GOGTE INSTITUTE OF TECHNOLOGY · 2. The graduates will ... 2. Develop Research Aptitude: Ability to...

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 Computer Science & Engineering

Scheme and Syllabus (2018 Scheme)

I Semester M.Tech Computer Science & Engineering

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.

MISSION

To train the students, to cultivate inquisitive mindset for identifying and analyzing real life

problems and develop optimal computer solutions for the benefit of the society.

PROGRAM EDUCATIONAL OBJECTIVES (PEOs)

1. The graduates will acquire core competence in basic science and engineering

fundamentals necessary to formulate, analyze and solve engineering problems and to

pursue advanced study.

2. The graduates will acquire necessary techno-managerial and life-long learning skills to

succeed as computer engineering professionals with an aptitude for higher education

and entrepreneurship.

3. The graduates will maintain high professionalism and ethical standards and also develop

the ability to work in teams on IT as well as multidisciplinary domains.

DEPARTMENT VISION

To be recognized as center of Excellence for Education, research and entrepreneurial skills in

the field of Computer Science and Engineering with an aim of building creative IT

professionals to meet global challenges.

PROGRAM OUTCOMES (POs)

1. Scholarship of Knowledge: Acquire in-depth knowledge of specific discipline or

professional area, including wider and global perspective, with an ability to discriminate,

evaluate, analyse and synthesise existing and new knowledge, and integration of the same for

enhancement of knowledge.

2. Critical Thinking: Analyse complex engineering problems critically, apply independent

judgement for synthesising information to make intellectual and/or creative advances for

conducting research in a wider theoretical, practical and policy context. 3. Problem Solving: Think laterally and originally, conceptualise and solve engineering

problems, evaluate a wide range of potential solutions for those problems and arrive at feasible,

optimal solutions after considering public health and safety, cultural, societal and

environmental factors in the core areas of expertise.

4. Research Skill : Extract information pertinent to unfamiliar problems through literature

survey and experiments, apply appropriate research methodologies, techniques and tools,

design, conduct experiments, analyse and interpret data, demonstrate higher order skill and

view things in a broader perspective, contribute individually/in group(s) to the development of

scientific/technological knowledge in one or more domains of engineering. 5. Usage of modern tools: Create, select, learn and apply appropriate techniques, resources,

and modern engineering and IT tools, including prediction and modelling, to complex

engineering activities with an understanding of the limitations.

6. Collaborative and Multidisciplinary work : Possess knowledge and understanding of

group dynamics, recognise opportunities and contribute positively to collaborative-

multidisciplinary scientific research, demonstrate a capacity for self-management and

teamwork, decision-making based on open-mindedness, objectivity and rational analysis in

order to achieve common goals and further the learning of themselves as well as others. 7. Project Management and Finance: Demonstrate knowledge and understanding of

engineering and management principles and apply the same to one’s own work, as a member

and leader in a team, manage projects efficiently in respective disciplines and multidisciplinary

environments after considerisation of economical and financial factors.

8. Communication : Communicate with the engineering community, and with society at large,

regarding complex engineering activities confidently and effectively, such as, being able to

comprehend and write effective reports and design documentation by adhering to appropriate

standards, make effective presentations, and give and receive clear instructions. 9. Life-long Learning: Recognize the need for, and have the preparation and ability to engage

in life-long learning independently, with a high level of enthusiasm and commitment to

improve knowledge and competence continuously.

10. Ethical Practices and Social Responsibility: Acquire professional and intellectual

integrity, professional code of conduct, ethics of research and scholarship, consideration of the

impact of research outcomes on professional practices and an understanding of responsibility to

contribute to the community for sustainable development of society.

11. Independent and Reflective Learning: Observe and examine critically the outcomes of

one’s actions and make corrective measures subsequently, and learn from mistakes without

depending on external feedback.

PROGRAM SPECIFIC OUTCOMES (PSOs)

1. Analyzing and Modeling skills: Ability to analyze and use of mathematical concepts

and algorithms along with tools to solve real world problems.

2. Develop Research Aptitude: Ability to identify research problem statement, carryout

experimentation, draw inferences and present them at national and international level.

3. Professional skills and Entrepreneurship: Ability to demonstrate professional and

leadership qualities required to pursue innovative career in Information Technology,

self-employment and research activities.

Karnatak Law Society’s

GOGTE INSTITUTE OF TECHNOLOGY

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

M.Tech. (Computer Science and Engineering)

ELECTIVE – I

First Semester

S.No.

Course

Code Course

Credits Total

credits

Contact

Hrs/wk

Marks

L - T - P CIE SEE TOTAL

1. 18SCS11 Applied Mathematics PC1 4 – 0 - 0 4 4 50 50 100

2. 18SCS12 Digital Image Processing PC2 4 – 0 - 0 4 4 50 50 100

3. 18SCS13 Cloud Computing PC3 4 – 0 - 0 4 4 50 50 100

4. 18SCS14X Elective-I PE- I 4 – 0 - 0 4 4 50 50 100

5. 18SCS15 Digital Image Processing Lab PC 0 – 0 – 3 1.5 3 25 25 50

6. 18SCS16 Cloud Computing Lab PC 0 – 0 – 3 1.5 3 25 25 50

7. 18SCS17 Seminar PC 0 – 0 - 1 2 1 25 25

Total 21 23 525

18SCS141 Advances in Computer Networks

18SCS142 Data Mining & Warehousing

18SCS143 Programming with Python

18SCS144 Advances in operating system

PG%20first%20sem/Data%20Mining.docx

Applied Mathematics

Subject Code 18SCS11 Credits 4

Course Type PC1 CIE Marks 50

Hours/Week: L-T-P 4-0-0 SEE Marks 50

Total Hours 45 SEE Duration 3

Course Learning Objectives (CLOs)

1. To introduce the fundamental concepts of Probability and study their applications

2. To study various probability distribution functions and their characteristics.

3. To present statistical approaches and drawing inferences.

4. To present various regression techniques and study their effectiveness.

5. To introduce number theoretic theorems and study their applications.

Unit I 9 Hours

Introduction

Introduction, Collection of Data, Mean, Median, Standard deviation, Statistical modeling,

Scientific interpretation, Graphical diagnostics, Role of Probability, Sample space, Events,

Counting Sample points, Permutations and Combinations, Probability of events, Rules and

Axioms of Probability, Conditional Probability, Baye’s Rule.

Unit II 9 Hours

Probability Distribution Functions

Concept of random variables, Probability Distributions : Mathematical expectation, Variance

and Co-variance, Discrete distributions: Binomial and Multinomial, Poisson distribution and

Poisson process, Geometric and Hyper-geometric distributions and their properties. Continuous

distributions: Uniform, Normal, Area under the curve, Applications of Normal distribution,

Gamma and exponential distributions and their properties.Weibull distribution.

Unit III 9 Hours

Statistics and Inference methods

Fundamentals of Sampling : Random sampling, Population and samples. Important statistics,

Mean, Sample variance. Data display and Graphical methods. BoxPlot, Quantile Plot. Sampling

distributions, Mean and Variance, Central Limit theorem, t-Distributions and its Applications.

One and Two-Sample hypothesis testing. Null and Alternate hypothesis. Testing a statistical

hypothesis. One and two tailed tests.

Unit IV 9 Hours

Regression

Introduction to Regression: Simple, Linear Regression. Least square and fitted model.

Properties of Least squares. Measure of Quality of fit. Inferences covering the regression

coefficients. Prediction, Analysis of variance, Simple regeression, case-study. Multiple

regression. Linear Regression using matrices. Analysis of Variance – ANOVA, Chi-Square and

F-test.

Unit V 9 Hours

Number Theory & its applications

Natural numbers and Division Theorem. GCD – Euclid’s method. Modular linear equations,

Extended Euclid Algorithm. Modulo Inverse and Chinese remainder theorem. Modular

exponentiation. Fermat’s theorem and primality testing. Miller-Rabin Primality testing. RSA

algorithm, Message Digest and Asymetric Cryptosystem. Elliptic Curves and its applications.

Text Books:

1. Walpole, Mayers, Ye, Probability and Statistics for Engineering and Scientits. 7th

Edition, Pearson.

2. Corman, Advanced Algorithms, 3rd

Edition, PHI, 2007.

Reference Books

1. Purnachandra, Biswal, Probability and Statistics, PHI, 2007.

2. Murray and Spiedel, John Schiller, Probability and Satatistics, 2nd

Edition, Schaum’s

Series, Tata McGraw Hill.

Course Outcomes (COs)

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

1. Apply basic principles of probability and theorems to a wide variety of

applications[L3]

2. Apply statistical models to problems in different domains and draw inferences.

3. Compare and contrast various regression models.[L4]

4. Apply number theory to build and analyze cryptosystems [L3].

Program Outcome of this course (Cos) PO No.

1. Application of Knowledge: Acquire in-depth knowledge of specific discipline 1

or professional area, including wider and global perspective, with an ability to

discriminate, evaluate, analyse and synthesise existing and new knowledge, and

integration of the same for enhancement of knowledge.

2. Problem Solving: Think laterally and originally, conceptualize and solve engine 3

ering problems, evaluate a wide range of potential solutions for those problems and

arrive at feasible, optimal solutions after considering public health and safety,

cultural, societal and environmental factors in the core areas of expertise.

Scheme of Continuous Internal Evaluation (CIE):

The total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10 marks

each and quiz/course seminar/course project of 10 marks each). The weightage of CIE is as

shown in the table below.

Component Average of

best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project Total

Marks

Maximum marks

Marks

30 10 10 50

Writing two IA test is compulsory.

Minimum qualifying marks for CIE: 20 marks.

Scheme of Semester End Examination (SEE):

o Semester end examination will be conducted for 100 marks which will be

reduced to 50 marks for the calculation of SGPA and CGPA.

Minimum marks required in SEE to pass: 40 marks.

Digital Image Processing


Course Type PC2 CIE Marks 50



Course objectives:

1. To understand the importance and applications of Digital Image Processing.

2. To understand the image representation and image formation in Gray

3. To understand the image fundamentals and mathematical transforms necessary for

image processing and study the image enhancement techniques.

4. To introduce the various image processing techniques in spatial and frequency domains.

Prerequisites: Basics of Mathematical Analysis, Vectors, Matrices, Probability & Statistics

Computer Programming.

Unit I 9 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 a digital

processing system.

Digital Image Fundamentals: Elements of visual perception, a simple image formation model,

basic steps in image Sampling and Quantization, representing digital images, zooming and

shrinking of digital images, some basic relationships between Pixels, Linear and Non-linear

operations.

Unit II 9 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.

Unit III 9 Hours

Image Enhancement in the Frequency Domain: Introduction to the Fourier Transform and

the Frequency Domain, Smoothing Frequency-Domain Filters, Sharpening Frequency-

Domain Filters.

Unit IV 9 Hours

Image Restoration: 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 Filtering.

Unit V 9 Hours

Image Transforms: Introduction, Need for transforms, Classification of Image transforms,

Walsh transform, Hadamard Transform, HAAR transform.

Text Books:

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

Edition 2005.

2. S. Jayaraman S. Esakkirajan, T.Veerakumar: Digital Image Processing, McGraw Hill Ed.

(India) Pvt. Ltd. 2013.

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 Outcomes:

At the end of the course, students will be able to,

1. Explain the importance of DIP and its applications [L1].

2. Explain the image formation and representation of digital images [L1].

3. Apply the spatial and frequency domain image processing techniques in gray & color [L3].


1. Scholarship of Knowledge: Acquire in-depth knowledge of specific discipline 1





ering problems, evaluate a wide range of potential solutions for those problems a

nd arrive at feasible, optimal solutions after considering public health and safety,


3. Usage of modern tools: Create, select, learn and apply appropriate techniques, res 5

ources, and modern engineering and IT tools, including prediction and modelling,

to complex engineering activities with an understanding of the limitations.

4. Collaborative and Multidisciplinary work: Possess knowledge and understanding 6

of group dynamics, recognise opportunities and contribute positively to collaborate

ve-multidisciplinary scientific research, demonstrate a capacity for self-management

and teamwork, decision-making based on open-mindedness, objectivity and rational

analysis in order to achieve common goals and further the learning of themselves

as well as others.






best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks

Maximum marks

30 10 10 50




1. Semester end examination will be conducted for 100 marks which will be


2. Minimum marks required in SEE to pass: 40 marks.

Cloud Computing

Course Code 18SCS13 Credits 4

Course type PC3 CIE Marks 50 marks

Hours/week: L-T-P 4 – 0 – 0 SEE Marks 50 marks

Total Hours: 45 SEE Duration 3

Course learning objectives

1. To understand various basic concepts related to cloud computing technologies.

2. To learn how to use Cloud Services.

3. To apply Map-Reduce concept to applications.

4.

To understand role of Virtualization and resource management in enabling Cloud

Computing.

Pre-requisites: Distributed Computing.

Unit I 9 Hours

Evolution of Computing, Cloud Computing Basics

Introduction to Mainframe architecture; Client-server architecture; Cluster Computing; Grid

Computing; Parallel Computing and Distributed Computing; Evolution of sharing on the

Internet; Utility Computing; Autonomic Computing; Cloud Computing; Introduction of Cloud

Computing; Service Models; Deployment Models; Characteristics of Cloud Computing;

Advantages and Obstacles in cloud computing; Ethical issues in cloud computing.

Unit II 9 Hours

Cloud Infrastructure

Cloud Vulnerabilities, NIST reference model, Cloud computing at Amazon, Cloud computing

the Google perspective, Microsoft Windows Azure and online services, Open-source software

platforms for private clouds, Cloud storage diversity and vendor lock-in, Energy use and

ecological impact, Service level agreements.

Unit III 9 Hours

Cloud Computing: Application Paradigms.

Challenges of cloud computing, Architectural styles of cloud computing, Workflows:

Coordination of multiple activities, Coordination based on a state machine model: The

Zookeeper, The Map Reduce programming model, A case study: The Grep The Web

application, Cloud for science and engineering, High-performance computing on a cloud,

Cloud computing for Biology research.

Unit IV 9 Hours

Cloud Resource Virtualization

Virtualization, Layering and virtualization, Virtual machine monitors, Virtual Machines,

Performance and Security Isolation, Full virtualization and paravirtualization, Hardware

support for virtualization, Case Study: Xen a VMM based paravirtualization, The dark side of

virtualization.

Self-Study: vBlades: Performance comparison of virtual machines.

Unit V 9 Hours

Cloud Resource Management and Scheduling.

Policies and mechanisms for resource management, Application of control theory to task

scheduling on a cloud, Stability of a two level resource allocation architecture, Feedback

control based on dynamic thresholds, Resourcing bundling: Combinatorial auctions for cloud

resources ,Scheduling algorithms for computing clouds, Fair queuing, Start-time fair queuing,

Borrowed virtual time, Cloud; scheduling subject to deadlines, Scheduling Map Reduce

applications subject to deadlines, Resource management and dynamic scaling.

Self-Study: Introduction to Cloud Simulator.

Text Books:

1. Cloud Computing by Dr. Kumar Saurabh, Wiley India, 2011 and onwards.

2. Dan C Marinescu: Cloud Computing Theory and Practice. Elsevier (MK) 2013 and

onwards.

Reference Books:

1. Cloud Computing Principles and Paradigms by Rajkumar Buyya, Wiley India 2011 and onwards.

2. John W Rittinghouse, James FRansome: Cloud Computing Implementation,

Management and Security, CRC Press 2013.

Course Outcomes (COs)

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

1. Discuss cloud computing and control considerations within cloud

computing environments.

L2

2. Identify various cloud services. L2

3. Explain various concepts related to virtualization. L2

4. Apply Map-Reduce concept L3

5. Analyze resource allocation and scheduling algorithms in cloud

computing

L3

6. Demonstrate working of cloud simulator. L3

Program Outcome of this course (POs) PO No.

1. Scholarship of Knowledge: Acquire in-depth knowledge of specific discipline

or professional area, including wider and global perspective, with an ability

to discriminate, evaluate, analyse and synthesise existing and new knowledge,

and integration of the same for enhancement of knowledge.

1

2. Problem Solving: Think laterally and originally, conceptualize and solve

engineering problems, evaluate a wide range of potential solutions for those

problems and arrive at feasible, optimal solutions after considering public

health and safety, cultural, societal and environmental factors in the core areas

of expertise.

3

Course delivery methods Assessment methods

1. Chalk and board 1. Internal assessment

2. PPT 2. Assignment

3. Video lectures 3. Quiz

4. Seminar / project






best 2 Tests

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks

Maximum marks

Marks

30 10 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

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)

Advances in Computer Networks

Course Objectives:

1. To become familiar with the basics of Computer Networks.

2. To learn the concepts of fundamental protocols.

3. To identify application of TCP for routing in ad-hoc networks.

4. To understand the Software defined networks (SDN) and BlockChain

Technology.

Prerequisite: Knowledge of Computer Networks.

Unit I 9 Hours

Foundation Building a Network, Requirements, Perspectives, Scalable Connectivity, Cost-Effective

Resource sharing, Support for Common Services, Manageability, Performance, Introduction

to Wired networks, Wireless Networks and Wireless sensor network.

Unit II 9 Hours

End-to-End Protocols

Simple De-multiplexer (UDP), Reliable Byte Stream(TCP), End-to-End Issues,

Segment Format, Connecting Establishment and Termination, Triggering Transmission,

Adaptive Retransmission, TCP Extensions, Fair Queuing, TCP Congestion Control,

Additive Increase/ Multiplicative Decrease, Slow Start, Fast Retransmit and Fast Recovery

Queuing Disciplines, FIFO.

Unit III 9 Hours

AD-HOC Network Routing & TCP:

Issues, Classifications of routing protocols – Hierarchical and Power aware. Multicast routing –

Classifications, Tree based, Mesh based. Ad Hoc Transport Layer Issues. TCP Over Ad Hoc –

Feedback based, TCP with explicit link, TCP-Bus, Ad Hoc TCP, and Split TCP.

Unit IV 9 Hours

Evolution of Switches and Control Planes, Cost, SDN Implications for Research and Innovation, Data

Center Innovation, Data Center Needs, The Genesis of SDN: Abstract, The Evolution of Networking

Subject Code: 18SCS141 Credits: 4

Course Type: PE CIE Marks: 50

Hours/week: L – T – P 4 – 0 – 0 SEE Marks: 50

Total Hours: 45 SEE Duration: 3

Technology, Forerunners of SDN, Software Defined Networking is Born, Sustaining SDN

Interoperability, Open Source Contributions, Legacy Mechanisms Evolve Toward SDN, Network

Virtualization, How SDN Works: Abstract, Fundamental Characteristics of SDN, SDN Operation, SDN

Devices, SDN Controller, SDN Applications, Alternate SDN Methods.

Unit V 9 Hours

Blockchain Technology: Origin of blockchain technology, The birth of blockchain, Revolutionizing

the Traditional Business Network, Exploring a blockchain application, Recognizing the key business

benefits, Building trust with blockchain, What Makes a Blockchain Suitable for Business, Identifying

Participants and Their Roles, Use of Blockchain in Internet of Things.

Text books:

1. Larry Peterson and Bruce S Davis “Computer Networks :A System Approach”

5th Edition, Elsevier -2014

2. C. Siva Ram Murthy and B.S Manoj, “AdHoc Wireless Networks – Architectures and

Protocols” , Pearson Education, 2004 and onwards.

3. Software Defined Networks: A Comprehensive Approach, by Paul Goransson and

Chuck Black, Morgan Kaufmann, June 2014, Print Book ISBN: 9780124166752,

eBook ISBN: 9780124166844

4. Manav Gupta, Blockchain For Dummies, IBM Limited Edition, John Wiley & Sons, Inc

Reference Books:

1. Ulysses Black “Computer Networks, Protocols, Standards and Interfaces”, 2nd

Edition-

PHI

2. Behrouz A Forouzan “TCP/IP Protocol suite”, 4th Edition – Tata McGraw-Hill

Course Outcomes:


1. Classify different types of computer network and their protocol structures [L1].

2. Demonstrate the knowledge of basic fundamental protocols used for

communication and networking [L4, L3].

3. Explain the use of TCP for routing in Ad-hoc networks [L2]

4. Elucidate basic working of SDN and Block Chains[L1].






2. Critical Thinking: Analyze complex engineering problems critically, apply indep 2

endent judgment for synthesizing information to make intellectual and /orcreative

advances for conducting research in a wider a wider theoretical, practical and pol

icy context.


The total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10

marks each and quiz/course seminar/course project of 10 marks each). The weightage of

CIE is as shown in the table below.


best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks

Maximum marks

Marks

30 10 10 50




a. Semester end examination will be conducted for 100 marks which will be


b. Minimum marks required in SEE to pass: 40 marks.

Data Mining & Warehousing


Course Type PE CIE Marks 50



Course Objectives:

1. To introduce the basic concepts and techniques of data mining and data

warehousing

2. To develop the skills using recent data mining software for solving practical

problems.

3. To assess the strengths and weaknesses of various data mining methods and

algorithms.

Prerequisite: Database Management System, Information Management.

Unit I 9 Hours

Introduction:

What is a Data Warehouse?, A Multidimensional Data Model, Data Warehouse

Architecture, Data Warehouse Implementation, Data cube Technology, From Data

warehousing to Data Mining, Data Mining Functionalities, Data cleaning, Data

Integration and Transformation, Data Reduction.

Unit II 9 Hours

Data Mining primitives, Languages and System Architecture: Data Mining

primitives, Presentation and Visualization of Discovered patterns, A Data Mining

Query Language. Association Rule Mining Single–dimensional Boolean Association

Rules From Transactional Databases, Mining Multilevel Association Rules from

Transactional Databases.

Unit III 9 Hours

Classification and Prediction: Issues regarding Classification and Prediction,

classification by Decision tree induction, Bayesian classification, Classification by

back propagation, Classification Based on the concepts from association rule mining.

Other classification methods, prediction.

Unit IV 9 Hours

Cluster Analysis: What is Cluster Analysis? Types of data in cluster Analysis: a

Categorization of Major Clustering Methods, Partitioning Methods, Hierarchical

methods, Density-Based Methods, Model-Based Clustering Methods: Statistical

Approach, Neural Network Approach Outliner Analysis.

Unit V 9 Hours

Application and Trends in Data Mining: Data mining application, Data mining

system Products research Prototypes, Additional Themes on Data Mining, Data Mining

and Intelligent Query Answering, Trends in Data Mining.

Text Books:

1. Jiawei Han, Michelin Kamber, "Data Mining Concepts and Techniques",

Morgan KaufMann Publishers, 3rd

edition, July 2011

Reference Books:

1. Alex Berson and Stephen J Smith, “Data Warehousing, Data Mining and

OLAP” (Data Warehousing/Data Management). New Delhi : Tata Mcgraw-

Hill, 2004

2. Arun K Pujari, “Data Mining Techniques”, Universities Press, Oct 2013

Course Outcomes


1. Identify the key processes of data mining, data warehousing and knowledge

discovery process[L1].

2. Describe the basic principles and algorithms used in practical data mining and

understand their strengths and weaknesses [L1].

3. Apply data mining techniques to solve problems in interdisciplinary domains [L3].

4. Demonstrate the classification, Regression & clustering technique [L3].






2. Critical Thinking: Analyze complex engineering problems critically, apply indep 2

endentjudgment for synthesizing information to make intellectual and /orcreative

advances forconducting research in a wider a wider theoretical, practical and pol

icy context.








http://www.amazon.in/Arun-K-Pujari/e/B0034PZ2GK/ref=dp_byline_cont_book_1


best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks

Maximum marks

Marks

30 10 10 50




1. Semester end examination will be conducted for 100 marks which will be reduced to 50

marks for the calculation of SGPA and CGPA.


Programming with Python





Course Objectives:

1. To acquire programming skills in core Python

2. To write programs in Python for simple problems.

3. To develop the skill of writing GUIs.

4. To develop an ability to write simple graphics animations with sound.

Prerequisite: Knowledge of Programming Concepts .

Unit I 9 Hours

Getting started:

The Game over program, Examining the game over program, introducing python,

setting up python on windows and other operating systems, introducing IDLE, back to

the game over program. Types, variables and simple I/O, introducing the useless trivia

program, using quotes with strings, using escape sequences with strings, concatenating

and repeating strings, working with numbers, user input, string methods, right types,

converting values, back to the trivia program. Branching, while loops and program

planning, using the if, else, elif statements, creating while loops, infinite loops, values as

conditions, compound conditions, planning your programs, guess my number game.

Unit II 9 Hours

FOR loops, strings and tuples:

Using for loops, using sequence operators and functions with strings, indexing strings,

string immutability, building a new string, slicing strings, tuples, jumble game. Lists

and dictionaries – using Lists, list methods, understanding when to use tuples and lists,

nested sequences, shared references, dictionaries, hangman game. Functions, creating

functions, parameters and return values, keyword arguments, default parameters, global

variables, tic tac toe game.

Unit III 9 Hours

Files and Exceptions:

Reading and writing to text files, storing complex data, handling exceptions, trivia

Challenge game. Software objects, caretaker program, object oriented basics, creating

Classes, methods and objects, constructors, attributes, class attributes and static

methods, object encapsulation, private attributes and methods, attribute access, critter

caretaker program. Object oriented programming – sending and receiving messages,

combining objects, inheritance, extending a class through inheritance, altering behavior

of inherited methods, understanding polymorphism, creating Units, blackjack game.

Unit IV 9 Hours

GUI development:

Examining GUI, understanding event driven programming, root window, labels,

buttons, creating a GUI using a class, binding widgets and event handlers, text and entry

widgets and Grid layout manager, check buttons, radio buttons, adlib program. Graphics

– Pizza panic game, creating a graphics window, setting background image,

understanding the graphics coordinate system, displaying sprite, text, message,

moving sprites, dealing with screen boundaries, mouse input, collisions.

Unit V 9 Hours

Introduction to Data Analytics and Pandas

Basics of Dataframe, Reading and writing Excel and CSV files, Handling missing data:

fillna, dropna, interpolate, replace function, Split and apply combine strategy,

Concatenating data frames, merging data frames, pivot and pivot_table, Reshaping data

frames using melt, Stacking and unstacking data

Text Books:

1. Python Programming, Michael Dawson, 3rd Edition, Course technology PTR, 2010

2. Python for Data Analysis, Wes McKinney, O’Reilly Media, Inc., 2010.

Reference Books:

1. Programming Python by Mark Lutz, O'Really, 4th Edition, 2011.

2. Head First Python by Paul Barry, O'Reilly Shroff Publishers, 2010.

Course Outcomes:


1. Write and execute programs for simple problems using Python [L6].

2. Design and develop GUI in Pyhton [L6].

3. Design and develop Object oriented applications in Python [L6].

4. Demonstrate use of Python in data analysis [L3].









http://codebasicshub.com/tutorial/cs-concepts/handle-missing-data-fillna-dropna-interpolate






The total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10

marks each and quiz/course seminar/course project of 10 marks each). The weightage of

CIE is as shown in the table below.


best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks

Maximum marks

Marks

30 10 10 50







Advances in Operating Systems





Course Objectives:

1. Define the fundamentals of Operating Systems.2. Explain distributed operating system concepts that includes architecture, Mutual exclusion

algorithms, Deadlock detection algorithms and agreement protocols3. Illustrate distributed resource management components viz. the algorithms for

implementation of distributed shared memory, recovery and commit protocols4. Identify the components and management aspects of Real time, Mobile operating Systems

Unit I

Operating System Overview, Process description & Control: Operating System 9 Hours Objectives and Functions, The Evolution of Operating Systems, Major Achievements,

Developments Leading to Modern Operating Systems, Microsoft Windows Overview,

Traditional UNIX Systems, Modern UNIX Systems, What is a Process?, Process States,

Process Description, Process Control, Execution of the Operating System, Security

Issues.

Unit II

Threads, SMP, and Microkernel, Virtual Memory: Processes and Threads, 9 Hours Symmetric Multiprocessing (SMP), Micro Kernels, Windows Vista Thread and SMP

Hours Management, Linux Process and Thread Management. Hardware and Control

Structures, Operating System Software, UNIX Memory Management, Windows Vista

Memory Management, Summary

Unit III

Multiprocessor and Real-Time Scheduling: Multiprocessor Scheduling, Real-Time 9 Hours Scheduling, Linux Scheduling, UNIX PreclsSl) Scheduling, Windows Vista Hours

Scheduling, Process Migration, Distributed Global States, Distributed Mutual Exclusion,

Distributed Deadlock

Unit IV

Embedded Operating Systems: Embedded Systems, Characteristics of Embedded 9 Hours Operating Systems, eCOS, TinyOS, Computer Security Concepts, Threats, Attacks, and

Assets, Intruders, Malicious Software Overview, Viruses, Worms, and Bots, Rootkits.

Unit V

Kernel Organization: Using Kernel Services, Daemons, Starting the Kernel, Control in 9 Hours the Machine , Modules and Device Management, MODULE Organization, MODULE

Installation and Removal, Process and Resource Management, Running Process

Manager, Creating a new Task , IPC and Synchronization, The Scheduler , Memory

Manager , The Virtual Address Space, The Page Fault Handler , File Management. The

windows NT/2000/XP kernel: Introduction, The NT kernel, Objects , Threads,

Multiplication Synchronization,Traps,Interrupts and Exceptions, The NT executive ,

Object Manager, Process and Thread Manager , Virtual Memory Manager, I/o Manager,

The cache Manager Kernel local procedure calls and IPC, The native API, subsystems.

ext Books: 1. William Stallings: Operating Systems: Internals and Design Principles, 6th

Edition, Prentice Hall, 2013. 2. Gary Nutt: Operating Systems, 3rd Edition, Pearson, 2014.

Reference Books:

1. Silberschatz, Galvin, Gagne: Operating System Concepts, 8th Edition, Wiley, 2008 2. Andrew S. Tanenbaum, Albert S. Woodhull: Operating Systems, Design

and Implementation, 3rd

Edition, Prentice Hall, 2006. 3. Pradeep K Sinha: Distribute Operating Systems, Concept and Design, PHI, 2007.

Course Outcomes:


1. Demonstrate the Mutual exclusion, Deadlock detection and agreement protocols of Distributed operating system [L3] 2. Explain the various resource management techniques for distributed systems [L2]

3. Identify the different features of real time and mobile operating system [L4] 4. Modify existing open source kernels in terms of functionality or features used [L4]


1. Critical Thinking: Analyze complex engineering problems critically, apply inde 2

Pendent judgment for synthesizing information to make intellectual and /orcreative

advances for conducting research in a wider a wider theoretical, practical and pol

icy context.






The total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10 marks each

and quiz/course seminar/course project of 10 marks each). The weightage of CIE is as shown in the

table below.


best 2 Tests

Test-2

Average of 2

Assignments

Quiz/Seminar/

Project

Total

Marks

Maximum marks

30 10 10 50







Digital Image Processing Laboratory

Course Code 18SCS15 Credits 1.5

Course type PC CIE Marks 25

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


Course Objectives:

1. To understand basic knowledge and practice about the use of computer algorithms to

perform image processing on digital images.

2. To understand how to process two dimensional image data.

LABORATORY WORK

1. Introduction to Matlab /Scilab with focus on Digital Image Processing.

2. Implement the following basic gray level transformations on the given image(s):

i) Image Negative

ii) Log transformation

iii) Power Law transformation

iv) Contrast stretching

v) Gray level slicing

vi) Bit plane slicing

vii) Histogram plotting

viii) Histogram equalization

ix) Arithmetic operation like image subtraction

x) AND /OR logic operations

3. Implementation of Low-pass and High-pass Butterworth filters in the frequency domain

for various cut off frequencies and comment on their performance.

4. Prove the convolution theorem: “convolution in spatial domain is equal to

multiplication in frequency domain” using the two images and the necessary operations.

5. Compute the edges of the given image using the following edge detectors and comment

on their performance:

i) Roberts

ii) Prewitt

iii) Sobel

iv) Canny

Course Outcomes (COs):


1. Apply the basic image processing techniques for solving real problems.

2. Be familiar with the image processing tools and software.

3. Have knowledge of Image transform and their applications.

Programme Outcomes (POs)

1. Graduates will apply the knowledge of mathematics, science, engineering

fundamentals, and an engineering specialization to the solution of complex

engineering problems. [PO1]

2. Graduates will demonstrate an ability 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. [PO 3]

3. 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

[PO 12].


CIE lab attendance/conducting the experiment 10

25 Journal writing 15


Rules to be followed for SEE exams:

1. Students have to execute a program selected from a lot of all experiments.

2. The breakup of evaluation will be

SEE

Initial write up 10

50 Conduct of experiments 20

Viva- voce 20

3. Minimum passing criteria is: 40% SEE marks.

4. Change of experiment is allowed only once. (20% of the total marks would be

deducted for change in experiment).

Cloud Computing Laboratory

Course Code 18SCS16 Credits 1.5

Course type PC CIE Marks 25

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


Course learning objectives

1. To learn how to use Cloud Services.

2. To implement Virtualization.

3. To understand the concept of Hadoop.

Prerequisites: Cloud Computing

List of experiments:

Introduction to CloudSim(tool)

1. Create a datacenter with one host and run one cloudlet on it.

2. Create two datacenters with one host and a network topology each and run two cloudlets on

them.

3. Create two datacenters with one host each and run cloudlets of two users on them.

4. Create simulation entities (a DatacenterBroker) in run-time using a globar manager entity

(GlobalBroker).

5. Simulation of a heterogeneous non-power aware data center (all hosts consume maximum

power all the time).

Mini- Project: (Group of students must implement one of the below mentioned problem

definition)

1. Demonstration of Full Virtualization using an Operating System and VMware

2. Demonstration of Para Virtualization using an Operating System and VMware

3. Demonstration of Installation and Configuration of Hadoop.

Text Books

1. Cloud Computing by Dr. Kumar Saurabh, Wiley India, 2011 and onwards.

2. Dan C Marinescu: Cloud Computing Theory and Practice. Elsevier (MK) 2013 and

onwards.

Reference Books

1. Cloud Computing Principles and Paradigms by RajkumarBuyya, Wiley India 2011 and onwards.

2. John Writing house, James F Ransome: Cloud Computing Implementation, Management

and Security, CRC Press 2013.

Course Outcome (COs)

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

Level

1. Explain various concepts related to virtualization. L2

2. Demonstrate the different services of cloud. L3

3. Demonstrate the installation of Hadoop. L3

Program Outcome of this course (POs) PO No.

Engineering Graduates will be able to:

1. Scholarship of Knowledge: Acquire in-depth knowledge of specific

discipline or professional area, including wider and global perspective,

with an ability to discriminate, evaluate, analyse and synthesise

existing and new knowledge, and integration of the same for enhancement

of knowledge.

PO1

Problem Solving: Think laterally and originally, conceptualize and solve

engineering problems, evaluate a wide range of potential solutions for those

problems and arrive at feasible, optimal solutions after considering public

health and safety, cultural, societal and environmental factors in the core areas

of expertise.

PO3


CIE

lab attendance/conducting the experiment 10

25 Journal writing 10

Mini Project(demonstration) 05


Rules to be followed for SEE exams:

1. Students have to execute a program selected from a lot of all experiments. 2. The breakup of evaluation will be

SEE

Initial write up 10

50 Conduct of experiments 15

Mini Project(Demonstration & report) 05

Viva- voce 20

3. Minimum passing criteria is: 40% SEE marks.

4. Change of experiment is allowed only once. (20% of the total marks would be deducted for change in experiment).

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


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