M.Tech. Programme in COMPUTER SCIENCE AND ENGINEERING Science and Engineering-gvp.pdf · Pushdown Automata – Pushdown Automata and Context-Free Grammar – Properties of Context-Free

M.Tech. Programme in

COMPUTER SCIENCE AND

ENGINEERING

2019 Regulations

GAYATRI VIDYA PARISHAD COLLEGE OF ENGINEERING

(AUTONOMOUS)

AFFILIATED TO JNTU- KAKINADA

M.Tech. in Computer Science and Engineering

1

MADHURAWADA, VISAKHAPATNAM

Programme Educational Objectives(PEOs):

At the end of the programme the student shall be able to:

PEO-1 Carry out research in frontier areas of computer science and engineering with the

capacity to learn independently and pursue lifelong learning.

PEO-2 Exhibit effective technical, communication and project management skills in their

Profession

PEO-3 Use modern tools/techniques through a quantitative, analytical and systematic

approach to solve problems relevant to society and environment.

PEO-4 Design secure systems and manage them.


2

Programme Specific Outcomes(PSOs):

At the end of the programme the student shall be able to:

PSO-1 Specify, architect, design and build system and application software

PSO-2 Design and develop techniques and tools of data science to extract meaningful

information from Big Data.

PSO-3 Ensure information security and manage in all the phases of system life cycle


3

Programme Outcomes(POs)

PO1 Graduates will demonstrate knowledge in core subjects of Computer Science and

Engineering and the ability to learn independently.

PO2 Graduates will demonstrate the ability to design a software application or process

which meets desired specifications within the constraints.

PO3 Graduates will demonstrate the ability to solve problems relevant to industries and

research organizations.

PO4 Graduates will develop innovative thinking capabilities to promote research in the

core and trans-disciplinary areas.

PO5 Graduates will be familiar with modern engineering software tools and equipment to

analyze computer science and engineering problems.

PO6 Graduates will demonstrate the ability to collaborate with engineers of other

disciplines and work on projects requiring multidisciplinary skills.

PO7 Graduates will acquire Information security management abilities.

PO8 Graduates will be able to communicate effectively in both verbal and written forms.

PO9 Graduates will engage themselves in lifelong learning in the context of rapid

technological changes in computer science and engineering.

PO10 Graduates will demonstrate an appreciation of ethical and social responsibilities in

Professional and societal context.

PO11 Graduates will demonstrate the ability in carrying out tasks independently and by

reflective learning.


4

GAYATRI VIDYA PARISHAD COLLEGE OF ENGINEERING (A)

(Approved by AICTE & Affiliated to JNTU Kakinada)

Re-accredited by NAAC with “A” with a CGPA of 3.47/4.00

Madhurawada, Visakhapatnam 530048

Department Computer Science and Engineering

Semester – I

S.No. Course

Code

Course Name Category L T P C

1 19CS2101 Foundations of Computer Science Core 3 0 0 3

2 19CS2102 Advanced Data Structures Core 3 0 0 3

3 19CS2103 No SQL Databases Core 3 0 0 3

4 19CS2150 Data Science PE1 3 0 0 3

19CS2151 Data Security and Access Control

19CS2152 Information Systems Security

Management

5 19CS2153 Distributed Processing of Large

Datasets

PE2 3 0 0 3

19CS2154 Natural Language Processing

19CS2155 Wireless Sensor Networks

6 Research Methodology and IPR AC 2 0 0 2

7 19CS2104 Advanced Data Structures Lab Lab 0 0 3 1.5

8 19CS2157 Databases Lab Lab-E 0 0 3 1.5

19CS21M1 Natural Language Processing (virtual)


5

Lab (IIIT Hyderabad)

19CS21M2 Wireless sensor networks (virtual) Lab

(IIT Khargapur)

Total 17 0 6 20


6






Semester –II

S.No. Course

Code

Course Name Category L T P C

1 19CS2105 Parallel Algorithms Core 3 0 0 3

2 19CS2106 Distributed Operating Systems Core 3 0 0 3

3 19CS2107 Machine Learning Core 3 0 0 3

4 19CS2158 Knowledge Discovery PE3 3 0 0 3

19CS2159 Secure Software Design and Enterprise

Computing

19CS2160 Computer Vision

5 19CS2161 Data Visualization PE4 3 0 0 3

19CS2162 Ethical Hacking

19CS2163 Biometrics

6 19CS2108 Machine Learning Lab Lab 0 0 3 1.5

7 19CS2164 Ethical Hacking Lab Lab -E 0 0 3 1.5

19CS2165 Computer Vision Lab

19CS2166 Parallel Algorithms Lab

8 19CS21P1 Open Elective (Business Analytics) OE 2 0 0 2

Total 17 0 6 20


7






Semester -III

S.No. Course

Code

Course Name Category

L T P C

1 Audit Course 1:English for Research Paper

Writing / Sanskrit for Technical Knowledge

AC-1 1 0 0 0

2 Audit Course 2: Stress Management by Yoga /

Personality Development through Life

Enlightenment Skills.

AC-2 1 0 0 0

4 19CS21IT/

19CS21PT

Industrial Training / Pedagogy Training 2

5 19CS21T1 Dissertation (Phase-I) PW 10

Total 4 0 0 12






Semester -IV

S.No. Course Code Course Name Category L T P C

1 19CS21T2 Dissertation (Phase-II) PW - - - 16

Total - - - 16


8

FOUNDATION OF COMPUTER SCIENCE Semester I

Course Code: L P C

3 0 3

Prerequisites: Nil

Course Outcomes: At the end of the course the student shall be able to

CO 1:Implement mathematical logic and solve recurrence relations.

CO 2:Apply Divide-and-Conquer and Greedy Methods to solve various problems.

CO 3:Apply Dynamic Programming to solve different problems.

CO 4:Discuss Finite Automata and Regular Expressions.

CO 5:Examine Context-Free Grammars and Pushdown Automata.

UNIT-I 11 Lectures

Mathematical logic : Connectives, negation, conjunction, disjunction, conditional and bi-conditional,

well-formed formulae, tautologies, equivalence of formulae, duality, tautological implications,

functionally complete set of connectives, principal disjunctive and conjunctive normal forms, inference

calculus, rules of inference, indirect method of proof, conditional proof.

Recurrence relations: Recurrence relations, solving linear recurrence relations by characteristic roots

method, system of recurrence relations, non - linear recurrence relations.

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

1. find equivalence formulas, implementation of logic for mathematical proofs (L1).

2. apply inference theory to verify the consistency of data ( L3).

3. formulate recurrence relations of the sequences ( L6).

4. solve homogeneous linear recurrence relations ( L6).

5. evaluate complementary function and particular integral for non-homogeneous linear recurrence

relations ( L5)

6. apply substitution method to solve non-linear recurrence relations ( L3)


9

UNIT-II 9 Lectures

Introduction to algorithms: Time complexity, Asymptotic Notations, Divide and Conquer: General

method, Applications-Binary search, Quick sort, Merge sort, Greedy Method: General method,

Applications - Fractional Knapsack Problem, Job sequencing with deadlines, Single-source shortest paths

problem.


1. discuss asymptotic notations to express time complexities of algorithms(L6).

2. apply Divide and Conquer technique to solve different problems(L3).

3. apply Greedy Method to solve different problems(L3).

UNIT-III 9 Lectures

Dynamic Programming: General method, Applications - 0/1 knapsack problem, All-pairs shortest paths

problem, Travelling salesperson problem, Reliability design.


1. apply Dynamic Programming to solve 0/1 knapsack problem(L3).

2. solve Travelling salesperson problem by using Dynamic Programming(L3).

3. solve Reliability design problem by using Dynamic Programming(L3)

UNIT-IV 12 Lectures

Introduction To Finite Automata: Alphabets and languages- Deterministic Finite Automata – Non-

Deterministic Finite Automata – Equivalence of Deterministic and Non-Finite Automata – Languages

Accepted by Finite Automata – Finite Automata and Regular Expressions – Properties of Regular sets &

Regular Languages and their applications.


1. discuss Examine Finite Automata (L6).

2. examine Languages Accepted by Finite Automata (L4).

3. discuss Regular Expressions and Languages (L6).


10

UNIT-V 11 Lectures

Context Free Languages: Context –Free Grammar – Regular Languages and Context-Free Grammar –

Pushdown Automata – Pushdown Automata and Context-Free Grammar – Properties of Context-Free

Languages – pushdown automata and Equivalence with Context Free Grammars.


1. explain Context –Free Grammars(L2)

2. discuss Pushdown Automata (L6)

3. illustrate Equivalence of Context-Free Grammars and Pushdown Automata(L2)

Text Books:

1. J.P. Tremblay and R. Manohar, Discrete Mathematical Structures with Applications to Computer

Science, 1st Edition, Tata McGraw Hill, 1997.

2. Joe L. Mott, Abraham Kandel and T. P. Baker, Discrete Mathematics for computer scientists and

mathematicians, 2nd Edition, Prentice Hall, Inc. Upper Saddle River, NJ, USA @1986. ISBN:0-

8359-1391-0.

3. Ellis Horowitz, Satraj Sahni and Rajasekharam, Fundamentals of Computer Algorithms, 2nd Edition,

University Press,2008.

4. Hopcroft H.E. and Ullman J. D, Introduction to Automata Theory Languages and Computation, 3rd

Edition, Pearson Education, 2011.

References:

1. Charles E. Leiserson, Clifford Stein, Ronald Rivest, and Thomas H. Cormen, Introduction to

Algorithms, 3rd Edition, MIT Press, 2009.

2. Mishra and Chandrashekaran, Theory of Computer Science – Automata Languages and

Computation, 3rd Edition, PHI, 2009.

3. Keneth. H. Rosen, Discrete Mathematics and its Applications, 6th Edition, Tata McGraw-Hill, 2009.


11

ADVANCED DATA STRUCTURES Semester I

Course Code: L P C

3 0 3

Prerequisites: Computer Programming


CO 1:Understand the implementation of data structures and dictionaries.

CO 2:Prepare Skip Lists and its related operations and implement hashing techniques.

CO 3:Develop and analyze algorithms for red-black trees, B-trees and Splay trees.

CO 4:Develop algorithms for text processing applications.

CO 5:Identify suitable data structures and develop algorithms for computational geometry problems.

UNIT-I 10 Lectures

Introduction to Data Structures: Stacks, Queues, Single Linked list, double linked list, Circular linked

lists. Dictionaries: Definition, Dictionary Abstract Data Type, Implementation of Dictionaries.

At the end of the module the students will be able to:

1. understand the concepts of stacks and queues(L2)

2. compare different types of linked lists(L2)

3. explain the applications of dictionaries(L2)

UNIT-II 8 Lectures

Hashing: Review of Hashing, Hash Function, Collision Resolution Techniques in Hashing, Separate

Chaining, Open Addressing, Linear Probing, Quadratic Probing, Double Hashing, Rehashing, Extendible

Hashing.

Skip Lists: Need for Randomizing Data Structures and Algorithms, Search and Update Operations on

Skip Lists, Probabilistic Analysis of Skip Lists, Deterministic Skip Lists.


1. compare various collision resolution techniques(L2).

2. understand and analyze the concepts of a skip list. (L4).

3. develop skip lists with deterministic skip lists. (L6).


12

UNIT-III 10 Lectures

Trees: Binary Search Trees, AVL Trees, Red Black Trees, 2-3 Trees, B-Trees, Splay Trees


1. analyze the performance of various binary search trees(L4)

2. understand and analyze the concepts of B-trees. (L4)

3. implement AVL trees (L3).

UNIT-IV 12 Lectures

Text Processing: Sting Operations, Brute-Force Pattern Matching, The Boyer-Moore Algorithm, The

Knuth-Morris-Pratt Algorithm, Standard Tries, Compressed Tries, Suffix Tries, The Huffman Coding

Algorithm, The Longest Common Subsequence Problem (LCS), Applying Dynamic Programming to the

LCS Problem.


1. analyze the performance of pattern matching algorithms(L4).

2. create pseudo-code for standard tries (L6).

3. apply dynamic programming to LCS problem (L3).

UNIT-V 12 Lectures

Computational Geometry: One Dimensional Range Searching, Two Dimensional Range Searching,

Constructing a Priority Search Tree, Searching a Priority Search Tree, Priority Range Trees, Quadtrees, k-

D Trees.


1. understand the concepts of computational geometry(L2)

2. create a priority search tree. (L6)

3. explain the concepts of quadtrees and k-D trees (L2)

Text Books:

1. Mark Allen Weiss, Data Structures and Algorithm Analysis in C++, 2nd Edition, Pearson, 2004.

2. Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars, Computational Geometry:

Algorithms and Applications, 3rd edition, Springer, 2008

https://www.amazon.in/s/ref=dp_byline_sr_book_1?ie=UTF8&field-author=Mark+de+Berg&search-alias=stripbooks

https://www.amazon.in/Otfried-Cheong/e/B07HR213YN/ref=dp_byline_cont_book_2

https://www.amazon.in/Otfried-Cheong/e/B07HR213YN/ref=dp_byline_cont_book_2

https://www.amazon.in/s/ref=dp_byline_sr_book_3?ie=UTF8&field-author=Marc+van+Kreveld&search-alias=stripbooks

https://www.amazon.in/s/ref=dp_byline_sr_book_3?ie=UTF8&field-author=Marc+van+Kreveld&search-alias=stripbooks

https://www.amazon.in/Mark-Overmars/e/B07HR38CCF/ref=dp_byline_cont_book_4

https://www.amazon.in/Mark-Overmars/e/B07HR38CCF/ref=dp_byline_cont_book_4


13

References:

1. M T Goodrich, Roberto Tamassia, Algorithm Design, John Wiley, 2002, ISBN: 978 - 1 - 118 -

29027 - 9.

2. Thomas H. Cormen , Charles E. Leiserson , Ronald L. Rivest , Clifford Stein., Introduction to

Algorithms, MIT Press, 2001.

3. Narasimha karumanchi, Data Structures and algorithms made easy, 5th Edition 2017.

https://www.amazon.in/s/ref=dp_byline_sr_book_1?ie=UTF8&field-author=Thomas+H.+Cormen&search-alias=stripbooks

https://www.amazon.in/s/ref=dp_byline_sr_book_2?ie=UTF8&field-author=Charles+E.+Leiserson&search-alias=stripbooks

https://www.amazon.in/s/ref=dp_byline_sr_book_2?ie=UTF8&field-author=Charles+E.+Leiserson&search-alias=stripbooks

https://www.amazon.in/s/ref=dp_byline_sr_book_3?ie=UTF8&field-author=Ronald+L.+Rivest&search-alias=stripbooks

https://www.amazon.in/s/ref=dp_byline_sr_book_3?ie=UTF8&field-author=Ronald+L.+Rivest&search-alias=stripbooks

https://www.amazon.in/s/ref=dp_byline_sr_book_4?ie=UTF8&field-author=Clifford+Stein&search-alias=stripbooks

https://www.amazon.in/s/ref=dp_byline_sr_book_4?ie=UTF8&field-author=Clifford+Stein&search-alias=stripbooks

https://www.amazon.in/s/ref=dp_byline_sr_book_1?ie=UTF8&field-author=Thomas+H.+Cormen&search-alias=stripbooks


14

NO SQL DATABASE Semester I

Course Code: 19CS2103 L P C

3 0 3

Prerequisites: UG level course in Database Management System

Course Outcomes: On completion of this course, the student will be able to

CO 1:Understand about Database Management System.

CO 2:Understand the concept of NoSQL using MongoDB

CO 3:Analyze various Query features on NoSQL

CO 4:Understand and Examine the relationship among data and its operations using MongoDB

CO 5:Develop Web applications with NoSQL and its administration

UNIT-I 10 Lectures

Introduction To Management: History of Database Systems. Database System Applications, database

System VS file System. Data Models: ER Model, Relational Model and Other Models .Database

Languages: DDL, DML. Introduction to the Relational Model – Integrity Constraint Over relations –

Enforcing Integrity constraints – Querying relational data – Logical database Design – Introduction to

Views – Destroying /altering Tables and Views. Introduction of Object Database Systems, Structured

Data types, operations on structured data, Encapsulation and ADTS, Inheritance.


1. understand database system applications (L2).

2. understand and applying database languages(L3).

3. analyze queries on relational data(L4).

UNIT-II 10 Lectures

Introduction To Nosql: Overview and History of NoSQL, Types of NoSQL Database, The Value of

Relational Databases, Getting at Persistent Data, Concurrency, Integration, Impedance Mismatch,

Application and Integration Databases, Attack of the Clusters, The Emergence of NoSQL, Key Points

.Comparison of relational databases to new NoSQL stores.

Introduction to MongoDB: MongoDB, Cassandra use and deployment, Application, RDBMS approach,

Challenges NoSQL approach, Key-Value and Document Data Models, Column-Family Stores,

Aggregate-Oriented Databases.


1. understand NoSQL and comparing with relational data(L4).

2. understand MongoDB, Cassandra and its applications.(L2).

3. apply and differentiate various Challenges in NoSQL and RDBMS(L4).


15


Data And Distribution Models: Replication and Sharding, Map-Reduce on databases. Distribution

Models, Single Server, Sharding, Master-Slave Replication, Peer-to-Peer Replication, Combining

Sharding and Replication. NoSQL Key/Value databases using MongoDB, Document Databases, Features,

Consistency, Transactions, Availability, Query Features, Scaling, Suitable Use Cases, Event Logging,

Content Management Systems, Web Analytics or Real-Time Analytics, Queries against Varying

Aggregate Structure.

At the end of the module the students will be able

1. understand the replication and Sharding (L2).

2. analyze various Query features on NoSQL using MongoDB (L4).

3. understand and examine the queries against various aggregate structure(L4).

UNIT-IV 10 Lectures

Key-value Databases: NoSQL Key/Value databases using Riak, Key-Value Databases, Key-Value Store

Features, Consistency, Transactions, Query Features, Structure of Data, Scaling, Suitable Use Cases,

Storing Session Information, User Profiles, Preferences, Shopping Cart Data, Relationships among Data,

Multi operation Transactions, Query by Data, Operations by Sets.


1. Understand NoSQL Key values using Riak (L2).

2. Unalyze consistency, transactions and query features(L4).

3. Understand and examine the relationship among data and its operations.(L4).

UNIT-V 12 Lectures

Developing Web Application with NOSQL and NOSQL Administration: Php and MongoDB, Python and

MongoDB, Creating Blog Application with PHP, NOSQL Database Administration. Graph NoSQL

databases using Neo4, NoSQL database development tools and programming languages, Introduction to

Graph Databases, features.


1. understand Graph NoSQL using Neo4 and MongoDB (L2).

2. unalyze database development tools and programming languages.(L4).

3. develop web applications with NoSQL and its administration(L6).

TextBooks:

1. Raghuramakrishnan and Johannes Gehrke, “Database Management Systems”, 3rd Edition, TMH,

2006.

2. Sadalage, P. & Fowler, M., NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot

Persistence. (1st Ed.). Upper Saddle River, NJ: Pearson Education, In, 2012.


16

References:

1. Gauravvaish, Getting started with NoSQL , PACKT publishing, ISBN: 978184969488

2. Redmond, E. & Wilson, J., Seven Databases in Seven Weeks: A Guide to Modern Databases and

the NoSQL Movement (1st Ed.), 2012

3. Raleigh, NC: The Pragmatic Programmers, LLC. ISBN-13: 978- 1934356920 ISBN-10:

1934356921


17

DATA SCIENCE Semester I

Course Code: L P C

3 0 3

Prerequisites: UG level course Data Structures


CO 1:Describe about Data Science and its process.

CO 2:Differentiate between the classification and regression methods.

CO 3:Apply clustering and evaluate the methods.

CO 4:Understand and analyze the text mining and time series forecasting applications.

CO 5:Assess different feature selection methods and use in applications.

UNIT-I 08 Lectures

Introduction : Data Science, Data Science Process: Process, Data, Data Preparation, Modeling.

Data Exploration: Objectives, Types of data, Descriptive Statistics, Data Visualization, Roadmaps for

data Exploration.


1. understand the process and preparation of Data Science(L2).

2. identify different types of data and modeling (L1).

3. explore the roadmaps for data (L3).

UNIT-II

11 Lectures

Classification Methods: K-Nearest Neighbors, Decision Trees, Rule Induction, Naive Bayesian,

Ensemble Learners.

Regression Methods: Linear Regression, Logistic Regression.


1. compare classification versus regression (L2).

2. understand the concepts of various classification methods. (L2)

3. analyze different regression methods. (L4)


18


Clustering: k-means, DBSCAN, Self-Organizing Maps.

Model Evaluation: Confusion Matrix, ROC and AUC, Lift Curves, Implementation.


1. analyze the performance of Self-Organizing Maps (L4).

2. understand different clustering algorithms. (L2)

3. apply and evaluate the models with different parameters (L5).

UNIT-IV 12 Lectures

Recommendation Engines: Concepts, Collaborative Filtering, Content Based Filtering, Hybrid

Recommendation.

Time Series Forecasting: Decomposition, Smoothing, Regression and Machine Learning Methods,

Performance Evaluation.


1. predict the preferences using recommendation engine (L3).

2. understand the concept of time series forecasting (L2).

3. create machine learning model for time series (L6).

UNIT-V 12 Lectures

Anomaly Detection: Concepts, Distance and Density based Outlier Detection, Local Outlier Factor,

Feature Selection: Classifying Feature Selection Methods, PCA, and Information Theory based

Filtering; Chi-Square based Filtering, Wrapper type feature selection.


1. detect anomalies based on the outliers. (L5)

2. Choose different feature selection methods for the given data. (L5)

3. Explain the importance of dimensionality reduction (L2)

Text Books:

1. Vijay Kotu, BalaDeshpande, Data Science Concepts and Practice, Second Edition, Morgan

Kaufmann Publishers, An imprint of Elsevier, 2019.

References:

1. Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk From The Frontline.

O’Reilly 2014.


19

DATA SECURITY AND ACCESS CONTROL Semester I


3 0 3

Prerequisites: UG level course Database Management Systems


CO 1:Provide fundamentals of database security, access control techniques.

CO 2:Understand and implement classical models and algorithms.

CO 3:Analyze the data and identify the problems.

CO 4:Choose the relevant models and algorithms to apply.

CO 5:Assess the strengths and weaknesses of various access control models and to analyze their behavior.

UNIT-I 08 Lectures

Introduction: Introduction to Access Control, Purpose and fundamentals of access control, brief history,

Policies of Access Control, Models of Access Control, and Mechanisms, Discretionary Access Control

(DAC), Non- Discretionary Access Control, Mandatory Access Control (MAC). Capabilities and

Limitations of Access Control Mechanisms: Access Control List (ACL) and Limitations, Capability

List and Limitations.

At the end of the module, the students will be able to:

1. understand access control mechanisms(L2)

2. differentiate the various kinds of access control mechanisms(L4)

3. analyze the limitations of access control mechanisms(L4)

UNIT-II 10

Lectures

Role-based Access Control: Introduction to Access Control, Purpose and fundamentals of access

control, brief history, Policies of Access Control, Models of Access Control, and Mechanisms,

Discretionary Access Control (DAC), Non- Discretionary Access Control, Mandatory Access Control

(MAC). Capabilities and Limitations of Access Control Mechanisms: Access Control List (ACL) and

Limitations, Capability List and Limitations.


1. differentiate various types of RBAC’s (L4)

2. understand the limitations of RBAC(L2)

3. understand MAC Access control policy (L2)


20


Models: Biba’s integrity model, Clark-Wilson model, Domain type enforcement model, mapping the

enterprise view to the system view, Role hierarchies- inheritance schemes, hierarchy structures and

inheritance forms, using SoD in real system Temporal Constraints in RBAC, MAC AND DAC.

Integrating RBAC with enterprise IT infrastructure: RBAC for WFMSs, RBAC for UNIX and JAVA

environments Case study: Multi-line Insurance Company


1. analyze different types of models(L4)

2. understand enterprise and system views(L2)

3. methodically examine the temporal constraints(L3)

4. analyze RBAC with respective to different Operating systems(L4)

UNIT-IV 10 Lectures

Smartcard Security: Smart Card based Information Security, Smart card operating system fundamentals,

design and implantation principles, memory organization, smart card files, file management, atomic

operation, smart card data transmission ATR, PPS Security techniques- user identification, smart card

security, quality assurance and testing, smart card life cycle-5 phases, smart card terminals.


1. understand the basics of Information security and smart cards(L2).

2. analyze data transmissions(L4).

3. examine the smart cards life cycle phases(L3)

UNIT-V 12 Lectures

Database Security: Recent trends in Database security and access control mechanisms. Case study of

Role-Based Access Control (RBAC) systems. Recent Trends related to data security management,

vulnerabilities in different DBMS.


1. examine the recent trends in security and access control(L4)

2. analyze case studies(L4)

3. understand the vulnerabilities in different databases(L2)

Text Books:

1. David F. Ferraiolo, D. Richard Kuhn, RamaswamyChandramouli, Role Based Access Control,

Artech House, 2003.

References:

1. http://www.smartcard.co.uk/tutorials/sct-itsc.pdf : Smart Card Tutorial.


21

INFORMATION SYSTEM SECURITY MANAGEMENT Semester I

Course Code: L P C

3 0 3

Prerequisites: UG level course Network security


CO1:Knowledge on various security threats and issues and how to overcome those issues.

CO2:Analyze various cybercrimes.

CO3:Learning various issues involved in threats overcome methods.

CO4:Learning Forensic analysis and risk analysis.

CO5:Learn inner investigation models for overcoming cyber crimes

UNIT-I 10 Lectures

Computer Security: The State of threats against computers, and networked systems, Overview of

Computer Security and why they fail Vulnerability assessment, managing firewalls and VPNs, Overview

of Instruction Detection and Intrusion prevention Network and host-based IDS. Classes of attackers, Kids/

hackers/ sophisticated groups, automated: Drones, Worms and Viruses A general IDS model and

taxonomy.


1. state the threats and vulnerabilities (L3)

2. understand intrusion detection (L2)

3. analyze the taxonomy (L4)

UNIT-II 10 Lectures

Information security risk analysis fundamentals: Importance of physical security and biometrics

controls for protecting information systems assets, Security considerations for the mobile workforce,

Network security perspectives, networking and digital communications (Overview only), security of

wireless networks.


1. explain the importance of Biometrics(L2)

2. analyze the security considerations(L4)

3. understand the security of wireless networks(L2)


22


Models, Frameworks and Privacy: Security models and frameworks and standards through introduction

to the ISO 27001, SSE CMM (systems security engineering – CapabilityMaturity Model), COBIT

(Control Objectives for Information and related technologies) and the Sarbanes-Oxley Act (SOX) and

SAS 70 (statement on auditing standards), Privacy Fundamentals, business practices, impact on data

privacy, technological impact on data privacy, privacy issues in web services and Applications based on

web services.


1. methodically examine the security models(L3)

2. understand and differentiate various technologies (L4)

3. Explain the impact of data privacy (L2)

UNIT-IV 11 Lectures

Internet/Computer Demographics: Computer/network user statistics; Computer crime statistics. Types

of Computer and Internet Crime: Types of crimes involving computers; Computer crimes; Network

crimes; Criminals, hackers, and crackers Investigations: The investigation life cycle; Legal methods to

obtain the computer; Jurisdictions and agencies; Internet investigations (e-mail, IRC, chat rooms, etc.); IP

addresses and domain names; Investigative methods, Digital Evidence. Evidence Collection: Working

with ISPs and telephone companies; Examining computer server, and network logs; Anonymous services.


1. analyze various crimes and explain them(L4)

2. examine the concept of investigations(L3)

3. apply the concept of IP’s and network logs(L6)

UNIT-V 11 Lectures

Cyber Crime: Introduction to Information System Security, Offensive and Defensive Information

Warfare: Cyber Crime: Fraud and Abuse; National Security, Offensive Information Warfare; Privacy

Rights, Ethics, Censorship, Harassment. Prevention Techniques: Access Control, Misuse Detection;

Vulnerability Monitoring, Security Policy, Risk Management, Incident Handling; Law Enforcement and

Cyber Crime, Emerging Concept in Cyber Crime.


1. differentiate various types of information(L3)

2. understand risk management(L2)

3. analyze incident handling and cybercrime(L4)


23

TextBooks:

1. Nina S. Godbole, Information Systems Security : Security Management, Metrics, Frameworks

And Best Practices , John Wiley & Sons, 2008.

2. Ross Anderson, Security Engineering : A Guide to Building Dependable Distributed Systems,

John Wiley & Sons, 05-Nov-2010.

3. Harold Tpton & Micki Krause, Information Security Management Handbook, CRC Press, 19-

Apr-2016.

References:

1. W. Stallings, Network Security Essentials: Applications and Standards ,Pearson Education, 2003.


24

DISTRIBUTED PROCESSING OF LARGE DATASETS Semester I

Course Code: L P C

3 0 3

Prerequisites :UG level course Data Structures and Database Management System


CO 1:Understand big data analytics as the next wave for businesses looking for competitive advantage

CO 2:Examine the correlation between data processing and its related technologies

CO 3:Access and Process Data on Distributed File System using Hadoop concepts.

CO 4:Manage Job Execution in Hadoop Environment

CO 5:Understand Apache Spark

UNIT-I 10 Lectures

Introduction: Introduction to Data processing and data computing.Definition, Features,Challenges of

Data Computing and processing. Introduction to Big Data.

Related Technologies: Relationship between Hadoop and Big Data,Relationship Between Machine

learning and Big Data.

Data Generation And Acquisition: Big Data Generation-Enterprise Data, IoT Data, Internet Data, Bio-

medical Data, Data Generation from Other Fields, Big Data Acquisition - Data Collection, Data

Transportation, Data Pre-processing.(TextBook 1 )


1. understand the concept of Big data for business intelligence (L2)

2. compare various Technologies like Cloud Computing, IoT, Hadoop (L3)

3. understand Storage Mechanism for Big Data (,L2)

UNIT-II 11

Lectures

Data Storage: Storage System for Massive Data, Distributed Storage System, Storage Mechanism for

Big Data - Database Technology, Design Factors, Database Programming Model.

Data Analysis: Traditional Data Analysis, Big Data Analytic Methods, Architecture for Big Data

Analysis - Real-Time vs. Offline Analysis, Analysis at Different Levels, Analysis with Different

Complexity, Tools for Big Data Mining and Analysis.(TextBook 1 )


1. analyze Big Data Analysis (L4)

2. differentiate Real-Time vs. Offline Analysis (L2)

3. understand Database Programming (L2).


25


Introduction to Hadoop: Hadoop Architecture, Hadoop Storage: HDFS, Common Hadoop Shell

commands , Anatomy of File Write and Read., NameNode, Secondary NameNode, and DataNode.

Hadoop Ecosystem: Hadoop ecosystem components - Schedulers - Fair and Capacity

Pig : Introduction to PIG, Execution Modes of Pig, Comparison of Pig with Databases, Grunt, Pig Latin,

User Defined Functions, Data Processing operators.

Hive : Hive Shell, Hive Services, Hive Metastore, Comparison with Traditional Databases.


1. understand Hadoop Architecture(L2)

2. apply Hadoop Ecosystem components(L3)

3. illustrate the database structures of Pig and Hive (L3)

UNIT-IV 10 Lectures

Map-reduce analytics using Hadoop:

Map-Reduce workflows, test data and local tests, anatomy of Map-Reduce job run, classic Map-reduce,

Job, Task trackers - Cluster Setup – SSH & Hadoop Configuration – HDFS Administering –Monitoring

& Maintenance.

YARN: YARN, failures in classic Map-reduce and YARN, Map-Reduce types, input formats, output

formats.

Hadoop Tools:

H-Base, Cassandra, Hive, data types and file formats, HiveQL data definition, HiveQL data manipulation,

HiveQL queries.(TextBook 2 )


1. Understanding Map-Reduce analytics using Hadoop(L2)

2. Implement the basic commands in Hive QL (L5)

3. Analyze the Hadoop tools (L4).

UNIT-V 10 Lectures

Apache Spark:

Introduction, Components,Resilient Distributed Datasets,Data Sharing using Spark RDD,

Spark Installation,Basic programming in RDD,Deployment, Spark SQL(Textbook 3)


1. implement the basic commands Spark RDD(L5)

2. understand Spark SQL(L2).


26

Text Books:

1. Min Chen, Shiwen Mao, Yin Zhang,Victor C.M. Leung, “Big Data: Related Technologies,

Challenges and Future Prospects”, Springer; 2014 edition.

2. Tom White, “Hadoop- The Definitive Guide”, O’reilly, 2nd Edition.

3. Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured

Streaming And Spark Machine Learning Library By HienLuu

References:

1. Eric Sammer, "Hadoop Operations", O'Reilley, 2nd Edition.

2. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.

3. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.

4. Alan Gates, "Programming Pig", O'Reilley, 2011.


27

NATURAL LANGUAGE PROCESSING Semester I


3 0 3

Prerequisites:


CO 1:Gain knowledge in automated Natural Language Generation and Machine Translation.

CO 2:Provide the student with knowledge of various levels of analysis involved in NLP.

CO 3:Understand the applications of NLP

CO 4:Analyze the semantic analysis of natural language

CO 5:Understand language generation and discourse analysis

UNIT-I 10 Lectures

OVERVIEW AND MORPHOLOGY: Introduction – Models -and Algorithms - -Regular Expressions

Basic Regular Expression Patterns – Finite State Automata Understand the wireless sensor network

principles. Morphology -Inflectional Morphology - Derivational Morphology. Finite-State Morphological

Parsing -- Porter Stemmer

At the end of the module the students will be able to

1. understand regular expressions and finite automata (L2).

2. analyze the internal structure of a word of the natural language (L4).

3. apply derivational morphology to create new lexemes (L3).

UNIT-II 10 Lectures

WORD LEVEL AND SYNTACTIC ANALYSIS: N-grams Models of Syntax - Counting Words -

Unsmoothed N-grams .Smoothing- Back-off Deleted Interpolation – Entropy - English Word Classes -

Tag sets for English Part of Speech Tagging-Rule Based Part of Speech Tagging - Stochastic Part

of Speech Tagging - Transformation-Based Tagging


1. apply N-grams rules to identify word patterns (L3)

2. understand probability of word count in language processing(L2)

3. understand and identify the finite tag in a word (L2)


28


CONTEXT FREE GRAMMARS: Context Free Grammars for English Syntax- Context-Free Rules

and Trees -Understand the network simulation tools. Sentence- Level Constructions–Agreement – Sub

Categorization .Parsing – Top-down – Early Parsing -feature Structures – Probabilistic Context-Free

Grammars


1. understand the context free grammar(L2)

2. explain parsing algorithm (L2)

3. discuss Probabilistic Context-Free Grammars(L2)

UNIT-IV 11 Lectures

SEMANTIC ANALYSIS: Representing Meaning-Meaning Structure of Language-First Order Predicate

Calculus Representing Linguistically Relevant Concepts -Syntax-Driven Semantic Analysis - Semantic

Attachments -Syntax-Driven Analyzer. Robust Analysis - Lexemes and Their Senses - Internal

Structure - Word Sense Disambiguation -Information Retrieval


1. compare and contrast the meaning of the word(L3)

2. discuss syntax driven semantic analysis (L2)

3. understand the basic unit of a word (L2)

UNIT-V 11 Lectures

LANGUAGE GENERATION AND DISCOURSE ANALYSIS: Discourse -Reference Resolution -

Text Coherence -Discourse Structure – Coherence. Dialog and Conversational Agents - Dialog Acts –

Interpretation -Conversational Agents. Language Generation–Architecture-Surface Realizations -

Discourse Planning .Machine Translation -Transfer Metaphor– Interlingua – Statistical Approaches


1. categorise the relation between tokens (L4)

2. explain about automatic language generating software (L2)

3. discuss automatic machine translation procedure (L2)


29

Text Books:

1. Daniel Jurafsky and James H Martin, ”Speech and Language Processing: An introduction

to Natural Language Processing, Computational Linguistics and Speech Recognition”,

Prentice Hall, 2nd Edition, 2008

2. C. Manning and H. Schutze, “Foundations of Statistical Natural Language

Processing”, MIT Press. Cambridge, MA:,1999

References:

1. C. Manning and H. Schutze, “Foundations of Statistical Natural Language

Processing”, MIT Press. Cambridge, MA:,1999

2. Bharati A., Sangal R., ChaitanyaV.. Natural language processing: a Paninian perspective, PHI,

2000

3. Siddiqui T., Tiwary U. S. Natural language processing and Information retrieval, OUP 2008


30

WIRELESS SENSOR NETWORKS Semester I

Course Code: L P C

3 0 3

Prerequisites:


CO 1:Understand and analyze the various architectures of wireless sensor network.

CO 2:Explain about operating system for wireless sensor networks.

CO 3:Discuss simulation tools for wireless sensor networks.

CO 4:Analyze the communication protocols.

CO 5:Understand the security principles in wireless sensor networks

UNIT-I 10 Lectures

Fundamentals Of Wireless Sensor Networks: Introduction to wireless sensor networks, challenges of

wireless sensor Networks,Single node architecture,network architecture, sensor network scenario,network

Design principles of wireless sensor networks.


1. understand the wireless sensor network principles.(L2)

2. analyze the network architecture.(L4)

3. understand the concept of source and sink.(L2)

UNIT-II 08 Lectures

Operating Systems And Power Management: Operating Systems: functional and nonfunctional

aspects, prototypes, Tiny OS, Contiki, LiteOS, SOS. Power management in wireless sensor networks.


1. understand the functional aspects of WSN.(L2)

2. analyze the operating system prototypes.(L4)

3. evaluate the energy consumption in WSN.(L5)


Simulation and Programming tools: C, nesC. Performance comparison of wireless sensor networks

simulation and experimental platforms like open source (ns-2) and commercial (QualNet, Opnet).


1. understand the network simulation tools.(L2)

2. analyze the wireless sensor networks performance.(L4)

3. understand the open source systems for WSN.(L2)


31

UNIT-IV 12 Lectures

Protocols: Communication protocols: MAC protocols and Network layer protocols :Flooding and

gossiping, data centric routing, proactive routing, On-Demand routing, Hierarchical routing, location

based routing.


1. understand the physical layer principles in WSN.(L2)

2. understand the routing techniques in WSN(L2).

3. analyze the routing protocols.(L4)

UNIT-V 12 Lectures

Synchronization And Security: Time Synchronization in Wireless Sensor Networks, Basics of Time

Synchronization, Fundamentals of Network Security, Challenges of Security in Wireless Sensor

Networks , Security Attacks in Sensor Networks, Protocols and Mechanisms for Security, Zig Bee

Security.


1. understand the speed mismatch between source and destination (L2)

2. analyze the various security attacks in WSN.(L4)

3. apply the security mechanisms in WSN.(L3)

TextBooks:

1. H. Karl and A. Willig, “Protocols and Architectures for Wireless Sensor Networks”, 1st edition,

John Wiley & Sons, India, 2012.

2. Waltenegus Dargie, Christian Poellabauer, “Fundamentals of Wireless Sensor Networks: Theory

and Practice”, 2nd edition, Wiley publications, 2010.

References:

1. C. S. Raghavendra, K. M. Sivalingam, and T. Znati, Editors, “Wireless Sensor Networks”,

Springer Verlag, 1st Indian reprint, 2010.

2. F. Zhao and L. Guibas, “Wireless Sensor Networks: An Information Processing Approach”,

Morgan Kaufmann, 1st Indian reprint, 2013.

3. Mohammad S. Obaidat, SudipMisra, “Principles of Wireless Sensor Networks”, Cambridge,

2014.


32

ADVANCED DATA STRUCTURES LAB Semester I

Course Code: L P C

0 3 1.5

Prerequisites:UG level course in Data Structures


CO1:Implement various types of linear and non-linear data structures.

CO2:Implement different hashing techniques.

CO3:Implement basic and advanced binary search trees.

CO4:Implement various types of pattern matching algorithms.

CO5:Implement the concepts of KD-tree

List of Programs:

1. Write a program to implement stack operations using linked list.

2. Write a program to implement circular queue operations using arrays.

3. Write a program to implement the following operations on singly linked list

a) Creation of a linked list b) Insert a node into linked list c) Delete a node from linked list

4. Write a program to implement the following operations on doubly linked list

a) Creation of a linked list b) Insert a node in to linked list c) Delete a node from linked list

5. Write a program to implement the following.a)Linear probing b)Quadratic probing c)Double

hashing.

6. Write a program to implement the following in binary search tree a) Creation b) Insertion c)

Deletion.

7. Write a program to implement AVL trees a) Creation b) Insertion c) Deletion

8. Write a program to create a Red-black tree for the given elements

9. Write a program to create a splay tree for the given elements

10. Write a program to implement Boyer-Moore pattern matching algorithm

11. Write a program to implement KMP pattern matching algorithm

12. Write a program to create a Kd tree for the given element.

13. Write a program to construct a binomial heap for the given elements.

14. Write a program to construct a leftist tree for the given elements.


33

DATABASES LAB Semester I

Course Code: L P C

0 3 1.5

Prerequisites: UG level course in Databases


CO 1:Learn basic MongoDB functions and its implementation.

CO 2:Implement various types of operations in MongoDB.

CO 3:Implement the concepts of limit records and sort records.

CO 4:Implement Indexing, Advanced Indexing and Hashing using MongoDB.

CO 5:Analyze and apply aggregation and Map Reduction in MongoDB.

List of Programs:

1. Installation of MongoDB on Windows & Linux.

2. Implementation of mongo Shell, Create database and display the database.

3. Execute the Commands of MongoDB and operations in MongoDB : Insert, Query, Update,

Delete and Projection.

4. Implementation of Where Clause, AND,OR operations in MongoDB.

5. Implementation of MongoDB count() cursor method.

6. Execute Aggregation Pipeline and its operations.

7. Execute Limit Records and Sort Records operation in MongoDB.

8. Implementation of Aggregation and Map Reduce functions in MongoDB.

9. Implementations of Indexing, Advanced Indexing using MongoDB.

10. Implementations of Hashing using MongoDB.

11. Practice with ' McDonalds ' collection data for document oriented database.Import restaurants

collection and apply some queries to get specified output.

12. Create a backup for an entire server to back up a Database with Mongo dump.

13. Establish a connection with a database or access any tabular data source using Java Driver,

Python Driver, PHP Driver to do the following operations.

a) Send various MongoDB statements.

b) Retrieve and process the results received from the database.


34

NATURAL LANGUAGE PROCESSING (VIRTUAL) Semester I

Course Code: L P C

0 3 1.5


CO1: Implement and learn about morphological features of a word by analysing it.

CO2: Calculate bigrams from a given corpus.

CO3: Implement POS tagging using different models.

CO4: Build POS tagger

CO5: Implement the concept of chunking and get familiar with the basic chunk tagset.

List of Programs:

1. Word Analysis

2. Word Generation

3. Morphology

4. N-Grams

5. N-Grams Smoothing

6. POS Tagging: Hidden Markov Model

7. POS Tagging: Viterbi Decoding

8. Building POS Tagger

9. Chunking

10. Building Chunker

11. HMM tagging

12. Word Count

References:

1. http://nlp-iiith.vlabs.ac.in/

2. Jurafsky and Martin: "Speech and Language Processing", Prentice Hall, 2000.

3. Akshar Bharati, Rajeev Sangal and Vineet Chaitanya: "Natural Language Processing: A

Paninian Perspective", Prentice-Hall of India , New Delhi, 1995.

http://nlp-iiith.vlabs.ac.in/


35

WIRELESS SENSOR NETWORKS (VIRTUAL) Semester I

Course Code: L P C

0 0 2


CO1: Design a local area network

CO2: Apply RF signal for demonstrating Sensor nodes.

CO3: Design a topology for network establishment

CO4: Analyze the Network performance metrics.

CO5: Identify different applications of the wireless sensor networks.

List of Programs:

1. Connect the computers in local area network.

2. Demonstration of a "Hello World" Application.

3. RF communication using Wireless sensor nodes.

4. Selecting different transmission range with respect to the available power levels.

5. Wireless Sensor Network Duty Cycle Implementation vs. Analysis of Power

Consumption.

6. Sensor Data Acquisition.

7. Wireless Sensor Network Data Collection Frequency and transmission vs. Analysis of

Power Consumption

8. Wireless Propagation.

9. Design wireless sensor network topologies and experiment data sending and reception at

various power levels.

10. Program the WSN to acquire sensor data, transmit it to the nearby nodes, and aggregate

it.

11. Design, develop, and implement, different wireless sensor network algorithms for

grouping the nodes.

12. Design, develop and implement different time synchronization algorithms for wireless

sensor networks and study its real world characteristics.

13.

References:

1. https://vlab.amrita.edu/index.php?sub=78&brch=256


36

PARALLEL ALGORITHMS Semester II

Course Code: L P C

3 0 3

Prerequisites: UG level course in Operating Systems


CO1:Explain different models of parallel algorithms.

CO2:Illustrate the speed-up, cost optimal algorithms.

CO3:Outline CREW/EREW/MCC models .

CO4:Demonstrate various techniques Vector-Matrix Multiplication, Solution of Linear equations, Root

finding.

CO5: Understand different Graph Algorithms.

UNIT-I 08 Lectures

Introduction: Sequential model, need of alternative model, parallel computational 8 models such as

PRAM, LMCC, Hypercube, Cube Connected Cycle, Butterfly, Perfect Shuffle Computers, Tree model,

Pyramid model, Fully Connected model, PRAM-CREW, EREW models, simulation of one model from

another one.


1. understand different parallel computing models (L2).

2. analyse different parallel architectures. (L3)

3. apply various parallel PRAM, LMCC model. (L4)

UNIT-II 10

Lectures

Performance Measures of Parallel Algorithms, speed-up and 8 efficiency of PA, Cost- optimality, An

example of illustrate Cost- optimal algorithms- such as summation, Min/Max on various models


1. analyze different measures of parallel algorithms (L4)

2. apply speed up and efficiency of parallel algorithms. (L3)

3. calculate the speed up and cost optimal algorithms. (L3)


37


Parallel Sorting Networks, Parallel Merging Algorithms on 8 CREW/EREW/MCC, Parallel Sorting

Networks CREW/EREW/MCC/, linear array.


1. analyze various Parallel Sorting Networks concepts. (L4)

2. analyze various concepts of different Merging Algorithms. (L4)

3. understand parallel sorting concepts. (L2)

UNIT-IV 10 Lectures

Parallel Searching Algorithm, Kth element, Kth element in X+Y on 8 PRAM, Parallel Matrix

Transportation and Multiplication Algorithm on PRAM, MCC, Vector-Matrix Multiplication, Solution of

Linear equations, Root.


1. explain various measures of parallel searching algorithms. (L2)

2. analyze various concepts such as PRAM , MCC, Vector Matrix (L4)

3. design various concepts how to solve the various Solutions of Linear Equations. (L6)

4. analyze data transmissions(L4).

5. examine the smart cards life cycle phases(L3)

UNIT-V 12 Lectures

Graph Algorithms - Connected Graphs, search and traversal, 8 Combinatorial Algorithms-Permutation,

Combinations.


1. explain the performance measures connected graphs. (L2)

2. analyse various concepts of search and traversal. (L4)

3. design various concepts combinatorial algorithms. (L6)

Text Books:

1. S.G. Akl, The Design and Analysis of Parallel Algorithms, 1st edition, Prentice Hall Inc., 1992 .

References:

1. Grama, An Introduction to Parallel Computing: Design and Analysis of Algorithms, 2nd edition,

Pearson Education India, 2008.


38

DISTRIBUTED OPERATING SYSTEMS Semester II

Course Code: L P C

3 0 3

Prerequisites: UG level course in Operating Systems


CO 1:Knowledge about advanced concepts in OS

CO 2:Ability to develop OS for distributed systems

CO 3:Analyze the distributed resource management i.e., memory and load

CO 4:Prepare and solve the issues related with distributed database management

CO 5:Analyze different resource security and protection mechanisms

UNIT-I 10 Lectures

MULTIPROCESSOR OPERATING SYSTEMS: System Architectures, Structures of OS – OS design

issues – Process synchronization – Process Scheduling - Memory management.


1. analyze various design issues(L4)

2. apply and analyze various process scheduling, synchronization concepts(L4)

3. differentiate various memory management schemes(L4)

UNIT-II 10 Lectures

DISTRIBUTED OPERATING SYSTEMS: System Architectures- Types, issues, communication

network, communication primitives, Theoretical Foundation- Inherent limitations of distributed systems,

Lamport’s logical clock, Vector Clocks, Causal ordering of messages

Distributed mutual exclusion – Classification of ME algorithm, Non token based Lamport’s algorithm,

token based algorithm, Singhal heuristic algorithm.

Distributed Deadlock detection – Deadlock handling strategy, control organizations for distributed

deadlock detection - centralized distributed deadlock detection.


1. understand various system architectures(L2)

2. understand and analyze the concept of clocks(L4)

3. apply and analyze deadlock methods(L4)


39


DISTRIBUTED RESOURCE MANAGEMENT: Distributed File System- Architecture, Mechanism,

Design Issues, Distributed Shared memory – Architecture, Algorithms for implementing DSM, Memory

coherence, coherence protocol, design issues, Distributed Scheduling – Issues in load distributing, load

distributing algorithm.


1. understand the design issues of the file systems(L2)

2. understand and apply distributed shared memory algorithms(L3)

3. analyze memory coherence and its protocols(L4)

4. analyze load distribution algorithms (L4)

UNIT-IV 12 Lectures

Database Operating Systems: Introduction, Concurrency Control: theoretical aspects, Concurrency

Control Algorithm – Introduction, Basic synchronization primitives, Lock based algorithm, Timestamp

based algorithm.


1. understand concurrency control(L2)

2. examine methodically the synchronization primitives(L4)

3. apply and differentiate various algorithms(L4)

UNIT-V 10 Lectures

PROTECTION AND SECURITY: Access and flow control: Introduction, Preliminaries, Access Matrix

Model,Implementation of Access Matrix,Safety in the Access matrix model, Advanced models of

protection: The Take-Grant Model,Bell-LaPadula Model,Case Studies.


1. understand the potential security violations, Policies and Mechanisms (L2)

2. analyze various safety in Access Matrix(L4)

3. apply and differentiate various advanced models for protection(,L4)

TextBooks:

1. M Singhal and NG Shivaratri , Advanced Concepts in Operating Systems, Tata McGraw Hill Inc,

2001.

2. Understanding Operating Systems, AnnMcHoes, Ida M. Flynn., 6th Edition,Cengage

learning,2011

References:

1. A. S Tanenbaum, Distributed Operating Systems, 2nd Edition, Pearson Education Asia, 2001.

2. George Coulouris, Jean Dollimore, Distributed Systems Concepts and Design,5th Edition,

Pearson Education Limited, 06-Nov-2013.


40

MACHINE LEARNING Semester II


3 0 3

Prerequisites: Probability & Statistics


CO1:Implement basic python programs.

CO2:evaluate different classification algorithms based on performance measures.

CO3: Distinguish between regression and classification

CO4: Implement the concepts of multi layer perceptron.

CO5:Analyze different deep learning architectures.

UNIT-I 10 Lectures

Introduction to Python programming language: Operators and Expressions, Decision and loop control

statements, Functions, Data types, File handling , Classes, Numpy, and Pandas.

At the end of the module the student shall be able to:

1. apply basics in python in problem solving (L4)

2. create lists, tuples, strings, sets and dictionaries and explain their necessity and importance in

programming. (L6)

3. explain object oriented features and file handling concepts. (L2)

4. understand Numpy and Pandas in machine learning. (L2)

UNIT-II 08 Lectures

The Machine Learning Landscape: What is machine learning, Types of machine learning-

Supervised/Unsupervised Learning , Batch and Online Learning , Instance-Based Versus Model-Based

Learning. Main challenges of machine learning, testing and validating, Performance measures- cross

validation, confusion matrix, Precision and Recall, ROC. Classification: Binary classifier, Multi class,

Multi label, Multi output classification. Case study: MNIST.


1. understand the concept of machine learning (L4)

2. analyze different types of machine learning (L4)

3. apply different classification methods on real time applications. (L3)


41


Regression: Linear regression, Gradient descent, Polynomial Regression, Logistic Regression. Support

Vector Machines: Linear and Non linear SVM classification, SVM regression. Ensemble learning:

Voting classifiers, Bagging and Pasting, Random Forest, Boosting, Radiant Boosting, Stacking.


1. classify different regression methods. (L4)

2. differentiate between linear and non linear SVM. (L2)

3. understand the various ensembling methods. (L2)

UNIT-IV 12 Lectures

Feedforward Neural Networks: Biological neuron, Logical computation with neurons, The perceptron,

Activation function, Multilayer perceptron , Gradient descent and backpropagation, Remarks on Back

Propagation algorithm, training with MLP and DNN with tensorflow, fine tuning neural network hyper

parameters.


1. illustrate the concepts of neural networks. (L4)

2. understand the concepts of activation function (L2)

3. explain the back propagation on feed forward networks. . (L2)

UNIT-V 12 Lectures

Deep Learning networks and architectures : Convolution neural network, Recurrent neural networks,

Auto encoders, Generative Adversarial Networks, Applications of deep learning.


1. Apply different neural network architectures on different models. (L3)

2. Understand Adversarial Generative Network.(L2)

3. Discuss different applications of deep learning. (L2)

TextBooks:

1. Aurelien Geron, Hands-on Machine Learning with Scikit-Learn & TensorFlow, 5th edition,

Oreilly, 2017.

References:

1. Bengio, Yoshua, IanJ. Goodfellow, and Aaron Courville, Deep learning, An MIT Press, 2015.

2. Tom M. Mitchell, Machine Learning, 14th edition, McGraw Hill Education Pvt. Ltd., 2016.

3. Manaranjan Pradhan, U Dinesh Kumar, Machine Learning with Python, Wiley, 2019.


42

KNOWLEDGE DISCOVERY Semester II

Course Code: L P C

3 0 3

Prerequisites: Data structures, Basic Statistics


CO 1:Able to have knowledge of various knowledge representation methods.

CO 2:Apply various data preprocessing techniques.

CO 3:Apply rules for classification and prediction.

CO 4:Evaluate and compare solutions by various clustering algorithms.

CO 5:Able to predict numeric methods.

UNIT-I 10 Lectures

INTRODUCTION KDD AND DATA MINING: Introduction KDD and Data Mining - The KDD

Process, Data Mining within the Complete Decision Support System, KDD and DM Research

Opportunities and Challenges, KDD & DM Trends.

Data Cleansing: Applying Data Cleansing.

1. At the end of the module the students will be able to:·

2. Understanding KDD process(L2)

3. Analyzing the Data Mining System.(L2)

4. Understanding the opportunities and trends of DM.(L4)

5. Apply Data Cleansing.(L2)

UNIT-II 08 Lectures

HANDLING MISSING ATTRIBUTE VALUES: Handling Missing Attribute Values-Sequential

Methods, Parallel Methods.

Feature Extraction and Dimensional Reduction, Feature Selection, Discretization Methods, Outlier

Detection.

At the end of the module the students will be able to:·

1. Apply Feature Extraction process(L2)

2. Apply Dimensional Reduction, Feature Selection (L3,L2)

3. Apply Discretization Methods(L4)

4. Methodically examine the Preprocessing steps.(L4)


CLASSIFICATION TREES- DECISION TREES: Classification Trees- Decision Trees: Divide and

Conquer, Calculating Information, Entropy, Pruning, Estimating Error Rates, The C4.5 Algorithm.

Evaluation of Learned Results- Training and Testing, Predicting Performance, Cross-Validation.



43

1. Differentiate methods of Decision Trees(L2)

2. Analyzing Training and Testing (L4)

3. Examine the performance(L4)

4.

UNIT-IV 12 Lectures

CLASSIFICATION RULES: Classification Rules - Inferring Rudimentary Rules, Covering Algorithms

for Rule Construction, Probability Measure for Rule Evaluation, Association Rules, Item Sets, Rule

Efficiency.


1. Applying various classification rules(L2)

2. Analyzing rules(L4,L3)

UNIT-V 12 Lectures

ARTIFICIAL NEURAL NETWORKS: Numeric Predictions - Linear Models for Classification and

Numeric Predictions, Numeric Predictions with Regression Trees.

Artificial Neural Networks – Perception’s, Multilayer Networks, The Back propagation Algorithm

Clustering - Iterative Distance-based Clustering, Incremental Clustering, The EM Algorithm.


1. Predict Numeric Methods(L2,L3)

2. Basics of Neural Networks.(L3)

3. Apply Clustering Algorithms (L4).

TextBooks:

1. Oded Maimon, Lior Rokach, Data Mining and Knowledge Discovery Handbook, Springer

Science & Business Media, 10-Sep-2010.

References:

1. Ronen Feldman, James Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing

Unstructured Data, Cambridge University Press, 2007


44

SECURE SOFTWARE DESIGN AND ENTERPRISE COMPUTING Semester II

Course Code: L P C

3 0 3

Prerequisites: Computer Programming, UG level course in Software Engineering


CO 1:Differentiate between various software vulnerabilities.

CO 2:Software process vulnerabilities for an organization.

CO 3:Monitor resources consumption in a software.

CO 4:Interrelate security and software development process.

CO 5:Explain SQL Injections

UNIT-I 10 Lectures

SECURE SOFTWARE DESIGN: Identify software vulnerabilities and perform software security

analysis, Master security programming practices, Master fundamental software security design concepts,

Perform security testing and quality assurance.


1. Compare different types of software vulnerabilities (L2)

2. Understand master security programming practices (L2)

3. Explain security testing and quality assurance.(L2)

UNIT-II 12 Lectures

ENTERPRISE APPLICATION DEVELOPMENT: Describe the nature and scope of enterprise

software applications, Design distributed N-tier software application, Research technologies available for

the presentation, business and data tiers of an enterprise software application, Design and build a database

using an enterprise database system, Develop components at the different tiers in an enterprise system,

Design and develop a multi-tier solution to a problem using technologies used in enterprise system,

Present software solution.


1. Design distribute N-tier software application. (L6)

2. Understand the concepts of enterprise software application. (L2)

3. Analyze different components at different tiers in an enterprise system (L4)


45


ENTERPRISE SYSTEMS ADMINISTRATION: Design, implement and maintain a directory-based

server infrastructure in a heterogeneous systems environment, Monitor server resource utilization for

system reliability and availability, Install and administer network services (DNS/DHCP/Terminal

Services/Clustering/Web/Email).


1. Demonstrate the administer network services (L3)

2. Understand the concepts of heterogeneous systems environment. (L2)

3. Explain the application of monitor server resource utilizations (L2)

UNIT-IV 08 Lectures

TROUBLESHOOTING: Obtain the ability to manage and troubleshoot a network running multiple

services, Understand the requirements of an enterprise network and how to go about managing them.


1. Analyze troubleshooting of a network running multiple services (L4)

2. Outline the manage the network running multiple services. (L4)

3. Understand the requirements of an enterprise network (L2)

UNIT-V 12 Lectures

SQL INJECTIONS: Handle insecure exceptions and command/SQL injection, Defend web and mobile

applications against attackers, software containing minimum vulnerabilities and flaws.

Case study of DNS server, DHCP configuration and SQL injection attack.


1. Understand the concepts of insecure exceptions (L2)

2. Create a SQL injection attack. (L6)

3. Explain the concepts of defending web and mobile applications against attackers. (L2)

TextBooks:

1. Theodor Richardson, Charles N Thies, Secure Software Design, Jones & Bartlett Learning, 2013.

Reference Books :

1. Kenneth R. van Wyk, Mark G. Graff, Dan S. Peters, Diana L. Burley, Enterprise Software

Security, Addison Wesley, 2015.


46

COMPUTER VISION Semester II

Course Code: L P C

3 0 3

Prerequisites: Basic Mathematics


CO 1:Understand and Apply morphological algorithms and image processing techniques in both the

spatial and frequency (Fourier) domains

CO 2:Understand various image segmentation approaches

CO 3:Understand 3D vision and motion analysis

CO 4:Apply the object recognition techniques

CO 5:Understand and Apply the deep learning and image classification techniques

UNIT-I 10 Lectures

IMAGE PRE-PROCESSING:

Elements of digital image processing, Sampling and Quantization, Relationships between pixels, Spatial

filtering: Smoothing, Median, & Sharpening, Color Models. Morphological operation: Dilation and

Erosion, Opening and Closing, Convex hull, Region filling, boundary extraction.


1. understand the elements of digital image processing(L2)

2. apply filtering techniques on an image (L3)

3. apply Morphological operations on an image(L3)

UNIT-II 08 Lectures

IMAGE SEGMENTATION:

Thresholding-basic global thresholding, optimum global thresholding using otsu’s method. Edge based

segmentation: point, line, edge detection, Region based segmentation - region growing, split and merging.

Canny edge detection, region segmentation using graph cuts. mean shift segmentation, digital

watermarking.


1. understand and apply thresholding (L2, L3)

2. apply segmentation techniques (L3)

3. analyze the digital watermarking on images (L4)


47


3D GRAPH BASED IMAGE SEGMENTATION:

3D Vision and Video analysis: Basic Projective Geometry, 3D information from Radiometric

measurement: Shapes from Shading, Photometric Stereo. Video tracking, Background modeling, Kalman

filter, Particle filter.


1. understand 3D imaging (L2)

2. understand the motion analysis concepts(L2)

3. analyze the videos using filters (L4)

UNIT-IV 10 Lectures

OBJECT RECOGNITION:

Knowledge representation, statistical pattern recognition- classification principles, classifier setting,

classifier learning, support vector machines, cluster analysis. Neural nets- feed forward networks,

unsupervised learning, hop field networks. Recognition as graph matching.


1. understand pattern recognition concepts (L2)

2. apply classification techniques on images (L3)

3. build the neural network for unsupervised learning (L6)

UNIT-V 12 Lectures

DEEP LEARNING :

Understanding deep learning - Perceptron, activation functions, artificial neural network, training neural

networks, CNN, RNN, LSTM. Deep learning for Computer Vision, Development environment setup,

Image Classification.


1. understand deep learning concepts (L2)

2. analyze different deep learning architectures for computer vision problems (L4)

3. build the required environment setup to use deep learning concepts (L6)

TextBooks:

1. R.C. Gonzalez & R.E. Woods, Digital Image processing, Addison Wesley/ Pearson

education,2nd Edition,2010.

2. Sonaka, Vaclav Hivac and Roger Boyle, Digital Image processing and Computer Vision,

Cengage Learning, 2008

3. Rajalingappaa Shanmugamani, Deep Learning for Computer Vision, Packt Publishing, 2018.

References:

1. A.K.Jain, “Fundamentals of Digital Image Processing”, PHI, 1st Edition, Prentice Hall, 1989.

2. William K. Pratt, John Wilely, Digital Image processing, 3rd Edition, Wiley Publications, 2004.

4. Forsyth and Ponce, Computer Vision: A Modern Approach, 2nd Edition, 2011.


48

DATA VISUALIZATION Semester II

Course Code: L P C

3 0 3

Prerequisites: Computer Graphics, and Image Processing.


CO 1:Identify and recognize visual perception and representation of data.

CO 2:Illustrate about projections of different views of objects.

CO 3:Apply various Interaction and visualization techniques.

CO 4:Analyze various groups for visualization.

CO 5:Evaluate visualizations

UNIT-I 10 Lectures

INTRODUCTION TO DATA VISUALIZATIONS AND PERCEPTION: Introduction of visual

perception, visual representation of data, Gestalt principles, Information overload.


1. understand visual representation of data (L2)

2. analyze Gestalt principles (L4)

3. understand information overloads (L2)

UNIT-II 08 Lectures

VISUAL REPRESENTATIONS: Creating visual representations, visualization reference model, visual

mapping,visual analytics, Design of visualization applications.


1. create various visual representations (L6)

2. understand visual reference model and mapping (L2)

3. analyze different applications of visualizations (L4)


CLASSIFICATION OF VISUALIZATION SYSTEMS: Classification of visualization systems,

Interaction and visualization techniques misleading, Visualization of one, two and multi-dimensional

data, text and text documents.


1. compare different types of visualization systems. (L3)

2. analyze various types of data (L4)

3. understand Interaction and visualization techniques (L2)


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UNIT-IV 12 Lectures

VISUALIZATION OF GROUPS: Visualization of groups, trees, graphs, clusters, networks, software,

Metaphorical visualization. Various visualization techniques, data structures used in data visualization.


1. understand visualization for different structures (L2)

2. apply various visualization techniques (L3)

3. create data visualizations using data structures (L6)

UNIT-V 12 Lectures

VISUALIZATION OF VOLUMETRIC DATA AND EVALUATION OF VISUALIZATIONS:

Visualization of volumetric data, vector fields, processes and simulations,Visualization of maps,

geographic information, GIS systems, collaborative visualizations, Evaluating visualizations


1. understand visualization of maps. (L2)

2. compare GIS systems and Collaborative visualizations (L3)

3. evaluate Visualizations (L5)

TextBooks:

1. Ward, Grinstein, Keim, Interactive Data Visualization: Foundations, Techniques, and

Applications. Natick, 2nd edition,A K Peters, Ltd 2015.

Reference Books:

1. Tamara Munzner,Visualization Analysis & Design ,1st edition,AK Peters Visualization Series

2014

2. Scott Murray,Interactive Data Visualization for the Web ,2nd Edition, 2017


50

Ethical Hacking Semester II

Course Code: L P C

3 0 3

Prerequisites:


CO 1:Learn various hacking methods through passwords.

CO 2:Discuss system security vulnerability through registry editing.

CO 3:Perform system vulnerability exploit attacks.

CO 4:Produce a security assessment with Perl.

CO 5:Examine various issues related to Viruses.

UNIT-I 10 Lectures

Introduction about Passwords:

Hacking Windows: BIOS Passwords, Windows Login Passwords, Changing Windows Visuals, Cleaning

Your Tracks, Internet Explorer Users, URL Address Bar, Netscape Communicator, Cookies, URL

History, The Registry, Babysitter Programs.


1. Understanding about various passwords (L2)

2. Understanding about securing the passwords (L2)

3. Applying and Analyzing software related issues related to Cookies (L4)

UNIT-II 10 Lectures

Booting Process and Registry Editing:

Advanced Windows Hacking: Editing your Operating Systems by editing Explorer.exe, The Registry,

The Registry Editor, Description of .reg file, Command Line Registry Arguments, Other System Files,

Some Windows & DOS Tricks, Customize DOS, Clearing the CMOS without opening your PC, The

Untold Windows Tips and Tricks Manual, Exiting Windows the Cool and Quick Way, Ban Shutdowns: A

Trick to Play, Disabling Display of Drives in My Computer, Take Over the Screen Saver, Pop a Banner

each time Windows Boots, Change the Default Locations, Secure your Desktop Icons and Settings.


1. Understanding about bootable files (L2)

2. Understanding and analyzing the concept of registry(L2,L4)

3. Applying and analyzing fragmentation of drives (L4)


51


Password Recovery:

Getting Past the Password: Passwords: An Introduction, Password Cracking, Cracking the Windows

Login Password, The Glide Code, Windows Screen Saver Password, XOR, Internet Connection

Password, Sam Attacks, Cracking Unix Password Files, HTTP Basic Authentication, BIOS Passwords,

Cracking Other Passwords.


1. Understanding about password recovery (L2)

2. Understanding and applying Windows and Unix passwords (L2, L3)

3. Analyzing the role of HTTP Authentication (L4)

UNIT-IV 10 Lectures

Hacking with Perl:

The Perl Manual: Perl: The Basics, Scalars, Interacting with User by getting Input, Chomp() and Chop(),

Operators, Binary Arithmetic Operators, The Exponentiation Operator(**), The Unary Arithmetic

Operators, Other General Operators, Conditional Statements, Assignment Operators. The ?: Operator,

Loops, The While Loop, The For Loop, Arrays, The For-Each Loop: Moving through an Array,

Functions Associated with Arrays, Push() and Pop(), Unshift() and Shift(), Splice(), Default Variables,

$_, @ARGV, Input Output, Opening Files for Reading, Another Special Variables.


1. Understanding the basic commands of Perl (L2)

2. Practically examine the Perl commands (L4)

3. Apply and differentiate various FILE operations (L3,L4)

UNIT-V 12 Lectures

Viruses:

Virus, Working principle of Virus, Boot Sector Viruses (MBR or Master Boot Record), File or Program

Viruses, Multipartite Viruses, Stealth Viruses, Polymorphic Viruses, Macro Viruses, Blocking Direct

Disk Access, Recognizing Master Boot Record (MBR) Modifications, Identifying Unknown Device

Drivers, Making of own Virus, Macro Viruses, Using Assembly to Create your own Virus, Hiding the

Virus from Scan, Create New Virus Strains, Simple Encryption Methods.


1. Understanding about Virus(L2)

2. Analyzing various types of viruses (L4)

3. Practically examine to create new viruses (L4)


52

Text Books:

1. Patrick Engbreston,“The Basics of Hacking and Penetration Testing: Ethical Hacking

and Penetration Testing Made Easy”,1stEdition, Syngress publication,2011.

2. Ankit Fadia,“Unofficial Guide to Ethical Hacking”, 3rd Edition, McMillan India

Ltd,2006

References:

1. Simpson, backman, corley, “Hands-on Ethical Hacking & Network Defense”, 2nd

Edition, Cengage, 2011.


53

BIOMETRICS Semester II

Course Code: L P C

3 0 0

Prerequisites: Image Processing.


CO1:Understand the benefits of biometrics

CO2:Perceive fingerprint, face, and iris recognition

CO3:Explain retina and hand recognition

CO4:Analyze signature and handwriting technology, keyboard / keystroke dynamics

CO5:Examine multi biometrics and Voice recognition

UNIT-I 10 Lectures

INTRODUCTION: Biometrics- Introduction- benefits of biometrics over traditional authentication

systems. Benefits of biometrics in identification systems-selecting a biometric for a system –Applications

- Key biometric terms and processes - biometric matching methods -Accuracy in biometric systems.


1. understand benefits of biometrics over traditional authentication systems(L2)

2. understand benefits of biometrics in identification systems (L2)

3. define Key biometric terms and processes(L1)

UNIT-II 10 Lectures

PHYSIOLOGICAL BIOMETRIC TECHNOLOGIES: Physiological Biometric Technologies:

Fingerprints - Technical description –characteristics - Competing technologies - strengths – weaknesses –

deployment. Facial scan - Technical description - characteristics - weaknesses-deployment. Iris scan -

Technical description – characteristics - strengths – weaknesses – deployment.


1. perceive fingerprint recognition(L5)

2. perceive iris recognition (L5)

3. perceive face recognition(L5)


RETINA VASCULAR PATTERN: Retina vascular pattern - Technical description – characteristics -

strengths – weaknesses – deployment. Hand scan - Technical description-characteristics - strengths –

weaknesses deployment. DNA biometrics.


1. explain retina recognition(L2)

2. explain hand recognition (L2)

3. examine DNA biometrics(L4)


54

UNIT-IV 11 Lectures

SIGNATURE AND HANDWRITING TECHNOLOGY: Signature and handwriting technology -

Technical description – classification. Keyboard / keystroke dynamics. Voice – data acquisition - feature

extraction - characteristics - strengths – weaknesses-deployment.


1. examine Signature and handwriting technology (L4)

2. examine Keyboard / keystroke dynamics(L4)

3. perceive voice recognition(L5)

UNIT-V 12 Lectures

MULTI BIOMETRICS AND MULTI FACTOR BIOMETRICS: Multi biometrics and multi factor

biometrics. Two-factor authentication with passwords-Tickets and tokens. Executive decision -

implementation plan.


1. illustrate Multi biometrics and multi factor biometrics(L2)

2. analyze Tickets and tokens(L4)

3. examine Executive decision(L4)

TextBooks:

1. Samir Nanavathi, Michel Thieme, and Raj Nanavathi, Biometrics - Identity verification in a

network world, 1st Edition, Wiley Eastern Publication, 2002.

2. John Chirillo and Scott Blaul, Implementing Biometric Security, 1 st Edition, Wiley Eastern

Publication, 2005.

Reference Books :

1. D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition,

Springer Verlag, 2003

2. Anil Jain, Arun A. Ross, Karthik Nanda kumar, Introduction to biometric, Springer, 2011.


55

MACHINE LEARNING LAB Semester II

Course Code: L P C

0 3 1.5

Prerequisites: Mathematics


CO1:Implement the basics in python programming

CO2:Implement the programs in Numpy and Pandas

CO3: Develop a classifier model for real time applications. .

CO4:Implement neural network architecture..

CO5:Build CNN and RNN models for real time datasets.

1. Create and assign variables and write a program with loops, functions and conditions in python

language.

2. Implement and demonstrate the data types of List, Strings, Sets and Tuples

3. Implement Dictionaries and classes.

4. Implement the concept of Arrays in Numpy and Pandas.

5. Build a classifier for the titanic dataset.

6. Build a classifier for the MNIST digit dataset using binary classifier.

7. Build a spam classifier for email and messages.

8. Build a model to distinguish dogs from cats.

9. Build a neural network for AND gate.

10. Build a DNN model with five hidden layers of 100 neurons each with Relu activation function

11. Build your own CNN and try to achieve the highest possible accuracy on MNIST.

12. Tackle the problem “how much it rain “ in kaggle competition using RNN.

References:

1.https://scikit-learn.org/stable/tutorial/index.html

2.https://www.tensorflow.org/

3.https://www.kaggle.com

https://scikit-learn.org/stable/tutorial/index.html

https://www.tensorflow.org/

https://www.kaggle.com/


56

ETHICAL HACKING LAB Semester II

Course Code: L P C

0 3 1.5

Prerequisites: UG level course in Operating System


CO 1:To implement various types of windows passwords

CO 2:To implement windows configurations and editing the registry.

CO 3:To implement various password recovery mechanisms.

CO 4:To implement various types commands in Perl.

CO 5:To Create and Hide the Viruses.

Programs:

1. In Windows environment perform the following, a) Set and Reset BIOS Passwordb. Jumper

Settingsc. Change BIOS Password

2. In Windows Browser perform the following,

a) Clear History b) Disable Cookies

3. In Windows Browser perform the following,

a) Saved Login/Passwords

b) Remove the Passwords that are Stored

4. In Windows environment perform the following,

a) Enabling and Disabling of Files

b) Enabling and Disabling of Desktop Icons

c) Password Protection for Files

5. In Windows environment perform the following,

a. REGEDIT b. MSCONFIG c. FRAGMENTATION

6. a. To perform the setting and resetting of Windows Login Password.

b. Reset the windows logon password using USB

7. Perform the UNIX Password Commands

a. Create Password b. Change Password c. Password Status Information

d. Password Expiry e. Set Minimum and Warning Days

8. Write the Perl Code to perform the following operations,

a. Push( ) and Pop ( ) b. Unshift( ) and Shift( )

9. Write the Perl Code to perform the following operations,

a. Chop ( ) and Chomp ( )

10. Write the Perl code to perform FILE-READ and FILE-WRITE Operations.


57

11. Write the Shell code to perform the following operations,

a) echo command b) bat command c) tskill command

12. Write a program to include shell commands to create a Virus.

References:

1.https://www.academia.edu/.../Ethical_Hacking_and_Countermeasures_v6_Lab_Manu.

2.https://oer.galileo.usg.edu/cgi/viewcontent.cgi?article=1009&context=compsci-collections

3.https://doc.lagout.org/security/ethical%20hacking%2C%20student%20guide.pdf

https://www.academia.edu/27427025/Ethical_Hacking_and_Countermeasures_v6_Lab_Manual_EC-Council_Module_26_Penetration_Testing

https://oer.galileo.usg.edu/cgi/viewcontent.cgi?article=1009&context=compsci-collections

https://doc.lagout.org/security/ethical%20hacking%2C%20student%20guide.pdf


58

COMPUTER VISION LAB Semester II

Course Code: L P C

0 3 1.5

Prerequisites: Mathematics and Computer Programming


CO 1: Select appropriate design techniques to solve real world problems.

CO 2: Apply the filtering techniques on images.

CO 3: Apply the segmentation techniques on images.

CO 4: Apply the pattern algorithms to identify features of an image.

CO 5: Develop object recognition models.

List of Experiments:

1.a) Write a program to display grayscale image.

b)Write a program to convert a 2D array into a grayscale image.

c)Write a program to convert gray images into an array of numbers.

d)Write a program to convert gray image into binary image.

2.Write a program to find histogram value and display histograph of a grayscale and color image.

3.Write a program using spatial filtering.

4.Write a program to detect edges of an image using Operators.

5.Write a program to apply 2-D DFT and DCT on an image and display the result.

6.Write a program to eliminate the high frequency components of an image.

7.a) Write a program to display of color image.

b) Write a program to convert an image into different color models.

c) Write a program to convert a 2D array into a color image.

d) Write a program to convert color images into an array of numbers.

8. Write a program to apply morphological algorithms on an image.

9. Write a program to perform discrete wavelet transform on image.

10. Write a program to perform segmentation on an image.


59

11. Write a program to perform optical character recognition..

12. Write a program to identify objects in an image.

13. Write a program to perform Ada boost algorithm on an image.


60

PARALLEL ALGORITHMS LAB Semester II

Course Code: L P C

0 3 1.5

Prerequisites: Design and analysis of algorithms


CO1: Understand parallel processing approaches

CO2:Describe different parallel processing platforms involved in achieving High Performance

Computing.

CO3:Discuss different design issues in parallel programming

CO4:Develop efficient and high performance parallel programming

CO5:Design algorithms suited for Multicore processor and GPU systems using OpenMp, CUDA

List of Programs:

I. The goal is to learn to program in shared memory model. Implement parallel LU decomposition. In

particular, implement the following functions:

1. int luDecompose(double *A, double* l, double* u, int n);

2. int luDecomposeP(double *A, double* l, double* u, int n);

luDecompose is the serial version and luDecomposeP os the parallel version.

3. They should return an error code less than 0 on error and the value of 0 on successful completion.

The input matrix A is nXn. Expect n to be large -- it may exceed 106.

4. Compile the functions into a library called luDecompose - that is luDecompose.so or

luDecompose.a, so the test code can directly call your function.

5. Submit the source code along with a makefile that builds the library.

6. You should write your own application program to test the library. The scoring will be based on

correctness, speed and scalability on multicore shared memory systems. It will be run on

computers with different core counts.

II. The goal is to learn to program in distributed memory model. Implement parallel LU decomposition.

In particular, implement the following function:

7. int luDecomposeMPI(char *filename, double* l, double* u, int *n);

luDecomposeMPI is the MPI version.

8. It should return an error code less than 0 on error and the value of 0 on successful completion.

The input matrix A is in a file, with n returned from the function.

9. Expect n to be large -- it may exceed 106. This time the entire matrix may not fit in any single

node.


61

III. The goal is to learn to program GPUs using CUDA. Implement parallel merge sort using CUDA.

Implement a CPU function

10. int mergesort(int *list, int *sorted, int n)

mergesort must use CUDA for its entire sorting and then return the sorted array in the user provided space

pointed to by sorted.

11. Compile the functions into a library called cuMergesort - that is cuMergesort.so or

libcuMergesort.a, so the test code can directly call your function.

12. You should write your own application program to test the library. The scoring will be based on

correctness, speed and scalability on a single K40.


62

BUSINESS ANALYTICS


3 0 3

Prerequisites: Nil


CO1:Understand the role of business analytics within an organization.

CO2:Gain an understanding of how managers use business analytics to support managerial decision

making.

CO3:Use technical skills in predictive and prescriptive modeling to support business decision-making.

CO4:Apply linear programming with respect to business analytics.

CO5:Use decision-making tools/Operations research techniques.

UNIT-I 10 Lectures

Introduction: What are business analytics, Why are business analytics important, resource considerations.


1. Understand basics of business analytics(L2))

2. Analyze the importance of resources in business analytics(L4)

3. Examine large data set to extract the information (l3)

UNIT-II 10 Lectures

How do we align resources to support business analytics in an organization: Organization structure and

management issues, Predictive Analytics.


1. Understand types of analytics(L2)

2. Methodically examine the organization and management issues(L4)

3. Perform the predictive analysis to unknown future events (L2)


Prescriptive Analysis, A Final business analytics Case Study, Prescriptive Modelling, nonlinear

Optimization


1. Differentiate types of Prescriptive Analytics(L2)

2. Analyze case study(L4)

3. Identify the best course of action for given situation (L2)


63

UNIT-IV 11 Lectures

Linear Programming, Duality and sensitivity Analysis in linear programming, Integer Programming.


1. Understand linear programming and its essentials(L2)

2. Examine Duality and sensitivity analysis(L3)

3. Solve Integer programming problems(L5)

UNIT-V 12 Lectures

What is Simulation, types of simulation, Decision Theory :Introduction, Model elements ,Decision

environment, decision making under certainty, uncertainty and risk


1. Understand simulation(L2)

2. Understand Decision theory(L2)

3. Differentiate decision making under different scenarios(L2)

TextBooks:

1. An Integrated Approach to Software Engineering, Third Edition by PankajJalote

Reference Books :

1. Bernd Bruegge, Allen H.zDutoit, “Object Oriented Software Engineering Using UML, Patterns

and Java”, Second Edition, Pearson Education, 2004.

2. Stephen R Schach “Object Oriented & Classical Software Engineering” Fifth Edition TMH-

2002.

Documents

M.Tech. Programme in COMPUTER SCIENCE AND ENGINEERING Science and Engineering-gvp.pdf · Pushdown Automata – Pushdown Automata and Context-Free Grammar – Properties of Context-Free