriminant, Analysis, Decision Tree

Text Books And Reference Books:

1)TEXT BOOKS

1.Darren George|Paul Mallery, “SPSS for Windows Step by Step”, Pearson, Tenth Edition, 2012.

 

Essential Reading / Recommended Reading

2)REFERENCE BOOKS

1.Andy field, “Discovering Statistics Using SPSS”, SAGE Publications, Second Edition, 2006.

 

Evaluation Pattern

CIA-1

MID SEM (CIA-2)

CIA -3

BTGE735 - DIGITAL MEDIA (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

This course provides students the insight on search engine optimization, social media and digital marketing techniques that helps them understand how each of the social media platforms works and how to strategize for any type of objectives from clients. Students will discover the potential of digital media space and will have hands on experience with different digital platforms.

Course Outcome

 

  • Students would be able to optimize the website and social media platforms which will be search engine friendly and as well as user friendly.
  • Students would be able to develop a digital strategy for a business’s online objectives.

 

 

Unit-1
Teaching Hours:10
Concepts
 

Website Hosting/Design/Development/Content, Website Optimization, Fundamentals of SEO, Voice Search Optimization, Local SEO, Advanced/Technical SEO, SEO Audit, Competition Analysis, App Store Optimization, Concepts of Digital Marketing

Unit-2
Teaching Hours:10
Marketing
 

Marketing on platforms – Facebook/Twitter/LinkedIn/Instagram/YouTube, Quora, Basics of Video Editing, Inbound Marketing, Email Marketing, Digital Marketing Planning and Strategy, Marketing Automations and Tools

Unit-3
Teaching Hours:10
Growth Hacking
 

Ethical vs. Unethical, Funnels, KPI’s, Viral Coefficient, Cohorts, Segments, Multivariate Testing, Lifetime Value of a Customer, Customer Acquisition Cost, Analytics Types, Tools, Project

Text Books And Reference Books:

Phillip J. Windley, "Digital Identity" O'Reilly Media, 2005

Essential Reading / Recommended Reading

Dan Rayburn, Michael Hoch, "The Business of Streaming and Digital Media", Focal Press, 2005

Evaluation Pattern
  • CIA 1 - Evaluated out of 20, which will be converted to 10
  • CIA 2 - Mid Semester Exam evaluated out of 50, which will be converted to 25
  • CIA 3 - Evaluated out of 20, which will be converted to 10
  • Total CIA Marks after conversion - 45
  • Attendance Marks - 5
  • ESE Evaluated out of 100, which will be converted to 50
  • Total Marks = CIA (Total) + ESE + Attendance = 45 + 50 + 5 = 100

 

BTGE736 - INTELLECTUAL PROPERTY RIGHTS (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

The course consists of five units. Theories behind the protection of intellectual property and its role in promoting innovations for the progress of the society is the focus of first unit. Second unit deals with protection of inventions through patent regime in India touching upon the process of obtaining international patents. The central feature of getting patent is to establish new invention through evidence. This is done through maintaining experimental/lab records and other necessary documents. The process of creating and maintain documentary evidence is dealt in Unit 3. Computers have become an integral part of human life. Till 1980, computer related inventions were not given much importance and lying low but today they have assumed huge significance in our economy. Computer related inventions and their protection which requires special treatment under legal regimes are discussed in Unit 4. The last module deals with innovations in   e-commerce environment.

Course Outcome

  1. Understand the meaning and importance of intellectual property rights as well as different categories of intellectual property
  2. Understand the meaning of patentable invention, the procedure for filing patent applications, rights of the patentee and the different rights of patentee.
  3. Construct research records in the patent process, the process of patent document searching and how to interact with patent agent or attorney.
  4. Understand the issues related to patenting of software, digital rights management and database management system.
  5. Understand the intellectual property issues in e-commerce, evidentiary value of electronic signature certificates, protection of websites and the protection of semiconductor integrated circuits

Unit-1
Teaching Hours:6
Philosophy of intellectual property
 

Intellectual Property & Intellectual Assists – Significance of IP for Engineers and Scientists – Types of IP – Legal framework for Protection of IP – Strategies for IP protection and role of Engineers and Scientists

Learning Outcome: After the completion of this module the students will be able to understand the meaning and importance of intellectual property rights as well as different categories of intellectual property

Unit-2
Teaching Hours:6
Patenting Inventions
 

Meaning of Invention – Product and Process Patents – True inventor – Applications for Patent – Procedures for obtaining Patent – Award of Patent – rights of patentee – grounds for invalidation – Legal remedies – International patents
Learning Outcome: At the completion of this unit, the students will be able to understand the meaning of patentable invention, the procedure for filing patent applications, rights of the patentee and the different rights of patentee.

Unit-3
Teaching Hours:6
Inventive Activities
 

Research Records in the patent process – Inventorship - Internet patent document searching and interactions with an information specialist - Interactions with a patent agent or attorney - Ancillary patent activities - Technology transfer, patent licensing and related strategies
Learning Outcome: After completing this unit, the students will know how to maintain research records in the patent process, the process of patent document searching and how to interact with patent agent or attorney.

Unit-4
Teaching Hours:6
Patents and software
 

Business Method Patents – Data protection – Administrative methods – Digital Rights Management (DRM) – Database and Database Management systems - Billing and payment – Graphical User Interface (GUI) – Simulations – E-learning – Medical informatics – Mathematical models
Learning Outcome: At the completion of this unit, the students will be able to understand the issues related to patenting of software, digital rights management and database management system.

Unit-5
Teaching Hours:6
Innovations in e-commerce
 

IP issues in e-commerce - Protection of websites – website hosting agreements – Copyright issues – Patentability of online business models – Jurisdiction – Digital signatures – Evidentiary value of Electronic signature certificates – Role of Certifying Authorities – Protection of  Semiconductor ICs
Learning Outcome: After completing this unit, the students will be able to understand the intellectual property issues in e-commerce, evidentiary value of electronic signature certificates, protection of websites and the protection of semiconductor integrated circuits

Text Books And Reference Books:
  • Burton A. Amernick, Patent Law for Non-Lawyer, Van Nostrand Reinhold (2nd Edition, 1991);
  • Avery N. Goldstein, Patent Law for Scientists and Engineers, Taylor & Francis (1st Edition, 2005);
  • Daniel Closa et al., Patent Law for Computer Scientists, Springer (2010);
  • P Narayanan, Patent Law, Eastern Law House (2017);
Essential Reading / Recommended Reading
  • P Narayanan, Law of Trademarks and Passing Off, Eastern Law House (2016);
  • Elizabeth Verkey, Intellectual Property Law and Practice, Eastern Book Co. (2015);
  • Dr. B.L. Wadhera, Law relating to Intellectual Property (2011).
  • V K Ahuja, Intellectual Property Rights in India, LexisNexis (2009).
Evaluation Pattern

CIA 1: 10 M
CIA 2 MSE: 25 M
CIA 3:10 M
ESE: 50M
Attendance: 5M

BTGE738 - CORPORATE SOCIAL RESPONSIBILITY (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

Course Description:

This course will familiarize the students with the concept ofcorporate social responsibility. The evolution of CSR has far reaching consequences on the development sector in India. The collaboration of companies and NGOs with the community has initiated a new paradigm of change in the country. The students will have an overview of the theories and the frameworks developed in the area of CSR. The paper will discuss a few prominent case studies of CSR.

 

Course Objectives

 

  • To understand the concept of CSR and the theoretical underpinnings.
  • To understand the stakeholder approaches.
  • Provide an experiential, integrative, substantive, and high quality experience surrounding issues of Corporate Social Responsibility
  • To provide participating students with a truly unique curriculum experience with field experience.

 

Course Outcome

The student will understand the different dimensions of the concept of CSR. They will understand the theoretical framework of CSR and the legal guidelines developed to undertake CSR.

Unit-1
Teaching Hours:7
Corporate social responsibility
 

 

Defining CSR. Aim and Objectives, Components of CSR, Key  drivers,  History  and  Evolution  of  CSR  in  the  Indian  and international  context,  CSR  policies  and  Governance,  Laws  and Regulations. Competencies of CSR Professionals. 

Unit-2
Teaching Hours:7
Stakeholder Engagement
 

Stakeholder engagement, Interaction in a Multi-Stakeholder Context: CSR role on internal environment: Employees, Human Resource Management - labour security and human rights, Health and Safety.CSR role on External environment: 1) Customers: Consumer rights and movements affecting CSR; (2) Community: Community involvement, (3) Shareholders (4) Suppliers.

Unit-3
Teaching Hours:6
CSR towards Environment and Biodiversity
 

 

Environment: Need for Environmental assessments. Governments’ response to CSR. Role of Biodiversity, Climate change and Environment in business. Environmental compliance.    

Unit-4
Teaching Hours:5
Sustainability models
 

Benefits of CSR to Business. Factors hindering CSR activities in companies

Unit-5
Teaching Hours:5
Theories of CSR
 

Theories of CSR: A.B Carroll, Wood, and stakeholders Theories.  The triple bottom line approach.  Stakeholder engagement, Standards and Codes – SA 8000, the Global Compact, GRI, etc as well as international standards including ISO 26000.

Text Books And Reference Books:

§  Agarwal, S. (2008). Corporate social responsibility in India. Los Angeles: Response.

§  Visser, W. (2007). The A to Z of corporate social responsibility a complete reference guide to concepts, codes and organisations. Chichester, England: John Wiley & Sons.

 

  •  Crane, A. (2008). Corporate social responsibility: Readings and cases in a global context. London: Routledge.'
  •        Werther, W., & Chandler, D. (2006). Strategic corporate social responsibility: Stakeholders in a global environment. Thousand Oaks: SAGE Publications.

 

Essential Reading / Recommended Reading
  • Baxi, C. (2005). Corporate social responsibility: Concepts and cases: The Indian experience. New Delhi, India: Excel Books.
  • Visser, W. (2011). The age of responsibility CSR 2.0 and the new DNA of business. Chichester, West Sussex: John Wiley & Sons.

 

Evaluation Pattern

CIA I=10

CIA II =25

CIA III-10

Attendance - 05

BTGE739 - CREATIVITY AND INNOVATION (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

1.      To equip students with skill and aptitude for creativity and innovation through

2.      Analysizing Problems:

To stimulate curiosity in students to identify the areas of gaps and opportunities and solutions that can be provided

3.      Creating Ideas:

To stimulate creativity in students to come up with ideas for the areas of gaps and opportunities

To understand the creative process: Smart storming

4.      Engineering Solutions:

To understand Proof of Concept, Minimum Viable Proposition, and the Rapid Iteration Process

Course Outcome

At the end of the course

1.       Students will have developed and aptitude for creative thinking and problem solving in the areas that drive their interest.

2.      Students will have appreciated the benefits of team work and collaborative thinking

3.      Students will understand the three keys aspects of the creative process viz. ACES

4.      Students will have carried out hands on projects to understand the various principles and elements of creativity and innovation

5.      Students groups might have emerged with projects which may be patentable, design and copyright protected.

Unit-1
Teaching Hours:9
Introduction: Creativity and Creative Thinking
 

Activity based introduction to creativity, Creativity and Innovation - Activity based introduction to InnovationA journey through major breakthrough innovations around the world, Team work in Creativity: Theory and Practice, Communicating Ideas Effectively

Unit-2
Teaching Hours:9
The Creative Process Part I (Analyzing Problems)
 

Analyzing Problems (Smart Storming) Theory and practice - Rethinking, Thinking,  Imagination, Observing, Abstracting,  Recognizing Patterns Forming Patterns

Unit-3
Teaching Hours:9
The Creative Process Part II (Creating Ideas)
 

Creative Thinking Techniques and Methods

Body Thinking

Empathizing (Design Thinking)

Dimensional Thinking

Evolution and Evaluation of Ideas through design Thinking

Unit-4
Teaching Hours:9
The Creative Process Part III (Engineering Solutions)
 

Proof of Concept, Minimum Viable Proposition, Rapid Iteration Process

Unit-5
Teaching Hours:9
Innovation and IPR
 

Patents, Designs, Copyrights, Geographical Indications, Trademarks, Trade Secret

Text Books And Reference Books:

Activity based teaching learning. So no additional references. 

Essential Reading / Recommended Reading

Activity based teaching learning. So no additional references. 

Evaluation Pattern

This course will have an overall CIA which will be evaluated out of 100 marks and converted out of 100 marks.  Students will have a portfolio prepared as per the classes that they have undergone which will be evaluated vis-à-vis the learning aspects associated at the appropriate course level. 

BTGE741 - GERMAN (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

To learn a new language, viz.German.  To understand the culture and differences of the new environment and be prepared to adapt.

 

 Sensitize the students to the environment of a foreign country. To enable the students adapt to a new environment and culture.

 

 

Course Outcome

 

Can understand and use familiar, everyday expressions and very simple sentences, which relate to the satisfying of concrete needs.

Can introduce oneself and others as well as ask others about themselves – e.g. where they live, whom they know and what they own – and can respond to questions of this nature.

Can handle everyday situations like shopping, eating out, visiting places, travelling, holidaying, requesting for information, making an appointment, cancelling an appointment, filling up a form etc

 

Unit-1
Teaching Hours:6
INTRODUCTION, SELF AND OTHERS
 

Introduction: Greeting and saying goodbye, Introducing yourself and others, Talking about yourself and others.

Numbers, telephone numbers and mail-addresses, the alphabet (spelling), countries and languages.

Question words, sentences, verbs and personal pronouns.

 

Unit-2
Teaching Hours:6
AROUND YOU :FRIENDS, COLLEAGUES
 

Hobbies, meeting friends, Weekdays, months and seasons, work and working times

Articles, verbs, Yes/ no questions, Plurals, The verbs : to have and to be.

 

Unit-3
Teaching Hours:6
PLACES TO VISIT
 

Places in the city, asking for directions, Means of transport. Orientation in a city.

Imperative sentences.

 

Unit-4
Teaching Hours:6
FOOD
 

Shopping for food, conversation during food shopping, ordering food and drinks, general greetings during eating out.

Word position in sentence, accusative case.

 

Unit-5
Teaching Hours:6
TIME WITH FRIENDS
 

Telling time and organizing meetings with family and friends.

Making plans, Birthday invitations, in Restaurants.

Finding information in a text, event tips in the radio, leisure activities, brochures.

Possessive articles, Modal verbs ,simple Past tense (to have and to be)

 

Text Books And Reference Books:

Netzwerk – Deutsch als Fremdsprache A1,

Publisher- Langenscheidt

 

 

Essential Reading / Recommended Reading

Netzwerk – Deutsch als Fremdsprache A1,

Publisher- Langenscheidt

 

 

Evaluation Pattern

EC

NO.

EVALUATION COMPONENT

MODULE

DURATION

(MIN)

DATE, TIME AND VENUE

NATURE OF THE COMPONENT

1

CIA I

 

Test 20 marks

 

60 MIN

 

ONLINE EXAM

2

CIA II

 

MSE

60 MIN

 

ONLINE EXAM

3

CIA III

 

Test 20 marks

 

60 MIN

 

ONLINE EXAM

4

Semester Exam

ESE

2HR

 

ONLINE EXAM

 

BTGE749 - PAINTING AND SKETCHING (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

Objective 

Global elective for a beginner level artist course, will focus on establishing the necessary basics for the students to start art practice.  The course is ideal for students who are keen on developing their skills. The course looks into basic aspects of different media and strengthens the basic understanding necessary for a beginner level artist. 

Course Outcome

Curriculum aims to help students:

  • Develop creativity, critical thinking and communication skills and nurture aesthetic sensitivity and cultural awareness.
  • Develop art skill, enjoyment and satisfaction through participating in arts activities; and pursue a lifelong interest in the art.

Unit-1
Teaching Hours:6
Module 1: Pencil/Pen - 3class/6hours
 
  • Week 1 Free hand sketching, Form & object study 

  • Week 2 Rendering technique, Light & shadow 

  • Week 3 Composition 

Unit-1
Teaching Hours:6
Module 1: Pencil/Pen - 3class/6hours Materials
 

 

Sl.no

Materials

1

Staedtler Mars Lumograph Drawing Pencils Metal Box (6pc set)

2

A1 size Cartridge Sketching & Drawing sheets (2pc)

3

A1 size News print Sheets (5pc)

4

Eraser, sharpener, cutter

Unit-2
Teaching Hours:6
Module 2: Charcoal and Pastels - 3class/6hours
 

 

  • Week 4 Tonal values, shadows & highlights 

  • Week 5 Explore thin & thick lines, composition 

  • Week 6 Different technique method in charcoal 

Unit-2
Teaching Hours:6
Module 2: Charcoal and Pastels - 3class/6hours Materials
 

 

Sl.no

Materials

1.

Camel Soft Pastel 20shade set

2.

A1 size Cartridge Sketching & Drawing sheets (2pc)

3.

Sudha 68 Crayons (black, brown, white)

4.

Camel fixative spray

5

Paper masking tape (1 inch)

Total

Unit-3
Teaching Hours:6
Module 3: Watercolour - 3class/6hours
 

 

  • Week 7 Introduction of water colour, colour palate & gradient 

  • Week 8 Introduction to basic, techniques (wet on wet & wash) 

  • Week 9 Composition (landscape/still life) Project work 

Unit-3
Teaching Hours:6
Module 3: Watercolour - 3class/6hours
 

 

Sl.no

Materials

1.

A4 size brustro water colour pad (300gsm) 

2.

Camel Artist 5ml water colour set (12 shades)   

3.

Brustro Artist Gold Taklon set of 10 brush for acrylic/water/oil (round &flat)

4.

Brustro Rectangle 20 well palette

Unit-4
Teaching Hours:2
Module 4: Creative Thinking & Studio Practice 1class/2hour
 

 

  • Week 10 Modern/contemporary art related documentary screening followed by Q&A.

  • Materials: note pad and basic stationary.

Unit-5
Teaching Hours:6
Module 5: Acrylic 3class/6 hour
 

Week 11 Introduction to Acrylic media, Conceptualising of subject, Basic-techniques

 

Week 12-13 Process of working on canvas & completion (final Project) 

Unit-5
Teaching Hours:6
Module 5: Acrylic 3class/6 hour Materials
 

 

Sl.no

Materials

1

Camel Artist 20ml acrylic Colour set (12 shades)

2

Canvas Stretched (3x3)feet

3

palette

Unit-6
Teaching Hours:4
Exhibition & Submission 2class/4hours
 

Week 14-15 Exhibition & Portfolio Submission

Evaluation Pattern
Assessment based and continues evaluation.
   Consistency,abilities to understand the concept and explore.time management,precision etc.

BTGE750 - PHOTOGRAPHY (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 
  • To learn to properly expose and develop B and W film to high craftsman like skills.
  • To learn how to craft a fine print using traditional b and w paper in the darkroom.
  • -To fully understand the workings of your camera in particular and broad concepts in general
    such as reciprocity in aperture and shutter.

Course Outcome

  • Learn how to "see photographically" That includes developing a fine appreciation for seeing light and the way light works on photo sensitive materials to produce expressive, elegant prints.
  • Learn how space works in the frame to create intentional, elegant design.
  • Understand the metaphoric possibilities in the images we create that transcend the
    literalness of the object(s) photographed.

Unit-1
Teaching Hours:6
Introduction and Development of Films
 

Introduction to the course, discussion of lenses, aperture, shutter, depth of field, loading film, Places to shoot. Functions and Usage of a camera.

Unit-2
Teaching Hours:6
Printing
 

Role of natural light in creating expressive print, Development of Coherent Portfolio, Printing Skills.

Unit-3
Teaching Hours:6
Unit - III
 

Role depth of field, lens choice, focal point and strong design plays. Group discussion on prints.

Unit-4
Teaching Hours:6
Urban Shooting and Portrait
 

Angles,  reflections, corners, street life. Emphasis on design and abstraction, Creation of visual relationships.

Unit-5
Teaching Hours:6
Final Portfolio
 

Revisit of your favourite place, printing for final critique, Matting of prints.

Text Books And Reference Books:
  1. Schaefer, John P., Basic Techniques of Photography, An Ansel Adams Guide: Little Brown and Company, Boston, 1992. 
  2. Horenstein, Henry, Beyond Basic Photography, A Technical Manual: Little Brown and Company, Boston, 1977.
Essential Reading / Recommended Reading

1. Craven, George M., Object and Image, An Introduction to Photography. Prentice Hall, Englewood Cliffs, New Jersey, 1990.

Evaluation Pattern

Overall CIA - 100.

ESE - 100.

Note :Students are expected to publish papers in reputed journal

BTGE754 - FUNCTIONAL ENGLISH (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

 

This course aims at strengthening English proficiency amongst 7th semester students. It focuses on three key areas –

1.Spoken English

2.Written English (Workplace and Academic writing)

3.Public Speaking

The course deploys BLENDED AND EXPERIENTIAL LEARNING as CAPS will use classroom teaching, hands on CIAs and E-learning modules.

 

Course Outcome

Students will be able to develop a clear understanding principles and characteristics of communication in professional settings. They would have developed skills for grammatical accuracy, precise vocabulary, clear style and appropriate tone for formal, professional communication

Unit-1
Teaching Hours:15
UNIT 1: VERBAL
 

·       Training on Nouns, Pronouns, Homophones, Homonyms

·       Verbs and Gender

·       Training on Tenses

·       Active Voice, Passive Voice and Sentence Formation

·       Direct and Indirect Speech

·       Adjectives and Adverbs

·       Barriers of communication and effective solutions

·       Workplace English

·       Pleasantries and networking

·       Cross-cultural understanding

Unit-2
Teaching Hours:15
UNIT- 2 WRITTEN Workplace English
 

·       Professional Writing

                Analytical

                Instructional including writing MOMs

                Project Planning

                Creative writing

                Blogging

·       Event management proposal meeting

·       Professional communication – Email Etiquette, Cover letters, Resume

Unit-2
Teaching Hours:15
Academic Writing
 

 

·       Application in technical fields and written communication

·       Project writing, essays and theories

·       Paper presentation skills and creative writing

Final project writing

Unit-3
Teaching Hours:15
UNIT-3 PUBLIC SPEAKING
 

·       Training on Presentation Skills

·       Body Language and Accent Training

·       Voice projection

·       Group Discussion Do’s and Don’ts

·       Getting individual feedback

·       Training on appropriate grooming code and body language in a professional workplace and delivery of apt elevator pitch.

Text Books And Reference Books:

 

https://www.wikipedia.org/

https://christuniversity.in/caps/ etc

Essential Reading / Recommended Reading

 

https://economictimes.indiatimes.com/topic/Inshorts

Evaluation Pattern

 

1. CIA 1- CLASS presentation

The students need to convince their teachers to give a presentation to their own class or a different class. The teachers will be given choices of CAPS modules from which a module/topic will be chosen and will be delivered to the class.

 

2. CIA 2 - Content creation

The students will be divided into groups of 5 each. They will develop content for a current, idea based topic/ concept eg: TED Ex type topic with handout and PPT with maximum 30 slides.

 

 

3. CIA -3 -

(The students will be evaluated on one of the following)

Stress Interview/ Panel Discussion/ Group Discussion

Blog Exploration - The blog exploration could be a whole class exercise or students could work in small groups.

CS731 - ARTIFICIAL INTELLIGENCE (2017 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 strong foundation of fundamental concepts in Artificial Intelligence.
  • To provide a basic exposition to the goals and methods and
  • To enable the student to apply these techniques in applications which involve perception, reasoning and learning.

Course Outcome

 
 

Sl. NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Identify the fundamental knowledge of Intelligent agents, searching strategies and syntax and semantics of first order logic.

L3

2.

Discover the complex problem solving agents, constraint satisfaction problems and optimal decisions in game.

L4

3.

Inspect the knowledge engineering in first order logic, knowledge representation and chaining mechanisms, knowledge in learning and different forms of learning.

L4

4.

Determine and build planning strategies, Communication and analysis of grammar and its interpretation.

L5

5.

Asses a system that utilize artificial intelligence to a complicated task with limited resources in the form of time and computations.

L5

Unit-1
Teaching Hours:12
INTRODUCTION
 
 

Intelligent Agents – Agents and environments - Good behavior – The nature of environments – structure of agents - Problem Solving - problem solving agents – example problems – searching for solutions – uniformed search strategies - avoiding repeated states – searching with partial information.

Unit-2
Teaching Hours:12
SEARCHING TECHNIQUES
 
 

Informed search and exploration – Informed search strategies – heuristic function – local search algorithms and optimistic problems – local search in continuous spaces – online search agents and unknown environments - Constraint satisfaction problems (CSP) – Backtracking search and Local search for CSP – Structure of problems - Adversarial Search – Games – Optimal decisions in games – Alpha – Beta Pruning – imperfect real-time decision – games that include an element of chance.

Unit-3
Teaching Hours:12
KNOWLEDGE REPRESENTATION
 
 

First order logic – representation revisited – Syntax and semantics for first order logic – Using first order logic – Knowledge engineering in first order logic - Inference in First order logic – prepositional versus first order logic – unification and lifting – forward chaining – backward chaining - Resolution - Knowledge representation - Ontological Engineering - Categories and objects – Actions - Simulation and events - Mental events and mental objects.

Unit-4
Teaching Hours:12
LEARNING
 

Learning from observations - forms of learning - Inductive learning - Learning decision trees - Ensemble learning - Knowledge in learning – Logical formulation of learning – Explanation based learning – Learning using relevant information – Inductive logic programming - Statistical learning methods - Learning with complete data - Learning with hidden variable - EM algorithm - Instance based learning - Neural networks - Reinforcement learning – Passive reinforcement learning - Active reinforcement learning - Generalization in reinforcement learning.

Unit-5
Teaching Hours:12
APPLICATIONS
 
 

Planning  – planning as search  –  partial order planning  – construction and use of planning graphs  Communication – Communication as action – Formal grammar for a fragment of English – Syntactic analysis – Augmented grammars – Semantic interpretation – Ambiguity and disambiguation – Discourse understanding – Grammar induction

Text Books And Reference Books:
 
 
  1. Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, 3rd Edition, Pearson Education / Prentice Hall of India, 2018.
  2. Elaine Rich and Kevin Knight, “Artificial Intelligence”, 3rd Edition, Tata McGraw-Hill, 2017.
Essential Reading / Recommended Reading
 
 
  1. Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd., 2018.
  2. George F. Luger, “Artificial Intelligence-Structures and Strategies for Complex Problem Solving”, Pearson Education / PHI, 2018.
Evaluation Pattern
 
 

CIA ASSESSMENT DETAILS - THEORY

CIA for Theory: Continuous Internal Assessment 50 Marks (50 Marks out of 100 Marks)

CIA 1: 10 Marks

CIA 2: 25 Marks

CIA 3: 10 Marks

Attendance : 5 Marks

End Semester Exam for Theory: 50 Marks ( 50 Marks out of 100 Marks)

Total: 100 Marks

 

CS732 - CLOUD COMPUTING (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Specifically, the course has the following objectives. Students will learn:

•The fundamental ideas behind Cloud Computing, the evolution of the paradigm, its applicability; benefits, as well as current and future challenges; 

•The basic ideas and principles in data center design and management; 

•About cloud storage technologies and relevant distributed file systems; 

•The variety of programming models and develops working experience in one of them. 

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT) LEVEL

1.

Illustrate the core concepts of the cloud computing paradigm.

L2

2.

Interpret the functioning of data-centers, its trade-offs in power, efficiency and cost.

L2

3.

Summarize the concept of virtualization and its role in providing services under cloud computing model.

L2

4.

Utilize the services of cloud storage systems.

L3

5

Examine the various cloud application from various vendors.

L4

Text Books And Reference Books:
Essential Reading / Recommended Reading
Evaluation Pattern
 
 

CIA ASSESSMENT DETAILS - THEORY

CIA for Theory: Continuous Internal Assessment 50 Marks (50 Marks out of 100 Marks)

CIA 1: 10 Marks

CIA 2: 25 Marks

CIA 3: 10 Marks

Attendance : 5 Marks

End Semester Exam for Theory: 50 Marks ( 50 Marks out of 100 Marks)

Total: 100 Marks

 

CS733P - MOBILE APPLICATION DEVELOPMENT (2017 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

·       To know the concepts corresponding to the Mobile applications.

·       To be in a position to develop applications for all the mobile manufacturers.

·       To learn and know the various architectures for different mobile systems.

·       To design mobile systems that meets the need for the current world requirements.

·       Know the components and structure of mobile application development frameworks for Android and windows OS based mobiles.

·       Understand how to work with various mobile application development frameworks.

·       Learn the basic and important design concepts and issues of development of mobile applications.

·       Understand the capabilities and limitations of mobile devices.

 

 

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Explain the concepts in mobile applications and its development.

L2

2.

Build interface for mobile applications and web applications

L6

3.

Construct mobile application for Windows platform

L6

4

Design mobile application for Android platform.(L6)

L6

5

Develop mobile application for IOS platform.

L3

Unit-1
Teaching Hours:15
INTRODUCTION
 

Introduction to mobile applications – cost of development - Market and business drivers for mobile applications – Publishing and delivery of mobile applications – Requirements gathering and validation for mobile applications. Third party Frameworks. - Mobile Content- Mobile Applications.

Unit-2
Teaching Hours:9
BASIC DESIGN
 

Introduction to Web Services– Web service language Format –Creating a Web service using Microsoft stack – Using the Linux Apache MySQL PHP (LAMP) Stack-Debugging Web Services. Mobile User Interface Design.-Mobile Web Apps Using HTML5.Designing applications with multimedia and web access capabilities – Integration with GPS and social media networking applications – Accessing applications hosted in a cloud computing environment – Design patterns for mobile applications.

Unit-3
Teaching Hours:9
TECHNOLOGY I WINDOWS 7
 

Introduction- architecture of windows 7- Establishing the development environment-Tools- Hardware- Visual studio and windows phone SDK- Windows 7 Project-Building the Derby App in Windows 7-Offline Storage-Notifications-GPS-Accelerometer-Web Services.

Unit-4
Teaching Hours:33
TECHNOLOGY II : ANDROID
 

Introduction – Establishing the development environment – Android architecture – Activities and views – Interacting with UI – Persisting data using SQLite – Packaging and deployment – Interaction with server side applications – Using Google Maps, GPS and Wifi – Integration with social media applications.

Unit-5
Teaching Hours:9
TECHNOLOGY III - IOS
 

Introduction to Objective C – iOS features – UI implementation – Touch frameworks – Data persistence using Core Data and SQLite – Location aware applications using Core Location and Map Kit – Integrating calendar and address book with social media application – Using Wifi – CASE STUDY- iPhone marketplace and mobile application development.

Text Books And Reference Books:

1.      Jeff McWherter and Scott Gowell, "Professional Mobile Application Development", Wrox, 2012,Wiley Publications.

2.      Charlie Collins, Michael Galpin and Matthias Kappler, “Android in Practice”, DreamTech, 2012.

3.    Dawn Griffiths, David Griffiths “Head First Android Development: A Brain-Friendly Guide 1st Edition” O'Reilly Media; 1 edition (July 3, 2015)

4.      James Dovey and Ash Furrow, “Beginning Objective C”, Apress, 2013

Essential Reading / Recommended Reading

1.   David Mark, Jack Nutting, Jeff LaMarche and Frederic Olsson, “Beginning iOS 6 Development: Exploring the iOS SDK”, Apress, 2013.

Evaluation Pattern

Assessment of each paper:

CIA 1: 10 Marks

CIA 2 (MSE): 10 Marks

CIA3: 10 Marks

Laboratory: 35 Marks

Attendance :05 Marks

End Semester Examination: 30 Marks

Total : 100 Marks

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA 1- (Component 1) Quiz

Unit 1 , Unit 2,Unit 4

CO1,CO2,CO4

L3

2

CIA1- (Component 2) Mini Project Blueprint design

Unit 4

CO4

L6

3

CIA2-MSE

Unit 1, Unit 4 and half of Unit 2

CO1, CO2, CO3

L4

4

CIA 3-(Component 1) Mini Project (Design and implementation)

Unit 1, Unit2, Unit 4

CO1,CO2,CO4

L5

5

CIA 3-(Component 2) Quiz

Unit 3, Unit 5

CO1,CO4

L3

CS735E01 - NATURAL LANGUAGE PROCESSING (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

 

·        Teach student about various Linguistics Rules, representation and understanding of Natural Language Processing

·        Make students to understand the concepts of morphology, syntax, semantics and pragmatics of the language and that they are able to give the appropriate examples that will illustrate the above-mentioned concepts.

·        Enable students to recognize the significance of language model for Natural Language Processing

 

 

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Demonstrate the representation of language with the levels of language analysis

L2

2.

Illustrate the process of top down parsing and bottom up parsing of strings and morphological analysis of lexicons

L2

3.

Experiment the techniques for handling questions and ambiguity resolution with analyzing movement phenomenon in language

L3

4

Examine the semantic interpretation of words and linkage between and syntax and semantics

 

L4

5

Explains language models required to verify its significance with recent trends in natural language

 

L2

Unit-1
Teaching Hours:9
Introduction to Natural Language Processing
 

Introduction to Natural Language Processing, Different Levels of language analysis, Representation and understanding, Linguistic background.

Unit-2
Teaching Hours:9
Grammars and parsing
 

Grammars and parsing, Top down and Bottom up parsers, Transition Network Grammars, Feature systems and augmented grammars, Morphological analysis and the lexicon, Parsing with features, Augmented Transition Networks.

Unit-3
Teaching Hours:9
Grammars for natural language
 

Grammars for natural language, Movement phenomenon in language, Handling questions in context free grammars, Hold mechanisms in ATNs, Gap threading, Human Preference in parsing, Shift reduce parsers, Deterministic parsers, Statistical methods for Ambiguity resolution.

Unit-4
Teaching Hours:9
Semantic Interpretation
 

Semantic Interpretation, word senses and ambiguity, Basic logical form language, Encoding ambiguity in logical from, Thematic roles, Linking syntax and semantics, Recent trends in NLP.

Unit-5
Teaching Hours:9
Language Model
 

Language Model: the Milton Model , THE META MODEL, Vision for the Future’, Strategies, NLP Change Techniques, Principle based NLP, Reframing, and Chunking Patterns, Recent Trends, Research Issues, Case studies.

Text Books And Reference Books:

TEXT BOOKS

1.      Steven Bird, Ewan Klein, Edward Loper, “Natural Language Processing with Python”, O'Reilly Media; 1 edition (July 10, 2009)

2.      Pushpak Bhattacharyya, “Machine Translation”, Chapman and Hall/CRC; 1 edition (January 22, 2015)

3.      Matthew A Russell , “Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More”, O'Reilly Media; Second Edition edition (October 20, 2013)

Essential Reading / Recommended Reading

REFERENCE BOOKS

1.      James Allen, Natural Language Understanding, Second Edition, 2003, Pearson Education.

2.      Daniel Jurafsky & James H.Martin, “ Speech and Language Processing”, Pearson Education (Singapore) Pte. Ltd., 2002.

Evaluation Pattern

Components of CIA

Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks)

CIA I:  Assignment 1 and 2                                                                     : 10 marks

CIA II: Mid Semester Examination                                                         : 25 marks

CIA III: Mini Project and Presentation                                                    : 10marks

Attendance                                                                                               : 5marks

 Total                                                                                                        : 50marks

 End Semester Examination (ESE) : 50% (50 marks out of 100 marks) 

 

S.NO

CIA Details

Component

Unit

CO

RBT LEVEL

1

CIA I Component 1

Assignment - 1

I

CO1

L2

2

CIA I Component 2

Assignment - 2 on Practical approaches of NLP

II

CO2

L2

3

CIA II

Mid Semester Examination

I, II & III

CO1, CO2 and CO3

L3

4

CIA III Component 1

Mini Project

II, III, IV and V

CO1, CO2, CO3, CO4 and CO5

L4

5

CIA  III Component 2

Presentation and Report Writing

II, III, IV and V

CO1, CO2, CO3, CO4 and CO5

L4

CS735E02 - RESEARCH METHODOLOGY (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 
  • To orient the student to make an informed choice from the large number of alternative methods and experimental designs available.
  • To enable the student to present a good research proposal.
  • To familiarize the student with the nature of research and scientific writing
  • To empower the student with the knowledge and skills they need to undertake a research project, to present a conference COURSE and to write a scientific article.

Course Outcome

CO1: Explain the research methodology and defining the research problem   

CO2: Experiment thestatistical techniques such as scaling, skewness, correlation, and association techniques to process the data   

CO3: Examine the algorithm to find the order of growth of best, worst and average cases.

CO4: Experiment thepopulation using sampling fundamentals, variance and covariance methods

CO5: Evaluate the research results and presenting the research report 

Unit-1
Teaching Hours:9
INTRODUCTION
 

An Introduction Meaning of Research, Objectives of Research , Motivation in Research , Types of Research , Research approaches ,Research Method versus Methodology ,Research and Scientific Method, Importance of Knowing How Research is Done

, Research Process, Criteria of Good Research, problem Encountered by Researchers in India.

Defining the Research Problem: Definition of Research Problem, Selecting the Problem, Necessity of Defining the Problem Technique Involved in Defining a Problem

Unit-2
Teaching Hours:9
Measurement and Scaling Technique
 

Measurement in Research, Measurement Scales, Sources of Error in Measurement, Tests of Sound Measurement, Technique of Developing Measurement Tools, Scaling, Meaning of Scaling, Scale Classification Bases, Important Scaling Techniques, Scale Construction Techniques.

Unit-3
Teaching Hours:9
Analysis of Algorithms
 

Analysis of algorithm: The role of algorithm in computing –Insertion sort–Analyzing and designing algorithms

Unit-4
Teaching Hours:9
Sampling Fundamentals
 

Need for Sampling, Some Fundamental Definitions, Central Limit Theorem, Sampling Theorem, Sandler’s A-test, Concept of Standard Error, Estimation, Estimating the Population Mean, Estimating the Population Proportion, Sample size and its Determination, Determination of Sample Size through the Approach, Based on Precision Rate and Confidence Level, Determination of Sample Size through the Approach, Based on Bayesian Statistics.

Unit-5
Teaching Hours:9
Interpretation and Report Writing
 

Meaning Of Interpretation, Technique of Interpretation: Precaution in Interpretation, Significance of Report Writing, Different Steps in Writing Report, Layout of the Research Report, Types of Reports, Oral Presentation, Mechanics of Writing a Research Report, Precautions for Writing a Research Report, Case study .

Text Books And Reference Books:

1. Kothari C.R., “Research Methodology – Methods and Techniques”, New Age International , New Delhi, (reprint 2011)

2. Montgomery, Douglas C., “Design and Analysis of Experiments”, Willey India, 2007

3. Montgomery, Douglas C. & Runger, George C. “Applied Statistics & Probability for Engineers”, Wiley India , 2010

Essential Reading / Recommended Reading

1. Krishnaswamy, K.N. Sivkumar , Appa Iyer and Mathiranjan M., “Management Research Methodology: Integration of Principles, Method and Techniques”, Pearson Education, New Delhi, 2006.

2. Charlie Catlett, Wolfgang Gentzsch, Lucio Grandinetti, Gerhard Joubert, and José Luis Vasquez-Poletti, “Cloud computing and Big data”, Published/Distributed: Amsterdam : Washington, DC : IOS Press, [2013]

Evaluation Pattern

·                Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●       End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications                      : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

CS736E01 - GRAPH THEORY (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

·       Understand basic notions of Graph Theory
·       Knowing Fundamental Theorems in Graph Theory
·       Study of algorithmic Graph Theory

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Explain the basic terminologies of graph theory. 

L2

2.

Examine the fundamentals of planar graphs, circuits and network flows.

L4

3.

Solve the problems on chromatic numbers, matrix and Euler graphs.

L4

4.

Inspect the algorithms and set of fundamental circuits for the graphs

L4

5.

Determine the shortest path algorithm and planarity testing

L4

Unit-1
Teaching Hours:9
INTRODUCTION
 

Graphs – Introduction – Isomorphism – Sub graphs – Walks, Paths, Circuits – Connectedness – Components – Euler Graphs – Hamiltonian Paths and Circuits – Trees – Properties of trees – Distance and Centers in  Tree – Rooted and Binary Trees.

Unit-2
Teaching Hours:9
Spanning trees
 

Spanning trees – Fundamental Circuits –Spanning Trees in a Weighted Graph – Cut Sets – Properties of Cut Set – All Cut Sets – Fundamental Circuits and Cut Sets – Connectivity and Separability – Network flows – 1-Isomorphism – 2-Isomorphism – Combinational and Geometric Graphs – Planer Graphs – Different Representation of a Planer Graph.

Unit-3
Teaching Hours:9
Incidence matrix
 

Incidence matrix – Submatrices – Circuit Matrix – Path Matrix – Adjacency Matrix – Chromatic Number – Chromatic partitioning – Chromatic polynomial - Matching  - Covering – Four Color Problem – Directed Graphs – Types of Directed Graphs – Digraphs and Binary Relations – Directed Paths and Connectedness – Euler Graphs – Adjacency Matrix of a Digraph.

Unit-4
Teaching Hours:9
Algorithms-1
 

Algorithms: Connectedness and Components – Spanning tree – Finding all Spanning Trees of a Graph –Set of Fundamental Circuits – Cut Vertices and Separability – Directed Circuits.

Unit-5
Teaching Hours:9
Algorithms-2
 

Algorithms: Shortest Path Algorithm – DFS – Planarity Testing – Isomorphism-Case studies.

Text Books And Reference Books:

·       Narsingh Deo, “Graph Theory: With Application to Engineering and Computer Science”, PHI, 2009.
·       Jonathan L. Gross, Jay Yellen, Ping Zhang , “Handbook of Graph Theory”, Second Edition, December 17, 2013 by Chapman and Hall/CRC Handbook.
·       Santanu Saha Ray ,” Graph Theory with Algorithms and its Applications: In Applied Science and Technology” , Springer, 2013.

Essential Reading / Recommended Reading

·       R.J. Wilson, “Introduction to Graph Theory”, Fourth Edition, Pearson Education, 2003.

Evaluation Pattern

Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :  Subject Assignments / Online Tests/Quiz                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                       : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative 

Assignments/presentations/publications                             : 10 marks

Attendance                                                                            : 05 marks

 

  Total                                                                                     : 50 marks

 

 End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

 

CS736E03 - WIRELESS NETWORKS (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To learn the basics of wireless communication, cellular communication, GSM and CDMA technologies and emerging wireless technologies in wireless networks.

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Outline the basic concepts and terminologies in wireless communication systems

L2

2.

Interpret modulation techniques in wireless communication

L4

3

Make use of the principles in cellular communications and multiple access techniques to solve real-world problems

L3

4

Examine the principles of GSM, GPRS and DECT Standards in real time environments

L4

5

Explain the emerging wireless technologies with respect to different parameters

L2

Unit-1
Teaching Hours:9
INTRODUCTION TO WIRELESS NETWORKS
 

Elements of a wireless communication system – signal and noise   - the radio – frequency  spectrum  –Analog  modulation  schemes  -Amplitude  modulation  – frequency  and  phase  modulation  –  sampling  –  pulse  code  modulation  –  delta modulation – data compression.

Unit-2
Teaching Hours:9
DIGITAL MODULATION AND RADIO PROPAGATION
 

Digital communication- sampling –pulse code modulation – delta modulation -   Frequency shift     keying – Phase shift keying – Multiplexing and Multiple access – spread spectrum systems   - radio propagation.

Unit-3
Teaching Hours:9
PRINCIPLES OF CELLULAR COMMUNICATION AND MULTIPLE ACCESS TECHNIQUES
 

Cellular  terminology  -  Cell  structure  and  Cluster  –  Frequency  reuse  concept  – Cluster  size  and  system  capacity  –  method  of  locating  co  channel  cells  – frequency  reuse  distance  –  frequency  division  multiple  access  –  time  division multiple access – space division multiple access – code division multiple access.  

Unit-4
Teaching Hours:9
GSM AND CDMA DIGITAL CELLULAR STANDARDS
 

GSM network architecture –GSM signaling protocol architecture – Identifiers in GSM – GSM channels –GSM handoff procedures – Edge technology – wireless local loop – DECT system – GPRS.

Unit-5
Teaching Hours:9
EMERGING WIRELESS TECHNOLOGIES
 

 IEEE  802.11    system    architecture  –  mobile  ad  hoc  networks  –  Mobile  IP    and mobility  management  –  Mobile  TCP    -  wireless  sensor  networks  –  RFID technology  –  Blue  tooth  –  Wi  –Fi      standards  –  Wimax  standards.  – Femtocell network – Push -to –talk   technology for SMS.  Case Study.

Text Books And Reference Books:

1.       Roy Blake, “Wireless communication technology”      CENGAGE Learning, Sixth Indian reprint 2010.  (Chapter 1, 2, 3, 4, 7, 14)

2.       Singal T.L.  , “Wireless communication”    Tata    Mc Graw    Hill    Education. Private limited, 2011. (Chapter 4, 8, 11, 13, 14)

3.    Dharma Prakash Agrawal, Qing –An Zeng, “Introduction to wireless and Mobile systems”   CENGAGE Learning, first edition 2012.(Chapter 16).

Essential Reading / Recommended Reading

1.       Upena  Dalal,  “Wireless  communication”    Oxford    University  press,  first edition 2009.

2.       Kaveh  Pahlavan,  Prashant  Krishnamurthy,  “Wireless  Networks”  PHI. Learning Private Limited.

Evaluation Pattern

Continuous Internal Assessment (CIA ) for theory papers : 50% (50 marks out of 100 marks)

End Semester Examination (ESE): 50 % (50 marks out of 100 marks)

Evaluation

Area of Evaluation

Marks

CIA 1

10 Marks

CIA 2

25 Marks

CIA 3

10 Marks

Attendance

5 Marks

End Semester

50 Marks

Total

100 Marks

 

 

 

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA 1 Component 1  Poster Presentation

I & II

CO1,CO2

L4

2

CIA 1 Component 2 Assignment

II

CO2

L4

3

CIA 2 MSE

I ,II,III

CO1,CO2,CO3

L4

4

CIA 3 Component 1 MCQ

III & IV

CO3,CO4

L4

5

CIA 3 Component 2 Closed Book Test

V

CO5

L2

CS771 - INTERNSHIP (2017 Batch)

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

Course Objectives/Course Description

 

To create an opportunity to the students to experience the industry working process.

Course Outcome

Gaining knowledge about working infrastucture and latest market trends.

Unit-1
Teaching Hours:30
INTRODUCTION
 

All students should complete internship either in Industry/MOOC/Research labs before 7th semester. This component carries 2 credits.

§  Continuous Internal Assessment:2 credits

o   Presentation assessed by Panel Members

Pursuing internships is beneficial because they provide the opportunity to:

          Get an inside view of an industry and organization/company

          Gain valuable skills and knowledge

          Make professional connections and enhance student's network

          Get experience in a field to allow the student  to make a career transition

Regulations

1.The student shall undergo an Internship for 60 days starting from the end of 2nd semester examination and completing it during the initial period of 7th semester. Due to the pademic Situations department has allowed students to complete the requirement of internship by completing a MOOC approved by the internal guide.

2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of  the students

3. The students shall report the progress of the internship/MOOC to the mentor/guide at regular intervals and may seek his/her advise.

4. The Internship shall be completed before the mid semester of  7th semester.

5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance.

6. Students can also undergo internships arranged by the department during vacation.

7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.

8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor  and a presentation on the internship given to department constituted panel.

Text Books And Reference Books:

Relevant to the internship domain

Essential Reading / Recommended Reading

Recommended based on the area of the internship domain.

Evaluation Pattern

Maximum Marks = 50(Only credit will be displayed in the score card)

Passing marks 40% min

Do not have ESE and completely evaluated through continuous assessment only

Continuous Internal Assessment is based upon

  • Total No. of Internship/ Mooc Course Hours (5 marks)  
  • Learning Objectives (10 marks)
  • Performance Contribution (10 marks)
  • Personal and Professional Development (10 marks)
  • Quality of Study/work/paper (10 marks)
  • Submission of Report (5 marks)


CS772 - SERVICE LEARNING (2017 Batch)

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

Course Objectives/Course Description

 

 This course attempts to utilize the academic capability and skill of the B.Tech students to develop and suggest practicable solutions to enduring societal problems. Thus the course inculcates among the students the ability of utilizing acquired knowledge to explore strategies to overcome practical problems, while helping them to become a socially aware global citizen.

Course Outcome

  • Students will improve their higher order thinking skills through analysis and understanding of complex problems. 
  • Students will connect the learning in the classroom to the service experience
  • Student will analyze their skill set growth related to potential careers.Students will gain an understanding of their role as an active member of the society.
  • Students will experience personal growth through challenges and will develop new skills

Unit-1
Teaching Hours:2
INTRODUCTION
 

Conceptualization, Security essentials

 

Unit-2
Teaching Hours:1
Security Essentials
 

Service attacks, Spoofing, Phishing

Unit-3
Teaching Hours:1
Security Threats
 

Threats , security, counter measure, payment systems, Penetration Testing

Unit-4
Teaching Hours:20
Community Projects
 

Perform a survey to identify the community and their needs. This will start in the first week immediately after the introduction class. Define problem statement ,upon completion of survey. Design of methodology, frequent interaction with the community. 

Unit-5
Teaching Hours:6
Practical implementations
 

Testing and validation of the project, Practical implimentation of the proposed project will be evaluated by community as well as the academic panel.  

Text Books And Reference Books:

1. Jason Andress, “The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice”, Elsevier Science & Technology, ISBN: 978-1-59749-653-7. Jun 10, 2011

2. Michael Bazzell, “Personal Digital Security: Protecting Yourself from Online Crime Paperback  – Import”, 31 Jul 2013

 

Essential Reading / Recommended Reading

 

1.  PatrickEngebretson ,”The Basics of Hacking and Penetration Testing: Ethical Hacking and Penetration Testing Made Easy”. Elsevier Science & Technology, ISBN: 978-1-59749-655-1, Jun 10, 2011,

 

 

2.  Bruce Schneier , “Secrets and Lies: Digital Security in a Networked World”, January 2004,.

 

3.   Arthur Mikel., “Digital Security: Protect Your Online Products With Digital Protection Which Are Cheap And Easy To Implement “ Paperback – 2 Jul 2012, 

 

Note : Apart from the above mentioned books, reading meterials based on the selected project domain are mandatory. 

Evaluation Pattern

Content and creativity                                                  - 10 marks

Weekly progress                                                          - 20 marks

Team work                                                                   - 5 marks

Evaluation from the participating community                  - 10 marks

Report Submission                                                       - 5 marks

Delivery/Demo                                                            - 50 marks

IT735E01 - INFORMATION SECURITY (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To understand the basics of Information Security, legal, ethical and professional issues, aspects of risk management, aware of various standards in this area and technological aspects of Information Security

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

CO1: Explain the potential sources of vulnerability within an information system.

 

L2

2.

CO2: Illustrate the need of security and outline threats, attacks, legal issues.

L2

3.

CO3: Experiment with various risks, vulnerable and possible controls.

L3

4.

CO4: Understand the policies, standards and practices of information security.

L2

5.

CO5: Examine the IDS, scanning, tools and access control devices. 

L4

Unit-1
Teaching Hours:9
INTRODUCTION
 

History, what is Information Security, Critical Characteristics of Information, NSTISSC Security Model, Components of an Information System, Securing the Components, Balancing Security and Access, The SDLC, The Security SDLC.

Unit-2
Teaching Hours:9
SECURITY INVESTIGATION
 

Need for Security, Business Needs, Threats, Attacks, Legal, Ethical and Professional Issues

Unit-3
Teaching Hours:9
SECURITY ANALYSIS
 

Risk Management: Identifying and Assessing Risk, Assessing and Controlling Risk

Unit-4
Teaching Hours:9
LOGICAL DESIGN
 

Blueprint for Security, Information Security Policy, Standards and Practices, ISO 17799/BS 7799, NIST Models, VISA International Security Model, Design of Security Architecture, Planning for Continuity.

Unit-5
Teaching Hours:9
PHYSICAL DESIGN
 

Security Technology, IDS, Scanning and Analysis Tools, Cryptography, Access Control Devices, Physical Security, Security and Personnel, Case Study.

Text Books And Reference Books:

1.     T1. Michael E Whitman and Herbert J Mattord, ―Principles of Information Security, Cengage Learning India 2011.

2.     T2. Micki Krause, Harold F. Tipton, ―Handbook of Information Security Management, Vol 6 CRC Press LLC, 2012.

3.     T3. Stuart Mc Clure, Joel Scrambray, George Kurtz, ―Hacking Exposed, 7th Edition Tata McGraw-Hill, 2012.

Essential Reading / Recommended Reading

R1. Matt Bishop, ―Computer Security Art and Science, Pearson/PHI, 2009

Evaluation Pattern

Sl No

CIA Component

Marks

Unit(s) Covered

CO

RBT Level

1

CIA-I: Component-1 MOOC Course - Information Security

10

Unit 1 and 2

CO1,CO2

L2

2

CIA-I: Component-2 Quiz - MCQ

 Unit 1 and 2

CO1, CO2

L2

3

MSE

25

Unit 1,2 and 3

CO1,CO2,CO3

L2

4

CIA-III: Component-1 Poster Presentation

10

Unit 4 and 5

CO4,CO5

L2

5

CIA-III: Component-2 Closed Book Test

Unit 4 and 5

 

CO4,CO5

L4

6

Attendance

5

 

 

 

7

ESE

50

 

 

 

 

Total Marks

100

 

 

 

IT736E01 - SIMULATION AND MODELING (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To understand the concept of simulation, modeling, testing randomness, various case studies like inventory, traffic flow networks, practice on simulation tools and impart knowledge on building simulation systems.

Course Outcome

1

Understand the concepts which include the technique of simulation, major application areas, concept of a system, environment, continuous and discrete systems models

L2

2

Study of probability concepts in simulation including discrete and continuous, probability functions, numerical evaluation of continuous probability functions etc.

L3

3

Developing Simulation experiments and sampling concepts.

L3

4

Analyze discrete system and Continues system simulation and study on different simulation languages.

L4

5

Analyze the role of simulation studies in practical systems.

L4

Unit-1
Teaching Hours:9
INTRODUCTION
 

Systems, modeling, general systems theory, Concept of simulation, Simulation as a decision making tool, types of simulation.

Unit-2
Teaching Hours:9
RANDOM NUMBERS
 

Pseudo random numbers, methods of generating random variables, discrete and continuous distributions, testing of random numbers.

Unit-3
Teaching Hours:9
DESIGN OF SIMULATION EXPERIMENTS
 

Problem formulation, data collection and reduction, time flow mechanism, key variables, logic flow chart, starting condition, run size, experimental design consideration, output analysis and interpretation validation.

Unit-4
Teaching Hours:9
SIMULATION LANGUAGES:
 

Comparison and selection of simulation languages, study of anyone simulation language.

Unit-5
Teaching Hours:9
CASE STUDIES:
 

Development of simulation models using simulation language studied for systems like queuing systems, Production systems, Inventory systems, maintenance and replacement systems and Investment analysis.

Text Books And Reference Books:

1. Geoffrey Gordon, “System Simulation”, 2nd Edition, Prentice Hall, India, 2011

   2. NarsinghDeo, “System Simulation with Digital Computer, “Prentice Hall, India, 2009.

Essential Reading / Recommended Reading

1. Jerry Banks and John S.Carson, Barry L. Nelson, David M.Nicol, “Discrete Event System Simulation”, 3rd Edition, Prentice Hall, India, 2002.

2. Shannon, R.E. “Systems simulation: The art and science”, Prentice Hall, 1975.

3. Thomas J. Schriber, “Simulation using GPSS”, John Wiley, 1991.

Evaluation Pattern

1)      CIA COMPONENTS – EVALUATION RUBRICS

CIA I

Component 1: Assignment Description/Technical presentation

·                           An Assignment will be given in order to ensure that students have understood the topics and following up well with the curriculum.

 

o   Max. Marks              : 10

o   Tentative Date         : 20 – 27 June2020.

o   Venue                        : Class room/Online

 

Learning Outcome(s)

(i)        Understand the concepts of Systems, Systems Environment

(ii)     Illustration of Discrete and Continuous Systems

(iii)   Describing various the Mathematical models

(iv)   Simulation as a Decision making tool

 

Evaluation Rubrics

 

4

3

2

1

Correctness

Modeling with example and its analysis

 

Algorithms/Concepts with example

Trying to give the algorithm/Concept of the problem

Just the theoretical information

 

 

Component 2: Closed Book/Online Mode

A closed book test to be conducted in order to ensure that students are following up well with the curriculum

Marks: 10

Tentative date: 05/07/2020

Venue: Classroom/Google meet/ Online

Learning objectives:

1.  To make them practice with Continues and Discrete Systems and Role of Models in Simulation Studies  and problems of 1 &2 units.

2.  To equip them well for further examinations

Evaluation Rubrics:

 

4

3

2

1

Correctness

Modeling with example and its  analysis

 

Algorithms with example

Trying to give the algorithm of the problem

Just the theoretical information

 

CIA III

Component 1:

1.      Technical – Individual  Presentations & Group Presentations on Simulation studies

Assignment Description:

Presentations to be conducted in order to ensure that students are following up well with the curriculum and able to apply the simulation principles in real time environment.

Marks: 10

Tentative date: 02/09/2020

Venue: Classroom

Learning objectives:

1.  To make them practice with Design of Models in Simulation Studies  and problems of 3, 4 & 5 units.

2.  To equip them well for further examinations

Evaluation Rubrics:

 

4

3

2

1

Correctness

Case studies on Design of Models  and their analysis with example and its  analysis

 

Algorithms with example without analysis of the same.

Trying to give the algorithm of the problem

Just the theoretical information

 

Component 2: Closed Book

 

A closed book test to be conducted in order to ensure that students are following up well with the curriculum

Marks: 10

Tentative date: 20/10/2020

Venue: Classroom

Learning objectives:

1.  To make them practice with Design and Modeling with simulation languages of Simulation Studies and to solve problems of 3 & 4 units.

2.  To equip them well for further examinations

Evaluation Rubrics:

 

4

3

2

1

Correctness

Design  of Simulation Models with example and its  analysis

 

Design of Algorithms with out much analysis of it and with example

Trying to give the algorithm of the problem

Just the theoretical information

IT736E03 - ADVANCED DATABASES (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To understand the basics of DBMS and Object-Oriented databases.

 To interpret XML, Database Schemas and validate using DTD

 To perform query and transaction processing for optimization.

 To incorporate concurrency control and Recovery mechanisms for a Database.

 To analyse various Database security issues.

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

  1.  

Interpret ER models for relational database design

L2

  1.  

Experiment with Object Databases and XML for advanced databases.

L3

  1.  

Identify query optimization parameters and appropriate scheduling for improved transactions.

L3

  1.  

Compare the working principles of concurrency and recovery methods for a database.

L3

  1.  

Examine solutions to problems pertaining to security aspects for a database.

L4

Unit-1
Teaching Hours:9
DATABASE MANAGEMENT
 

Relational Data Model – SQL - Database Design - Entity-Relationship Model – Relational Normalization – EER- Relational Database Design Using ER-to-Relational Mapping -Mapping EER Model Constructs to Relations.

Unit-2
Teaching Hours:9
ADVANCED DATABASES
 

Object Databases – Object database Extensions to SQL-ODMG and ODL –Object Database Conceptual Design – XML Hierarchical model– XML Documents DTD and XML Schema – Distributed Data bases – Types and Architectures- data fragmentation, Replication and Allocation Techniques.

Unit-3
Teaching Hours:9
QUERY AND TRANSACTION PROCESSING
 

Query Processing Basics – Heuristic Optimization –Selectivity, Cost, Size Estimation – Transaction and System Concepts-Properties of Transactions – Architecture – Characterizing Schedules Based on Recoverability – Characterizing Schedules Based on Serializability – Transaction Support in SQL.

Unit-4
Teaching Hours:9
CONCURRENCY CONTROL AND RECOVERY
 

Concurrency Control – Two-Phase Locking Techniques for Concurrency Control - Concurrency Control Based on Timestamp Ordering - Multiversion Concurrency Control Techniques - Granularity of Data Items and Multiple Granularity Locking- Recovery Concepts- Recovery Techniques Based on Immediate Update- The ARIES Recovery Algorithm - Recovery in Multidatabase Systems

Unit-5
Teaching Hours:9
DATABASE SECURITY
 

Introduction to Database Security Issues- Discretionary Access Control Based on Granting and Revoking Privileges- Mandatory Access Control and Role-Based Access Control for Multilevel Security - SQL Injection- Statistical Database Security- Flow Control- Encryption and Public Key Infrastructures- Privacy Issues and Preservation- Challenges of Database Security- Oracle Label-Based Security

Text Books And Reference Books:

1.     R. Elmasri and S.B. Navathe, “Fundamentals of Database Systems”, 6th Edition, Addison Wesley, 2014

2.     Abraham Silberschatz, Henry. F. Korth, S.Sudharsan, “Database System Concepts”, 6th Edition., Tata McGraw Hill, 2014

Essential Reading / Recommended Reading

1.     Raghu Ramakrishnan & Johannes Gehrke, “Database Management Systems”, 3rd Edition, TMH, 2003

2.     Philip M. Lewis, Arthur Bernstein, Michael Kifer, “Databases and Transaction Processing: An Application-Oriented Approach”, Addison-Wesley, 2002.

Evaluation Pattern

Assessment of each paper Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) 

 End Semester Examination (ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I:   Closed Book Test and Quiz                                                      : 10 marks                

CIA II:  Mid Semester Examination (Theory)                                      : 25 marks

CIA III: Closed Book Test and Quiz                                                      : 10 marks

                    Attendance                                                            : 05 marks

IT736E04 - NETWORK ADMINISTRATION (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

In this syllabus the basics of network planning and Red Hat installation and configuration is taught. Configuring a database server, creating a VNC server, monitoring performance, providing Web services, exploring SELinux security basics, and exploring desktops

Course Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Illustrate the basic principles of system administration and role of System administrator.

L2

2.

Apply the concept of network services to configure a database server and a VNC server.

L3

3.

Explain the different types network services with their configuration and optimization.

L2

4.

Identify the responsibilities of system administrator with respect to data storage.

L3

5.

Analyze the network security issues and troubleshoot them.

L4

Unit-1
Teaching Hours:9
Introduction
 

Introduction to System Administration, The Unix Way, Essential Administrative Tools and Techniques, Startup and Shutdown. System and Network Administration Defined,  Duties of the System Administrator,  Planning the Network, Standard Installation , Kick start Installation , Exploring the Desktops,  System Startup and Shutdown , The File System Explained , Examining the System Configuration Files.

Unit-2
Teaching Hours:9
Network Services
 

Network Services, Managing the X Window System , Configuring Printers , TCP/IP Networking , Managing user and groups, Security, managing network services, The Network File System,  The Network Information System ,Connecting to Microsoft and Novell Networks, Configuring a Database Server, Creating a VNC Server,  Providing Additional Network Services, Optimizing Network Services.

Unit-3
Teaching Hours:9
Internet Services
 

Internet Services, Configuring BIND: The Domain Name System, Configuring Mail Services, Configuring FTP Services, Configuring a Web Server, Providing Web Services, Optimizing Internet Services.

Unit-4
Teaching Hours:9
System Administration
 

System Administration, Upgrading and Customizing the Kernel, Configuring the System at the Command Line , Administering Users and Groups , Installing and Upgrading Software Packages, Backing Up and Restoring the File System , Performance Monitoring

Unit-5
Teaching Hours:9
System Security and Problem Solving
 

System Security and Problem Solving, Exploring SELinux Security, Implementing Network Security, Troubleshooting and Problem Solving, Case studies.

Text Books And Reference Books:

1.  Thomas A. Limoncelli, ”The Practice of System and Network Administration”, Addison-Wesley Professional, second edition ,Published Feb 2012.

2.  Terry Collings, Kurt Wall, “Red Hat Linux Networking and System Administration”, 3rd Edition

3.  Leen Frisch, “Essential System Administration”, 3rd Edition , O'Reilly Media, 2002.

Essential Reading / Recommended Reading

 

1.  Evi Nemeth, Garth Snyder, Trent R. Hein, Ben Whaley, “Unix and Linux System Administration Handbook” , Prentice Hall

 

 

 

Evaluation Pattern

Assessment of each paper ·

Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) ·

End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

 

Components of CIA

CIA 1     Assignment and MCQ                       -   10 marks

CIA 2     Mid Semester Examination               -   25 marks

CIA 3     MOOC Course and Closed book test  -   10 marks

                               Attendance                    - 5  marks

 

 

Sl No

CIA Component

Unit(s) Covered

CO

RBT Level

1

CIA-I: Component-1

Assignment

Unit 1 and 2

CO1,CO2

L2

2

CIA-I: Component-2 Quiz - MCQ

 Unit 1 and 2

CO1, CO2

L2

3

MSE

Unit 1,2 and 3

CO1,CO2,CO3

L2

4

CIA-III: Component-1 MOOC Course - System Administration and IT Infrastructure Services

Unit 4 and 5

CO4,CO5

L2

5

CIA-III: Component-2 Closed Book Test

Unit 4 and 5

CO4,CO5

L4

 

BTCY01 - CYBER SECURITY (2017 Batch)

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

Course Objectives/Course Description

 

 This course is aimed at providing a comprehensive overview of the different facets of Cyber Security.  In addition, the course will detail into specifics of Cyber Security with Cyber Laws both in Global and Indian Legal environments.

Course Outcome

 Providing knowledge about different Cyber Crimes, Threats and Laws .Creating awareness about  risk management and protection from the cyber threats.

Unit-1
Teaching Hours:6
Security Fundamentals
 

As Architecture Authentication Authorization Accountability, Social Media, Social Networking and Cyber Security.

Cyber Laws, IT Act 2000-IT Act 2008-Laws for Cyber-Security, Comprehensive National Cyber-Security Initiative CNCI – Legalities.

Unit-2
Teaching Hours:12
Cyber Attack and Cyber Services
 

Computer Virus – Computer Worms – Trojan horse.

Vulnerabilities -  Phishing -  Online Attacks – Pharming - Phoarging    Cyber Attacks  -  Cyber Threats -  Zombie- stuxnet - Denial of Service Vulnerabilities  - Server Hardening-TCP/IP attack-SYN Flood.

Unit-3
Teaching Hours:12
Cyber Security Management
 

Risk Management and Assessment - Risk Management Process - Threat Determination Process -Risk Assessment - Risk Management Lifecycle.

Security Policy Management - Security Policies - Coverage Matrix, Business Continuity Planning – Disaster Types  -  Disaster Recovery Plan - Business Continuity Planning Process.

Unit-4
Teaching Hours:12
Vulnerability
 

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Black box- white box., Architectural Integration:  Security Zones - Devicesviz Routers, Firewalls, DMZ. Configuration Management - Certification and Accreditation for Cyber-Security.

Unit-5
Teaching Hours:12
Authentication and Cryptography
 

Authentication and Cryptography: Authentication - Cryptosystems - Certificate Services Securing Communications:  Securing Services -  Transport    Wireless  -  Steganography and NTFS Data Streams., Intrusion Detection and Prevention Systems:   Intrusion -  Defense in Depth  -  IDS/IPS  -IDS/IPS Weakness and Forensic Analysis, Cyber Evolution: Cyber Organization - Cyber Future

Text Books And Reference Books:

REFERENCES

1.      Matt Bishop, Introduction to Computer Security, Pearson, 6th impression, ISBN: 978-81-7758-425-7.

2.      Thomas R, Justin Peltier, John, Information Security Fundamentals, Auerbach Publications.

3.      AtulKahate, Cryptography and Network Security 2nd Edition, Tata McGrawHill.


Essential Reading / Recommended Reading

  Nina Godbole, SunitBelapure, Cyber Security, Wiley India 1st Edition 2011.

5.      Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, Cyber Security Policy Guidebook, Wiley; 1 edition , 2012,  ISBN-10: 1118027809

6.      Dan Shoemaker and Wm. Arthur Conklin, Cybersecurity: The Essential Body Of Knowledge,   Delmar Cengage Learning; 1 edition (May 17, 2011) ,ISBN-10: 1435481690

 

7.      Stallings, “Cryptography & Network Security - Principles & Practice”, Prentice Hall, 3rd Edition 2002. 

Evaluation Pattern

Evaluation based on CIAI, CIAII and on ESE

ESE will be based multiple choice questions

CS831E01 - QUANTUM COMPUTING (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 
  • To understand the fundamental principles of quantum computing
  • To understand the building blocks of a quantum computer.
  • To understand the principles, quantum information and limitation of quantum operations formalizing.
  • To understand the quantum error and its correction.

Course Outcome

 CO1:   Outline basic concepts and practices of Quantum Computing.(L3)

CO2:   Identify the key components in the field of Quantum Computing.(L3)

CO3:   Demonstrate the components of Quantum Computers with respect to Quantum principles.(L3)

CO4:   Categorize the Quantum Information with respect to Quantum Computing process.(L4)

CO5:   Understand the error handling process in Quantum Computing.(L4)

Unit-1
Teaching Hours:9
FUNDAMENTAL CONCEPTS
 

Global Perspectives, Quantum Bits, Quantum Computation, Quantum Algorithms, Quantum Information, Postulates of Quantum Mechanisms.

Unit-2
Teaching Hours:9
QUANTUM COMPUTATION
 

Quantum Circuits – Quantum algorithms, Single Orbit operations, Control Operations, Measurement, Universal Quantum Gates, Simulation of Quantum Systems, Quantum Fourier transform, Phase estimation, Applications, Quantum search algorithms – Quantum counting – Speeding up the solution of NP – complete problems – Quantum Search for an unstructured database.

Unit-3
Teaching Hours:9
QUANTUM COMPUTERS
 

Guiding Principles, Conditions for Quantum Computation, Harmonic Oscillator Quantum Computer, Optical Photon Quantum Computer – Optical cavity Quantum electrodynamics, Ion traps, Nuclear Magnetic resonance.

Unit-4
Teaching Hours:9
QUANTUM INFORMATIONS
 

Quantum noise and Quantum Operations – Classical Noise and Markov Processes, Quantum Operations, Examples of Quantum noise and Quantum Operations – Applications of Quantum operations, Limitations of the Quantum operations formalism, Distance Measures for Quantum information.

Unit-5
Teaching Hours:9
QUANTUM ERROR CORRECTION
 

Introduction, Shor code, Theory of Quantum Error –Correction, Constructing Quantum Codes, Stabilizer codes, Fault – Tolerant Quantum Computation, Entropy and information – Shannon Entropy, Basic properties of Entropy, Von Neumann, Strong Sub Additivity, Data Compression, Entanglement as a physical resource. Case study.

Text Books And Reference Books:

1.      Michael A. Nielsen. & Issac L. Chiang, “Quantum Computation and Quantum Information”, Cambridge University Press, 2007.

Essential Reading / Recommended Reading

1.      Mika Hiravensalo, “Quantum computing” II edition, ACM computing classification, Springer- 2004

Evaluation Pattern

·    Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●       End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications                      : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

CS831E02 - GRID COMPUTING (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

·         To provide in-depth knowledge in Computing techniques with grid as the platform.

·         To know the concepts pertaining to the grid Computing environment.

·         To understand various techniques to enhance the performance and scalability of Grids.

·         To nurture the students to design and applications and infrastructures for Grid.

·         To understand Grid technology for the present market scenarios.

Course Outcome

 

CO1 Illustrate the core concepts of the grid computing paradigm and applications.
CO2 Interpret the functioning of scheduling and security concepts in grid.
CO3 Summarize the concept of grid Service Oriented Architecture and Open Grid Services Architecture.
CO4 Utilize grid based applications to solve multiple grid Computers problem.
CO5 Examine the various components in Globus Toolkit.

Unit-1
Teaching Hours:9
INTRODUCTION TO GRID COMPUTING
 

Introduction to Grid Computing: Grid Computing Concept, History of Distributed Computing

Computational Grid Applications, Grid Computing Infrastructure Development, Grid Computing

Software Interface Job Submission: Introduction, Globus Job Submission, Transferring Files.

Unit-2
Teaching Hours:9
SCHEDULING AND SECURITY
 

Schedulers: Scheduler Features, Scheduler Examples, Grid Computing Meta-+Schedulers,

Distributed Resource Management Application (DRMAA).

Security Concepts: Introduction, Symmetric Key Cryptography, Asymmetric Key Cryptography, (Public Key Cryptography), Public Key Infrastructure, Systems/Protocols Using Security Mechanisms.

Grid Security: Introduction, Grid Security Infrastructure (GSI), Delegation, Higher-Level

Authorization Tools.

Unit-3
Teaching Hours:9
GRID INFRASTRUCTURE
 

System Infrastructure I Web Services: Service-Oriented Architecture, Web Services and Web

Service Implementation.

System Infrastructure II: Grid Computing Services: Grid Computing and Standardization Bodies, Interacting Grid Computing Components, Open Grid Services Architecture (OGSA), WSRF.

User-Friendly Interfaces: Introduction Grid Computing Workflow Editors, Grid Portals.

Unit-4
Teaching Hours:9
APPLICATIONS IN GRID COMPUTING
 

Grid-Enabling Applications: Introduction, Parameter Sweep, Using an Existing Program on Multiple Grid Computers, Writing an Application Specifically for a Grid, Using Multiple Grid Computers to Solve a Single Problem.

Unit-5
Teaching Hours:9
CASE STUDIES
 

Globus: Overview of Globus Toolkit 4, Installation of Globus, GT4 Configuration, Main Components and programming Model, Using Globus.

gLite: Introduction, Internal Workings of gLite, Logging and Bookkeeping (LB), Security

Mechanism Using gLite.

Resource management using Gridway and Gridbus.

Scheduling using Condor, SGE, PBS, LSF Grid scheduling with QoS.

Text Books And Reference Books:

1.      Barry Wilkinson, "Grid Computing Techniques and Applications", CRC Press, 2010.

2.      Frederic Magoules, Jie Pan, Kiat-An Tan, Abhinit Kumar, “Introduction to Grid Computing”, CRC Press, 2009.

Essential Reading / Recommended Reading

1.      Vladimir Silva, "Grid Computing for Developers ", Dreamtech Press, 2006.

2.      Ian Foster, Carl Kesselman. "The Grid 2-  Blueprint for a new computing Infrastructure",       Elsevier Series, 2004.

3.      Fran Berman, Geoffrey Fox. Anthony J.G Hey, "Grid Computing: Making the Global Infrastructure a Reality", Wiley, 2003.

4.      Joshey Joseph, Craig Fellenstein, "Grid computing", IBM Press, 2004.

Evaluation Pattern

·     Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●      End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications                      : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

CS831E03 - MOBILE COMPUTING (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

At the end of the course, the student should understand to provide basics for various techniques in Mobile Communications and Mobile Content services. To learn the basics of wireless voice and data communications technologies. To build working knowledge on various telephone and satellite networks. To study the working principles of wireless LAN and its standards. To build knowledge on various mobile computing algorithms. To build skills in working with wireless application protocols to develop mobile content applications.

Course Outcome

 

CO1 Demonstrate basis of wireless technology and media access schemes for classical systems.
CO2 Identify different wireless communication systems and to show how they transfer data between communication partners.
CO3 Illustrate MAC Layer Protocols for wireless communication.
CO4 Compare the performance of different routing algorithms supported by mobile communication.
CO5 Analyze TCP performance and WAP features in Mobile environment.

Unit-1
Teaching Hours:9
WIRELESS COMMUNICATION FUNDAMENTALS
 

Introduction – Wireless transmission – Frequencies for radio transmission – Signals – Antennas – Signal Propagation – Multiplexing – Modulations – Spread spectrum – MAC – SDMA – FDMA – TDMA – CDMA – Cellular Wireless Networks. 

Unit-2
Teaching Hours:9
TELECOMMUNICATION NETWORKS
 

Telecommunication systems – GSM – GPRS – DECT – UMTS – IMT-2000 – Satellite Networks - Basics – Parameters and Configurations – Capacity Allocation – FAMA and DAMA – Broadcast Systems – DAB - DVB. 

Unit-3
Teaching Hours:9
WIRLESS LAN
 

Wireless LAN – IEEE 802.11 - Architecture – services – MAC – Physical layer – IEEE 802.11a -802.11b standards – HIPERLAN – Blue Tooth. 

Unit-4
Teaching Hours:9
MOBILE NETWORK LAYER
 

Mobile IP – Dynamic Host Configuration Protocol - Routing – DSDV – DSR – Alternative Metrics. 

Unit-5
Teaching Hours:9
TRANSPORT AND APPLICATION LAYERS
 

Traditional TCP – Classical TCP improvements – WAP, Case Study.

Text Books And Reference Books:

1. Jochen Schiller, ―Mobile Communications‖, PHI/Pearson Education, Second Edition, Reprint edition 2012.

2. William Stallings, ―Wireless Communications and Networks‖, PHI/Pearson Education, 2009. (Unit I Chapter – 7&10-Unit II Chap 9)

Essential Reading / Recommended Reading

1. Kaveh Pahlavan, Prasanth Krishnamoorthy, ―Principles of Wireless Networks‖, PHI/Pearson Education, 2003.

2. Uwe Hansmann, Lothar Merk, Martin S. Nicklons and Thomas Stober, ―Principles of Mobile Computing‖, Springer, New York, 2003.

3. Hazysztof Wesolowshi, ―Mobile Communication Systems‖, John Wiley and Sons Ltd, 2002.

Evaluation Pattern

·     Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●      End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications            : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

CS832E01 - SOFTWARE TESTING (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To give an overview of the software testing techniques. To design and understand test cases, various levels of testing and related concepts.

Course Outcome

 

CO1 Identify the reason for bugs and device mechanism forpreventing /fixing bugswith respect tothe principles in software testing .
CO2 Interpret the existing procedures for software testing which would enhance the software quality.
CO3 Construct a software test plan to validate the software with respect to defined test scenarios.
CO4 Justify the test processes applied in the testing framework and incorporate the procedures as a formatted report.
CO5 Analyze the available techniques in software testing which would validate any given software product in a commercial environment.

Unit-1
Teaching Hours:9
INTRODUCTION
 

Testing as an Engineering Activity – Role of Process in Software Quality – Testing as a Process – Basic Definitions – Software Testing Principles – The Tester‘s Role in a Software Development Organization – Origins of Defects – Defect Classes – The Defect Repository and Test Design – Defect Examples – Developer/Tester Support for Developing a Defect  Repository.

Unit-2
Teaching Hours:9
TEST CASE DESIGN
 

Introduction to Testing Design Strategies – The Smarter Tester – Test Case Design Strategies – Using Black Box Approach to Test Case Design Random Testing – Requirements based testing – positive and negative testing – Boundary Value Analysis – decision tables -Equivalence Class Partitioning state-based testing – cause effect graphing – error guessing -compatibility testing – user documentation testing – domain testing Using White–Box Approach to Test design – Test Adequacy Criteria – static testing vs. structural testing – codefunctional testing - Coverage and Control Flow Graphs – Covering Code Logic – Paths – Their Role in White–box Based Test Design – code complexity testing – Evaluating Test Adequacy Criteria.

Unit-3
Teaching Hours:9
LEVELS OF TESTING
 

The Need for Levels of Testing – Unit Test – Unit Test Planning –Designing the Unit Tests.The Test Harness – Running the Unit tests and Recording results – Integration tests – Designing Integration Tests – Integration Test Planning – scenario testing – defect bash elimination -System Testing – types of system testing - Acceptance testing – performance testing - Regression Testing – internationalization testing – ad-hoc testing - Alpha – Beta Tests – testing OO systems – usability and accessibility testing

Unit-4
Teaching Hours:9
TEST MANAGEMENT
 

People and organizational issues in testing – organization structures for testing teams – testing services - Test Planning – Test Plan Components – Test Plan Attachments – Locating Test Items – test management – test process - Reporting Test Results – The role of three groups in Test Planning and Policy Development – Introducing the test specialist – Skills needed by a test specialist – Building a Testing Group.

Unit-5
Teaching Hours:9
CONTROLLING AND MONITORING
 

Software test automation – skills needed for automation – scope of automation – design and architecture for automation – requirements for a test tool – challenges in automation - Test metrics and measurements –project, progress and productivity metrics – Status Meetings – Reports and Control Issues – Criteria for Test Completion – SCM – Types of reviews –Developing a review program – Components of Review Plans– Reporting Review Results. – Evaluating software quality – defect prevention – testing maturity model – Case Studies.

Text Books And Reference Books:

1. Boris Beizer, ―Software Testing Techniques‖, Dreamtech. Second Edition, 2009

2. Srinivasan Desikan and Gopalaswamy Ramesh, ―Software Testing – Principles and Practices‖, Pearson education, 2008.

Essential Reading / Recommended Reading

1. Elfriede Dustin, ―Effective Software Testing‖, Pearson Education, First Edition, 2008.

2. Edward Kit, ―Software Testing in the Real World‖, Pearson Education, 2008.

3. Aditya P.Mathur, ―Foundations of Software Testing‖, Pearson Education, 2011.

Evaluation Pattern

·     Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●      End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications            : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

CS832E02 - SOFTWARE PROCESS AND PROJECT MANAGEMENT (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To provide basics for various Process and Project management models, also it provide students a systematic approach to initiate, plan, execute, control and close a software project and understanding of the best practices, and techniques used in project management processes, knowledge of ISO 9000 and CMMI, and process improvement techniques.

Course Outcome

 

CO1 Explain the Software maturity frame work and process assessment.
CO2 Demonstrate the software process reference model and techniques.
CO3 Utilize the classical models of software management.
CO4 Examine the life cycle phases of software process management
CO5 Analyze the various process in software management disciplines

Unit-1
Teaching Hours:9
SOFTWARE PROCESS MATURITY
 

Software maturity Framework, Principles of Software Process Change, Software Process Assessment, The Initial Process.

Unit-2
Teaching Hours:9
PROCESS REFERENCE MODELS
 

Capability Maturity Model (CMM), CMMi, PCMM, PSP, TSP, IDEAL, Process Definition Techniques.

Unit-3
Teaching Hours:9
SOFTWARE PROJECT MANAGEMENT RENAISSANCE
 

Conventional Software Management, Evolution of Software Economics, Improving Software Economics, The old way and the new way.

Unit-4
Teaching Hours:9
SOFTWARE MANAGEMENT PROCESS FRAMEWORK
 

A Software management process framework: life-cycle phases, artifacts of the process, model based software architecture, work flow process, check points of the process.

Unit-5
Teaching Hours:9
SOFTWARE MANAGEMENT DISCIPLINES
 

Software management disciplines: iterative process planning, project organization and responsibilities, Process automation.

CCPDS-R Case Study and Future Software Project Management Practices

Modern Project Profiles, Next-Generation software Economics, Modern Process Transitions

Text Books And Reference Books:

1. Watts S. Humphrey, ―Managing the Software Process‖, Pearson Education 2012.

2. Walker Royce, ―Software Project Management‖, Pearson Education 2010.

Essential Reading / Recommended Reading

1. Watts S. Humphrey, ―An Introduction to the Team Software Process‖, Pearson Education 2007.

2. Watts S. Humphrey, ―A Discipline to Software Engineering‖, Pearson Education 2008.

3. Pankaj Jalote, ―Software Project Management in Practice‖, Pearson Education 2010.

4. Chris Kemerer, ―Software Project Management Readings and Cases‖, 2010.

Evaluation Pattern

·     Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●      End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications            : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

CS833E02 - INTRODUCTION TO DATA SCIENCE (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

●To study the concepts of data pre-process.

•To study various pattern discovery methods.

•To study the basic concepts of classification techniques.

•To study the basic concepts of clustering techniques.

•To learn about the recent trends in Data Science

 

Course Outcome

CO1:   Demonstrate the fundamental concepts, applications and preprocessing of data science.

CO2:   Illustrate the concepts of association rule mining for various applications.

CO3:  Examine different classification algorithms with data sets.

CO4:   Apply and evaluate various clustering techniques and principles in mining the knowledge.

CO5:   Analyze the latest trends in data science.

Unit-1
Teaching Hours:9
INTRODUCTION AND DATA PRE-PROCESSING
 

Why Data Mining?, What Is Data Mining?, What Kinds of Data Can Be Mined?, What Kinds of Patterns Can Be Mined?, Which Technologies Are Used? Which Kinds of Applications Are Targeted?, Major Issues in Data Mining, Data Pre-processing: An Overview, Data Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization

Unit-2
Teaching Hours:9
Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
 

Basic Concepts, Frequent Itemset Mining Methods, Which Patterns Are Interesting?—Pattern Evaluation Methods , Advanced Pattern Mining: Pattern Mining: A Road Map, Pattern Mining in Multilevel, Multidimensional Space, Constraint-Based Frequent Pattern Mining, Mining High-Dimensional Data and Colossal Patterns,  Mining Compressed or Approximate Patterns

Unit-3
Teaching Hours:9
CLASSIFICATION: BASIC CONCEPTS
 

Basic Concepts, Decision Tree Induction, Bayes Classification Methods, Rule-Based Classification, Model Evaluation and Selection, Techniques to Improve Classification Accuracy, Support Vector Machines, Lazy Learners (or Learning from Your Neighbors)

Unit-4
Teaching Hours:9
CLUSTER ANALYSIS: BASIC CONCEPTS AND METHODS
 

Cluster Analysis, Partitioning Methods, Hierarchical Methods, Density-Based Methods, Grid-Based Methods,  Evaluation of Clustering, Clustering High-Dimensional Data, Clustering Graph and Network Data

Unit-5
Teaching Hours:9
Data Mining Trends and Research Frontiers
 

Mining Complex Data Types, Other Methodologies of Data Mining, Data Mining Applications, Data Mining and Society, Data Mining Trends

Text Books And Reference Books:

1. J. Han, M. Kamber, “Data Mining: Concepts and Techniques”, Harcourt India / Morgan Kauffman, 2011.

2. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson Education, 

 

Essential Reading / Recommended Reading

1. K.P.Soman, ShyamDiwakar, V.Ajay: Insight into Data Mining – Theory and   Practice, PHI, 2012 

2. David Hand, Heikki Manila, PadhraicSymth, “Principles of Data Mining”, PHI 2012.

3. W.H.Inmon, “Building the Data Warehouse”, 3rd Edition, Wiley, 2011.

4. Alex Bezon, Stephen J.Smith, “Data Warehousing, Data Mining & OLAP”, MeGraw-Hill Edition, 2001

5. PaulrajPonniah, “Data Warehousing Fundamentals”, Wiley-Interscience Publication, 2003.

 

Evaluation Pattern

·     Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●      End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications            : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

CS833E03 - SOFT COMPUTING (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The course will provide students to understand the techniques of soft computing, ideas of fuzzy sets, optimization associated with neural network learning and adaptive neuro-fuzzy inferencing systems which differ from conventional AI and computing in terms of its tolerance to imprecision and uncertainty. It also provide case studies utilizing the above and illustrate the intelligent behavior of programs based on soft computing.

Course Outcome

 

CO1 Solve Fuzzy set, relation, reasoning and rulebased problems.
CO2 Explain basic concepts in Genetic Algorithm,Simulated Annealing, random search and Downhill simplex search.
CO3 Experiment with the concepts in Steepest Descent method,Genetic Algorithm, Simulated Annealing, random search and Downhill simplex search in optimization problems.
CO4 Explain the basic concepts in Artificial neural networks.
CO5 Build ANN techniques based solutions for Classification and Clustering problems.

Unit-1
Teaching Hours:9
FUZZY SET THEORY
 

Overview of clustering , classification, regression-Introduction to Neuro – Fuzzy and Soft Computing – Fuzzy Sets – Basic Definition and Terminology – Set-theoretic Operations –Member Function Formulation and Parameterization – Fuzzy Rules and Fuzzy Reasoning – Extension Principle and Fuzzy Relations – Fuzzy If-Then Rules – Fuzzy Reasoning – Fuzzy Inference Systems.

Unit-2
Teaching Hours:9
OPTIMIZATION
 

Derivative-based Optimization – Descent Methods – The Method of Steepest Descent – Classical Newton‘s Method – Step Size Determination – Derivative-free Optimization

Unit-3
Teaching Hours:9
GENETIC ALGORITHM
 

Genetic Algorithm (GA): Biological terminology –elements of GA: encoding, types of selection, types of crossover, mutation, reinsertion–a simple genetic algorithm –Theoretical

foundation: schema, fundamental theorem of GA, building block hypothesis. Simulated Annealing – Random Search – Downhill Simplex Search.

Unit-4
Teaching Hours:9
NEURAL NETWORKS
 

Supervised Learning Neural Networks – Perceptrons - Adaline – Backpropagation Mutilayer Perceptrons – Radial Basis Function Networks – Unsupervised Learning Neural Networks – Competitive Learning Networks – Kohonen Self-Organizing Networks. 

Unit-5
Teaching Hours:9
SOFT COMPUTING SYSTEMS
 

Introduction to Extreme Learning Machines- Convolutional Neural networks – Deep Neural Networks. Hybrid Systems – ANFIS. Case studies (ONE EACH)- Fuzzy systems, Genetic Algorithm, ANN

Text Books And Reference Books:

1. J.S.R.Jang, C.T.Sun and E.Mizutani, ―Neuro-Fuzzy and Soft Computing‖, PHI, 2004, Pearson Education 2004.

2. Kumar S Ray, ―Soft Computing and Its Applications, Volume One: A Unified Engineering Concept‖, Apple Academic Press; 1 edition (September 16, 2014)

3. Timothy J. Ross, ―Fuzzy Logic with Engineering Application‖, Wiley 2011. 

4. Davis E. Goldberg, ―Genetic Algorithms: Search, Optimization and Machine Learning

5. S.Rajasekaran and G.A.V.Pai, ―Neural Networks, Fuzzy Logic and Genetic Algorithms‖, PHI, 2003.

6. R.Eberhart, P.simpson and R.Dobbins, ―Computional Intelligence PC Tools‖, AP Professional,Boston Pearson 2002. 

Essential Reading / Recommended Reading

1. S. N. Sivanandam, S. Sumathi, S. N. Deepa, ―Introduction to Neural Networks using MATLAB 6.0‖, Tata McGraw Hill, New Delhi, 2006.

2. S. N. Sivanandam, S.N. Deepa, ―Principles of Soft Computing‖, Wiley India, 2008.

Evaluation Pattern

·     Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●      End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications            : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

CS833E04 - DIGITAL IMAGE PROCESSING (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

It provide a strong foundation in image processing concepts and technique like image enhancement, image restoration, image compression , image segmentation, representation, pattern recognition and interpretation of the application areas.

Course Outcome

CO1: Identify the fundamental concepts of image formation and image transformations.

 CO2: Interpret histograms and their use to enhance quality of images based on matching and specification techniques

 CO3: Demonstrate the use of degradation function for distorted images and compare compression techniques

CO4: Evaluate Morphological processing for image representation

 CO5: Utilize descriptors and patterns to describe an image for Object recognition

Unit-1
Teaching Hours:9
DIGITAL IMAGE FUNDAMENTALS
 

Image formation (chapter1, chapter 2 Gonzalez), Image transforms – Fourier transforms

Unit-2
Teaching Hours:9
IMAGE ENHANCEMENT
 

Histogram modification techniques - Image smoothening - Image Sharpening

Unit-3
Teaching Hours:9
IMAGE RESTORATION, COMPRESSION
 

Introduction to Image Restoration -Compression Models -– Region based segmentation

Unit-4
Teaching Hours:9
MORPHOLOGY AND REPRESENTATION
 

Morphology-Representation schemes

Unit-5
Teaching Hours:9
DESCRIPTION AND OBJECT RECOGNITION
 

Boundary descriptors- Regional descriptors - Relational Descriptors Patterns and pattern classes - Structural methods-Case studies

Text Books And Reference Books:

1. Gonzalez.R.C & Woods. R.E., ―Digital Image Processing‖, 3rd Edition, Pearson Education, Indian edition published by Dorling Kindersely India Pvt. Ltd. Copyright © 2009, Third impression 2011.

2. Gonzalez.R.C & Woods. R.E., ―Digital Image Processing using MATLAB‖, 2nd Edition, McGraw Hill Education (India) Pvt Ltd 2011 (Asia)

3. Madan, ―An Introduction to MATLAB for Behavioral Researchers‖ , Sage Publications, 2014

Essential Reading / Recommended Reading

1. Anil Jain.K, ―Fundamentals of Digital image Processing‖, Prentice Hall of India, 2011.

Evaluation Pattern

·     Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●      End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications            : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

CS871 - PROJECT WORK (2017 Batch)

Total Teaching Hours for Semester:180
No of Lecture Hours/Week:12
Max Marks:200
Credits:6

Course Objectives/Course Description

 

students were expected to develop products using differnet languages using their class works

Course Outcome

 

CO1 Design engineering solutions to complex real world problems using research literature for societal applications through independent study.
CO2 Use appropriate hardware and software depending on the nature of the project with an understanding of their limitations.
CO3 Demonstrate teamwork and leadership skills with professional ethics and prepare a project report in the prescribed format.
CO4 Understand the impact of the developed projects on environmental factors.
CO5 Demonstrate project management skills including handling the finances in doing projects for given real world societal problems.

Unit-1
Teaching Hours:180
Assessment of Project Work
 


§ 
Continuous Internal Assessment:100 Marks

¨      Presentation assessed by Panel Members

¨      Assessed by Guide

 

§  End Semester Examination:100 Marks

¨      Viva Voce

¨      Demonstration

¨      Project Report

 

 

Text Books And Reference Books:

Based on the project domain the reference meterials will be suggested

Essential Reading / Recommended Reading

Guide will give the recommendations based on their project work 

Evaluation Pattern

 

 

CIA 100 MARKS

 

ESE 100 MARKS

REVIEW 1

REVIEW 2

REVIEW 3

 

REVIEW COMMITTEE

GUIDE

REVIEW COMMITTEE

GUIDE

REVIEW COMMITTEE

GUIDE

EXAMINERES

20

05

20

10

20

25

100

TOTAL

25

TOTAL

30

TOTAL

45

 

CS872 - COMPREHENSION (2017 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:4
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Aim to develope the presentation skills of the students.

Course Outcome

 

CO1 Use research based methodologies through independent study to solve complex engineering problems.
CO2 Demonstrate oral communication through presentation of the research topic.
CO3 Exhibit teamwork skills with professional ethics.

Unit-1
Teaching Hours:30
Assessment of Comprehension
 

§  Continuous Internal Assessment:50 Marks

 

¨      Presentation assessed by Panel Members

Text Books And Reference Books:

Based on the topic selected the reading meterials will be recommended.

Essential Reading / Recommended Reading

Based on the topic selected the reading meterials will be recommended.

Evaluation Pattern

Comprehension

Maximum Marks = 50

 

Passing marks 40% min

 

Do not have ESE and completely evaluated through continuous assessment only,

The evaluation (minimum 2 presentations) shall be based on the

      Topic / report :40%

       Presentation: 40%

       Response to the questions asked during presentation : 20%.

 

IT832E02 - WEB SERVICES AND SERVICE ORIENTED ARCHITECTURE (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

Students are expected to understand the meaning of service-oriented paradigm and the aspects affecting the efficient utilization of it. Students achieve understanding of SOA for

sustainable service development. Students are able to design and implement service-oriented applications.

Course Outcome

CO1: Demonstrate the applicability of SOA Concepts and the goals of the REST Architectural Style

CO2: Apply requirements towards the creation of a REST web service , Design Principles and Constraints.

CO3: Analyze Service Modeling, Service Contract in SOA and Service Oriented Desgin With REST.

CO4: Develop RESTful services as part of service-oriented solutions in conjunction with service-oriented architecture (SOA).

CO5: Design solutions for web services that follow the REST architectural style.

Unit-1
Teaching Hours:9
UNIT I INTRODUCTION TO SOA - TERMINOLOGY, CONCEPTS AND GOALS
 

Service Terminology - Service Terminology Context - Basic Terminology and Concepts - Further Reading - Case Study Example - REST Constraints - Goals of the REST Architectural

Style

Unit-2
Teaching Hours:9
SERVICE CONTRACTS AND SERVICE-ORIENTATION WITH REST
 

Uniform Contract Elements - REST Service Capabilities and REST Service Contracts - REST Service Contracts vs. Non-REST Service Contracts - The Role of Hypermedia - REST Service

Contracts and Late Binding - ―SOA vs. REST" or "SOA + REST"? - Design Goals - Design Principles and Constraints

Unit-3
Teaching Hours:9
SOA METHODOLOGY, ANALYSIS AND SERVICE MODELING AND SERVICE-ORIENTED DESIGN WITH REST
 

Service Inventory Analysis - Service-Oriented Analysis (Service Modeling) - Service-Oriented Design (Service Contract) - Service Logic Design - Service Discovery - Service Versioning and Retirement - Uniform Contract Modeling and REST Service Inventory Modeling - REST  Service Modeling - Uniform Contract Design Considerations - REST Service Contract Design - Complex Method Design

Unit-4
Teaching Hours:9
FUNDAMENTAL AND ADVANCED SERVICE COMPOSITION WITH REST WITH CASE STUDY
 

Service Composition Terminology - Service Composition Design Influences - Composition Hierarchies and Layers - REST Service Composition Design Considerations - A Step-by-Step Service Activity - Service Compositions and Stateless - Cross-Service Transactions with REST - Event-Driven Interactions with REST - Service Composition with Dynamic Binding and Logic Deferral - Service Composition Across Service Inventories - Revisiting the Confer Student Award Process - Application Submission and Task Service Invocation - Confer Student Award Service Composition Instance - Review of Pending Applications and Task Service Invocation

Unit-5
Teaching Hours:9
DESIGN PATTERNS, SERVICE VERSIONING WITH REST AND UNIFORM CONTRACT PROFILES
 

REST-Inspired SOA Design Patterns - Other Relevant SOA Design Patterns - Versioning Basics - Version Identifiers - Uniform Contract Profile Template - REST Service Profile Considerations – Case Study Example

Text Books And Reference Books:

1. Thomas Erl, Benjamin Carlyle, Cesare Pautasso, Raj Balasubramanian, ―SOA with REST: Principles, Patterns & Constraints for Building Enterprise Solutions with

REST‖, Prentice Hall Service Technology 2012.

2. Arnon Rotem-Gal-Oz, ―SOA Patterns, Manning‖.

Essential Reading / Recommended Reading

1. Java Web Services: Up and Running, 2nd Edition, A Quick, Practical, and Thorough Introduction‖, O'Reilly 2013.

2. Bill Burke, ―Restful Java with JAX-RS 2.0, Designing and Developing Distributed Web Services‖, 2nd Edition, O'Reilly 2013.

3. Developing RESTful Services with JAX-RS 2.0, WebSockets, and JSON, A complete and practical guide to building RESTful Web Services with the latest Java EE7 API‖,

Packet Publishing, 2013.

Evaluation Pattern

·     Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●      End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications            : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks

IT833E04 - PROFESSIONAL ETHICS AND HUMAN VALUES (2017 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

·         To create an awareness on Engineering Ethics and Human Values.

·         To instill Moral and Social Values and Loyalty

·         To appreciate the rights of Others

Course Outcome

 CO1: Outline professional ethics and human values by realizing the holistic attributes.

CO2: Specify the Engineering Professional Ethics to identify and solve problems related to society, safety, health & legal aspects.

CO3: Explain the basic perception of profession, professional ethics, various moral issues &uses of ethical theories

CO4: Review case studies related to safety, security, society, health, employee rights and intellectual Property Rights.

CO5: Summarize professional ethics, professional rights,and responsibilities of an engineer.

Unit-1
Teaching Hours:9
HUMAN VALUES
 

Morals, Values and Ethics – Integrity – Work Ethic – Service Learning – Civic Virtue – Respect for Others – Living Peacefully – caring – Sharing – Honesty – Courage – Valuing Time – Co-operation – Commitment – Empathy – Self-Confidence – Character – Spirituality

 

Unit-2
Teaching Hours:9
ENGINEERING ETHICS
 

                                            

Senses of 'Engineering Ethics' - variety of moral issued - types of inquiry - moral dilemmas - moral autonomy - Kohlberg's theory - Gilligan's theory - consensus and controversy – Models of Professional Roles - theories about right action - Self-interest - customs and religion - uses of ethical theories.

Unit-3
Teaching Hours:9
ENGINEERING AS SOCIAL EXPERIMENTATION
 

Engineering as experimentation - engineers as responsible experimenters - codes of ethics - a balanced outlook on law - the challenger case study

 

Unit-4
Teaching Hours:9
SAFETY, RESPONSIBILITIES AND RIGHTS
 

Safety and risk - assessment of safety and risk - risk benefit analysis and reducing risk - the Three Mile Island and Chernobyl case studies.

Collegiality and loyalty - respect for authority - collective bargaining - confidentiality - conflicts of interest - occupational crime - professional rights - employee rights - Intellectual Property Rights (IPR) - discrimination.

Unit-5
Teaching Hours:9
GLOBAL ISSUES
 

Multinational corporations - Environmental ethics - computer ethics - weapons development - engineers as managers-consulting engineers-engineers as expert witnesses and advisors -moral leadership-sample code of Ethics like ASME, ASCE, IEEE, Institution of Engineers (India), Indian Institute of Materials Management, Institution of electronics and telecommunication engineers (IETE),India, etc.

Text Books And Reference Books:

1. Mike Martin and Roland Schinzinger, “Ethics in Engineering”, McGraw-Hill, New York 1996.

2. Govindarajan M, Natarajan S, Senthil Kumar V. S, “Engineering Ethics”, Prentice Hall of India, New Delhi, 2004.

Essential Reading / Recommended Reading

1.   Charles D. Fleddermann, “Engineering Ethics”, Pearson Education / Prentice Hall, New Jersey, 2004 (Indian Repri

2. Charles E Harris, Michael S. Protchard and Michael J Rabins, “Engineering Ethics – Concepts and Cases”, Wadsworth Thompson Learning, United States, 2000 (Indian Reprint now available)

3. John R Boatright, “Ethics and the Conduct of Business”, Pearson Education, New Delhi, 2003.

4. Edmund G Seebauer and Robert L Barry, “Fundamentals of Ethics for Scientists and Engineers”, Oxford University Press, Oxford, 2001.

Evaluation Pattern

·     Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks)

●      End Semester Examination(ESE)      : 50% (50 marks out of 100 marks)

 

Components of the CIA

CIA I   :  Subject Assignments / Online Tests                 : 10 marks

CIA II  :   Mid Semester Examination (Theory)                : 25 marks                 

CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications            : 10 marks

Attendance                                                                            : 05 marks

                     Total                                                                              : 50 marks