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1

KARNATAK LAW SOCIETY‟S

GOGTE INSTITUTE OF TECHNOLOGY UDYAMBAG, BELAGAVI-590008

(An Autonomous Institution under Visvesvaraya Technological University, Belagavi)

(APPROVED BY AICTE, NEW DELHI)

Department of Information Science & Engineering

Scheme and Syllabus (2015 Scheme)

7th

Semester Information Science & Engineering

2

INSTITUTION VISION

Gogte Institute of Technology shall stand out as an institution of excellence in technical

education and in training individuals for outstanding caliber, character coupled with creativity

and entrepreneurial skills.

MISSION

To train the students to become Quality Engineers with High Standards of Professionalism and

Ethics who have Positive Attitude, a Perfect blend of Techno-Managerial Skills and Problem

solving ability with an analytical and innovative mindset.

QUALITY POLICY

Imparting value added technical education with state-of-the-art technology in a congenial,

disciplined and a research oriented environment.

Fostering cultural, ethical, moral and social values in the human resources of the institution.

Reinforcing our bonds with the Parents, Industry, Alumni, and to seek their suggestions for

innovating and excelling in every sphere of quality education.

DEPARTMENT VISION

Department of Information Science and Engineering shall provide excellent learning

environment with focus on innovation, research and entrepreneurship among aspiring

engineers to contribute to the workforce of the nation

MISSION

To impart Quality Technical Education in the field of Information Technology and enhance

intellectual and professional competence amongst the aspiring engineers

PROGRAM EDUCATIONAL OBJECTIVES (PEOs)

1. To develop the ability among students to synthesize data and technical concepts for

software design and development hence prepare students for successful careers in

software industry that meet the needs of Indian and multinational companies or to excel

in higher studies.

2. To inculcate professional and ethical attitude amongst students with effective

communication skills, teamwork skills, and an ability to relate engineering issues to

broader social context.

3. To provide students with an excellent academic environment, entrepreneur capabilities

and to enable students for life-long learning needed to lead a successful professional

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career.

PROGRAM OUTCOMES (POs)

1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering

fundamentals, and an engineering specialization to the solution of complex engineering

problems.

2. Problem analysis: Identify, formulate, review research literature, and analyze complex

engineering problems reaching substantiated conclusions using first principles of

mathematics, natural sciences, and engineering sciences.

3. Design/development of solutions: Design solutions for complex engineering problems

and design system components or processes that meet the specified needs with

appropriate consideration for the public health and safety, and the cultural, societal, and

environmental considerations.

4. Conduct investigations of complex problems: Use research-based knowledge and

research methods including design of experiments, analysis and interpretation of data,

and synthesis of the information to provide valid conclusions.

5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and

modern engineering and IT tools including prediction and modeling to complex

engineering activities with an understanding of the limitations.

6. The engineer and society: Apply reasoning informed by the contextual knowledge to

assess societal, health, safety, legal and cultural issues and the consequent responsibilities

relevant to the professional engineering practice.

7. Environment and sustainability: Understand the impact of the professional engineering

solutions in societal and environmental contexts, and demonstrate the knowledge of, and

need for sustainable development.

8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities

and norms of the engineering practice.

9. Individual and team work: Function effectively as an individual, and as a member or

leader in diverse teams, and in multidisciplinary settings.

10. Communication: Communicate effectively on complex engineering activities with the

engineering community and with society at large, such as, being able to comprehend and

write effective reports and design documentation, make effective presentations, and give

and receive clear instructions.

11. Project management and finance: Demonstrate knowledge and understanding of the

engineering and management principles and apply these to one‟s own work, as a member

and leader in a team, to manage projects and in multidisciplinary environments.

12. Life-long learning: Recognize the need for, and have the preparation and ability to

engage in independent and life-long learning in the broadest context of technological

change.

PROGRAM SPECIFIC OUTCOMES (PSOs)

1. Problem solving Skills: An ability to analyze a problem design, implement and

evaluate software solutions related to algorithms, system software, web design big data

analytics & networking.

2. Professional skills: An ability to develop standard software solutions for existing and

emerging industry verticals and research domains.

3. Career Skills: An ability to harness Information Science & Engineering knowledge

with ethics and societal concern for career and further educational abilities along with

entrepreneurial skills.

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Scheme of Teaching

Seventh Semester

S.No. Course

Code Course

Contact

Hours

Total

Contact

Hours/

week

Total

credits

Marks

L – T - P CIE SEE Total

1. 15IS71 Cryptography and Network Security PC1 3 – 1 – 0 4 4 50 50 100

2. 15IS72 Software Testing PC2 3 – 0 – 0 3 3 50 50 100

3. 15IS73 Data Science PC3 3 – 0 – 0 3 3 50 50 100

4. 15IS74 Embedded systems and Internet of

Things PC4 3 – 1 – 0 4 4 50 50 100

5. 15IS75X Elective – III PE 3 – 0 – 0 3 3 50 50 100

6. 15IS76X Elective – IV PE 3 – 0 – 0 3 3 50 50 100

7. 15ISL77 Data Science Lab L1 0 – 0 – 3 3 2 25 25 50

8. 15ISL78 Software Testing Laboratory

L2 0 – 0 – 3 3 2 25 25 50

9. 15IS79 Seminar on Project synopsis PR 0 – 0 – 2 2 2 25 - 25

Total 28 26 375 350 725

Electives

-III

Course

Code VII SEMESTER

Electives

-IV

Course

Code VII SEMESTER

1 15IS751 Big Data

Management 1 15IS761

Mobile Computing and

Applications

2 15IS752 Adhoc Sensor

Networks 2

15IS762 Network Programming

3 15IS753 Digital Image

Processing 3 15IS763

System Simulation and

Modeling

4 15IS754 Artificial

Intelligence 4 15IS764 C# .Net

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CRYPTOGRAPHY AND NETWORK SECURITY

(Theory)

Course learning objectives

1. Explain standard algorithms used to provide security.

2. Distinguish between various types of security applications

3. Deploy encryption techniques to secure data in transit across data networks

4. Implement security applications in the field of Information technology

Unit – I 8 Hours

Classical Encryption Techniques Symmetric Cipher Model:

Symmetric Cipher model, Substitution Techniques: Caesar Cipher, Mono alphabetic

Cipher, Play fair Cipher, Poly alphabetic Cipher, One Time Pad. Transposition

Techniques, Stream Ciphers and Block Ciphers, The data encryption standard: DES

encryption, DES decryption, A DES example, results, The avalanche effect, The

strength of DES

Course Code 15IS71 Credits 4

Course type PC CIE Marks 50 marks

Hours/week: L-T-P 3 – 1 – 0 SEE Marks 50 marks

Total Hours: 40 SEE Duration 3 Hours

Pre-requisites :

Computer Networks

Unit – II 8 Hours

Public-Key Cryptography and RSA:

Principles of public-key cryptosystems. Public key cryptosystems. Applications for

public-key cryptosystems, Requirements for public key cryptography, Public-key

cryptanalysis. The RSA algorithm: Description of the algorithm, Computational aspects,

The security of RSA, Deffie Heillman Key Exchange.

Unit – III 8 Hours

Security Technology & Cryptography:

Introduction, Physical Design, Protecting Remote Connections, Honey Pots, Honey

Nets, Attacks on Cryptosystems, Firewalls.

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Course Outcome (COs):

At the end of the course ,the student will be able to Bloom’s

Level

1. Analyze the vulnerabilities in any computing system and hence be

able to provide a proper solution.

L4

2. Explain the concept of RSA algorithm and its use in different

applications.

L3

3. Explain the importance of firewalls in providing a secure network. L2

4. Apply the concept of IP security during transmission of data across

networks.

L3

5. Discuss the different security threats in wireless networks. L2

Unit – IV 8 Hours

IP Security: IP Security Overview; IP Security Policy, Encapsulating Security Payload, Combining

Security Associations.

Unit – V 8 Hours

Wireless Network Security and Email security:

Wireless security: Wireless Network Threats, Wireless Security Measures, Mobile

Device Security: Security Threats, Mobile Device Security Strategy, IEEE 802.11

Wireless LAN review, E-Mail Security: PGP, S/MIME.

Text Book:

1. William Stallings, Cryptography and Network Security, Pearson 6th edition.

2. Michael E. and Herbart J.: Principles of Information Security, 2nd Edition 2005.

Reference Book:

1. Atul Kahate: Cryptography and Network Security McGraw-Hill Second edition.

Program Outcome of this course (POs)

PO No.

1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.

PO1

7

Scheme of Continuous Internal Evaluation (CIE):

Components Average of best

two IA tests out of

three

Average of

assignments

(Two) / activity

Quiz

Class

participation

Total

Marks

Maximum Marks:

50

25 10 5 10 50

Writing two IA test is compulsory.

Minimum marks required to qualify for SEE : 20

Self Study topics shall be evaluated during CIE (Assignments and IA tests) and

10% weightage shall be given in SEE question paper.

Scheme of Semester End Examination (SEE):

1. It will be conducted for 100 marks of 3 hours duration. It will be reduced to 50

marks for the calculation of SGPA and CGPA.

2. Minimum marks required in SEE to pass: 40 (out of 100 )

3. Question paper contains 08 questions each carrying 20 marks. Students have to

answer FIVE full questions. SEE question paper will have two compulsory

questions (any 2 units) and choice will be given in the remaining three units.

2 Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.

PO2

3 Design/development of solutions: Design solutions for complex

engineering problems and design system components or processes

that meet the specified needs with appropriate consideration for the

public health and safety, and the cultural, societal, and environmental

considerations.

PO3

Course delivery methods Assessment methods

1. Lecture & Board 1. Assignments

2. Power-point Presentation 2. Quizzes

3. Online Videos / Learning 3. Internal Assessment Tests

4. NPTEL / Edusat 4. Course Seminar

5. Class Room Exercises 5. Course Project (Mini project)

6. Case Studies

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SOFTWARE TESTING (Theory)


1. To introduce the terminology, testing, test-case, pseudo-codes / algorithms / flowcharts

of Triangle, NextDate & Commission programs.

2. To develop the skill of analyzing the Triangle, NextDate & Commission programs, with

the perspective of Boundary Value Analysis, Equivalence Class & Decision Table

Testing paradigms.

3. To practice quality assurance related processes / methods / standards.

Unit – I 8 Hours

A Perspective on Testing: Basic definitions, Test cases, Insights from a Venn diagram, Identifying test cases, Error and

fault taxonomies, Levels of testing.

Examples: Generalized pseudocode, The triangle problem, The NextDate function, The

commission problem.

Unit – II 8 Hours

The SATM (Simple Automatic Teller Machine) problem, The currency converter, Saturn

windshield wiper.

Boundary value analysis Equivalence Class Testing, Decision Table-Based Testing: Boundary value analysis, Robustness testing, Worst-case testing, Special value testing,

Examples, Random testing, Guidelines for Boundary Value Testing.


Equivalence Class Testing:

Equivalence classes, Equivalence test cases for the triangle problem, NextDate function, and the

commission problem, Guidelines and observations.

Decision Table–Based Testing:

Decision tables, Test cases for the triangle problem. Decision tables for NextDate function, and

the commission problem, Guidelines and observations.


Course type PC2 CIE Marks 50 Marks

Hours/week: L-T-P 3 – 0 – 0 SEE Marks 50 Marks


Pre-requisites:

Software Engineering

Graph Theory

Unit – IV 8 Hours

Path Testing, Data Flow Testing: DD paths, Test coverage metrics, Basis path testing, guidelines and observations. Definition-

Use testing. Slice-based testing, Guidelines and observations.

9


At the end of the course, the student should be able to: Blooms

Level

1. Define the test-case, testing, error taxonomy L1

2. Illustrate test-cases for Triangle, NextDate & Commission programs,

for boundary value analysis. L2

3. Design test-cases for Triangle, NextDate & Commission programs, for

equivalence class testing, decision table testing. L3

4. Demonstrate the importance of verification & validation in improving

the process of software development. L3

5. Examine the testing, verification and validation for an application. L4





4. NPTEL / EDUSAT 4. Course Seminar



Unit – V 8 Hours

Data Flow Testing:

Define/Use Testing, Slice-Based Testing, Program Slicing Tools, Examples, Guidelines and

observations.

Text Book:

1. Paul C. Jorgensen: Software Testing, A Craftsman‟s Approach, 3rd Edition, Auerbach

Publications, 2008.

Reference Book:

1. Aditya P. Mathur: Foundations of Software Testing, Pearson Education, 2008.

2. Srinivasan Desikan, Gopalaswamy Ramesh: Software testing Principles and Practices,

2nd Edition, Pearson Education, 2007.

Program Outcome of this course (POs) PO No.

1. Engineering knowledge: Apply the knowledge of mathematics, science,

engineering fundamentals, and an engineering specialization to the solution of

complex engineering problems. 1

2. Problem analysis: Identify, formulate, review research literature, and analyze

complex engineering problems reaching substantiated conclusions using first

principles of mathematics, natural sciences, and engineering sciences. 2

3. Modern tool usage: Create, select, and apply appropriate techniques,

resources, and modern engineering and IT tools including prediction and

modeling to complex engineering activities with an understanding of the

limitations.

5

4. Individual and team work: Function effectively as an individual, and as a

member or leader in diverse teams, and in multidisciplinary settings. 9

10

Components Average of best two

IA tests out of three

Average of

assignments

(Two) / activity

Quiz

Class

participation

Total

Marks

Maximum Marks: 50 25 10 5 10 50


Minimum marks required to qualify for SEE: 20

Self Study topics shall be evaluated during CIE (Assignments and IA tests) and 10%

weightage shall be given in SEE question paper.


1. It will be conducted for 100 marks of 3 hours duration. It will be reduced to 50 marks for

the calculation of SGPA and CGPA.

2. Minimum marks required in SEE to pass: 40 (out of 100)

3. Question paper contains 08 questions each carrying 20 marks. Students have to answer

FIVE full questions. SEE question paper will have two compulsory questions (any 2

units) and choice will be given in the remaining three units.

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DATA SCIENCE

(Theory)




Total Hours: 38 SEE Duration 3 Hours for

100 marks

Course Learning Objectives

1. To introduce importance of Data Science-Data Vs Big Data

2. To study and analyze various Data set for application and apply modeling technique.

3. To understand different machine learning algorithm used.

Pre-requisites : Basic Mathematics

Unit – I 8 Hours

Introduction: What Is Data Science? Big Data and Data Science Hype, Getting Past the Hype,

Why Now? Datafication, The Current Landscape (with a Little History), Data Science Jobs, A

Data Science Profile, Thought Experiment: Meta-Definition, OK, So What Is a Data Scientist,

Really? In Academia, In Industry

Unit – II 8 Hours

Statistical Analysis, Exploratory Data Analysis and Data Science Process: Statistical

Thinking in the Age of Big Data, Statistical Inference, Populations and Samples, Populations

and Samples of Big Data, Big Data Can Mean Big Assumptions, Can N=ALL?, Data is not

objective, Modeling, What is a model?, Statistical modeling, But how do you build a model?,

Probability distributions, Fitting a model, Over-fitting, Exploratory Data Analysis, Philosophy

of Exploratory Data Analysis.


Algorithms: Machine Learning Algorithms, Three Basic Algorithms, Linear Regression, Start

by writing something down, fitting the model, Extending beyond least squares, Adding in

modeling assumptions about the errors, Adding other predictors, Transformations.

K-Nearest Neighbors, Example with credit scores, similarity or distance metrics, Training and

test sets, pick an evaluation metric, putting it all together, choosing k, what are the modeling

assumptions? K-means.

Unit – IV 8 Hours

Thought Experiment: Learning by Example, Why won‟t Linear Regression work for

Filtering Spam? How about k-nearest Neighbors? Naïve Bayes, Bayes Law, A spam Filter for

Individual words, A spam Filter that Combines words: Naïve Bayes, Comparing Naïve Bayes

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to k-NN.

Logistic Regression: Classifiers, Runtime, Interpretability, Scalability M6D Logistics

Regression Case Study.

Unit – V 8 Hours

Data Engineering: Thought Experiment, MapReduce, word Frequence Problem, Enter

MapReduce, Other Example of MapReduce, On being Data Scientist, Data Abudance Versus

Data Scarcity, Designing Models, Mind the gap, Economic Interlude: Hadoop, A brief

Introduction to Hadoop.

Text Books:

1. Cathy O'Neil, Rachel Schutt “ Doing Data Science”,: O'Reilly Media, Inc.

Reference Books:

1. Sinan Ozdemir , “ Principles of Data Science”,Packt publisher December 2016.

Course Outcome (COs)

At the end of the course, the student will be able to Bloom’s

Level

1. Explain data, Big data and data model. L2

2. Analyze data set and Model for specific applications

L3

3. Apply machine learning algorithm to a application L4


PO No.


engineering fundamentals, and an engineering specialization to the solution

of complex engineering problems

PO1

2. Problem analysis: Identify, formulate, review research literature, and

analyze complex engineering problems reaching substantiated conclusions

using first principles of mathematics, natural sciences, and engineering

sciences.

PO2

3. Conduct investigations of complex problems: Use research-based

knowledge and research methods including design of experiments, analysis

and interpretation of data, and synthesis of the information to provide valid

conclusion.

PO4



modeling to complex engineering activities with an understanding of

limitations.

PO5

https://www.safaribooksonline.com/search/?query=author%3A%22Cathy%20O%27Neil%22&sort=relevance&highlight=true

https://www.safaribooksonline.com/search/?query=author%3A%22Rachel%20Schutt%22&sort=relevance&highlight=true

https://www.safaribooksonline.com/library/publisher/oreilly-media-inc/

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4. NPTEL / Edusat 4.

5. Class Room Exercises 5.




Average of two

assignments /

activity

Quiz

Class

participation

Total

Marks

Maximum Marks: 50 25 10 5 10 50


Minimum qualifying Marks :20 Marks (Minimum 10 Marks from IA is must)

Minimum marks required to qualify for SEE :20






2. Minimum marks required in SEE to pass:40


FIVE full questions. SEE question paper will have two compulsory questions (any 2 units)

and choice will be given in the remaining three units.

14

EMBEDDED SYSTEMS AND INTERNET OF THINGS

(Theory)


Course type PC4 CIE Marks 50 marks




1. To introduce the concepts of designing the Embedded systems using the microcontroller and

peripheral circuits.

2. To present the techniques of interfacing the sensors and actuators with microcontroller

development board.

3. To develop the skills of designing and developing the IOT applications

Pre-requisites: Microprocessors and Microcontrollers, Embedded C and python programming

Unit – I 8 hours

Embedded Computing: Introduction, Complex Systems and Microprocessors, Embedded Systems

Design Process, Formalism for System design.

Instruction Sets, CPUs: Preliminaries, ARM Processor, Programming Input and Output, Supervisor

mode, Exceptions, Traps, Coprocessors, Memory Systems Mechanisms, CPU Performance,

Self-Study: CPU Power Consumption design.

Unit – II 8 hours

Introduction To Internet Of Things : Definition and Characteristics of IoT, physical design of IoT, IoT Protocols, IoT communication models, IoT Communication APIs, Communication protocols,

Embedded Systems, IoT Levels and Templates. Overview of Microprocessor and Microcontroller, Basics

of Sensors and actuators.

Unit – III 8 hours

Prototyping IoT Objects Using Microprocessor/Microcontroller : Working principles of sensors and

actuators, Setting up the board - Programming for IOT, Reading from Sensors, Communication:

connecting microcontroller with mobile devices, communication through Bluetooth, Wi-Fi, Ethernet,

Zigbee, RFID, and NFC.

Unit – IV 8 hours

IoT Architecture And Protocols: Architecture Reference Model- Introduction, Reference Model and

architecture, IoT reference Model. Protocols- 6LowPAN, RPL, CoAP, MQTT.

Device Discovery Management:Device Discovery capabilities – Registering a device, De-register a

device, Querying for devices, Intel IoTivity, XMPP Discovery extension.

Unit – V 8 hours Cloud Services for IoT: Introduction to Cloud Storage models and communication APIs Web-Server

Web server for IoT, Cloud for IoT, Python web application framework designing a RESTful web API,

Self Study: Amazon Web services for IoT.

Text Books

1. Wayne Wolf: Computers as Components, Principles of EmbeddedComputing Systems Design, 2nd

Edition, Elsevier, 2008 and onwards.

2. ArshdeepBahga, Vijay Madisetti, “Internet of Things (A Hands-on-Approach)” , 1stEdition, VPT,

2014 and onwards. Reference Books

1. Olivier Hersent, David Boswarthick, Omar Elloumi, “The Internet of Things – Key applications

and Protocols”, Wiley, 2012 and onwards. 2. Marco Schwartz,“Internet of Things with Arduino: Build Internet of Things Projects With the

Arduino Platform”.

15

Course Outcome (COs) At the end of the course, the student will be able to Bloom’s

Level

1. Analyze given design problem and choose the various hardware components

including microcontroller and peripheral components. L4

2. Identify and demonstrate the skill of interfacing sensors and actuators with

microcontroller development boards. L3

3. Design and develop software programs using embedded C or python programming

languages for IOT applications. L3

4. Apply device discovery and cloud services for IoT applications L3


1. Chalk and talk 1. Quiz

2. Power Point Presentations 2. Assignment

3. Demos 3. IA Test

4. Audio and Videos




Average of

assignments (Two)

/ activity

Quiz

Class

participation

Total

Marks

Maximum Marks: 50 25 10 5 10 50



Self-Study topics shall be evaluated during CIE (Assignments and IA tests) and 10% weightage shall

be given in SEE question paper.


1. It will be conducted for 100 marks of 3 hours duration. It will be reduced to 50 marks for the

calculation of SGPA and CGPA.

2. Minimum marks required in SEE to pass:40


1 Engineering knowledge: Apply the knowledge of mathematics, science, engineering

fundamentals, and an engineering specialization to the solution of complex engineering

problems. 1

2 Problem analysis: Identify, formulate, review research literature, and analyze complex

engineering problems reaching substantiated conclusions using first principles of

mathematics, natural sciences, and engineering sciences. 2

3 Modern tool usage: Create, select, and apply appropriate techniques, resources, and

modern engineering and IT tools including prediction and modeling to complex

engineering activities with an understanding of the limitations.

5

4 Individual and team work: Function effectively as an individual, and as a member or

leader in diverse teams, and in multidisciplinary settings. 9

5 Communication: Communicate effectively on complex engineering activities

with the engineering community and with society at large, such as, being able to

comprehend and write effective reports and design documentation, make effective

presentations, and give and receive clear instructions.

10

16

3. Question paper contains 08 questions each carrying 20 marks. Students have to answer FIVE full

questions. SEE question paper will have two compulsory questions (any 2 units) and choice will be

given in the remaining three units.

List of Experiments for Tutorials

1. Study of simple GPIO programs to use the ports.

2. Study use of interrupts and peripherals like LCD 16x2.

3. Study of ADC programs Keyboard, seven segments.

4. Study of ADC programs and graphical LCD 128x64.

5. Study of UART programming.

6. Study of enabling the I2C with LCD 16x2

7. Study of enabling the I2C

8. Study use of PWM with DC motor/ servo motor

9. Study of Node MCU wireless features.

10. Use of SPI with LCD graphical 128X64

-----o0o-----

17

BIG DATA MANAGEMENT

(Elective III)


1. To understand Big data dimensions and its applications with case studies.

2. To explore Hadoop framework and architecture

3. To understand basics of NoSQL

4. To understand the importance of MapReduce framework

5. To explore Big Data Tools and Technologies : Pig and Hive

Unit – I 8 Hours

Introduction: Big Data Definition, History of Data Management-Evolution of Big Data,

Structuring Big Data, Elements of Big Data, Big Data Analytics, Careers in Big Data, Future

of Big Data, Use of Big Data in Social Networking, Use of Big Data in Preventing

Fraudulent Activities; Use of Big Data in Retail Industry

Unit – II 8 Hours

Hadoop Ecosystem: Understanding Hadoop Ecosystem, Hadoop Distributed File

System:HDFS Architecture,Concept of Blocks in HDFS Architecture, NameNodes and Data

Nodes, The Command-Line Interface, Using HDFS Files, Hadoop-Specific File System

Types, HDFS Commands, The org.apache.hadoop.io package,HDFS High

availability:Features of HDFS.


NoSQL: Introduction to NoSQL: Why NoSQL, Characteristics of NoSQL, History of NoSQL,

Types of NoSQL Data Models: Key-Value Data Model, Column-Oriented Data Model,

Document Data Model, Graph Databases, Schemaless Databases, Materialized views,

Distribution Models: CAP Theorem, Sharding.


Course type PE CIE Marks 50 marks


Total Hours: 40 SEE Duration 3 Hours for 100 marks

Pre-requisites :

Database Management System, Unix Shell Programming

Unit – IV 8 Hours

Understanding MapReduce: The MapReduce Framework: Exploring the Features of

MapReduce,Working of MapReduce, Exploring Map and Reduce Functions, Uses of

MapReduce.

YARN Architecture: Background; Advantages of YARN;YARN Architecture

18



Level

1. Outline the importance of Big Data, its characteristics and use of Big

Data in different fields/sectors. L1

2. Explain the ecosystem of Hadoop L3

3. Infer why to use NoSQL databases in the Big Data Scenario. L2

4. Apply map reduce framework in analyzing the data and relate to YARN L3

5. Explain tools in analyzing the data and managing Big Data L2


Unit – V 8 Hours

Properties of Pig : Introducing Hive, Getting started with Hive, Hive Services, Data types in

Hive, Built-in Functions in Hive

Analyzing Data with Pig: Introduction to Pig: The Pig Architecture, Benefits of Pig,

Properties of Pig

Text Book:

1. DT Editorial Services,”Big Data:Black Book ,Comprehensive Problem Solver”,

Dreamtech Press. 2016 Edition [ Chapters - 1,2,4,5,11,12,13,15]

Reference Book:

1. Paul C. Zikopoulos, Chris Eaton, Dirk deRoos, Thomas Deutsch, George Lapis,

Understanding Big Data – Analytics for Enterprise Class Hadoop and Streaming Data,

McGraw Hill, 2012

2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging

World of

Polyglot Persistence", Addison-Wesley Professional, 2012.

3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilly, 2012.








sciences.

2

3. Design/development of solutions: Design solutions for complex engineering

problems and design system components or processes that meet the specified

needs with appropriate consideration for the public health and safety, and the

cultural, societal, and environmental considerations.

3



and interpretation of data, and synthesis of the

information to provide valid conclusions.

4

19




4. Case Studies




Average of

assignments (Two)

/ activity

Quiz

Class

participation

Total

Marks

Maximum Marks: 50 25 10 5 10 50










ADHOC SENSOR NETWORKS

(Elective III)


1. Introduce the basic design of Ad-Hoc networks, MANETS and routing protocols.

2. Understand the techniques of Broadcasting and Geocasting in Ad-Hoc network.

3. Learn the applicability of TCP over Ad-Hoc networks

4. Emphasize on design and applicability of Wireless Sensor Networks to solve real

world problems.





20

Unit – I 8 Hours

Introduction: Introduction to ad-hoc networks, Applications, challenges in ad-hoc

networks, Routing in Ad Hoc Networks: Topology based routing protocols- Proactive

Routing Approach, Reactive Routing Approach,

Self Learning: Hybrid Routing Approach.

Pre-requisites :

Basic concepts of Data Communications and Networking.

Unit – II 8 Hours

Broadcasting and Geocasting: The Broadcast Storm, Broadcasting in a MANET,

Flooding-Generated Broadcast Storm, Redundancy Analysis, Rebroadcasting Schemes,

Geocasting: Geocast Routing Protocols, Comparison.


TCP over Ad Hoc Networks: Introduction, TCP Protocol Overview- Designed and

Fine-Tuned to Wired Networks, TCP Basics, TCP Header Format, Congestion Control,

Round-Trip Time Estimation, TCP and MANETs- Effects of Partitions on TCP, Impact

of Lower Layers on TCP, Solutions for TCP over Ad Hoc - Mobility-Related.

Self Learning: Fairness-Related.

Unit – IV 8 Hours

Wireless Sensor Networks: Introduction, The Mica Mote, Sensing and

Communication Range, Design Issues, Energy Consumption, Clustering of Sensors:

Regularly placed Sensors, Heterogeneous WSNs, Mobile Sensors

Unit – V 8 Hours

Applications of Sensor Networks: Introduction, Habitat Monitoring , A remote

Ecological Micro sensor Network, Environmental Monitoring, Drinking Water Quality,

Soil Moisture Monitoring, Health Care Monitoring , Building, Bridge, and Structural

Monitoring, Smart Energy and Home /Office Applications, DARPA Efforts towards

Wireless Sensor Networks, Body Area Network.

Text Book:

21




Level

1. Illustrate the knowledge of message transmission modes in Ad-hoc

networks like MANET and WSN

2

2. Explain the process of routing in Ad-hoc networks 2

3.

4

Illustrate the method of imbibing TCP for ad-hoc networks

Explain the different issues related to wireless sensor networks.

2

2

4. Apply the design principles to develop sensor network applications 3

1. Carlos de Morais Cordeiro and Dharma Prakash Agrawal, “Ad Hoc & Sensor

Networks, Theory and Applications”, World Scientific, 2nd Edition, 2011 and

above.

Reference Book:

2. C. Siva Ram Murthy, and B. S. Manoj, "Ad Hoc Wireless Networks: Architectures

and Protocols ", Prentice Hall Professional Technical Reference, 2008.


1. Engineering knowledge: Apply the knowledge of mathematics,

science, engineering fundamentals, and an engineering specialization

to the solution of complex engineering problems.

1


analyze complex engineering problems reaching substantiated

conclusions using first principles of mathematics, natural sciences,

and engineering sciences.

2

3. Design/development of solutions: Design solutions for complex

engineering problems and design system components or processes

that meet the specified needs with appropriate consideration for

the public health and safety, and the cultural, societal, and

environmental considerations.

3

22

Course Delivery Methods:

Course delivery methods

Assessment methods


2. Power-point Presentation 2. Internal Assessment Tests

3. Class Room Exercises 3. Quiz

4. Assignments




Average of

assignments

(Two) / activity

Quiz

Class

participation

Total

Marks

Maximum Marks: 50 25 10 5 10 50










4. Conduct investigations of complex problems: Use research-

based knowledge and research methods including design of

experiments, analysis and interpretation of data, and synthesis of the


4

23

DIGITAL IMAGE PROCESSING

(Elective III)





100 marks

9 Course learning

objectives

1. To Explain image fundamentals and mathematical transforms necessary for image

processing.

2. To study the image enhancement techniques.

3. To Demonstrate the image segmentation and representation techniques.

4. To study How image are analyzed to extract features of interest.

Pre-equisites

Data Structures

Basics of Matrices

Basics of „C‟ Programming

Unit – I

Hours Introduction:

What is Digital Image Processing, Origins of Digital Image Processing, Examples of fields that

use DIP, Fundamental Steps in Digital Image Processing, Components of an Image Processing

System. Digital Image Fundamentals: Elements of Visual Perception, A Simple Image Formation

Model, Basic Concepts in Sampling and Quantization, Representing Digital Images, Spatial and

Gray-level Resolution, Zooming and Shrinking Digital Images, Some Basic Relationships

Between Pixels, Linear and Nonlinear Operations.

. Unit – II

7 Hours

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, Combining Spatial Enhancement Methods.

Unit – III

7 Hours Image Enhancement In Frequency Domain:

Introduction to the Fourier Transform and the Frequency Domain, Smoothing frequency-

Domain Filters, Sharpening Frequency-Domain Filters, Homomorphic Filtering.

Unit – IV 9

Hours ImageRestoration:

A Model of the Image degradation/Restoration process, Noise Models, Restoration in the

Presence of Noise Only– Spatial Filtering, Periodic Noise Reduction by Frequency Domain

24

Filtering, Linear, Position-Invariant Degradations, Estimating the Degradation Function, Inverse

Filtering ,Minimum Mean Square Error (Wiener) Filtering, Constrained Least Square Filtering,

Geometric Mean Filter.

Unit – V 7 Hours

Color Fundamentals: Color Models, Pseudocolor Image Processing, Basics of Full-Color

Image Processing, Color Transformations, Smoothing and Sharpening, Color Segmentation,

Noise in Color Images, Color Image Compression.

Text Books:

1. Rafael C Gonzalez and Richard E. Woods: Digital Image Processing, PHI 2nd

Edition 2005.

Reference Books:

1. A. K. Jain: Fundamentals of Digital Image Processing, Pearson, 2004.

2. Scott E. Umbaugh: Digital Image Processing and Analysis, CRC Press, 2014.

Course Outcome (COs) At the end of the course, the student will be able to Blooms level descscriptor

1.

Explain the fundamental DIP algorithms and

implementation

2 Understanding

2. Study image processing algorithms 4 Analyzing

3. Choose image processing algorithms to real problems. 3 Applying


1. Graduates will demonstrate the knowledge of mathematics, science,


complex engineering problems

PO1

2. Graduates will demonstrate the ability to identify, formulate, review research

literatute and analyze complex engineering problems reaching substantiated

conclusions using first principles of mathematics, natural sciences and

engineering sciences.

PO2

3. An ability to conduct investigations of complex problems by methods that

include appropriate experiments, analysis and interpretation of data, and

synthesis of information in order to reach valid conclusions.

PO3

4. An ability to create, select, apply, adapt, and extend appropriate techniques,

resources, and modern engineering tools to a range of engineering activities,

from simple to complex, with an understanding of the associated limitations.

PO6

25






5. Class Room Exercises 5. Course Project (Mini

project) 6. Case Studies


Components

Average of best

two

IA tests out of

three

Average of

assignments

(Two)

/ activity

Quiz

Class

participation

Total

Marks

Maximum Marks:

50

25 10 5 10 50






1. It will be conducted for 100 marks of 3 hours duration. It will be reduced to 50 marks

for the calculation of SGPA and CGPA.





ARTIFICIAL INTELLIGENCE

(Elective III)





100 marks


1. To understand various symbolic knowledge representations to specify domains and

reasoning tasks of a situated software agent 2. To understand different logical systems for inference over formal domain

representations and trace how a particular inference algorithm works on a given

problem specification

3. To understand various learning techniques and agent technology

Pre-requisites : Discrete Mathematical Structures, Probability

Unit – I 8 Hours

Introduction to Artificial Intelligence: Introduction, What is AI, Strong Methods and weak

Methods.Uses and Limitations:

Knowledge Representation: Need for good representation, semantic nets, Inheritance,

Frames, Object oriented programming, Search Spaces, Semantics Tress, Search Trees,

Combinatorial Explosion, Problem reduction, Goal Trees

Unit – II 8 Hours

Search Methodologies: Introduction, Problem solving as search, Data driven or goal driven

search, Generate and test, Depth First Search, Breadth First Search, Properties of search

methods, Implementing Depth-First and Breadth-First Search, Using Heuristics for Search,

Hill Climbing, Best-First Search, Identifying Optimal Paths, Constraint Satisfaction search,

Forward Checking, Local Search and Meta heuristics, Simulated Annealing


Game Playing: Game Trees, Minimax, Alpha beta pruning

Prepositional and Predicate Logic: Introduction, What is Logic, Why Logic is used in Artificial

Intelligence, Logical Operators, Translating between English and Logic Notation, Truth Tables:

Not, And, Or, Implies, if, Complex Truth Tables, Tautology, Equivalence, Propositional logic,

Deduction, The deduction Theorem, Soundness, Completeness, Decideability, Monotonicity,

Abduction and Inductive reasoning, Modal logics and possible worlds, Dealing with change.

Unit – IV 8 Hours

Inference and Resolution for Problem Solving: Introduction, Resolution in prepositional logic:

Applications of Resolution, Resolution in Predicate Logic, Normal forms for predicate logic,

Skolemization, Resolution Algorithms, Resolution for problem solving, Rules for knowledge

representation, Rule-Based systems, Rule based expert systems, CLIPS, Backward chaining, CYC .

Unit – V 8 Hours

Advanced Knowledge Representation: Representations and semantics, Blackboard Architecture,

Scripts, Copycat Architecture, Nonmonotonic Reasoning, Reasoning about change, Case-based

Reasoning,

Intelligent Agents: Properties of Agents, Agent Classification, Reactive Agents, Interface Agents,

Mobile Agents, Information Agents, Multiagent Systems, Collaborative agents, Agent architectures,

Accessibility, Learning Agents, Robotic Agents

Text Book

1. Ben Coppin, Artificial Intelligence Illuminated, Jones and Bartlett Publishers, 1st

Edition, 2004.

Reference Books

1. Elaine Rich Kevin Knight, Shivashankar B Nair: Artificial Intelligence, Tata McGraw

Hill 3rd

edition 2013. 2. Stuart Russel, Peter Norvig: Artiificial Intelligence A Modern Approach, Pearson 3

rd

edition 2013.


At the end of the course, the student will be able to Bloom‟s

Level

1.

Design intelligent agents for problem solving, reasoning, planning,

decision making and learning for specific design and performance

constraints and when needed, design variants of existing algorithms

L4

2. Apply AI techniques on current applications L3

3. Demonstrate ability for problem solving, knowledge representation,

reasoning and learning L3


1.

Engineering knowledge: Apply the knowledge of mathematics, science,


complex engineering problems 1

2.

Problem analysis: Identify, formulate, review research literature, and analyze


principles of mathematics, natural sciences, and engineering sciences.. 2


1. Lecture 1. Internal Assessment Test

2. Demonstration 2. Assignment

3. Hands on 3. Quiz

4. Presentation 4. Programming Exercises




Average of

assignments (Two)

/ activity

Quiz

Class

participation

Total

Marks

Maximum Marks: 50 25 10 5 10 50


Minimum marks required to qualify for SEE :

Self-Study topics shall be evaluated during CIE (Assignments and IA tests) and 10% weightage

shall be given in SEE question paper.




2. Minimum marks required in SEE to pass:


questions. SEE question paper will have two compulsory questions (any 2 units) and choice will

be given in the remaining three units.

MOBILE COMPUTING AND APPLICATIONS

(Elective IV)





100 marks


1. To introduce the fundamental concepts of wireless networks and design considerations

of mobile computing environment. 2. To familiarize with the concepts of location management, mobility management and

tracking management of Cellular networks. 3. To familiarize with SMS, GSM and GPRS Technologies and Smart client Architecture

4. To conceptualize the use of different Forms (APIs) for mobile applications

Pre-requisites: 1. Fundamentals of computer network, Wireless Sensor Networks.

2. Fundamental of JAVA

Unit – I 8 Hours Introduction to Mobile Computing: Mobile Computing, Mobile Computing, Dialogue Control,

Networks, Architecture for Mobile Computing, Three tier architecture, Design Considerations for

Mobile Computing

Unit – II 8 Hours Wireless Technology: Wireless Broadband, Mobile IP, Cellular IP, Global system for Mobile

Communications- Architecture, Entities, Call Routing, PLMN Interfaces, Gsm addresses and

Identifiers.

Unit – III 8 Hours Short Message Service(SMS): Mobile Computing over SMS, SMS services, Value added services

through SMS, GPRS and Packet Data network- GPRS network architecture, network operations.

Unit – IV 8 Hours Building Smart client Applications: Smart client architecture-the client and the server. Smart client

development Process-Needs analysis phase and the design phase. Thin client wireless internet

applications-Client overview (client and middleware), Processing a wireless request, WAP model,

WAP components, Wireless Application Environment

Unit – V 8 Hours Client Programming using J2ME-JAVA in Handset, Programming for Connected Limited Device

Configuration(CLDC), GUI in Mobile Information Device Profile (MIDP), UI Design issues, Record

Management system, Communication in MIDP and Security Considerations.

Text Books:

1. Asoke Talukder, Roopa Yavagal-Mobile Computing-Technlogy, Applications and Service

Creation, Tata McGraw Hill Publishers-2nd

Edition onwards.

2 Martyn Mallick-Mobile and Wireless Design Essentials,Wiley Publications-2016 print onwards

Reference Books:

1. Jochen Schiller-Mobile Communications, Pearson Education Publications-2nd

Edition onwards.



Level 1. Explain the architecture for mobile computing and its design considerations. L2

2. Describe the working of SMS computing, its service and GPRS network architecture

and its operations. L2

3. Explain the design steps of Smart client and Thin client wireless applications

development module.

L2

4. Compare the different mobile technological concepts learnt to prepare a survey

report on their performance analysis parameters. L4


Student should be able to

PO No.

1. Identify the different mobile technologies used in the present context and understand

their working. 1,2

2. Understand and appreciate the use of mobile and thin client architectures in mobile

applications development.

1,2




3. Class Room Discussion 3. Internal Assessment Tests

4. Course Seminar




Average of

assignments (Two)

/ activity

Quiz

Class

participation

Total

Marks

Maximum Marks: 50 25 10 10 5 50

1. Writing two IA test is compulsory.

2. Minimum marks required to qualify for SEE : 20

Self-Study topics shall be evaluated during CIE (Assignments and IA tests) and 10% weightage

shall be given in SEE question paper.




2. Minimum marks required in SEE to pass: 40


questions. SEE question paper will have two compulsory questions (any 2 units) and choice will

be given in the remaining three units.

SYSTEM SIMULATION & MODELING

(Elective IV)


1. To bring out importance of simulation and simulation components in engineering

problems.

2. To introduce mathematical and statistical models in continuous and discrete distributions.

3. To present random number generation methods and tests for random number.

4. To realize the importance of analysis of simulation data and validation of simulation

models.

Unit – I 8 Hours

Introduction: When simulation is the appropriate tool and when it is not appropriate,

Advantages and disadvantages of Simulation, Systems and system environment; Components

of a system; Discrete and continuous systems; Model of a system; Types of Models; Discrete-

Event System Simulation; Simulation examples: Simulation of queuing systems(single server

and two server), Simulation of (M,N) inventory system.

General Principles, Simulation Software: Concepts in Discrete-Event simulation: The event-

scheduling / time-advance algorithm.

Unit – II 8 Hours

Statistical Models in Simulation: Review of terminology and concepts; Useful statistical

models; Discrete distributions: Binomial distribution, Poisson distribution; Continuous

distributions: Uniform distribution, Exponential distribution, Triangular distribution.


Random-Number Generation: Properties of random numbers; Generation of pseudo-random

numbers; Techniques for generating random numbers; Tests for Random Numbers: frequency

tests;

Random-Variate Generation: Inverse transform technique: Exponential distribution, Uniform

distribution, Triangular distribution.





Pre-requisites :

Engineering Mathematics, Discrete mathematics

Unit – IV 8 Hours

Input Modeling: Data Collection; Identifying the distribution with data; Parameter estimation;

Goodness of Fit Tests; Selecting input models without data.

Unit – V 6 Hours

Verification, Calibration, Validation and Optimization

Model building, verification and validation; Verification of simulation models; Calibration and

validation of models, input-output validation using historical input data.



Level

1. Classify and compare simulation models. 3

2. Solve simulation problems on queuing, inventory systems. 3

3. Identify types of distribution and apply statistical models for simulation. 3

4. Construct random number generator and test for the random numbers 4

5. Explain validation and verification models 2

Course Delivery Methods:


Assessment methods






6. Case Studies

Text Book:

3. Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol: Discrete-Event System

Simulation, 4th Edition onwards, Pearson Education, 2010.

Reference Book:

3. Lawrence M. Leemis, Stephen K. Park: Discrete – Event Simulation: A First Course,

Pearson Education, 2006.

4. Averill M. Law: Simulation Modeling and Analysis, 4th Edition, Tata McGraw-Hill,

2007.


PO No.







sciences.

2





3



and interpretation of data, and synthesis of the


4




Average of

assignments

(Two) / activity

Quiz

Class

participation

Total

Marks

Maximum Marks: 50 25 10 5 10 50











1. To learn different protocols such as TCP, UDP and STCP.

2. To understand the concept of sockets.

3. To understand the use of semaphores, message queues and FIFOs.

4. To learn concept of remote login and its applications.

Pre-requisites :

Computer Networks, Operating Systems.

Unit – I 8 hours

Introduction to Network Programming: OSI model, Unix standards, UDP, TCP, STCP,

TCP connection establishment and termination: Three-way handshake, TCP options, TCP

connection termination, Buffer sizes and limitation, standard internet services, Protocol usage

by common internet application.

Self Learning: TCP State Transition Diagram.

Unit – II 8 hours

Sockets : Address structures, value – result arguments, Byte ordering, Byte manipulation

functions, inet_aton,inet_addr, and inet_ntoa functions, inet_pton and inet_ntop functions.

Elementary TCP sockets – Socket, connect, bind, listen, accept, fork and exec function,

concurrent servers, close function.

Self Learning: readn function, writen function.

Unit – III 8 hours

Elementary UDP sockets: Introduction, UDP Echo server: main function, dg_echo function,

UDP Echo client: main function, dg_cli function, lost datagram, summary of UDP example,

connect function with UDP, determining outgoing interface with UDP.

NETWORK PROGRAMMING

(Elective IV)



Hours/week: L-T-P 3– 0 – 0 SEE Marks 50 marks


100 marks

Self Learning: Elementary SCTP sockets: Introduction, Interface models and The One-to-

One style.

Unit – IV 8 hours

IPC: Introduction, File and record locking, Pipes, FIFOs streams and messages, Name

spaces, system IPC, Message queues, Semaphores.

Unit – V 8 hours

Remote Login: Terminal line disciplines, Pseudo-Terminals, Terminal modes, Control

Terminals, rlogin Overview, RPC Transparency Issues.

Books

1. W.Richard Stevens, UNIX Network Programming, Sockets API, Volume I, 3rd

Edition, PHI , 2010.

2. T Chan, “UNIX Systems Programming using C++”, 1st Edition, PHI, 2010.

References:

1. W.Richard Stevens, UNIX Network Programming, Volume II, 1st Edition, PHI, 2009.



Level

1. Differentiate between TCP and UDP. L3

2. Explain the advantages of sockets. L2

3. Explain the concept of Pipes and FIFOs in client server

communication. L2


1.

Engineering knowledge: Apply the knowledge of mathematics, science,

engineering fundamentals, and an engineering specialization to the solution

of complex engineering problems.

PO1

2.

Problem analysis: Identify, formulate, review research literature, and analyze


principles of mathematics, natural sciences, and engineering sciences.

PO2

3.

Design/development of solutions: Design solutions for complex engineering




PO3

4.

Life-long learning: Recognize the need for, and have the preparation and

ability to Engage in independent and life-long learning in the broadest

context of technological change.

PO12


Assessment methods







Average of

assignments

(Two) / activity

Quiz

Class

participation

Total

Marks

Maximum Marks: 50 25 10 5 10 50

Writing two IA tests is compulsory.











C#. NET

(Elective IV)


Pre-requisites :

Object Oriented Programming.

Unit – I 8 Hours

The Philosophy of .NET :

Introduction to .NET Platform, The Building Block of the .NET Platform, .NET Base Class

Libraries, An Overview of .NET Binaries, The Common Intermediate Language, The .NET

Metadata, Assembly Manifest, Compiling CIL to Platform – Specific Instructions,

Understanding the Common Type System, Intrinsic CTS Data Types, Common Languages

Specification, Common Language Runtime, .NET Namespaces,

Unit – II 8 Hours

Building C# Applications :

The Role of the Command Line Complier(csc.exe), Building C# Application using csc.exe

Working with csc.exe Response Files, Generating Bug Reports, C# Complier Options, The

Command Line Debugger (cordbg.exe), C# “Preprocessor:” Directives, The

System.Environment Class.


C# Language Fundamentals:

The Anatomy of Basic C# Class, Creating objects: Constructor Basics, The Composition of a

C# Application, Default assignment and Variable Scope, The C# Member Initialization

Syntax, Basic Input and Output with the Console Class, Value Types and Reference Types,

The Master Node: System, Object, The System Data Types, Boxing and Unboxing, Defining

Program Constants, C# Iteration Constructs, C# Controls Flow Constructs, C# Operators,

Defining Custom Class Methods, Static Methods, Methods Parameter Modifies, Array

Manipulation in C#.

Unit – IV 8 Hours





1. To present the basic building blocks of the C# and .NET platform

2. To create understanding about Object oriented programming using c# on .NET Platform

3. To introduce Exception Handling anduser defined exception.

4. To appreciate the use of C#.NET as a tool for writing of robust and efficient programs.

Object- Oriented Programming with C#: Forms Defining of the C# Class, Definition the “Default Public Interface” of a Type,

Recapping the Pillars of OOP, The First Pillars: C#‟s Encapsulation Services, Pseudo-

Encapsulation: Creating Read-Only Fields, The Second Pillar: C#‟s Inheritance Supports, The

“Protected” Keyword, Nested Type Definitions, The Third Pillar: C#‟s Polymorphic Support,

C# Casting Rules

Unit – V 8 Hours

Exceptions and Object Lifetime: Errors, Bugs, and Exceptions, The Role of .NET

Exception Handing. Exception Base Class, Throwing a Generic Exception, Catching

Exception, CLR System Level Exception (System. System Exception), Custom Application-

Level Exception (System. System Exception), Handling Multiple Exception, The Finally

Block.

Object Lifetime: the CIL of new, Understanding object generation, The Basics of Garbage

Collection, Building a Finalizable objects, Building Disposable objects, Building a Finalizable

and Disposable type.


Text Book:

1. Andrew Troelsen: Pro C# with .NET 3.0, Special Edition, Dream tech Press, India, 2007

onwards.

2. E. Balagurusamy: Programming in C#, 5th

Reprint, Tata McGraw Hill, 2004 onwards.

Reference Book:

1. Herberat Schildt: C# The Complete Reference, The McGrawHill, 2004onwards.



Level

1. Summarize basic concepts of C#. L2

2. Design applications using object oriented concepts with C#.NET L3

3. Illustrate custom exceptions handling in robust applications. L3

4. Understanding of object lifecycle and importance of garbage collection. L2

5. Analyze the problem in hand and design an application using C#.NET tool L4





Average of

assignments

(Two) / activity

Quiz

Class

Participation

Total

Marks

Maximum Marks: 50 25 10 5 10 50







complex engineering problems.

1





3



modeling to complex engineering activities with an understanding of the

limitations.

5

4. Life-long learning: Recognize the need for, and have the preparation and

ability to engage in independent and life-long learning in the broadest context

of technological change. 12


1. It will be conducted for 100 marks of 3 hours duration. It will be reduced to 50 marks

for the calculation of SGPA and CGPA.





DATA SCIENCE LABORATORY

(Lab)

Course Code 15ISL77 Credits 2

Course type L1 CIE Marks 25 marks


Total Hours: 40 SEE Duration 3 Hours for 50

marks

Course learning objectives 1. To study and analyze various Data set for application and apply modeling technique.

2. To understand different machine learning algorithm used.

Prerequisites: Any programming language

List of experiments(Using appropriate data analytical tools)

1. Predict the price of a house by applying linear regression to using suitable dataset of

real estate business.

2. Apply k-nearest neighbor algorithm to classify and analyze the ionosphere data.

3. Classify the messages as spam and ham using naïve bayes algorithm on sms dataset.

4. Implement logistic regression algorithm on the iris flower dataset to classify the flower

into different types.

Text Books:

Cathy O'Neil, Rachel Schutt “ Doing Data Science”,: O'Reilly Media, Inc.

Reference Books:

Sinan Ozdemir , “ Principles of Data Science”,Packt publisher December 2016.



Level

1. Analyze data set and Model for specific applications

L3

2. Apply machine learning algorithm to a application L4

https://www.safaribooksonline.com/search/?query=author%3A%22Cathy%20O%27Neil%22&sort=relevance&highlight=true

https://www.safaribooksonline.com/search/?query=author%3A%22Rachel%20Schutt%22&sort=relevance&highlight=true

https://www.safaribooksonline.com/library/publisher/oreilly-media-inc/

Program Outcomes of the course POs



complex engineering problems

PO1

2. Problem analysis: Identify, formulate, review research literature, and analyze


principles of mathematics, natural sciences, and engineering sciences.

PO2



and interpretation of data, and synthesis of the information to provide valid

conclusion.

PO4



modeling to complex engineering activities with an understanding of

limitations.

PO5

Assessment methods

1. Regular Journal Evaluation & Attendance Monitoring. 2. Lab Internal Assessment.


Components Project execution Journal submission Conduct of the lab

Total

Marks

Maximum Marks: 25 10 10 5 25

Submission and certification of lab journal is compulsory to qualify for

SEE.


Scheme of Semester End Examination (SEE): It will be conducted for 50 marks of 3 hours duration. It will be reduced to 25 marks for

the 1.

calculation of SGPA and CGPA. 2. Minimum marks required in SEE to pass: 40 %

Initial write up 20 marks Conduct of experiments 20 marks 50

marks Viva- voce 10 marks

SOFTWARE TESTING LAB

(Lab)

Subject Code: 15ISL78 Credits: 2

Course Type: Lab CIE Marks: 25

Hours/week: L – T – P 0 – 0 – 3 SEE Marks: 25

Total Hours: 40 SEE Duration: 3 Hours

Course Learning Objectives (CLOs):

CLO 1: Demonstrate the techniques of verification and validation as a means of testing a software.

CLO2: Implementing the software testing methods suitable for the computer program development.

CLO 3: Present the different techniques of testing by quality approach.

Prerequisites:

Software Engineering

Graph Theory

List of Experiments:

PART-A

1 1. Design and develop a program in a language of your choice to solve the triangle problem defined as

follows: Accept three integers which are supposed to be the three sides of a triangle and determine if

the three values represent an equilateral triangle, isosceles triangle, scalene triangle, or they do not

form a triangle at all. Derive test cases for your program based on decision-table approach, execute

the test cases and discuss the results.

2 Design and develop a program in a language of your choice to solve the triangle problem defined as



form a triangle at all. Assume that the upper limit for the size of any side is 10. Derive test cases for

your program based on boundary-value analysis, execute the test cases and discuss the results.

3 Design and develop a program in a language of your choice to solve the triangle problem defined as



form a triangle at all. Assume that the upper limit for the size of any side is 10. Derive test cases for

your program based on equivalence class partitioning, execute the test cases and discuss the results.

4 Design, develop, code and run the program in any suitable language to solve the commission

problem. Analyze it from the perspective of data flow testing, derive different test cases, execute

these test cases and discuss the test results..

5 Design, develop, code and run the program in any suitable language to solve the commission

problem. Analyze it from the perspective of decision table-based testing, derive different test cases,

execute these test cases and discuss the test results.

PART-B

6 Design, develop, code and run the program in any suitable language to implement the binary search

algorithm. Determine the basis paths and using them derive different test cases, execute these test

cases and discuss the test results.

7 Design, develop, code and run the program in any suitable language to implement the quicksort

algorithm. Determine the basis paths and using them derive different test cases, execute these test

cases and discuss the test results

8 Design, develop, code and run the program in any suitable language to implement an absolute letter

grading procedure, making suitable assumptions. Determine the basis paths and using them derive

different test cases, execute these test cases and discuss the test results.

9 Design, develop, code and run the program in any suitable language to implement the NextDate

function. Analyze it from the perspective of equivalence class value testing, derive different test

cases, execute these test cases and discuss the test results.

10 Demonstration of test case developments based on boundary values analysis or equivalence class

using the software testing tools like „SELENIUM‟.

Text Books:

1 Paul C. Jorgensen: Software Testing, A Craftsman‟s Approach, 3rd Edition, Auerbach

Publications, 2008.

Reference Books:

1 Aditya P Mathur: Foundations of Software Testing, Pearson Education, 2008.

2 Srinivasan Desikan, Gopalaswamy Ramesh: Software testing Principles and Practices, 2nd

Edition, Pearson Education, 2007.

3 Mauro Pezze, Michal Young: Software Testing and Analysis – Process, Principles and

Techniques, Wiley India, 2008.

Course Outcomes (COs):

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

1. Describe essential features of software testing to suite the user specific requirements.

2. Design and write the test-cases for any software program under test.

3. Identify, formulate, review & analyze the data with suitable software testing methods

4. Evaluation of various testing processes to ensure quality software product.

L1

L3

L4

L5

Program Outcomes (POs) of the course:

1 Apply the knowledge of mathematics, science, engineering fundamentals, and an

engineering specialization to the solution of complex engineering problems.

1

2 An ability to conduct investigations of complex problems by methods that include

appropriate experiments, analysis and interpretation of data, and synthesis of information in

order to reach valid conclusions.

3

3 An ability to create, select, apply, adapt, and extend appropriate techniques, resources, and

modern engineering tools to a range of engineering activities, from simple to complex, with

an understanding of the associated limitations.

6

4 An understanding of the roles and responsibilities of the professional engineer in society,

especially the primary role of protection of the public and the public interest. 10


CIE

Conduct of lab 10

25 Journal writing 10

Lab test 5


SEE

Initial write up 2*10 = 20

50 Conduct of experiments 2*10 = 20

Viva- voce 10

Minimum marks required to qualify for see 13

Submission and certification of lab journal is compulsory to qualify for SEE

Practical examination (SEE) of 3 hours duration will be conducted for 50 marks. It will be

reduced to 25 marks for the calculation of SGPA and CGPA.

Bloom’s Taxonomy of Learning Objectives

Bloom‟s Taxonomy in its various forms represents the process of learning. It was

developed in 1956 by Benjamin Bloom and modified during the 1990‟s by a new

group of cognitive psychologists, led by Lorin Anderson (a former student of Bloom‟s)

to make it relevant to the 21st century. The revised taxonomy given below emphasizes

what a learner “Can Do”.

Lower order thinking skills (LOTS)

L1 Remembering Retrieve relevant knowledge from memory.

L2

Understanding Construct meaning from instructional material, including oral, written, and

graphic communication.

L3

Applying Carry out or use a procedure in a given situation – using learned

knowledge.

Higher order thinking skills (HOTS)

L4

Analyzing Break down knowledge into its components and determine the relationships

of the components to one another and then how they relate to an overall

structure or task.

L5

Evaluating Make judgments based on criteria and standards, using previously learned

knowledge.

L6

Creating Combining or reorganizing elements to form a coherent or functional whole

or into a new pattern, structure or idea.

karnatak law society‟s gogte institute of technology€¦ · 2. power-point presentation 2....

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