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Saturday, 12 May 2018

AU B.TECH CSE 4.1 NEW SYLLABUS 2018

ANDHRA UNIVERSITY COLLEGE OF ENGINEERING(A) -

VISAKHAPATNAM

I – SEMESTER SCHEME OF INSTRUCTION AND EXAMINATION

Branch: COMPUTER SCIENCE AND ENGINEERING
IV/IV B.TECH (FOUR YEAR COURSE) & IV/IV B.TECH (SIX YEAR DOUBLE DEGREE COURSE) (With effect from 2015-2016 admitted batch onwards)

Under Choice Based Credit System











DOWNLOAD PDF FULL SYLLABUS

CSE 4.1.1                                            EMBEDDED SYSYTEMS                                               Credits:4

Instruction:     3 Periods & 1 Tut/week                                                                                  Sessional Marks: 30
Univ. Exam : 3 Hours                                                                                                                  Univ-Exam-Marks:70

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Course Objectives:

1)  To study the basics of embedded systems and its examples.

2)  To study the 8051 Microcontroller architecture and its instruction set.
3)  To discuss various software architectures in embedded systems.
4)  To discuss Inter Task Communication procedures in RTOS and design issues of RTOS.
5)  To study various embedded software development tools and debugging techniques.

Course Outcomes:

1)  Student will be understand the basic architecture of 8051 micro controller.

2)  ability to write ALP programs using 8051 instruction set.
3) Ability to understand the concepts related to RTOS and its Inter Task Communication methods.

4)  Ability to understand various design issues of RTOS.
5)  Understand about embedded software development tools.

1.      Introduction to Embedded Systems: Examples, Typical Hardware , Memory, Microprocessors , Busses; Introduction to 8051 Microcontroller , Architecture, Instruction set, Programming. Interrupts: Interrupt Basics, Shared-Data problem, Interrupt Latency.

2.      Software Architectures: Round-Robin Architecture, Round-Robin with Interrupts Architecture, Function-Queue Scheduling Architecture, Real-Time Operating Systems Architecture, Selection of Architecture.

3.      Real Time Operating System: Tasks and Task States, Tasks and Data, Semaphores and Shared Data, Semaphore Problems, Semaphore variants.

4.      Inter Task Communication: Message Queues, Mailboxes, Pipes, Timer Functions, Events, Memory Management, Interrupt Routines in RTOS Environment.

5.      Design issues of RTOS: Principles , Encapsulation Semaphores and Queues, Hard Real-Time Scheduling Considerations, Saving Memory Space, Saving Power.

6.      Embedded Software development Tools: Host and Target Machines , Linker/Locator for Embedded Software, Getting Embedded Software into the Target System.

7.      Embedded Software Debugging Techniques :Testing on your Host Machine, Instruction Set Simulators, Laboratory Tools used for Debugging.

Text Book:

1. The 8051 Microcontroller Architecture, Programming & Applications, Kenneth J. Ayala, Penram International.

2.  An Embedded Software Primer, David E. Simon, Pearson Education , 2005.

Reference Book:

1.    Embedded Systems: Architecture , Programming and Design, Raj Kamal, Tata McGraw-Hill Education, 2008



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CSE 4.1.2                          CRYPTOGRAPHY & NETWORK SECURITY                        Credits:4

Instruction:     3 Periods & 1 Tut/week                                                                                  Sessional Marks: 30
Univ. Exam : 3 Hours                                                                                                                   Univ-Exam-Marks:70

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Course Objectives:

1)     Introduction of the issues in network security- its need and importance, taxonomy and terminology.

2)  Discussion of various cryptographic techniques.

3)  Exploration of different types of security threats and remedies.

4)  Understanding of Internet security protocols and standards

Course Outcomes:

1)  Realize the need and importance of network and data security in the Internet and in the distributed environments.

2)  Identify the different types of network security issues and their remedies.

3)  Application various cryptographic tools and techniques in different contexts and as per need of security levels.

4)  Implementation of some Internet security protocols and standards

1     Overview: Computer Security Concepts, Threats, Attacks, and  Assets,   Security Functional Requirements, A Security Architecture for Open Systems, Computer Security
Trends,  Computer
Security  Strategy.
Cryptographic
Tools:  Confidentiality
with
Symmetric  Encryption,  Message  Authentication  and  Hash  Functions,  Public-Key
Encryption, Digital Signatures and Key Management, Random and  Pseudorandom
Numbers, Practical Application: Encryption of Stored Data. User Authentication: Means
of  Authentication,
Password-Based
Authentication,
Token-Based  Authentication,
Biometric Authentication, Remote User Authentication, Security Issues for User Authentication, Practical Application: An Iris Biometric System, Case Study: Security Problems for ATM Systems.

2        Access Control: Access Control Principles, Subjects, Objects, and Access Rights,

Discretionary Access Control, Example: UNIX File Access Control, Role-Based Access

Control, Case Study: RBAC System for a Bank. Database Security: The Need for Database Security, Database Management Systems, Relational Databases, Database Access Control, Inference, Statistical Databases, Database Encryption, Cloud Security.


3        Malicious Software: Types of Malicious Software (Malware), Propagation—Infected Content—Viruses, Propagation—Vulnerability Exploit—Worms, Propagation—Social Engineering—SPAM E-mail, Trojans, Payload—System Corruption, Payload—Attack Agent—Zombie, Bots, Payload—Information Theft—Key loggers, Phishing, Spyware,
Payload—Stealthing—Backdoors, Root kits, Countermeasures.

Denial-of-Service Attacks: Denial-of-Service Attacks, Flooding Attacks, Distributed Denial-of-Service Attacks, Application-Based Bandwidth Attacks, Reflector and Amplifier Attacks, Defenses Against Denial-of-Service Attacks, Responding to a Denial-of-Service Attack.

4         Intrusion Detection: Intruders, Intrusion Detection, Host-Based Intrusion Detection, Distributed Host-Based Intrusion Detection, Network-Based Intrusion Detection, Distributed Adaptive Intrusion Detection, Intrusion Detection Exchange Format, Honeypots, Example System: Snort. Firewalls and Intrusion Prevention Systems: The Need for Firewalls, Firewall Characteristics, Types of Firewalls, Firewall Basing, Firewall Location and Configurations, Intrusion Prevention Systems, Example: Unified Threat Management Products.
5         Buffer Overflow: Stack Overflows, Defending Against Buffer Overflows, Other Forms of Overflow Attacks, Recommended Reading and Web Sites, Key Terms, Review Questions, and Problems. Software Security: Software Security Issues, Handling Program Input, Writing Safe Program Code, Interacting with the Operating System and Other Programs,

Handling Program Output. Operating System Security: Introduction to Operating System Security, System Security Planning, Operating Systems Hardening, Application Security, Security Maintenance, Linux/Unix Security, Windows Security, Virtualization Security.

6         Symmetric Encryption and Message Confidentiality: Symmetric Encryption Principles, Data Encryption Standard, Advanced Encryption Standard, Stream Ciphers and RC4, Cipher Block Modes of Operation, Location of Symmetric Encryption Devices, Key Distribution. Public-Key Cryptography and Message Authentication: Secure Hash Function, HMAC, The RSA Public-Key Encryption Algorithm, Diffie-Hellman and Other Asymmetric Algorithms.

7         Internet Security Protocols and Standards: Secure E-mail and S/MIME, Domain Keys Identified Mail, Secure Socket Layer (SSL) and Transport Layer Security (TLS), HTTPS, IPv4 and IPv6 Security. Internet Authentication Applications: Kerberos, X.509, Public-Key Infrastructure, Federated Identity Management. Wireless Network Security: Wireless Security Overview, IEEE 802.11 Wireless LAN Overview, IEEE 802.11i Wireless LAN Security.

Text Book:

1.   Computer Security - Principles and Practices (Except the Chapters 13, 14, 15, 16, 17, 18, 19), 2nd Edition by William Stallings, Pearson Education, Inc.

Reference Books:

1.       Cryptography and Network Security by William Stallings, Pearson Education Asia, New Delhi.
2.       Network Security Essentials Applications and Standards, by William Stallings, Pearson Education Asia, New Delhi.








CSE 4.1.3                                   ARTIFICIAL INTELLIGENCE                                      Credits:4

Instruction:     3 Periods & 1 Tut/week                                                                                  Sessional Marks: 30

Univ. Exam : 3 Hours                                                                                                                  Univ-Exam-Marks:70
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Course Objectives:

1)  To learn about AI problem, Production Systems and their characteristics.

2)  To understand the importance of search and the corresponding search strategies for solving AI problem.

3)  To introduce to Planning, Natural Language Processing and Expert Systems.

Course Outcomes:

1)  The Student understands AI problem characteristics, state space approach for solving AI problem, Production System framework.

2)  The student learn several optimal search strategies and the use of heuristics.

3)  The student learns relational, inferential, inheritable and procedural knowledge and the corresponding knowledge representation approaches.

4)  The student is introduced to applying AI problem solving approaches to natural language processing, planning and expert systems.


1. Introduction to Artificial Intelligence: Artificial Intelligence, AI Problems, AI Techniques, Defining the Problem as a State Space Search, Problem Characteristics, Production Systems

2.  Search Techniques: Issues in The Design of Search Programs, Un-Informed Search, BFS,

DFS; Heuristic Search Techniques: Generate-And- Test, Hill Climbing, Best-First Search, A* Algorithm, Problem Reduction, AO*Algorithm, Constraint Satisfaction, Means-Ends Analysis.

3. Knowledge Representation using Rules: Procedural Vs Declarative Knowledge, Logic programming, Forward Vs Backward Reasoning, Matching Techniques, Partial Matching, RETE Matching Algorithm AI Programming languages: Overview of LISP and PROLOG, Production System in Prolog


4. Symbolic Logic: Propositional Logic, First Order Predicate Logic: Representing Instance and is-a Relationships, Computable Functions and Predicates, Unification & Resolution, Natural Deduction; Structured Representations of Knowledge: Semantic Nets, Partitioned Semantic Nets, Frames, Conceptual Dependency, Conceptual Graphs, Scripts

5.  Reasoning  under  Uncertainty:  Introduction  to  Non-Monotonic  Reasoning,  Truth

Maintenance Systems, Logics for Non-Monotonic Reasoning, Statistical Reasoning: Bayes Theorem, Certainty Factors and Rule-Based Systems, Bayesian Probabilistic Inference, Bayesian Networks, Dempster- Shafer Theory, Fuzzy Logic: Crisp Sets ,Fuzzy Sets, Fuzzy Logic Control, Fuzzy Inferences & Fuzzy Systems.

6. Natural Language Processing: Steps in The Natural Language Processing, Syntactic Processing and Augmented Transition Nets, Semantic Analysis, NLP Understanding Systems; Planning: Components of a Planning System, Goal Stack Planning, Non-linear Planning using Constrait Posting, Hierarchical Planning, Reactive Systems

7. Experts Systems: Overview of an Expert System, Architecture of an Expert Systems, Different Types of Expert Systems- Rule Based, Frame Based, Decision Tree based, Case Based, Neural Network based, Black Board Architectures, Knowledge Acquisition and Validation Techniques, , Knowledge System Building Tools, Expert System Shells.
Text Book:

1.   Artificial Intelligence, Elaine Rich and Kevin Knight, Tata Mcgraw-Hill Publications
2.   Introduction To Artificial Intelligence & Expert Systems, Patterson, PHI publications
References:

1. Artificial Intelligence, George F Luger, Pearson Education Publications
2. Artificial Intelligence : A modern Approach, Russell and Norvig, Printice Hall

3. Artificial Intelligence, Robert Schalkoff, Mcgraw-Hill Publications
4. Artificial Intelligence and Machine Learning, Vinod Chandra S.S., Anand Hareendran S.







CSE 4.1.4              PRINCIPLES OF ECONOMICS & MANAGEMENT                 Credits:4

Instruction:     3 Periods & 1 Tut/week                                                                                  Sessional Marks: 30
Univ. Exam : 3 Hours                                                                                                                 Univ-Exam-Marks:70

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Course Objectives:

1.  Apply economic reasoning to the analysis of selected contemporary economic problems.

2.  Understand how households (demand) and businesses (supply) interact in various market structures to determine price and quantity of goods and services produced and consumed.

3.  Analyze the efficiency and equity implications of government interference in markets.

4.  Recognize and identify situations leading to market failures and government failures.

5.  Evaluate the intent and outcomes of government stabilization policies designed to correct macroeconomic problems.

6.  Use economic problem solving skills to discuss the opportunities and challenges of the increasing globalization of the world economy.

Course Outcomes:

1.  Understand the links between production costs and the economic models of supply.

2.  Represent supply, in graphical form, including the upward slope of the supply curve and what shifts the supply curve.

3.  Understand the efficiency and equity implications of market interference, including government policy.

4.  Understand how different degrees of competition in a market affect pricing and output.

5.  Apply economic reasoning to individual and firm behavior.


1.       Introduction to Managerial Economics: Wealth, Welfare and Scarce Definitions of Economics; micro and Macro Economics; Demand- Law of Demand, Elasticity of Demand, types of Elasticity and factors of determining price elasticity of Demand: utility- Law of Diminishing Marginal Utility and its limitations.


2.       Conditions of Different Market Structures: Perfect Competition, Monopolistic Competition, Monopoly, Oligopoly, and Duopoly.


3.       Forms of Business Organizations: Sole Proprietorship, Partnership, Joint Stock Company- Private Limited and Public Limited Companies, Public Enterprises and their types.


4.       Introduction to Management: Functions of Management- Taylor’s Scientific management; Henry Fayol’s Principle of Management; Human Resource Management-basic Functions of HR Manager; Man Power Planning, Recruitment, Selection, Training, Development, Placement, Compensation and performance Appraisal( in brief).


5.       Production Management: Production Planning and Control, plant Location, Break-Even Analysis, assumptions and applications.


6.       Financial Management: Types of Capital: Fixed and Working Capital , and Methods

of Raining Finance; Depreciation: Straight Line and Diminishing Balance Methods.

Marketing Management: Functions of marketing and Distribution Channels.


7.       Entrepreneurship:   Entrepreneurial   Functions,   Entrepreneurial   Development:

Objectives, Training, Benefits: Phases of Installing a project


Textbooks

1.       K.K.DEWETT, Modern Economic Theory, S.Chand and Company, New Delhi-55.

2.       S.C. Sharma and Banga T. R., Industrial Organization & Engineering Economics,

khanna Publications, Delhi-6.

Reference Books

1.       A.R. AryaSri, Management Science, TMH publications, New Delhi-20.

2.       A.R. AryaSri, Managerial Economics and Financial Analysis, TMH Publications, new Delhi-20.




CSE 4.1.6




BIGDATA ANALYTICS




Credits:4



Instruction:     3 Periods & 1 Tut/week
Univ. Exam : 3 Hours


Sessional Marks: 30
Univ-Exam-Marks:70



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Course Objectives:

On completing this course student will be able to

1.    Understand big data and Apache Hadoop Eco system

2.    Understand distributed , parallel, cloud computing and SQL concepts

3.    Apply Hadoop concepts

4.    Understand concepts of map and reduce and functional programming


Course Outcomes :

1)
Gain conceptual understanding of analytics concepts, algorithms and statistical tests
2)
Students will be able to look at the core projects used for both batch
and real time
data processing such as Hadoop

3)   Students will be able to look at wider range of problems and data science based solutions


1.Introduction to Big Data: Big Data-definition, Characteristics of Big Data (Volume, Variety, Velocity, Veracity, Validity), Importance of Big Data , Patterns for Big Data Development, Data in the Warehouse and Data in Hadoop,

2.Introduction to Hadoop: Hadoop- definition, Understanding distributed systems and Hadoop, Comparing SQL databases and Hadoop, Understanding MapReduce, Counting words with Hadoop—running your first program, History of Hadoop, Starting Hadoop - The building blocks of Hadoop, NameNode, DataNode, Secondary NameNode, JobTracker and Task Tracker

3.MapReduce -A Weather Dataset, Analyzing the Data with Unix Tools, Analyzing the Data with Hadoop, Scaling Out, Hadoop Streaming, Hadoop Pipes, Developing a MapReduce Application - The Configuration API, Configuring the Development Environment, Running Locally on Test Data, Running on a Cluster, Tuning a Job, MapReduce Workflows


4.  HDFS: Components of Hadoop -Working with files in HDFS, Anatomy of a MapReduce program, Reading and writing the Hadoop Distributed File system -The Design of HDFS, HDFS Concepts, The Command-Line Interface, Hadoop Filesystem, The Java Interface, Data Flow, Parallel Copying with distcp, Hadoop Archives

5.   MapReduce Programming: Writing basic Map Reduce programs - Getting the patent data set, constructing the basic template of a Map Reduce program, Counting things, Adapting for Hadoop’s API changes, Streaming in Hadoop, Improving performance with combiners.

6.   MapReduce Advanced Programming: Advanced MapReduce - Chaining MapReduce jobs, joining data from different sources, creating a Bloom filter, Passing job-specific parameters to your tasks, probing for task-specific information, Partitioning into multiple output files, Inputting from and outputting to a database, keeping all output in sorted order

7.    Graph Representation in MapReduce: Modeling data and solving problems with graphs, Shortest Path Algorithm, Friends-of-Friends Algorithm, PageRank Algorithm, Bloom Filter, Parallelized Bloom filter creation in MapReduce, Map-Reduce semi-join with Bloom filters

Textbooks:

1.  Dirk deRoos, Chris Eaton, George Lapis, Paul Zikopoulos, Tom Deutsch ,“Understanding Big Data Analytics for Enterprise Class Hadoop and Streaming Data”, 1st Edition, TMH,2012.

2.  Hadoop: The Definitive Guide by Tom White, 3rd Edition, O’reilly

Reference Books:

1.Hadoop in Action by Chuck Lam, MANNING Publ.

2.  Hadoop in Practice by Alex Holmes, MANNING Publishers
3.  Mining of massive datasets, Anand Rajaraman, Jeffrey D Ullman, Wiley Publications.



CSE 4.1.7




KNOWLEDGE ENGINEERING LAB




Credits: 2



Instruction: 3 Hours


Sessional Marks: 50



Univ. Exam: 3 Hours


Univ-Exam-Marks: 50



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Course Objectives:

1)  To study the various data analysis techniques in R.

2)  To discuss about WEKA software and demonstrate about several datasets available in online.

3)  To apply the various data mining techniques such as Association Analysis, Classification

and

Clustering to various standard datasets and own datasets.

Course Outcomes:

1) Student will be able to execute programs to perform several operations on data using R language.
2)   Ability to understand the usage of WEKA software.

3) Ability to apply several data mining techniques to various datasets           in WEKA.

1. Introduction to exploratory data analysis using R

Load the ‘iris. CSV’ file and display the names and type of each column. Find statistics such as min, max, range, mean, median, variance, standard deviation for each column of data.

Generate histograms and density plots for each sepal length, sepal width, petal length, petal width.

Generate box plots for each of the numerical attributes. Identify the attribute with the highest variance.

2.    Study of homogeneous and heterogeneous data structures such as vector, matrix, array, list, data frame in R.

3.  Introduction to regression using R

Air Velocity (cm/sec)
20,60,100,140,180,220,260,300,340,380
Evaporation Coefficient(mm2
0.18, 0.37, 0.35, 0.78, 0.56, 0.75, 1.18, 1.36, 1.17, 1.65
/sec)


Use R to perform linear regression on the given the data.

Analyze the significance of residual standard-error value, R-squared value, F-statistic. Find the correlation coefficient for this data and analyze the significance of the

correlation value.

Use a Quantile-Quantile plot to determine whether the residuals are normally distributed.

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Perform a log transformation on the ‘Air Velocity 'column, perform linear regression again, and analyze all the relevant values.

4. Introduction to the WEKA machine learning toolkit

Create an ARFF (Attribute-Relation File Format) file and read it in WEKA. Explore the purpose of each button under the preprocess panel after loading the ARFF file. Also, try to interpret using a different ARFF file, weather.arff, provided with WEKA.

5. Performing data preprocessing in Weka – Part1

Study Unsupervised Attribute Filters such asReplaceMissingValues to replace missing values in the given dataset, Add to add the new attribute Average, Discretize to discretize the attributes into bins. Explore Normalize and Standardize options on a dataset with numerical attributes.

6. Perform data preprocessing in WEKA – Part 2

Study the Unsupervised Instance Filters such as Remove Range filter to remove the last two instances, R

7. Classification using the WEKA toolkit – Part 1

Demonstration of classification process using id3 algorithm on categorical dataset(weather).

Demonstration of classification process using naïve Bayes algorithm on categorical dataset (‘vote’).

Demonstration of classification process using Random Forest algorithm on datasets containing large number of attributes.

8. Classification using the WEKA toolkit – Part 2

Demonstration of classification process using J48 algorithm on mixed type of dataset after discretizing numeric attributes.

Perform cross-validation strategy with various fold levels. Compare the accuracy of the results.

9. Performing clustering in WEKA

Apply hierarchical clustering algorithm on numeric dataset and estimate cluster quality.

Apply DBSCAN algorithm on numeric dataset and estimate cluster quality.

Apply COBWEB clustering algorithm on categorical dataset and estimate cluster quality.

10. Association rule analysis in WEKA

Demonstration of Association Rule Mining on supermarket dataset using Apriori Algorithm.

Demonstration of Association Rule Mining on supermarket dataset using FP-Growth Algorithm.

11.  & 12. Rule based inference using any public domain software tool like CLIPS.

References:

Practical data science with R, Nina Zumel and John Mount- Dreamtech Press.



CSE 4.1.8




BIG DATA ANALYTICS LAB




Credits:2



Instruction: 3 Hours
Univ. Exam : 3 Hours


Sessional Marks: 50
Univ-Exam-Marks:50



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Getting Hadoop Up and Running in a cluster:

1.      Setting up Hadoop on standalone machine.

2.      Wordcount Map Reduce program using standalone Hadoop.

3.      Adding the combiner step to the Wordcount Map Reduce program.

4.      Setting up HDFS.

5.      Using HDFS monitoring UI

6.      HDFS basic command-line file operations.

7.      Setting Hadoop in a distributed cluster environment.

8.      Running the WordCount program in a distributed cluster environment.

9.      Using Map Reduce monitoring UI

Hadoop Map Reduce Applications:

10.  Choosing appropriate Hadoop data types.

11.  Implementing a custom Hadoop Writable data type.

12.  Implementing a custom Hadoop key type.

13.  Emitting data of different value types from a mapper.

14.  Choosing a suitable Hadoop Input Format for your input data format.

15.  Formatting the results of Map Reduce Computation – using Hadoop Output Formats.

Analytics

16.  Simple analytics using Map Reduce.

17.  Performing Group-By using Map Reduce.

18.  Calculating frequency distributions and sorting using Map Reduce.

19.  Plotting the Hadoop results using GNU plot.

20.  Calculating histograms using Map Reduce.

21.  Calculating scatter plots using Map Reduce.

22.  Parsing a Complex dataset with Hadoop.

23.  Joining two datasets using Map Reduce.

Learning Resources

Text Book:

1.      Hadoop Map Reduce Cookbook, Srinath Perera & Thilina Gunarathne, 2013, PACKT PUBLISHING.





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