Computer Science & Engineering III B.Tech. - II Semester ...

Department of

Computer Science & Engineering

III B.Tech. - II Semester

Course Handout 2019-20

Name of the student

Roll No.

Section

Kallam Haranadhareddy Institute of Technology NH-5, Chowdavaram, Guntur-522 019

Approved by AICTE, New Delhi; Affiliated to JNTUK, Kakinada Accredited by NBA, NAAC with ‘A’ Grade & An ISO 9001:2015 Certified Institution

III-II Course Handout-R16 A.Y:2019-20

Department of CSE, Kallam Haranadhareddy Institute of Technology, Guntur Page 2

INDEX

S.No Description Page No.

1 College Vision & Mission 3

2 Department Vision & Mission 3

3 Program Educational Objectives (PEOs) 3

4 Graduate Attributes (GAs) 4

5 Program Outcomes (POs) 4

6 Program Specific Outcomes (PSOs) 5

7 JNTUK Academic Calendar 5

8 Department Academic Process Calendar 6

9 Course Structure 7

10 Evaluation Pattern 7

11 Quality of Internal Question Papers and Assignment

Questions 9

12 Timetable 10

13

Details of All Theory & Lab Courses as per Course

Structure

11 Theory: CN, DWDM, DAA, STM, AI

Labs: NP lab, ST lab, DWDM lab

14 Non-Programming Laboratory Courses Assessment

Guidelines 58

15 Programming Laboratory Courses Assessment Guidelines 59

16 Laboratory Course Evaluation Rubrics 59



COLLEGE VISION & MISSION

Institute Vision:

To be a quality - oriented technical institution known for global

academic excellence and professional human values

Institute Mission:

To provide quality instruction with competent and knowledgeable faculty

and well - equipped laboratories to meet global standards.

To achieve academic distinction through novel teaching and learning

practice

To encourage students by providing merit scholarships

To prepare the graduates to accomplish professional practice,

employability, entrepreneurial development and higher education

To inculcate self-discipline, accountability and values in the learners for

effective and informed citizenship

To focus on MoUs with premier institutes and renowned industries for

effective industry-institution interaction to become an R&D centre

through skill development , professional up-gradation and innovation

DEPARTMENT VISION & MISSION

CSE Vision:

To impart quality technical education to students in the field of

computer science and engineering to produce technically competent

software and hardware personnel with advanced skills, knowledge and

behavior to meet the global computational challenges.

CSE Mission:

Providing strong theoretical and practical knowledge to students.

Providing students with training on latest technologies to meet the

industry needs.

Developing ethical values in students to lead the life with good

human values.

PROGRAM EDUCATIONAL OBJECTIVES (PEOs)

PEO1: Graduates shall effectively apply mathematics, science and

engineering methodologies for analysis, design and implementation of

real world problems.

PEO2: Graduates utilize breadth and depth of theoretical computer

science to adopt emerging technologies and tools for changing needs of

industry or for pursuing higher studies.

PEO3: Graduates shall continue to enhance technical skills through

lifelong learning, exhibit social and ethical responsibilities and effective

communication skills.

PEO4: Graduates shall be employed in software and hardware industries

or pursue higher studies or research or become entrepreneurs.



GRADUATE ATTRIBUTES (GAs) prescribed by NBA:

i. Engineering Knowledge

ii. Problem Analysis

iii. Design & Development of Solutions

iv. Investigation of Complex Problem

v. Modern Tools Usage

vi. Engineer and Society

vii. Environment & Sustainability

viii. Ethics

ix. Individual & Team work

x. Communication

xi. Lifelong Learning

xii. Project management & Finance

PROGRAM OUTCOMES (POs)

PO1: Graduates will be able to apply the principles of basic sciences,

mathematics and engineering fundamentals in finding solutions to

complex problems.

PO2: Graduates will acquire critical thinking skills, problem solving

abilities and familiarity with the computational procedures essential to

the field.

PO3: Graduates will be able to plan, analyze and design various types of

systems required for technical advancements and societal needs.

PO4: Graduates will be able to use research based knowledge to conduct

experiments and interpret experimental data.

PO5: Graduates gain hands on experience in using latest software and

hardware tools for obtaining solutions to engineering problems.

PO6: Graduates will be able to apply knowledge gained to tackle societal,

health, safety, legal and cultural issues.

PO7:Graduates will possess adequate knowledge required for sustainable

development keeping in view environmental effects and real life

problems.

PO8:Graduates will have professional ethics and the culture of practicing

the established norms of engineering.

PO9: Graduates will acquire the capability of working productively as

individuals, as members or leaders in teams in any environment.

PO10:Graduates will be able to articulate their ideas clearly with

excellent communication skills and prepare technical reports.

PO11: Graduates will acquire knowledge required for project and finance

management.

PO12: Graduates will have ability to engage in lifelong learning to keep

abreast of ever changing technology.



PROGRAM SPECIFIC OUTCOMES (PSOs)

PSO 1: To use mathematical methodologies to crack problem using

suitable mathematical analysis, data structure and suitable algorithm.

PSO 2: The ability to interpret the fundamental concepts and

methodology of computer systems. Students can understand the

functionality of hardware and software aspects of computer systems.

PSO 3: The ability to grasp the software development lifecycle and

methodologies of software systems. Possess competent skills and

knowledge of software design process. Familiarity and practical

proficiency with a broad area of programming concepts and provide new

ideas and innovations towards research.

JNTUK Academic Calendar for II B.Tech 2019-20 Batch



Academic Process Calendar for II B.Tech 2019-20 Batch

S.

No. Event/Activity Scheduled Dates

1 Commencement of class work (Semester-1) 10-06-2019

2 Talentio Training on Programming in C and Datastructures for IV B.Tech

02-06-2019 to 15-06-2019

3

KHIT Student Chapter Oranizing FDP on

Introduction to Machine Learning Speaker: Ramachandra Reddy,IIIT Sricity.

6-8 June 2019

4 Commencement of Class work for II,III,IV B.Tech 10-06-2019

5 ABC Technologies, Bangalore Bootstrap Training 24-06-2019

6 ABC Technologies, Bangalore Placement Drive-On

Campus 25-06-2019

7 Talentio Training Program Aptitude and Verbal for

TCS NINJA 28-06-2019 to 15-07-2015

8

KHIT CSI STUDENT CHAPTEROrganizingONE DAY NATIONAL WORKSHOPon Internet of Intelligent

Things Using Cloud Computing Speaker: Mr.J Srinivas and Rajesham, MJCET, Hyd.

28th June 2019

9 Sports Day August 2019

10 Mid-1 Examinations 05-08-2019 to 10-08-2019

11 Four Days FDP on Internet of Things in Association with APSSDC

5-8 AUGUST 2019

12 Two Day Workshop on Cloud Computing in

Association with APSSDC 27-28 Aug 2019

13

Three Day Workshop on Problem Solving and

Programming using Python in Association with APSSDC

04-06 Sep 2019

14 A National Level Students Tech Cultural Fest

SAMKALP 2K19 13th - 14th September 2019

15 ACM Students Chapter Organizing Project Expo Judge: Dr.D.Jagan Mohan Reddy, LBRCE

13th September 2019

16 ACM Student Chapter Organizing 30 Hrs Code

Hackathon 13th - 14th September 2019

17 CSI Student Chapter Organizing IDEATHON 13th September 2019

18 CSI Student Chapter Organizing Poster

Presentation 13th September 2019

19 Engineer’s Day 15thSeptember -2019


21 End Examinations 21-10-2019 to 02-11-2019

22

Cognizant Technologies-Industry Specifc training to IV CSE

Trainer: Mr.Subham, Senior Trainer Organization,

Tanentio Technologies, Hyderabad

2nd November 2019

23

Investor Awareness Program in association with AMFI&SEBI

Speaker:G.Rajasekhar Reddy No.of Faculty Attended:36

8th November 2019

24

Bridge Course: II BTech CSE Lateral Entry

Speakers: Mr.N.Md.Jubair Basha and

Mr.B.Satyanarayana Reddy No.of students attended:

11th -16th November 2019

25 Commencement of class work (Semester-2) 18-11-2019

26 Percolation Pits Dec-2019

2 APSSDC Workshop on Associate Cloud Architect 2-7 December 2019



28

APSSDC Workshop on Programming Contest &

Design

16-December- 2019

29 Tree Plantation Dec-2019

30 5K/10Krun Jan-2020

31 Industrial Visit December 1st or January1st

week 2020


33 Alumni Meet Jan, 1st or 2nd week

34 APSSDC Workshop on Web Development with

Python 20-25 January 2020

35 Medical Camp Jan 2020

36 ACM ESP/ACM DSP January 2020

37 CSI Guest Lecture Feb 2020

38 Blood Camp February 2020

39 Annual Day Feb 2020, 2nd week

40 ACM Code of Hour February 2020

41 Food Festival February 2020

42 CSI Technical Quiz February 2020

43 FDP on Machine Learning February 2020


45 ENVISAGE –A Poster Display March 2020

46 Students Feedback March 2020

47 End Examinations 06-04-2020 to 18-04-2020

48 APSSDC FDP on Machine Learning using Pythton May 2020

49 Next Academic Year Class Work 08/06/2020

B.TECH. COMPUTER SCIENCE AND ENGINEERING

III Year II Semester

COURSE STRUCTURE

S.No Subject L T P C

1 Computer Networks 4 - - 3

2 Data Warehousing and Mining 4 - - 3

3 Design and Analysis of Algorithms 4 - - 3

4 Software Testing Methodologies 4 - - 3

5 Artificial Intelligence (Open Elective) 4 - - 3

6 Network Programming Lab - - 3 2

7 Software Testing Lab - - 3 2

8 Data Warehousing and Mining Lab - - 3 2

9 IPR & Patents - 2 - -

TOTAL CREDITS 21



EVALUATION PATTERN

Distribution and weight age of marks

(i) The performance of a student in each semester shall be evaluated

subject - wise with a maximum of 100 marks for theory subject and 75

marks for practical subject. The project work shall be evaluated for 200

marks.

(ii) For theory subjects the distribution shall be 30 marks for Internal

Evaluation and 70 marks for the End-Examinations.

(iii) For theory subjects, during the semester there shall be 2 internal tests.

The weightage of internal marks for 30 consists of Descriptive - 15,

Assignment – 05, Objective – 10 (Conducted online with 20 Multiple choice

question with a weightage of 1/2 Mark each). The objective examination is

for 20 minutes duration. The subjective examination is for 90 minutes

duration. Each subjective type test question paper shall contain 3

questions and all questions need to be answered. The Objective

examination conducted for 10 marks and subjective examination

conducted for 15 marks are to be added to the assignment marks of 5 for

finalizing internal marks for 30. The best (80% +20%) of two tests will be

taken for internal marks. As the syllabus is framed for 6units, the first

mid examination (both Objective and Subjective) is conducted in 1-3

units and second test in 4-6 units of each subject in a semester.

(iv) The end semester examination is conducted covering the topics of all

units for 70marks. Part-A contains a mandatory question (

Brainstorming / Thought provoking/ case study) for 14marks. Part - B

has 6 questions (one from each Unit). The student has to answer 4 out

of 6 questions in Part -B and carries a weightage of 14marks each.

(v) For practical subjects there shall be continuous evaluation during the

semester for 25 internal marks and 50 end examination marks. The

Internal 25 marks shall be awarded as follows: day to day work - 10

marks, Record-5 marks and the remaining 10marks to be awarded by

conducting an internal laboratory test. The end examination shall be

conducted by the teacher concerned and external examiner.

(vi) For the subject having design and estimation, the distribution

shall be 30 marks for internal evaluation (20 marks for day to day

work and 10 marks for internal test) and 70marks for end

examination. There shall be two internal tests in a Semester and the

better of the two shall be considered for the award of marks for

internal tests.

(vii) For the seminar, the student shall collect the information on a

specialized topic and prepare a technical report, showing his

understanding over the topic and submit to the department, which

shall be evaluated by the Departmental committee consisting of Head

of the department, seminar supervisor and a senior faculty member.

The seminar report shall be evaluated for 50 marks. There shall be no



external examination for seminar.

(viii) Out of a total of 200 marks for the project work, 60 marks shall be

for Internal Evaluation and 140 marks for the End Semester

Examination. The End Semester Examination (Viva-Voce) shall be

conducted by the committee. The committee consists of an external

examiner, Head of the Department and Supervisor of the Project. The

evaluation of project work shall be conducted at the end of the IV year.

The Internal Evaluation shall be on the basis of two seminars given by

each student on the topic of his project and evaluated by an internal

committee.

(ix) Laboratory marks and the internal marks awarded by the College

are not final. The marks are subject to scrutiny and sealing by the

University whenever felt desirable. The internal and laboratory marks

awarded by the College will he referred to a Committee. The

Committee shall arrive at a scaling factor and the marks will be scaled

as per the scaling factor. The recommendations of the Committee are

final and binding. The laboratory records and internal test papers

shall be preserved in the respective departments as per the University

norms and shall be produced to the Committees of University as and

when they ask for.

Quality of Internal Question Papers and Assignment Questions

The quality of internal semester question papers and assignments are

assessed by the Module coordinators and classified as per level of

difficulty into three levels:

Level 1 – These are the questions that the students “must know” –These

questions constitute the fundamental concepts of a subject and it is

mandatory that every student knows these concepts. Further, these

questions are at the lower level of Blooms taxonomy like Remembering

and Understanding. Lack of these fundamental concepts would mean

that the student is not fit for passing this course.

Level 2 – These are the questions that the students “Need to Know” –

These questions test the skill of the student at a higher level of Blooms

Taxonomy like Applying and Analyzing, the student should be able to

apply the fundamental knowledge gained in a course to analyze a typical

problem and arrive at conclusions.

Level 3 – these are the questions that have the status of “Good to know”

– These questions test the highest skills levels of Blooms Taxonomy like

Evaluate and Create. A student would be considered to have achieved

proficiency in the subject if he/she is able to answer the questions in

Level 3 and is able to apply the concepts for finding engineering solutions.

The module coordinators regularly analyze the assignment and internal



papers and classify them into the above three levels and ensure that a

good balance is maintained for all the three levels. A recommended

distribution of marks at the three levels is as follows level 1 -60%, Level

2 -30%, Level 3 – 10%.

TIME TABLE



Details of All Theory & Lab Courses as per Course Structure

Course Title COMPUTER NETWORKS

Course Code C310

Regulation R-16 JNTUK

Course Structure Lectures Tutorials Practicals Credits

4 2 - 3

Course Coordinator Mr.A.Sandeep Kumar

Module Coordinator Mr. B. Rama Krishna

Course Coordinator

E-mail ID [email protected]

Course Coordinator

Availability yes

Pre-requisites

Courses Data Communication

Course Description:

Data communications, network architectures, communication protocols,

data link control, medium access control; introduction to local area

networks metropolitan area networks and wide area networks; introduction

to Internet, OSI, TCP/IP Reference models..

Overview of learning activities:

1. Lecture and Class Discussions.

2. Assignment work.

3. Tutorial/Quiz sessions

4. Power Point Presentations

Course Assessment Method:

Sessional Marks

Universit

y End-

Exam

Marks

Total

Marks

Descriptive + Quiz + Assignment

= 15 + 10 + 5 = 30 Marks

70 100 Mid Term

Max Marks-15

Online Quiz

Max Marks-10

Assignment

Max Marks-05

Mid-1 Mid-2 Quiz-

1

Quiz-

2

Assign-

1

Assign-2

15 15 10 10 5 5

COURSE OBJECTIVES

Understand state-of-the-art in network protocols, architectures, and

applications.

Process of networking research

Constraints and thought processes for networking research

Problem Formulation—Approach—Analysis—



COURSE OUTCOMES (COs)

Upon Completion of the course, the students will be able to have

Overview of learning resources:

Prescribed & Suggested Text Books

1. Tanenbaum and David J Wetherall, Computer Networks, 5th Edition,

Pearson Edu, 2010

2. Computer Networks: A Top Down Approach, Behrouz A. Forouzan,

FirouzMosharraf, McGraw Hill Education

Reference Books

1. Larry L. Peterson and Bruce S. Davie, “Computer Networks - A Systems

Approach” (5th ed), Morgan Kaufmann/ Elsevier, 2011

Overview of assessment: Internal Test. Quiz

Assignments. University Exams.

VERIFIED BY

Course Coordinator

Module Coordinator

Program Coordinator

HOD

MR.A.Sandeep Kumar

Mr.B.Rama Krishna

Mr.N.Md.Jubair Basha

Dr.K.Venkata Subba Reddy

SYLLABUS

UNIT – I:

Introduction: Network Topologies WAN, LAN, MAN. Reference models- The

OSI Reference Model- the TCP/IP Reference Model - A Comparison of the

OSI and TCP/IP Reference Models

UNIT – II:

Physical Layer – Fourier Analysis – Bandwidth Limited Signals – The

Maximum Data Rate of a Channel - Guided Transmission Media, Digital

Modulation and Multiplexing: Frequency Division Multiplexing, Time

CO1: Classify various types of network topologies, protocols &

Enumerate the layers of the OSI model and TCP/IP Model.

CO2: Explain about multiplexing.

CO3: Apply Error Detecting & Correcting methods.

CO4: Identify collision detection and apply avoidance methods. Describe

about various IEEE Standards

CO5: Discuss various types of routing and congestion control

algorithms.

CO6: Discuss about the client server communication.



Division Multiplexing, Code Division Multiplexing

Data Link Layer Design Issues, Error Detection and Correction,

Elementary Data Link

Protocols, Sliding Window Protocols

UNIT – III:

The Data Link Layer - Services Provided to the Network Layer – Framing –

Error Control – Flow Control, Error Detection and Correction – Error-

Correcting Codes – Error Detecting Codes, Elementary Data Link Protocols-

A Utopian Simplex Protocol-A Simplex Stop and Wait Protocol for an Error

free channel-A Simplex Stop and Wait Protocol for a Noisy Channel, Sliding

Window Protocols-A One Bit Sliding Window Protocol-A Protocol Using Go-

Back-NA Protocol Using Selective Repeat

UNIT – IV:

The Medium Access Control Sub layer-The Channel Allocation Problem-

Static Channel Allocation-Assumptions for Dynamic Channel Allocation,

Multiple Access Protocols-Aloha- Carrier Sense Multiple Access Protocols-

Collision-Free Protocols-Limited Contention Protocols- Wireless LAN

Protocols, Ethernet-Classic Ethernet Physical Layer-Classic Ethernet MAC

Sub layer Protocol-Ethernet Performance-Fast Ethernet Gigabit Ethernet-

10-Gigabit Ethernet- Retrospective on Ethernet, Wireless Lans-The 802.11

Architecture and Protocol Stack-The 802.11 Physical Layer-The802.11 MAC

Sublayer Protocol-The 805.11 Frame Structure-Services

UNIT – V:

Design Issues-The Network Layer Design Issues – Store and Forward Packet

Switching-Services Provided to the Transport layer- Implementation of

Connectionless Service-Implementation of Connection Oriented Service-

Comparison of Virtual Circuit and Datagram Networks, Routing Algorithms-

The Optimality principle-Shortest path Algorithm,

Congestion Control Algorithms- Approaches to Congestion Control-Traffic

Aware Routing-Admission Control-Traffic Throttling-Load Shedding.

UNIT – VI:

Transport Layer – The Internet Transport Protocols: Udp, the Internet

Transport Protocols: Tcp

Application Layer –The Domain Name System: The DNS Name Space,

Resource Records, Name Servers, Electronic Mail: Architecture and Services,

The User Agent, Message Formats, Message Transfer, Final

TEXT BOOKS:

1. Tanenbaum and David J Wetherall, Computer Networks, 5th Edition,

Pearson Edu, 2010

2. Computer Networks: A Top Down Approach, Behrouz A. Forouzan,

FirouzMosharraf, McGraw Hill Education

REFERENCE BOOKS:

1. Larry L. Peterson and Bruce S. Davie, “Computer Networks - A Systems

Approach” (5th ed), Morgan Kaufmann/ Elsevier, 2011



Mapping of COs with POs

LESSON PLAN

Prerequisite: Data Communication

Unit/T

opic

No.

Topic Name No of

Classes

Requir

ed I OSI overview: 2

1.1 TCP/IP and other networks models 1

1.2 Examples of Networks 1

1.3 Novell Networks 1

1.4 Arpanet 1

1.5 Internet 1

1.6 Network Topologies 1

1.7 WAN 1

1.8 LAN 1

1.9 MAN. 1

II Physical Layer – Fourier Analysis 1

2.1 Bandwidth Limited Signals – The Maximum Data

rate of channel

1

2.2 Guided Transmission Medi 1

2.3 , Digital Modulation 1

2.4 Multiplexing: frequency division multiplexing 1

Course Outcomes

PO

1

PO

2

PO

3

PO

4

PO

5

PO

6

PO

7

PO

8

PO

9

PO

1

0

PO

1

1

PO

1

2

PSO

1

PSO

2

PSO

3

CO1:Classify various

types of network

topologies, protocols

& Enumerate the

layers of the OSI

model and TCP/IP

Model.

3 2 3 3

CO2:Explain about

multiplexing. 3 1 3 3

CO3:Apply Error

Detecting &

Correcting methods.

2 2 3 3 1 2

CO4:Identify collision

detection and apply

avoidance methods.

Describe about

various IEEE

Standards

2 2 3 3 1 3 1

CO5:Discuss various

types of routing and

congestion control

algorithms.

3 3 2

CO6: Discuss about

the client server

communication.

3 3 3



2.5

2.6

wave length division multiplexing

multiplexing

1

2.6

Time division multiplexing 1

2.7 statistical time division multiplexing and cdm 1

III Data link layer 1

3.1 Data link layer Design issues, Framing 1

3.2 fixed size framing, variable size framing 1

3.3 flow control, error control, error

detection and correction, CRC,

1

3.4 Checksum: idea, one’s complement internet

checksum

1

3.5 services provided to Network

Layer,

1

3.6 simplex protocol, Simplex stop and wait 1

3.8 Sliding window protocol , One bit 1

3.9 Go back N & Selective repeat-Stop and wait

protocol

1

IV The Medium Access Control Sub layer 1

4.1 The Channel Allocation Problem-Static Channel

AllocationAssumptions for Dynamic Channel

Allocation

1

4.2 -Assumptions for Dynamic Channel Allocation 1

4.3 ALOHA, MAC addresses, Carrier sense multiple

access (CSMA)

1

4.4 CSMA with Collision Detection, CSMA with

Collision Avoidance

1

4.5 Controlled Access: Reservation, Polling, Token

Passing, Channelization

1

4.6 Limited Contention Protocols- Wireless LAN

Protocols,

1

4.7 Ethernet-Classic Ethernet Physical Layer-Classic

Ethernet MAC Sub layer Protocol 2

4.8 Ethernet Performance 1

4.9 Fast Ethernet, Gigabit Ethernet 2

4.10 10-Gigabit Ethernet- Retrospective on Ethernet 1

4.11 Wireless Lans-The 802.11 Architecture and

Protocol Stack 1

4.12 The 802.11 Physical Layer-The802.11 MAC

Sublayer Protocol

1

4.13 The 805.11 Frame Structure-Services 1

V Design Issues The Network Layer Design Issues

1

5.1 Store and Forward Packet Switching 1

5.2 Services Provided to the Transport layer 1

5.3 Implementation of Connectionless Service-

Implementation of Connection Oriented Service 2

5.4 Comparison of Virtual Circuit and Datagram

Networks

1

5.5 Routing Algorithms-The Optimality principle-

Shortest path Algorithm, 1

5.6 Congestion Control Algorithms 1

5.7 Approaches to Congestion Control 1

5.8 Traffic Aware Routing 1

5.9 Admission Control-Traffic Throttling 1

510 Load Shedding 1

VI Transport Layer – The Internet Transport

Protocols: Udp, 1

6.1 the Internet Transport Protocols: Tcp 1

6.2 Application Layer –The Domain Name System 1

6.3 The DNS Name Space, Resource Records, 2

6.4 Name Servers 1

6.5 Electronic Mail: Architecture and Services, 1

6.6 The User Agent, Message Formats, , Final

1

6.7 Message Transfer 1

Total 58



QUESTION BANK

Uni

t

No.

Sl.

No. Questions

Bloom’s

Taxonomy

level

Mapped

with CO

I

1. Explain in detail about the Novell

Network. 2

CO1

2. Discuss how Internet has

revolutionized many aspects of our

daily lives

1

3. Explain different Layers and their

functionalities in TCP/IP Model. 3

4. Discuss in detail about the LAN and

WAN. 5

5. Compare OSI Reference Model with

the TCP/IP Model. 4

6. Differentiate LAN, MAN and WAN

network topologies. 5

7. What are the different Layers in the

OSI Reference Model? Explain the

Functionalities of each Layer.

7

8. Give a brief notes of ARPA NET 4

II

1. Explain in detail about the

statistical time division multiplexing 3

CO2

2. Compare and contrast a circuit-

switched network and a packet-

switched network

5

3. Explain briefly about the

applications of FDM 2


synchronous time division

multiplexing.

4


Efficiency and Delay in Datagram

Networks.

2

6. What is Frequency Division

Multiplexing? Explain Multiplexing

process in Frequency Division

Multiplexing with a suitable

example.

7

7. What are the two phases required in

the Setup phase in Virtual Circuit?

Explain.

2

8. What is multiplexing? Explain the

basic format of multiplexed system. 2


Wavelength Division Multiplexing. 4

10. Discuss briefly about the multiple

slot allocation. 2

III 1 What are the services provided to 5 CO3



the Network Layer by Data Link

Layer? Explain.

2

Given 1101011011 data frame and

generator polynomial G(x) = x4 + x +

1. Derive the transmitted frame. [

8

3 Explain in detail about the Simplex

protocol for Noisy channel. 4

CO3

4.

Explain in detail about the sliding

window protocol using Selective

Repeat.

4

5. Give a brief note on the Multilink

Point to point protocol. 2

6. Explain briefly about one-bit sliding

window protocol. 3

7. Explain in detail about the point-to-

point protocol frame format. 2

8. What is the problem in Go-Back-N

protocol? How it can be solved. 5

9. Draw and explain HDLC frame

format. 2

IV

1. Describe in detail about the

Frequency Division Multiple Access. 3

CO4

2. Explain briefly about the shortest

path routing algorithm. 8,9

3. Explain how slotted aloha improves

the performance of pure aloha. 7

4. Discuss briefly about the token

passing. 4

5. What is Count to infinity problem?

Explain with suitable example. 2

6.

With a suitable example explain

Distance Vector Routing algorithm.

What is the serious drawback of

Distance Vector Routing algorithm?

Explain.

7

7. Write a short note on Fast Ethernet. 2

8. Describe in detail about the

Hierarchical routing. 8

V

1. Explain in detail about the Physical

layer in the Fast Ethernet. 2

CO5

2. Discuss briefly about the MAC

layers in the 802.11 standard. 2

3. Compare HDLC Frame with the LLC

and MAC frame formats. 1


addressing mechanism in 802.11. 2

5. What are the common Standard

Ethernet implementations? 4

6. Explain the fields in the 802.11

Frame Structure. 3



7. Explain in detail about the 802.3

MAC frame format and its fields. 3

8. What are the common Fast

Ethernet implementations? 2

VI

1. Explain in detail about the Client

and Server in World Wide Web. 4

CO6

2. Describe briefly about the HTTP

Operational Model. 3

3. Explain briefly about the

Architecture of WWW. 3

4. What are the different request types

available in HTTP? Explain. 2

5. Compare TCP and UDP 5

6. Explain about DNS 2

7.

Write a short note on E-Mail

architecture and Services 3

E-learning materials

NPTEL

1. http://nptel.ac.in/courses/106105084/

2. https://youtu.be/mhP0oYrUkJg?list=PLd3UqWTnYXOl89R8vSU8_EV

-MyHZPdeip

3. https://www.youtube.com/watch?v=68NuOrSerT8&list=PLd3UqWTnY

XOkofdmEC1VB42cX8ZUwNEB7

4.

Question-Papers html:

1. https://www.jntufastupdates.com/jntuk-b-tech-3-1-regular-supply-

exams-question-papers-collection/

Recommended books

1. R. Kent Dybvig, “The Scheme programming language”, Fourth

Edition, MIT Press, 2009.

2. Jeffrey D. Ullman, “Elements of ML programming”, Second Edition,

Prentice Hall, 1998.

3. Richard A. O'Keefe, “The craft of Prolog”, MIT Press, 2009.

4. W. F. Clocksin and C. S. Mellish, “Programming in Prolog: Using

the ISO Standard”, Fifth Edition, Springer, 2003

.

Prepared By

A. Sandeep Kumar, Asst. Prof.

Dept. of Computer Science and Engineering

http://nptel.ac.in/courses/106105084/

https://youtu.be/mhP0oYrUkJg?list=PLd3UqWTnYXOl89R8vSU8_EV-MyHZPdeip

https://youtu.be/mhP0oYrUkJg?list=PLd3UqWTnYXOl89R8vSU8_EV-MyHZPdeip

https://www.youtube.com/watch?v=68NuOrSerT8&list=PLd3UqWTnYXOkofdmEC1VB42cX8ZUwNEB7

https://www.youtube.com/watch?v=68NuOrSerT8&list=PLd3UqWTnYXOkofdmEC1VB42cX8ZUwNEB7

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Course Title DATA WAREHOUSING AND DATA MINING

Course Code C311



4 - - 4

Course Coordinator Dr.B.Tarakeswara Rao

Module Coordinator Mr. P.LAKSHMI KANTH

Course Coordinator

Mobile Number +91-9441045755

Course Coordinator


Course Coordinator

Availability

Wednesday: 10:00 Am to 12:30 PM

Friday: 11:00AM to 12:30 PM

Pre-Requisites of

the Course

Database Management Systems, SQL

COURSE DESCRIPTION:

Students should know about design issues of data warehousing, learn

various mining tools, able to identify the real time problems and able to

design solution using various mining tools, further take the R&D interest

and try to contribute some new methods to the area

OVERVIEW OF LEARNING ACTIVITIES:

1. Class Room Lecture.

2. Assignment work.

3. Quiz Sessions


COURSE ASSESSMENT METHOD:

Session Marks

University

End-Exam

Marks

Total Marks

Descriptive+Objective+Assignment

=15+10+5=30 Marks

70 100 Mid Term

Max Marks-15

Online Quiz

Max Marks-10

Assignment

Max Marks-05

Mid-1 Mid-2 Quiz-

1

Quiz-

2

Assign

-1

Assign

-2

15 15 10 10 5 5

COURSE OBJECTIVES:

The students will have a broad understanding of

mailto:[email protected]



1. Students will be enabled to understand and implement classical

models and algorithms in

data warehousing and data mining.

2. They will learn how to analyze the data, identify the problems, and

choose the relevant

models and algorithms to apply.

3. They will further be able to assess the strengths and weaknesses of

various methods and

algorithms and to analyze their behavior.

COURSE OUTCOMES:


CO 1: Understood the hierarchy of KDD fundamentals of data Mining

techniques.

CO 2: Understood preprocessing techniques.

CO 3: Gain the knowledge on classification.

CO 4: Discuss about various classifiers.

CO 5: Learnt different association rule mining algorithms.

CO 6: Gain the knowledge on cluster analysis techniques.

OVERVIEW OF LEARNING RESOURSES:

TEXT BOOKS:

1. Introduction to Data Mining: Pang-Ning Tan & Michael Steinbach, Vipin

Kumar, Pearson.

2. Data Mining concepts and Techniques, 3/e, Jiawei Han, Michel Kamber,

Elsevier.

REFERENCE BOOKS:

1. Data Mining Techniques and Applications: An Introduction, Hongbo Du,

Cengage Learning.

2. Data Mining : VikramPudi and P. Radha Krishna, Oxford.

3. Data Mining and Analysis - Fundamental Concepts and Algorithms;

Mohammed J. Zaki,

Wagner Meira, Jr, Oxford

4. Data Warehousing Data Mining & OLAP, Alex Berson, Stephen Smith,

TMH.

OVERVIEW OF ASSESSMENT:

Internal Test.

Quiz

Assignments.

University Exams.

VERIFIED BY

Course

Coordinator

Module

Coordinator

Program

Coordinator HOD

Dr.B.Tarakeswara

Rao

Mr. P.LAKSHMI

KANTH

Mr.N.Md.Jubair

Basha

Dr.K.Venkata

Subba Reddy

SYLLABUS

UNIT –I:



Introduction: Why Data Mining? What Is Data Mining? What Kinds of Data

Can Be Mined? What Kinds of Patterns Can Be Mined? Which Technologies

Are Used? Which Kinds of Applications Are Targeted? Major Issues in Data

Mining. Data Objects and Attribute Types, Basic Statistical Descriptions of

Data, Data Visualization, Measuring Data Similarity and Dissimilarity

UNIT –II:

Data Pre-processing: Data Preprocessing: An Overview, Data Cleaning,

Data Integration, Data Reduction, Data Transformation and Data

Discretization

UNIT –III:

Classification: Basic Concepts, General Approach to solving a classification

problem, Decision Tree Induction: Working of Decision Tree, building a

decision tree, methods for expressing an attribute test conditions, measures

for selecting the best split, Algorithm for decision tree induction.

UNIT –IV:

Classification: Alterative Techniques, Bayes’ Theorem, Naïve Bayesian

Classification, Bayesian Belief Networks

UNIT –V

Association Analysis: Basic Concepts and Algorithms: Problem

Definition, Frequent Item Set generation, Rule generation, compact

representation of frequent item sets, FP-Growth Algorithm. (Tan &Vipin)

UNIT –VI

Cluster Analysis: Basic Concepts and Algorithms: Overview: What Is

Cluster Analysis? Different Types of Clustering, Different Types of Clusters;

K-means: The Basic K-means Algorithm, K-means Additional Issues,

Bisecting K-means, Strengths and Weaknesses; Agglomerative Hierarchical

Clustering: Basic Agglomerative Hierarchical Clustering Algorithm DBSCAN:

Traditional Density Center-Based Approach, DBSCAN Algorithm, Strengths

and Weaknesses. (Tan &Vipin)

TEXT BOOKS:

1. Introduction to Data Mining: Pang-Ning Tan & Michael Steinbach, Vipin

Kumar, Pearson.

2. Data Mining concepts and Techniques, 3/e, Jiawei Han, Michel Kamber,

Elsevier.

REFERENCE BOOKS:

1. Data Mining Techniques and Applications: An Introduction, Hongbo Du,

Cengage Learning.

2. Data Mining : VikramPudi and P. Radha Krishna, Oxford.

3. Data Mining and Analysis - Fundamental Concepts and Algorithms;

Mohammed J. Zaki,

Wagner Meira, Jr, Oxford

4. Data Warehousing Data Mining & OLAP, Alex Berson, Stephen Smith,

TMH.

Mapping of COs with Pos



LESSON PLAN

S.NO UNIT NO Topics to be Covered No.of

Periods Total

1

UNIT-I

Introduction: Why Data Mining? 1

10

2 What Is Data Mining? 1

3 What Kinds of Data Can Be

Mined? 1

4 What Kinds of Patterns Can Be

Mined? 1

5 Which Technologies Are Used? 1

6 Which Kinds of Applications Are

Targeted? 1

7 Major Issues in Data Mining.

Data Objects and Attribute

Types

1

8 Basic Statistical Descriptions of

Data 1

9 Data Visualization 1

10 Measuring Data Similarity and

Dissimilarity 1

11

UNIT-II

Data Pre-processing: Data

Preprocessing: An Overview 1

10 12 Data Cleaning 2

13 Data Integration 2

14 Data Reduction 2

15 Data Transformation 2

Course Outcomes

PO

1

PO

2

PO

3

PO

4

PO

5

PO

6

PO

7

PO

8

PO

9

PO

1

0

PO

1

1

PO

1

2

PSO

1

PSO

2

PSO

3

CO1: Understood the

hierarchy of KDD

fundamentals of data

Mining techniques.

2 1 2

CO2: Understood

preprocessing

techniques.

1 1 1 2 1 1 1

CO3: Gain the

knowledge on

classification.

1 1 2

CO4: Discuss about

various classifiers. 1 2 3 1 2 1 1 3

CO5: Learnt different

association rule mining

algorithms.

1 1 3 1 2 1 1

CO6: Gain the

knowledge on cluster

analysis techniques.

1 2 3 1 2 1 1



16 Data Discretization 1

17

UNIT-III

Classification: Basic Concepts 1

10

18 General Approach to solving a

classification problem 1

19 Decision Tree Induction:

Working of Decision Tree 2

20 Building a decision tree 2

21 Methods for expressing an

attribute test conditions 1

22 Measures for selecting the best

split 2

23 Algorithm for decision tree

induction. 1

24

UNIT-IV

Classification: Alterative

Techniques 2

11

25 Bayes’ Theorem 3

26 Naïve Bayesian Classification 3

27 Bayesian Belief Networks 3

28

UNIT-V

Project Monitoring & Control

Resource Allocation 1

11

29 Creating a framework for

monitoring & control 1

30 Progress monitoring

1

31 Cost monitoring 1

32 Earned value Analysis

1

33 Defects Tracking 1

34

UNITVI

Cluster Analysis: Basic

Concepts and Algorithms:

Overview

1

14

35 What Is Cluster Analysis? 1

36 Different Types of Clustering, 2

37 Different Types of Clusters; 2

38 K-means: The Basic K-means

Algorithm, 2

39

K-means Additional Issues,

Bisecting K-means, Strengths

and Weaknesses;

2



40

Agglomerative Hierarchical

Clustering: Basic Agglomerative

Hierarchical Clustering

Algorithm

2

41

DBSCAN: Traditional Density

Center-Based Approach,

DBSCAN Algorithm, Strengths

and Weaknesses.

2

Total No. of Periods 66

QUESTION BANK

Uni

t

No.

Sl.

No. Questions

Bloom’s

Taxonomy

level

Mapped

with CO

I

1. What is data mining? Why is it

Importance 4

CO1

2. What kind of patterns can be

mined? 4

3. Explain data mining functionalities? 3

4. What are the various issues in data

mining? Explain each one in detail. 4

5. Which Kinds of Applications Are

Targeted? 3

6.

Explain Data Visualization,

Measuring Data Similarity and

Dissimilarity

4

II

1. List and describe the various types of

concept hierarchies? 8

CO2

2. Write about Data cleaning. 9

3. Write about Data transformation. 9

4. Write about Data Selection 9

5. Discuss various issues in Data

integration? 3

6. Explain the concept hierarchy generation

for categorical data? 3

III

1

What is Classification explain with

suitable example

2

CO3

2 Explain how to solve a Classification

problem. 2

3 Explain the methods for expressing

an attribute test conditions 8

4. Explain the measures for selecting

the best split 8

5. Briefly outline the major steps of decision

tree classification? 7

6. Discuss the major steps of decision tree

classification? 9



IV

1. Explain Naïve Bayesian classifier.

2

CO4

2. What is Bayesian Belief Networks?

Explain with an example. 3

3. What is Bayes theorem? Briefly explain

the concept with an example? 4

4. Briefly explain Classification

Alterative Techniques 7

V

1. Describe about Frequent Item Set

generation with an example. 9

CO5

2. Explain about apriori algorithm with an

example? 2

3. Write FP growth algorithm and explain

with an example. 4

4. Discuss compact representation of

frequent item sets. 9

5.

Briefly discuss about Association

Analysis: Basic Concepts and

Algorithms

7

6. Describe about Rule generation with an

example. 9

VI

1. Explain about k-means clustering 2

CO6

2.

Describe about Density based

Clustering? Describe DB Scan clustering

algorithm?

9

3. Explain about Agglomerative

Hierarchical Clustering 2

4. What Is Cluster Analysis? What are

the different Types of Clustering’s 2

5. What are issues in K-means? What

is Bisecting K-means 2

6.

Distinguish the strength and

weaknesses of different clustering

algorithms.

8

NPTEL

5. http://nptel.ac.in/courses/106102067/

6. http://nptel.ac.in/courses/106102067/25

7. http://www.nptel.ac.in/courses/106102067/40

Question-Papers html

2. https://www.jntufastupdates.com/jntuk-b-tech-3-1-regular-supply-

exams-question-papers-collection/

Prepared By

Dr. B. Tarakeswara Rao, Professor


Course Title Design and Analysis of Algorithms

http://nptel.ac.in/courses/106102067/

http://nptel.ac.in/courses/106102067/25

http://www.nptel.ac.in/courses/106102067/40





Course Code C 312



4 - - 4

Course Coordinator Dr.K.Venkata Subba Reddy

Module Coordinator Mr.Ch.Samsonu

Course Coordinator


Course Coordinator


Course Coordinator

Availability



Pre-Requisites of

the Course Data Structures

COURSE DESCRIPTION:

The primary objective of this course is to introduce the concept of

algorithm as a precise mathematical concept, and study how to design

algorithms, establish their correctness, study their efficiency and memory

needs. The course consists of a strong mathematical component in addition

to the design of various algorithms.



2. Assignment work.

3. Quiz Sessions



Session Marks

University

End-Exam

Marks

Total Marks


=15+10+5=30 Marks

70 100 Mid Term

Max Marks-15

Online Quiz

Max Marks-10

Assignment

Max Marks-05

Mid-1 Mid-2 Quiz-

1

Quiz-

2

Assign

-1

Assign

-2

15 15 10 10 5 5

COURSE OBJECTIVES:

Upon completion of this course, students will be able to do the following:



Analyze the asymptotic performance of algorithms.

Write rigorous correctness proofs for algorithms.

Demonstrate a familiarity with major algorithms and data structures.

Apply important algorithmic design paradigms and methods of

analysis.

Synthesize efficient algorithms in common engineering design

situations

COURSE OUTCOMES:


TEXT BOOKS:

1. Software Project Management, Bob Hughes & Mike Cotterell, TATA

Mcgraw-Hill

2. Software Project Management, Walker Royce: Pearson Education,

2005.

3. Software Project Management in practice, Pankaj Jalote, Pearson.

REFERENCE BOOKS:

1. Software Project Management, Joel Henry, Pearson Education.


Internal Test.

Quiz

Assignments.

University Exams.

VERIFIED BY

Course

Coordinator

Module

Coordinator

Program

Coordinator HOD

Dr.K.Venkata

Subba Reddy

Mr.Ch.Samsonu Mr.N.Md.Jubair

Basha

Dr.K.Venkata

Subba Reddy

SYLLABUS

CO-1 Analyze the running time and space complexity of algorithms using

asymptotic analysis..

CO-2 Apply divide and conquer to binary search, quick sort, merge sort

CO-3 Describe and apply greedy method to knapsack problem, job sequencing

with deadlines, prims, kruskal algorithms.

CO-4 Explain and Apply dynamic programming to optimal binary search

trees,0/1 knapsack problem, All pairs shortest path problem etc.

CO-5 Discuss and Apply Backtracking N-queen problem, sum of subsets problem,

graph coloring etc.

CO-6 Explain and Apply branch and bound to Travelling sales person problem,

0/1 knapsack problem



UNIT-I:

Introduction: What is an Algorithm, Algorithm Specification, Pseudocode

Conventions.Recursive Algorithm, Performance Analysis, Space Complexity,

Time Complexity, Amortized,Complexity, Amortized Complexity, Asymptotic

Notation, Practical Complexities, Performance Measurement.

UNIT-II:

Dived and Conquer: General Method, Defective Chessboard, Binary Search,

Finding the Maximum and Minimum, Merge Sort, Quick Sort, Performance

Measurement, Randomized Sorting Algorithms.

UNIT-III:

The Greedy Method: The General Method, Knapsack Problem, Job

Sequencing with Deadlines, Minimum-cost Spanning Trees, Prim’s

Algorithm, Kruskal’s Algorithms, An Optimal Randomized Algorithm,

Optimal Merge Patterns, Single Source Shortest Paths.

UNIT-IV:

Dynamic Programming: All - Pairs Shortest Paths, Single – Source Shortest

paths General Weights, String Edition, 0/1 Knapsack, Reliability Design,

UNIT-V:

Backtracking: The General Method, The 8-Queens Problem, Sum of

Subsets, Graph Coloring, Hamiltonian Cycles.

UNIT-VI:

Branch and Bound: The Method, Least cost (LC) Search, The 15-Puzzle: an

Example, Control Abstraction for LC-Search, Bounding, FIFO Branch-and-

Bound, LC Branch and Bound, 0/1 Knapsack Problem, LC Branch-and

Bound Solution, FIFO Branch-and-Bound Solution, Traveling Salesperson.

TEXT BOOKS:

1. Fundamentals of computer algorithms E. Horowitz S. Sahni,

University Press

2. Introduction to Algorithms Thomas H. Cormen, PHI Learning

REFERENCE BOOKS:

1. The Design and Analysis of Computer Algorithms, Alfred V. Aho, John

E. Hopcroft, Jeffrey D.Ullman

2. Algorithm Design, Jon Kleinberg, Pearson


Course

Outcomes

PO

-1

PO

-2

PO

-3

PO

-4

PO

-5

PO

-6

PO

-7

PO

-8

PO

-9

PO

-10

PO

-11

PO

12

PS0-1

PS0-2

PS0-3



LESSION PLAN

Unit/

TopicNo

.

Topic Name

No of

Classes

Required

Total

I UNIT-I

12

1.1 What is an Algorithm, Algorithm

Specification, 2

1.2 Pseudo code Conventions 1

1.3 Recursive Algorithm, Performance Analysis, 2

1.4 Space Complexity, Time Complexity, 2

1.5 Amortized Complexity, 1

1.6 Asymptotic Notation, 1

1.7 Practical Complexities, 1

1.8 Performance Measurement. 2

CO1: Analyze the

running time and space

complexity of algorithms

using asymptotic

analysis..

2 2 2 2 1 1

CO2:Apply divide and

conquer to binary

search, quick sort,

merge sort

2 1 2 2 3 1

CO3: Describe and

apply greedy method to

knapsack problem, job

sequencing with

deadlines, prims,

kruskal algorithms.

3 3 3 2 2 2

CO4: Explain and Apply

dynamic programming

to optimal binary search

trees,0/1 knapsack

problem, All pairs

shortest path problem

etc.

2 3 3 2 2 2

CO5: Discuss and Apply

Backtracking n-queen

problem, sum of subsets

problem, graph coloring

etc.

2 3 3 2 2 2

CO6: Explain and Apply

branch and bound to

Travelling sales person

problem, 0/1 knapsack

problem

2 3 3 2 2 2



II UNIT-II

12

2.1 Dived and Conquer: General Method, 1

2.2 Defective Chessboard, 1

2.3 Binary Search, 1

2.4 Finding the Maximum and Minimum, 2

2.5 Merge Sort, 2

2.6 Quick Sort, 1

2.7 Performance Measurement, 2

2.8 Randomized Sorting Algorithms. 2

III UNIT-III

10

3.1 The Greedy Method: The General Method 1

3.2 Knapsack Problem 1

3.3 Job Sequencing with Deadlines

2

3.4 Minimum-cost Spanning Trees

1

3.5 Prim’s Algorithm, Kruskal’s Algorithms

2

3.6 An Optimal Randomized Algorithm. 1

3.7 Optimal Merge Patterns 1

3.8 Single Source Shortest Paths 1

IV UNIT-IV

10

4.1 Dynamic Programming: All - Pairs Shortest

Paths

2

4.2 Single – Source Shortest paths General

Weights 2

4.3 String Edition 2

4.4 0/1 Knapsack, 2

4.5 Reliability Design 2

V UNIT-V

10

5.1 Backtracking: The General Method

2

5.2 The 8-Queens Problem

2

5.3 Sum of Subsets

2

5.4 Graph Coloring

2

5.5 Hamiltonian Cycles 2

VI UNIT-VI

10

6.1 Branch and Bound: The Method

1

6.2 Least cost (LC) Search,

1

6.3 The 15-Puzzle: an Example, Control

Abstraction for LC-Search 1



6.4 Bounding, FIFO Branch-and-Bound,

1

6.5 LC Branch and Bound,

1

6.6 0/1Knapsack Problem,

1

6.7 LC Branch-and Bound Solution,

1

6.8 FIFO Branch-and-Bound Solution,

1

6.9 Traveling Salesperson. 1

6.10 Random Forests 1

Total No. of hours:

64

QUESTION BANK

Unit

No. QUESTION

Blooms

Taxonomy

Level

Mapped

with CO

I

Discuss various the asymptotic notations used

for best case average case and worst case

analysis of algorithms

4

CO-1

Differentiate between priori analysis and

posteriori analysis

2

Discuss binary search algorithm and analyze its

time complexity

4

Explain probabilistic analysis 6

Discuss amortized analysis 4

II

Apply sorting techniques to the list of numbers

using merge sort: 78, 32, 42, 62, 98, 12, 34, 83

6

CO-2

Differentiate divide and conquer and greedy

method

2

Explain quick sort compute the time complexity 6

Explain finding the maximum and minimum

with an example?

6

Write short note on randomized sorting algorithm 2

III

Explain in detail job sequencing with deadlines

problem with example

4

CO-3

Explain single source shortest path problem with

example

4

Explain optimal merge patterns with example 4

Write an algorithm of minimum cost spanning

tree

4

Explain prims algorithm with example 4

Explain kruskal algorithm with example 4



Explain the concept of single source shortest

path with example.

4

IV

Explain 0/1 knapsack problem with example 4

CO-4

Explain all pairs shortest path problem with

example

4

Describe the travelling salesman problem and

discuss how to solve it using dynamic

programming?

6

Write short note on reliability design 2

V

Write an algorithm for N-queens problem using

backtracking

2

CO-5

Describe graph coloring problem and write an

algorithm for m-coloring problem

4

Write an algorithm for Hamiltonian cycle with an

example

2

Explain sum of subset problem with an example 4

VI

Explain properties of LC search 4 CO-6

Describe control abstraction for LC Search 6

Explain principle of FIFO branch and bound 4

Explain principle of LIFO branch and bound 4

Solve travelling sales person problem using

branch and bound

8

Explain TSP using branch and bound method

with example

4

FREELY ACCESSIBLE INTERNET SITES:

NPTEL

https://nptel.ac.in/courses/106101060/

RECOMMENDED BOOKS

1. Fundamentals of computer algorithms E. Horowitz S. Sahni, University

Press

Prepared by

Dr.K.Venkata Subba Reddy, HOD-CSE Dept, KHIT

https://nptel.ac.in/courses/106101060/



Course Title SOFTWARE TESTING METHODOLOGIES

Course Code



4 - - 3

Course Coordinator Mr. G. Mahesh Reddy

Module Coordinator Dr. B. Tarakeswara Rao

Course Coordinator


Course Coordinator


Course Coordinator

Availability



Pre-Requisites of

the Course

Software Engineering.

Software Testing Methodologies

COURSE DESCRIPTION:

The primary objective of this course is Students will study

fundamental concepts in software testing, including software testing

objectives, process, criteria, strategies, and methods beside discussing

various software testing issues and solutions in software unit test,

integration, regression, and system testing.



2. Assignment work.

3. Quiz Sessions



Session Marks

University

End-Exam

Marks

Total Marks


=15+10+5=30 Marks

70 100 Mid Term

Max Marks-15

Online Quiz

Max Marks-10

Assignment

Max Marks-05

Mid-1 Mid-2 Quiz-

1

Quiz-

2

Assign

-1

Assign

-2

15 15 10 10 5 5

COURSE OBJECTIVES:

Fundamentals for various testing methodologies.

Describe the principles and procedures for designing test cases.

Provide supports to debugging methods.

Acts as the reference for software testing techniques and strategies.



COURSE OUTCOMES:

CO -1: Know the basic concepts of software testing and its

essentials.

CO -2: Performing functional testing using transaction flow and

control flow graphs.

CO -3: Able to test a domain or an application and identifying the

nice and ugly domains.

CO -4: Able to make a path expression and reduce them very well

when needed.

CO -5: Follow an effective, step-by-step process for identifying

needed areas of testing, designing test conditions and building

and executing test cases.

CO -6: Apply appropriate software testing tools, techniques and

methods for even more effective systems during both the test

planning and test execution phases of a software development

project.


TEXT BOOKS:

2. Software testing techniques – Boris Beizer, Dreamtech, second edition.

3. Software Testing- Yogesh Singh, Camebridge

REFERENCE BOOKS:

1. The Craft of software testing - Brian Marick, Pearson Education.

2. Software Testing, 3rd edition, P.C. Jorgensen, Aurbach Publications

(Dist.by SPD).

3. Software Testing, N.Chauhan, Oxford University Press.

4. Introduction to Software Testing, P.Ammann & J.Offutt, Cambridge

Univ.Press.

5. Effective methods of Software Testing, Perry, John Wiley, 2nd Edition,

1999.

6. Software Testing Concepts and Tools, P.NageswaraRao, dreamtech

Press

7. Win Runner in simple steps by Hakeem Shittu, 2007Genixpress.

8. Foundations of Software Testing, D.Graham & Others, Cengage

Learning.


Internal Test.

Quiz

Assignments.

University Exams.

VERIFIED BY

Course

Coordinator

Module

Coordinator

Program

Coordinator HOD

Mr. G. Mahesh

Reddy

Dr.B.Tarakeswara

Rao

Mr.N.Md.Jubair

Basha

Dr.K.Venkata

Subba Reddy



SYLLABUS

OBJECTIVES:

Fundamentals for various testing methodologies.

Describe the principles and procedures for designing test cases.

Provide supports to debugging methods.

Acts as the reference for software testing techniques and strategies.

UNIT-I

Introduction: Purpose of Testing, Dichotomies, Model for Testing,

Consequences of Bugs, Taxonomy of Bugs.

Flow graphs and Path testing: Basics Concepts of Path Testing, Predicates,

Path Predicates and Achievable Paths, Path Sensitizing, Path

Instrumentation, Application of Path Testing.

UNIT-II

Transaction Flow Testing: Transaction Flows, Transaction Flow Testing

Techniques.

Dataflow testing: Basics of Dataflow Testing, Strategies in Dataflow Testing,

Application of Dataflow Testing.

UNIT-III:

Domain Testing: Domains and Paths, Nice & Ugly Domains, Domain

testing, Domains and Interfaces Testing, Domain and Interface Testing,

Domains and Testability.

Paths, Path products and Regular expressions: Path Products & Path

Expression, Reduction Procedure, Applications, Regular Expressions & Flow

Anomaly Detection.

UNIT-IV:

Syntax Testing: Why, What and How, A Grammar for formats, Test Case

Generation, Implementation and Application and Testability Tips.

Logic Based Testing: Overview, Decision Tables, Path Expressions, KV

Charts, and Specifications.

UNIT – V:

State, State Graphs and Transition Testing: State Graphs, Good & Bad

State Graphs, State Testing and Testability Tips.

Graph Matrices and Application: Motivational overview, matrix of graph,

relations, power of a matrix, node reduction algorithm.

UNIT -VI:

Software Testing Tools: Introduction to Testing, Automated Testing,

Concepts of Test Automation, Introduction to list of tools like Win runner,

Load Runner, Jmeter, About Win Runner ,Using Win runner, Mapping the

GUI, Recording Test, Working with Test, Enhancing Test, Checkpoints, Test

Script Language, Putting it all together, Running and Debugging Tests,

Analyzing Results, Batch Tests, Rapid Test Script Wizard.



OUTCOMES:

1. To study the fundamental concepts of software testing which

includes objectives, process, criteria, strategies, and methods.

2. To discuss various software testing types and levels of testing like

black and white box testing along with levels unit test, integration,

regression, and system testing.

3. Able to support in generating test cases and test suites.

4. It also helps to learn the types of bugs, testing levels with which

the student can very well identify a bug and correct as when it

happens.

5. It provides knowledge on transaction flow testing and data flow

testing techniques so that the flow of the program is tested as well.

6. To learn the domain testing, path testing and logic based testing to

explore the testing process easier.

7. To know the concepts of state graphs, graph matrixes and

transition testing along with testability tips to enhance the testing

process in different way.

8. To expose the advanced software testing topics, such as object-

oriented software testing methods, and component-based software

testing issues, challenges, and solutions.

9. To gain software testing experience by applying software testing

knowledge and methods to practice-oriented software testing

projects.

10. Apply tools to resolve the problems in Real time environment

TEXT BOOKS:

1. Software testing techniques – Boris Beizer, Dreamtech, second edition.

2. Software Testing- Yogesh Singh, Camebridge

REFERENCE BOOKS:


2. Software Testing, 3rd edition, P.C. Jorgensen, Aurbach Publications

(Dist.by SPD).

3. Software Testing, N.Chauhan, Oxford University Press.

4. Introduction to Software Testing, P.Ammann & J.Offutt, Cambridge

Univ.Press.

5. Effective methods of Software Testing, Perry, John Wiley, 2nd Edition,

1999.

6. Software Testing Concepts and Tools, P.NageswaraRao, dreamtech

Press


8. Foundations of Software Testing, D.Graham & Others, Cengage

Learning.




LESSON PLAN

S.NO UNIT NO Topics to be Covered No.of

Periods Total

1

UNIT-I

Introduction: Purpose of

Testing 1

12

2 Dichotomies 1

3 Model for Testing 1

4 Consequences of bugs 1

5 Taxonomy of Bugs 3

6 Flow graphs and Path testing: 1

Course Outcomes

PO

-1

PO

-2

PO

-3

PO

-4

PO

-5

PO

-6

PO

-7

PO

-8

PO

-9

PO

-10

PO

-11

PO

-12

PS0-1

PS0-2

PS0-3

CO1: Know the basic concepts of

software testing and its essentials 3 2

3

CO2: Performing functional testing

using transaction flow and control

flow graphs.

3 3 3 3 3

CO3: Able to test a domain or an

application and identifying the nice

and ugly domains.

3 3 3 2 2 3

CO4: Able to make a path expression

and reduce them very well when

needed.

3 2 2 3

CO5: Follow an effective, step-by-step

process for identifying needed areas

of testing, designing test conditions

and building and executing test

cases.

2 2 2 2 1

CO6: Apply appropriate software

testing tools, techniques and

methods for even more effective

systems during both the test

planning and test execution phases

of a software development project.

2 2 2 3



Basics Concepts of Path Testing

7 Predicates 1

8 Path Predicates and Achievable

Paths 1

9 Path Sensitizing, Path

Instrumentation 1

10 Application of Path Testing 1

11

UNIT-II

Transaction Flow Testing:

Transaction Flows 1

09 12 Transaction Flow Testing

Techniques 2

13 Dataflow testing: Basics of

Dataflow Testing 1

14 Strategies in Dataflow Testing 4

15 Application of Dataflow Testing 1

16

UNIT-III

Domains and Paths 1

15

17 Nice & Ugly Domains 2

18 Domain testing 1

19 Domains and Interfaces Testing 1

20 Domains and Testability 1

21

Paths, Path products and

Regular expressions: Path

Products & Path Expression

2

22 Reduction Procedure 2

23 Applications 4

24 Regular Expressions & Flow

Anomaly Detection 1

25

UNIT-IV

Syntax Testing:Why, What and

How 1

09

26 A Grammar for formats 1

27 Test Case Generation 1

28 Implementation and Application

and Testability Tips 1

29 Logic Based Testing:Overview,

Decision Tables 2

30 Path Expressions 1

31 KV Charts and Specifications 2

32

UNIT-V

State, State Graphs and

Transition Testing : State

Graphs

1

10 33 Good & Bad State Graphs 1

34 State Testing, and Testability

Tips 1



35

Graph Matrices and

Application: Motivational

overview

1

36 Matrix of graph 1

37 Relations 1

38 Power of a matrix 1

39 Node reduction algorithm 3

40

UNITVI

Introduction to Testing,

Automated Testing, Concepts of

Test Automation

1

09

41

Introduction to list of tools like

Win runner, Load Runner,

Jmeter, About Win Runner,

Using Win runner

2

42

Mapping the GUI, Recording

Test, Working with Test,

Enhancing Test, Checkpoints

2

43 Test Script Language, Putting it

all together 1

44 Running and Debugging Tests,

Analyzing Results 2

45 Batch Tests, Rapid Test Script

Wizard 1

Total No. of Periods 64

QUESTION BANK

Unit

No. S.No. Questions

Bloom’s

Taxonomy

level

Mapped

with CO

I

UNIT-I

CO1

1 Explain goals for testing and model

for testing in software testing? 1

2

Demonstrate nightmare list and

when to stop testing in the

consequences of bugs?

2

3 State and explain various

dichotomies in software testing? 2

4

Demonstrate structural bugs, coding

bugs, data bugs and system bugs

and discuss methods to catch these

bugs? And Discuss the classes of

bugs in the taxonomy of bugs?

2

5

Demonstrate path statement, path

testing criteria and explain branch

testing?

2

6 Define loops and explain different 1



types of loops and Explain nested

loops

7

Explain path nodes and links and

explain the effectiveness and

limitations of path testing?

2

8

Explain multi entry and multi exit

routines and describe path predicate

expression?

1

9

Define statement coverage (C1) and

branch coverage (C2)? Explain with

an example methods to select enough

paths to achieve C1+C2?

1

10 Write a short note on loop testing? 3

II

UNIT-II

CO2

1

Demonstrate the transaction flows?

Discuss their complications? And

Discuss about static and dynamic

anomaly detection?

2

2

Define the terms

i. Biosis

ii. Mitosis

iii. Absorption

iv. Conjugation

1

3

State and explain various transaction

flow junctions and mergers? And

Explain the terms inspections,

reviews and walkthroughs?

1

4 Explain Data Flow Anomaly State

Graph with example? 2

5 Compare static versus dynamic

anomaly detection? 1

6 Demonstrate du-path and define all

du-paths? 2

7

Explain the following terms in detail.

i. Definition clear path segment ii.

Loop free path segment iii. Simple

path segment

2

UNIT-III

CO3

III

1 Explain domain closure and define

domain dimensionality? 1

2

Define the bug assumptions for

domain testing. And Explain about

simple domain boundaries and

compound predicates?

2

3

Demonstrate meaning of domain

testing? Discuss various Applications

of domain Testing

2

4 List the restrictions of domain testing

and explain? 2

5 Explain different properties under 2



nice domains?

6 Define ugly domains? 1

7

Explain about

Interior Point

Boundary Point

Extreme Point

on-point

off-point

1

8

Explain the testing strategy for One

and Two-dimensional domains? And

Discuss the purpose of domain

testing?

2

9

Discuss the limitations and solutions

in node by node reduction

procedure?

1

10 Explain regular Expressions and

Flow anomaly detection? 1

11

Discuss about

PATH PRODUCTS

PATH EXPRESSION

PATH SUMS

LOOPS

1

12

Explain the methods of regular

expressions and flow anomaly

detection?

2

IV

UNIT-IV

CO4

1 Define decision table and how is a

decision table useful in testing? 1

2 Discuss about test case generation,

and its implementation? 1

3 Explain about KV Chart and its

specifications? 2

4 Explain about knowledge based

systems in logic based testing? 2

5

Write short notes on:

Distributive laws

Absorption Rule

Loops

Identity Elements

2

6 Explain BNF Operations? 2

7 Write short note on Boolean algebra? 3

V

UNIT-V

CO5

1 Write an algorithm for node

reduction? 3

2 Discuss the matrix operations in tool

building? 1

3 Define a state? Explain good and bad

state graphs? 1

4 Define graph matrices and evaluate 3



graph matrix with pictorial Graph

explains the basic algorithms? And

Demonstrate maximum element and

minimum element of a graph?

5

Discuss the linked list

representation? And Demonstrate the

matrix operations in tool building?

1

6

Demonstrate the operations does a

toolkit consist for the representation

of graphs Illustrate about matrix

powers and products

2

7

Explain the terms

1. No of states

2. Impossible states

3. Equivalent States

2

VI

UNIT-VI

CO6

1 Explain in detail about Automated

Testing? 1

2

Discuss expected outcomes in test

process? Describe any two of

outcomes?

1

3 Discuss about Batch Tests and Rapid

Test Script Wizard? 1

4

Explain about Win Runner? How to

perform

a. Mapping the GUI

b. Recording, Working,

Enhancing Test

c. Checkpoints

d. Running and Debugging Tests,

Analyzing Results

e. Batch Tests, Rapid Test Script

Wizard

2

FREELY ACCESSIBLE INTERNET SITES:

1. https://www.youtube.com/watch?v=gPE9emPFrwo

2.https://www.youtube.com/watch?v=g6zGd6ycktY&list=PLpeXMPUIySMI6

mt7xIMXdEPk88Ywoay0f

NPTEL

https://nptel.ac.in/courses/106/105/106105150/

RECOMMENDED BOOKS



Prepared by

Mr. G. Mahesh Reddy, CSE Dept, KHIT

https://www.youtube.com/watch?v=gPE9emPFrwo

https://www.youtube.com/watch?v=g6zGd6ycktY&list=PLpeXMPUIySMI6mt7xIMXdEPk88Ywoay0f

https://www.youtube.com/watch?v=g6zGd6ycktY&list=PLpeXMPUIySMI6mt7xIMXdEPk88Ywoay0f

https://nptel.ac.in/courses/106/105/106105150/



Course Title COMPUTER ORGANIZATION

Course Code C314



4 - - 3

Course Coordinator Mr. N Md Jubair Basha

Module Coordinator Mr. B Satyanaraya Reddy

Course Coordinator


Course Coordinator

Availability

Pre-requisites

Courses

Mathematical Foundations in Computer Science(C202),

Data Structures through C++(C205), Advanced Data

Structures(C211)

Course Description: This course relates to the study of intelligence in

artificial artifacts. Students will be able to explore the key paradigms of AI,

the core techniques and technologies used and algorithms for some of these

techniques. The fundamentals learnt in this course forms the basis for

advanced courses such as Machine Learning(C411) and Artificial Neural

Networks(412). The applications of AI will be found in wide range of fields

such as Medical diagnosis, Stock trading, Remote sensing, Robot control

etc.

Course Outcomes:

On successful completion of this course, students will be able to

1. Understand the Foundations and applications of AI.

2. Identify the Problem solving, Problem reduction and game playing

techniques.

3. Explain the Logic concepts like proportional logic, predicate logic.

4. Discuss knowledge representation using semantic networks.

5. Describe the Expert system and applications.

6. Understand probability theory, Fuzzy sets and fuzzy logic concepts.

Overview of learning activities:

5. Lecture and Class Discussions.

6. Assignment work.

7. Tutorial/Quiz sessions


Course Assessment Method:

Sessional Marks

Universit

y End-

Exam

Marks

Total

Marks

Descriptive + Quiz + Assignment

= 15 + 10 + 5 = 30 Marks

70 100 Mid Term

Max Marks-15

Online Quiz

Max Marks-10

Assignment

Max Marks-05

Mid-1 Mid-2 Quiz-

1

Quiz-

2

Assign-

1

Assign-2

15 15 10 10 5 5



COURSE OBJECTIVES

This course provides a strong foundation of fundamental concepts in

Artificial Intelligence It also introduces a basic exposition to the goals and

methods of problems related to Artificial Intelligence.This course enable the

students to apply these techniques in applications which involve reasoning,

learning, knowledge representation, expert system and fuzzy logic.

COURSE OUTCOMES (COs)


CO1: Understand the Foundations and applications of AI.

CO2: Identify the Problem solving, Problem reduction and game playing

techniques.

CO3: Explain the Logic concepts like proportional logic, predicate logic.

CO4: Discuss knowledge representation using semantic networks.

CO5: Describe the Expert system and applications.

CO6: Understand probability theory, Fuzzy sets and fuzzy logic concepts.

Overview of learning resources: Prescribed & Suggested Text Books

1. Artificial intelligence, structures and Strategies for Complex

problem solving, -George F Lugar, 5thed, PEA

2. Introduction to Artificial Intelligence, Ertel, Wolf Gang, Springer

3. Artificial Intelligence, A new Synthesis, Nils J Nilsson, Elsevier “. Reference Books

1. Artificial intelligence, structures and Strategies for Complex

problem solving, -George F Lugar, 5thed, PEA


3. Artificial Intelligence, A new Synthesis, Nils J Nilsson, Elsevier .

Overview of assessment: Internal Test. Quiz

Assignments. University Exams.

VERIFIED BY

Course

Coordinator

Module

Coordinator

Program

Coordinator HOD

Mr.N.Md.Jubair

Basha

Mr.B.Satynarayana

Reddy

Mr.N.Md.Jubair

Basha

Dr.K.Venkata

Subba Reddy

SYLLABUS

UNIT-I: Introduction to artificial intelligence: Introduction ,history,

intelligent systems, foundations of AI, applications, tic-tac-tie game playing,

development of ai languages, current trends in AI

UNIT-II: Problem solving: state-space search and control strategies

:Introduction, general problem solving, characteristics of problem,

exhaustive searches, heuristic search techniques, iterativedeepening a*,

constraint satisfaction Problem reduction and game playing: Introduction,

problem reduction, game playing, alphabeta pruning, two-player perfect

information games

UNIT-III: Logic concepts: Introduction, propositional calculus, proportional

logic, natural deduction system, axiomatic system, semantic tableau system



in proportional logic, resolution refutation in proportional logic, predicate

logic

UNIT-IV: Knowledge representation: Introduction, approaches to knowledge

representation, knowledge representation using semantic network, extended

semantic networks for KR, knowledge representation using frames advanced

knowledge representation techniques: Introduction, conceptual dependency

theory, script structure, cyc theory, case grammars, semantic web

UNIT-V: Expert system and applications: Introduction phases in building

expert systems, expert system versus traditional systems, rule-based expert

systems blackboard systems truth maintenance systems, application of

expert systems, list of shells and tools

UNIT-VI: Uncertainty measure: probability theory: Introduction, probability

theory, Bayesian belief networks, certainty factor theory, dempster-shafer

theory Fuzzy sets and fuzzy logic: Introduction, fuzzy sets, fuzzy set

operations, types of membership functions, multi valued logic, fuzzy logic,

linguistic variables and hedges, fuzzy propositions, inference rules for fuzzy

propositions, fuzzy systems.

TEXT BOOKS:

1. Artificial Intelligence- Saroj Kaushik, CENGAGE Learning,

2. Artificial intelligence, A modern Approach , 2nded, Stuart Russel, Peter

Norvig, PEA

3. Artificial Intelligence- Rich, Kevin Knight, Shiv Shankar B Nair, 3rded,

TMH

4. Introduction to Artificial Intelligence, Patterson, PHI

REFERENCE BOOKS:

1. Atificial intelligence, structures and Strategies for Complex problem

solving, -George F Lugar, 5thed, PEA



Mapping of COs with Pos

Course Outcomes

PO

1

PO

2

PO

3

PO

4

PO

5

PO

6

PO

7

PO

8

PO

9

PO

1

0

PO

1

1

PO

1

2

PSO

1

PSO

2

PSO

3

CO1: Understand the

Foundations and

applications of AI.

2 3 1 - - - - - - - - 1 - 1 -

CO2: Identify the

Problem solving,

Problem reduction and

game playing

techniques.

2 3 1 3 - - - - - - - 1 - 1 -

CO3: Explain the Logic

concepts like

proportional logic,

predicate logic.

2 3 1 - - - - - - - - 1 3 1 -



LESSON PLAN

UNIT

No. TOPIC NAME

No. of

classes

required

Total

UNIT-I

I

Course Objective, Course Outcome,

Pre-requisites [Reqd. Optional], Skill

Set Acquired, Real Time Applications,

Syllabus Overview, Suggested & Ref.

Books

Introduction: History Of AI,

1

8

Intelligent Systems 1

Foundations of AI 1

Subareas of AI, Applications 1

Tic-Tac-Toe Game Playing 2

Development of AI languages 1

Current trends in AI 1

UNIT-II

II

Problem Solving – State-Space

Search and Control strategies:

Introduction

1

14

General Problem Solving,

Characteristics of Problem

1

Exhaustive Searches, Heuristic

Search Techniques,

2

Iterative - Deepening A* 1

Constraint Satisfaction 2

Problem reduction and game playing:

Introduction,

1

problem reduction, game playing 2

Alpha beta pruning 2

two-player perfect information games

2

UNIT-III

CO4: Discuss

knowledge

representation using

semantic networks.

2 3 1 2 - 1 - - - - - 1 3 1 -

CO5: Describe the

Expert system and

applications.

2 3 1 - 1 - - - - - - 1 2 1 -

CO6: Understand

probability theory,

Fuzzy sets and fuzzy

logic concepts.

2 3 1 - - - - - - - - 1 2 1 -

AVG 2 3 1 2 1 1 - - - - - 1 2.

5 1 -



III

Logic Concepts and Logic

Programming: Introduction,

Propositional Calculus, Propositional

Logic

2

8

Natural Deduction System 2

Axiomatic System, Semantic Tableau

System in Propositional Logic

2

Resolution Refutation in

Propositional Logic

2

Predicate Logic, Logic Programming 2

UNIT-IV

IV

Knowledge Representation :

Introduction, Approaches to

Knowledge Representation

Knowledge Representation using

Semantic Network

2

12 Extended Semantic Networks for KR,

Knowledge representation using

Frames

2

advanced knowledge representation

techniques: Introduction

1

conceptual dependency theory, 1

script structure 1

cyc theory 1

case grammars 2

semantic web 2

UNIT-V

V

Expert System and Applications :

Introduction,

1

10

Phases in Building 1

Expert Systems 1

Expert System Architecture 2

Expert Systems vs Traditional

Systems

1

Truth Maintenance Systems, 1

Application of Expert Systems 1

List of Shells and Tools 2

UNIT-VI

VI

Uncertainty Measure- Probability

Theory : Introduction

1

14

Probability Theory 2

Bayesian Belief Networks 1

Certainity factor theory, dempster-

shafer theory

2

Fuzzy sets and fuzzy logic:

Introduction, fuzzy sets, fuzzy set

operations,

2

types of membership functions,multi

valued logic,

2

fuzzy logic, linguistic variables and 2



hedges

fuzzy propositions, inference rules for

fuzzy propositions, fuzzy systems

2

Total No. of classes 66

QUESTION BANK

Unit

No. S.No. Questions

Bloom’s

Taxonomy

level

Mapped

with CO

I

UNIT-I

CO1

1 Explain the various foundations of

AI?

6

2 Discuss the applications of AI? 4

3 What are the various current trends

in AI?

2

4 Define Intelligent Systems? 2

5 Describe about tic-tac-toe game

playing?

4

II

UNIT-II

CO2

1

What are the basic components of

AI problem solving methodology?

Describe them in detail. Illustrate

with an example.

2

2 What is Alpha-Beta Pruning ? 2

3 Write about A* algorithm with 8-

Puzzle Problem?

4

4 Write the algorithm for breadth first

and depth first search.

8

5

Describe the behavior of A* search

in terms of optimality, completeness

and complexity.

8

6 Discuss about Constraint

satisfaction?

6

7 What is heuristic search technique?

Explain it with example?

4

8

What is a depth first search of the

search tree? Write an algorithm to

conduct depth first search explain

with example and also mention

advantages and disadvantages.

4

UNIT-III

III

1 What is propositional calculus? 2

CO3

2 Discuss about natural deduction

system?

4

3 What is predicate logic?

2

4 What is resolution find the resolvent

of Clause (AUB,~AUD,CU~B)

4

5 Explain on semantic tableau system

using propositional logic

4



6

Explain the predicate logic

representation with reference to

suitable example?

6

IV

UNIT-IV

1 Explain knowledge representation

using semantic network?

4

2 What are Frames? What is it

purpose?

6

3 Discuss about cyc theory? 4

4 Describe about semantic web and

case grammars?

6

5 Explain briefly on conceptual

dependency theory?

4

6 What are the various approaches to

knowledge representations?

2

V

UNIT-V

CO5

1 Compare among expert systems vs

traditional systems

4

2 Explain about rule-based expert

systems?

6

3

Explain the process of knowledge

acquisition and validation for expert

systems.?

4

4 List the shells and tools for expert

systems?

2

5 Discuss the various phases for

building expert systems?

4

VI

UNIT-VI

CO6

1 Define certainty factor. What are the

components of certainty factor?

6

2 Explain Bayesian belief networks? 4

3 What is multi valued logic? 2

4 Explain about linguistic variables

and hedges?

6

5 Discuss about fuzzy systems? 4

6 Explain dempster-shafer theory? 6

7 What is probability theory? 2

Question-Papers html

1. http://www.khitguntur.ac.in/cse.php#cseqp.php

2. https://www.manaresults.co.in/download.php?subcode=RT41057

Recommended books

1. Atificial intelligence, structures and Strategies for Complex problem

solving, -George F Lugar, 5thed, PEA



Prepared By

Mr. N. N Md Jubair Basha,

Associate Professor,

Dept. of CSE, KHIT.

http://www.khitguntur.ac.in/cse.php#cseqp.php

https://www.manaresults.co.in/download.php?subcode=RT41057



Course Title

INTELLECTUAL PROPERTY RIGHTS AND

PATENTS

Course Code C 312



- 2 - 0

Course Coordinator Mr. I Vijay Kumar

Module Coordinator

Course Coordinator


Course Coordinator


Course Coordinator

Availability



COURSE DESCRIPTION:

In this course students will learn about the fundamentals of Intellectual

Property Rights and understand the various steps in attaining ownership

over Industrial properties. This course makes how to apply and retain

different creations of human mind. It also makes the individual to

understand National and International legal aspects and computer related

issues.



6. Quiz Sessions 7. Power Point Presentations

COURSE OBJECTIVES:

1. To understand the basics of Intellectual Property Rights, agencies that

protect IP issues and other related basic concepts of IPR

2. To know the Copyright Law and the rights of Copyright owners, its

registration and protection mechanisms nationally and internationally

3. To know the Patent law and registration procedures of Patents in India

and International wide, different legal issues related to patents

4. To understand the Trademark Law and issues related to Trademarks

like registration, maintenance, infringement and litigations nationally

and internationally

5. To get a view about Tradesecrets and its importance, maintenance

mechanisms, litigations of trade secrets etc.,

6. To know about the Cyber Law and protective Acts related to cyber

crime issues and data protection measures

COURSE OUTCOMES:

Upon Completion of the course, the students will be able to




VERIFIED BY

Course

Coordinator

Module

Coordinator

Program

Coordinator HOD

Mr. I Vijay Kumar Dr.K.Venkata Subba Reddy

SYLLABUS Unit I

Introduction to Intellectual Property Law – Evolutionary past – Intellectual

Property Law Basics - Types of Intellectual Property - Innovations

andInventions of Trade related Intellectual Property Rights –

AgenciesResponsible for Intellectual Property Registration – Infringement -

Regulatory – Over use or Misuse of Intellectual Property Rights -Compliance

and Liability Issues.

Unit II

Introduction to Copyrights – Principles of Copyright – Subject Matters

ofCopyright – Rights Afforded by Copyright Law –Copyright Ownership –

Transfer and Duration – Right to Prepare Derivative Works –Rights

ofDistribution – Rights of performers – Copyright Formalities and

Registration– Limitations – Infringement of Copyright – International

Copyright Law-Semiconductor Chip Protection Act.

Unit III

Introduction to Patent Law – Rights and Limitations – Rights under

PatentLaw – Patent Requirements – Ownership and Transfer – Patent

ApplicationProcess and Granting of Patent – Patent Infringement and

Litigation –International Patent Law – Double Patenting – Patent Searching –

PatentCooperation Treaty – New developments in Patent Law-

InventionDevelopers and Promoters.

CO-1 Basic information about Intellectual Property Rights and protecting agencies

CO-2 understand different issues related to Copyrights

CO-3 Information about Patent Registration and maintenance procedures

nationally and internationally

CO-4 To understand the Trademark Registration procedures nationally and

internationally

CO-5 To understand different Trade Secret Agreements and its protection

CO-6

Knowledge about cyber crimes and mechanisms to secure data in cyber

space.



Unit IV

Introduction to Trade Mark – Trade Mark Registration Process –

Postregistration procedures – Trade Mark Maintenance – Transfer of rights –

Interparties Proceedings – Infringement – Dilution of Ownership of Trade

Mark –Likelihood of confusion – Trade Mark claims – Trade Marks Litigation

–International Trade Mark Law

Unit V

Introduction to Trade Secrets – Maintaining Trade Secret – Physical

Security– Employee Access Limitation – Employee Confidentiality Agreement

–Trade Secret Law – Unfair Competition – Trade Secret Litigation – Breach

ofContract – Applying State Law.

Unit VI

Introduction to Cyber Law – Information Technology Act - Cyber Crime

andE-commerce – Data Security – Confidentiality – Privacy –

Internationalaspects of Computer and Online Crime.

REFERENCE BOOKS:

1. Deborah E.Bouchoux: “Intellectual Property”. Cengagelearning , NewDelhi

2. KompalBansal&ParishitBansal "Fundamentals of IPR for Engineers",BS

Publications Press)

3. Cyber Law. Texts & Cases, South-Western’s Special Topics Collections

4. PrabhuddhaGanguli: ‘ Intellectual Property Rights” Tata Mc-Graw –Hill,

New Delhi

5. Richard Stim: "Intellectual Property", Cengage Learning, New Delhi.

6. R. Radha Krishnan, S. Balasubramanian: "Intellectual

PropertyRights",Excel Books. New Delhi.

7. M. Ashok Kumar and Mohd.Iqbal Ali: “Intellectual Property Right”Serials

Pub.


Course Outcomes

PO

1

PO

2

PO

3

PO

4

PO

5

PO

6

PO

7

PO

8

PO

9

PO

1

0

PO

1

1

PO

1

2

CO1: Basic

information about

Intellectual Property

Rights and protecting

agencies.

√

CO2: Can

understand different

issues related to

Copyrights

√

CO3: Information

about Patent

Registration and

maintenance

procedures nationally

and internationally.

√ √



LESSON PLAN PERIODS List of contents UNIT NO.OF

PERIODS

1 Introduction to Unit 1 I 9 Periods

2 Basics of IPR I

3 Evolutionary past of IPR I

4 Types of Intellectual Property I

5 Innovations and inventions of TRIPs I

6 WIPO and GATT I

7 Agencies responsible for IP protection I

8 Infringement and Regulatory issues of IPR I

9 Use and misuse of IPR I

10 Introduction to Unit 2 COPYRIGHTS II 10 Periods

11 Principles of Copyrights, Subject matter of Copyrights

II

12 Rights afforded by Copyright law II

13 Copyright ownership – joint works II

14 Ownership – works made for hire II

15 Coyright transfer and duration II

16 Copyright Registration II

17 Copyright infringement II

18 Copyright International Law II

19 Semiconductor chip protection act II

20 Introduction to Unit – 3 - PATENTS III 7 Periods

21 Patent Registration procedure III

22 Patent ownership and transfer III

23 Patent infringement and litigation III

24 Patent cooperation Treaty III

25 New developments in Patent law III

26 Invention developers and promoters III

27 Introduction to unit- 4 - TRADEMARKS IV

CO4: To understand

the Trademark

Registration

procedures nationally

and internationally.

√

CO5: To understand

different Trade Secret

Agreements and its

protection.

√

CO6: Knowledge

about cyber crimes

and mechanisms to

secure data in cyber

space.

√



28 Trademark requirements IV

9 Periods 29 Trademark registration procedure IV

30 Post registration procedures IV

31 Inter partes proceedings, infringement IV

32 International trademark law IV

33 Trademark dilution IV

34 Likelihood of confusion IV

35 Trademark litigations and cases IV

36 Introduction to unit -5- TRADE SECRETS V

6 Periods

37 Maintainence of trade secrets V

38 Misappropriation of trade secrets V

39 Unfair competition V

40 Breach of contract and trade secret litigation V

41 Legal cases related to trade secrets V

42 Introduction to unit -6- CYBER LAW VI 6 periods

43 Information technology act and e-commerce VI

44 Cyber crimes VI

45 Data security VI

46 International aspects of online crimes VI

47 Case studies VI

Total No.of Periods 47 Periods

*** Note minimum classes: 47

Maximum clsasses: 50

Prepared by

Mr. I Vijay Kumar,

MBA Dept, KHIT



Course Title Network Programming Lab

Course Code C310



4 2 - 3

Course Coordinator Mr. A.SANDEEP KUMAR / G.MAHESH REDDY/

P.LAVANYA

Module Coordinator Mr. B. Rama Krishna

Course Coordinator


Course Coordinator

Availability Yes

Pre-requisites

Courses Data Communication

Aim and objective of the course:

Lab Objectives: The main objectives of this lab is to impart the students with

hands of experience on Unix system calls, Unix Inter Process

communication, Remote Procedure Call, Socket programming, Process

Synchronization.

OBJECTIVES:

• To write, execute and debug c programs which use Socket API.

• To understand the use of client/server architecture in application

development

• To understand how to use TCP and UDP based sockets and their

differences.

• To get acquainted with unix system internals like Socket files, IPC

structures.

• To Design reliable servers using both TCP and UDP sockets

OUTCOMES:

• Understand and explain the basic concepts of Grid Computing;

• Explain the advantages of using Grid Computing within a given

environment;

• Prepare for any upcoming Grid deployments and be able to get

started with a potentially

available Grid setup.

• Discuss some of the enabling technologies e.g. high-speed links and

storage area

networks.

• Build computer grids.

Syllabus:

List of Experiments (12 experiments to be done)

1. Understanding and using of commands like ifconfig, netstat, ping, arp,

telnet, ftp, finger, traceroute, whoisetc. Usage of elementary socket system

calls (socket (), bind(), listen(),



accept(),connect(),send(),recv(),sendto(),recvfrom()).

2. Implementation of Connection oriented concurrent service (TCP).

3. Implementation of Connectionless Iterative time service (UDP).

4. Implementation of Select system call.

5. Implementation of gesockopt (), setsockopt () system calls.

6. Implementation of getpeername () system call.

7. Implementation of remote command execution using socket system calls.

8. Implementation of Distance Vector Routing Algorithm.

9. Implementation of SMTP.

10. Implementation of FTP.

11. Implementation of HTTP.

12. Implementation of RSA algorithm.


Course Outcomes

PO

1

PO

2

PO

3

PO

4

PO

5

PO

6

PO

7

PO

8

PO

9

PO

10

PO

11

PO

12

PSO

1

PSO

2

PSO

3

CO1:Understand and

Explain the basic

commands

3 2 2 1

2

2 2 2

CO2: Compute

shortest path of a

graph by using

Dijikstra’s algorithm

and distance vector

algorithm.

2 3 2 2

2

2 3 2

CO3: Acquainted

with unix system

internals like Socket

files, IPC structures

1 1 3

3

2 2

CO4:Develop a

program for IPC. 3

2 2 2

CO5:Design &

Develop TCP Client

and Server

applications for

various methods.

2 2 3

3

2 2 3

CO6: Design &

Develop UDP Client

and Server

applications for

various methods.

1 2 3

2

2 1 2



Equipments & Software required:

Software:

i.) Computer Systems with latest specifications

ii) Connected in LAN (Optional)

iii) Operating system (Windows XP & Linux)

References:

Data Communications and Networks – Behrouz A. Forouzan.Third Edition

TMH.

2. Computer Networks, 5ed, David Patterson, Elsevier

3. Computer Networks — Andrew S Tanenbaum, 4th Edition. Pearson

Education/

1.An Engineering Approach to Computer Networks-S.Keshav, 2nd Edition,

Pearson Education

2. Understanding communications and Networks, 3rd Edition, W.A. Shay,

Thomson

E-Learning materials:

http://nptel.iitm.ac.in/courses/IIT-

MADRAS/Principles_of_Communication1/index.php

http://nptel.iitm.ac.in/video.php?courseId=1099

Precautions:

1. Care should be taken while performing the experiment

2. Loose connections must be avoided

3. Students should inform the Lab-in charge in the case of any hardware

problems

Prepared By

A. Sandeep Kumar, Asst. Prof.


http://nptel.iitm.ac.in/courses/IIT-MADRAS/Principles_of_Communication1/index.php

http://nptel.iitm.ac.in/courses/IIT-MADRAS/Principles_of_Communication1/index.php

http://nptel.iitm.ac.in/video.php?courseId=1099



Course Title Software Testing Lab

Course Code C316



- - 3 2

Course Coordinator Mr. G. Mahesh Reddy

Module Coordinator Dr B Tarakeswara Rao

Course Coordinator


Course Coordinator

E-mail ID

mahesh.gogula @gmail.com

Course Coordinator

Availability



Pre-Requisites of

the Course

Software Engineering Lab

COURSE DESCRIPTION:

In this course, Software Testing is a process of executing a program

with the intent of finding an error. A good test case is one that has high

probability of finding an as yet undiscovered error. A successful test is

one that uncovers an as yet undiscovered error.

COURSE OBJECTIVES:

1. To discuss the distinctions between validation testing and defect testing.

2. To describe the principles of system and component testing .

3. To describe strategies for generating system test cases.

4. To understand the essential characteristics of tool used for test

automation.

COURSE OUTCOMES:

CO-1: Find practical solutions to the problems of C.

CO-2: Solve specific problems for ATM applications.

CO-3: Create the test cases for the Banking application.

CO-4: Manage a project like Library system.

CO-5: To Study the Win Runner Testing tool and its

implementation.

CO-6: To Apply Win Runner testing tool implementation.

VERIFIED BY

Course

Coordinator

Module

Coordinator

Program

Coordinator HOD

Mr. G. Mahesh

Reddy

Dr. B.

Tarakeswara

Rao

Mr.N.Md.Jubair

Basha

Dr.K.Venkata

Subba Reddy




Software Testing Lab Syllabus

OBJECTIVES:

Demonstrate the UML diagrams with ATM system descriptions.

Demonstrate the working of software testing tools with c language.

Study of testing tools- win runner, selenium etc.

Writing test cases for various applications

Programming:

1. Write programs in ‘C’ Language to demonstrate the working of the

following constructs:

I. do...while

II. while….do

III. if…else

IV. switch

V. for

2. “A program written in ‘C’ language for Matrix Multiplication fails”

Introspect the causes for its failure and write down the possible

reasons for its failure.

3. Take any system (e.g. ATM system) and study its system specifications

and report the various bugs.

4. Write the test cases for any known application (e.g. Banking application)

5. Create a test plan document for any application (e.g. Library

Management System)

6. Study of Win Runner Testing Tool and its implementation

a) Win runner Testing Process and Win runner User Interface.

b) How Win Runner identifies GUI(Graphical User Interface) objects

in an application and describes the two modesfor organizing GUI

map files.

c) How to record a test script and explains the basics of Test Script

Language (TSL).

d) How to synchronize a test when the application responds slowly.

e) How to create a test that checks GUI objects and compare the

behaviour of GUI objects in different versions of the sample

application.

f) How to create and run a test that checks bitmaps in your

application and run the test on different versions ofthe sample

application and examine any differences, pixel by pixel.

g) How to Create Data-Driven Tests which supports to run a single

test on several sets of data from a data table.

h) How to read and check text found in GUI objects and bitmaps. i)

How to create a batch test that automatically runs the tests.

i) How to update the GUI object descriptions which in turn supports

test scripts as the application changes.

7. Apply Win Runner testing tool implementation in any real time

applications.

OUTCOMES:

Find practical solutions to the problems

Solve specific problems alone or in teams

Manage a project from beginning to end

Work independently as well as in teams

Define, formulate and analyze a problem




Prepared by

Mr.G. Mahesh Reddy, Asst.Professor-CSE Dept, KHIT

Course Outcomes

PO

-1

PO

-2

PO

-3

PO

-4

PO

-5

PO

-6

PO

-7

PO

-8

PO

-9

PO

-10

PO

-11

PO

-12

PS0-1

PS0-2

PS0-3

CO1: Find practical solutions to

the problems of C. 3 2 2 3 2 3 1 2

CO2: Solve specific problems for

ATM applications. 2 3 3 3 2 3 2 3

CO3: Create the test cases for the

Banking application. 3 3 2 2 3 2 2

CO4: Manage a project like Library

system. 2 2 1 3 2 3 2 2

CO5: To Study the Win Runner

Testing tool and its implementation. 3 3 2 2 3 2 2

CO6: To Apply Win Runner testing

tool implementation. 3 3 3 3 2 2 2 2



Course Title DATA WAREHOUSING AND DATA MINING-LAB

Course Code C317



-- - 3 4

Course Coordinator Dr.B.Tarakeswara Rao

Module Coordinator Mr. P.LAKSHMI KANTH

Course Coordinator


Course Coordinator


Course Coordinator

Availability



Pre-Requisites of

the Course

Database Management Systems, SQL

Aim and objective of the course:

The objectives of this lab are to develop

Practical exposure on implementation of well known data mining

tasks.

Exposure to real life data sets for analysis and prediction.

Learning performance evaluation of data mining algorithms in a

supervised and an unsupervised setting.

Handling a small data mining project for a given practical domain.

Course Outcomes:

At the end of this lab, the learner will get ability to

1. Apply data cleaning technique on data

2. Understand data integration techniques.

3. Apply Association rule process on dataset test

4. Identify the difference between apriori and FP tree algorithm

5. Learn different association rule mining algorithms.

6. Gain the knowledge on cluster analysis techniques.

Syllabus

1. Demonstration of preprocessing on dataset student.arff

2. Demonstration of preprocessing on dataset labor.arff

3. Demonstration of Association rule process on dataset contactlenses.arff

using apriori algorithm

4. Demonstration of Association rule process on dataset test.arff using

apriori algorithm.

5. Demonstration of classification rule process on dataset student.arff using



j48 algorithm.

6. Demonstration of classification rule process on dataset employee.arff

using j48 algorithm.


using id3 algorithm.


using naïve bayes algorithm.

9. Demonstration of clustering rule process on dataset iris.arff using simple

k-means.

10. Demonstration of clustering rule process on dataset student.arff using

simple k- means.


Prepared By

Dr. B. Tarakeswara Rao, Professor


Course Outcomes

PO

1

PO

2

PO

3

PO

4

PO

5

PO

6

PO

7

PO

8

PO

9

PO

10

PO

11

PO

12

PSO

1

PSO

2

PSO

3

CO1: Apply data

cleaning technique on

data

1 2 2 1 2 3 1 1 2 1 1

CO2: Understand

data integration

techniques.

1 2 2 1 2 3 1 1 1 1 1

CO3: Apply

Association rule

process on dataset

test

1 2 2 1 2 3 1 2 1 1

CO4: Identify the

difference between

apriori and FP tree

algorithm

1 2 2 1 2 1 3 1 1 1 3 1

CO5:Learn different

association rule

mining algorithms.

1 2 2 1 2 1 3 1 1 1 1 1

CO6: Gain the

knowledge on

clustering Algorithms

1 2 2 1 2 1 3 1 1 1 1 1



NON-PROGRAMMING LABORATORY COURSES ASSESSMENT

GUIDELINES

The number of experiments in each laboratory course shall be as per the

curriculum in the scheme of instructions provided by JNTUK. Mostly the

number of experiments is 10 in each laboratory course under semester

scheme. The students will maintain a separate note book for observations in

each laboratory course.

In each session the students will conduct the allotted experiment and

enter the data in the observation table. The students will then complete the

calculations and obtain the results. The course coordinator will certify the

results in the same session. The students will submit the record in the next

class. The evaluation will be continuous and not cycle-wise or at semester

end.

The internal marks of 25 are awarded in the following manner:

Laboratory record - Maximum Marks 15

Test and Viva Voce- Maximum Marks 10

Laboratory Record: Each experimental record is evaluated for a score of 50.

The rubric parameters are as follows:

Write up format - Maximum Score 15

Experimentation Observations & Calculations - Maximum Score 20

Results and Graphs - Maximum Score 10

Discussion of results - Maximum Score 5

While (a), (c) and (d) are assessed at the time of record submission,(b) is

assessed during the session based on the observations and calculations.

Hence if a student is absent for an experiment but completes it in another

session and subsequently submits the record, it shall be evaluated for a

score of 30 and not 50. The 15 marks of laboratory record will be scaled

down from the TOTAL of the assessment sheet.

The test and viva voce will be scored for 10 marks as follows:

Internal Test - 6 marks

Viva Voce/Quiz - 4 marks

The assessment of each experiment is recorded in the following format for

every student.

Exp.

No.

Title

of the

Exp

Date

conducted

Date

submitted

Observations

and

Calculations

(20)

Write

up

(15)

Results

and

Graphs

(10)

Discussion of

Results (5)

Total

(50)

1

2

3

Total

Avg.(Total/No of experiments conducted as per curriculum)

Scaled down to 15 marks(Avg./50 * 15)



PROGRAMMING LABORATORY COURSES ASSESSMENT GUIDELINES

The number of experiments/programs/sessions in each laboratory

course shall be as per the curriculum in the scheme of instructions

provided by JNTUK.

The students will maintain a separate note book for each laboratory

course in which all the related work would be done. In each session the

students will complete the assigned tasks of process development, coding,

compiling, debugging, linking and executing the programs. The students will

then execute the programme and validate it by obtaining the correct output

for the provided input. The course coordinator will certify the validation in

the same session.

The students will submit the record in the next class. The evaluation

will be continuous and not cycle- wise or at semester end.

The internal marks of 25 are awarded in the following manner:

Laboratory record - Maximum Marks 15

Test and Viva Voce - Maximum Marks 10

Laboratory Record: Each experimental record is evaluated for a score of 50.

Write up format - Maximum Score 20

Process development and coding - Maximum Score 10

Compile, debug, link and execute program -Maximum Score 15

Process validation through input-output - Maximum Score 5

While (a) is assessed at the time of record submission, (b), (c) and (d) are

assessed during the session based on the performance of the student in the

laboratory session. Hence if a student is absent for any laboratory session

but completes the program in another session and subsequently submits

the record, it shall be evaluated for a score of 20 and not 50.

The 15 marks of laboratory record will be scaled down from the TOTAL of

the assessment sheet.

The test and viva voce will be scored for 10 marks as follows:

Internal Test - 6 marks

Viva Voce / Quiz - 4 marks

The assessment of each experiment is recorded in the following format for

every student.

The rubric parameters are as follows:

Exp.

No.

Title of

the

Exp

Date

conducted

Date

submitted

Process

Development

and coding

(10)

Compilati

on,Debugging,

Linking and

Executing

(Max 15)

Process

Validation

(Max 5)

Write up

format

(Max20)

Total

Score

(Max

50)

1

2

Total



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