R2017 - M.E. Software Engineering Curriculum and Syllabus - REC

Department of IT | REC




SE17E37 SOFT COMPUTING L T P C

3 0 0 3

OBJECTIVES:

 Learn The Various Soft Computing Frame Works
 Be Familiar With Design Of Various Neural Networks
 Be Exposed To Fuzzy Logic
 Learn Genetic Programming
 Knowledge about the Soft Computing Based Hybrid Fuzzy Controllers

UNIT I INTRODUCTION 9

Artificial Neural Network: Introduction, Characteristics- Learning Methods – Taxonomy – Evolution Of
Neural Networks- Basic Models – Important Technologies – Applications. Fuzzy Logic: Introduction – Crisp
Sets- Fuzzy Sets – Crisp Relations and Fuzzy Relations: Cartesian Product Of Relation – Classical Relation,
Fuzzy Relations, Tolerance And Equivalence Relations, Non-Iterative Fuzzy Sets. Genetic Algorithm-
Introduction – Biological Background – Traditional Optimization and Search Techniques – Genetic Basic
Concepts


UNIT II NEURAL NETWORKS 9
McCulloch-Pitts Neuron – Linear Separability – Hebb Network – Supervised Learning Network: Perceptron
Networks – Adaptive Linear Neuron, Multiple Adaptive Linear Neuron, BPN, RBF, TDNN- Associative
Memory Network: Auto-Associative Memory Network, Hetero-Associative Memory Network, BAM,
Hopfield Networks, Iterative Auto associative Memory Network & Iterative Associative Memory Network –
Unsupervised Learning Networks: Kohonen Self Organizing Feature Maps, LVQ – CP Networks, ART
Network.

UNIT III FUZZY LOGIC 9
Membership Functions: Features, Fuzzification, Methods Of Membership Value Assignments-
Defuzzification: Lambda Cuts – Methods – Fuzzy Arithmetic And Fuzzy Measures: Fuzzy Arithmetic –
Extension Principle – Fuzzy Measures – Measures Of Fuzziness -Fuzzy Integrals – Fuzzy Rule Base And
Approximate Reasoning : Truth Values And Tables, Fuzzy Propositions, Formation Of Rules-Decomposition
Of Rules, Aggregation Of Fuzzy Rules, Fuzzy Reasoning-Fuzzy Inference Systems-Overview Of Fuzzy
Expert System-Fuzzy Decision Making.



UNIT IV GENETIC ALGORITHM 9
Genetic Algorithm And Search Space – General Genetic Algorithm – Operators – Generational Cycle –
Stopping Condition – Constraints – Classification – Genetic Programming – Multilevel Optimization – Real
Life Problem- Advances In GA.

UNIT VHYBRID SOFT COMPUTING TECHNIQUES & APPLICATIONS 9
Neuro-Fuzzy Hybrid Systems – Genetic Neuro Hybrid Systems – Genetic Fuzzy Hybrid And Fuzzy Genetic
Hybrid Systems – Simplified Fuzzy ARTMAP – Applications: A Fusion Approach Of Multispectral Images





Curriculum and Syllabus | M.E Software Engineering | R2017 Page 51

Department of IT | REC


With SAR, Optimization Of Traveling Salesman Problem Using Genetic Algorithm Approach, Soft
Computing Based Hybrid Fuzzy Controllers.

TOTAL: 45 PERIODS

OUTCOMES:

Upon Completion Of The Course, The Student Should Be Able To:

1. Apply Various Soft Computing Frame Works.
2. Design of Various Neural Networks.
3. Use Fuzzy Logic.
4. Apply Genetic Programming.
5. Discuss Hybrid Soft Computing.


TEXT BOOKS:

1. J.S.R.Jang, C.T. Sun And E.Mizutani, ―Neuro-Fuzzy And Soft Computing‖, PHI / Pearson Education
2004.
2. S.N.Sivanandam And S.N.Deepa, ―Principles Of Soft Computing‖, Wiley India Pvt Ltd, 2011.


REFERENCES:

1. S.Rajasekaran and G.A.VijayalakshmiPai, ―Neural Networks, Fuzzy Logic And Genetic Algorithm:
Synthesis & Applications‖, Prentice-Hall Of India Pvt. Ltd., 2006.
2. George J. Klir, Ute St. Clair, Bo Yuan, ―Fuzzy Set Theory: Foundations And Applications‖ Prentice
Hall, 1997.
3. David E. Goldberg, ―Genetic Algorithm In Search Optimization And Machine Learning‖ Pearson
Education India, 2013.
4. James A. Freeman, David M. Skapura, ―Neural Networks Algorithms, Applications, And
Programming Techniques, Pearson Education India, 1991.
5. Simon Haykin, ―Neural Networks Comprehensive Foundation‖ Second Edition, Pearson Education,
2005.









SE17E38 BUSINESS INTELLIGENCE L T P C
3 0 0 3















Curriculum and Syllabus | M.E Software Engineering | R2017 Page 52

Department of IT | REC



OBJECTIVES:
 Identify the enormous opportunities that currently exists in providing business intelligence
services
 Gain a practical understanding of the key data mining methods of classification, prediction, data
reduction and exploration
 Understand and help develop the strategies of modern enterprise decision makers
 Acquire knowledge in many scientific and technological fields including data warehouses, data
mining, content analytics, business process management, visual analytics
 Gain competences in information systems, web science, decision science, software engineering,
and innovation and entrepreneurship.


UNIT I INTRODUCTION
9
BI Basics – Meeting the BI challenge – BI user models – Basic reporting and querying – BI Markets - BI
and Information Exploitation – Value of BI – BI cycle – Bridging the analysis gap – BI Technologies– BI
Decision Support Initiatives – BI Project Team.

UNIT II BI BIG PICTURE 9
Advanced Emerging BI Technologies – Human factors in BI implementations – BI design and development
– OO Approach to BI - BI Environment – BI business process and information flow – Identifying BI
opportunities – Evaluating Alternatives - BI solutions – BI Project Planning.

UNIT III BI ARCHITECTURE 9
Components of BI Architecture – BI Design and prototyping – Importance of Data in Decision Making -
Data requirements Analysis - Using OLAP for BI – Data warehouse and Technical BI Architecture –
Business Rules – Data Quality – Data Integration – High performance BI - BI 2.0 – GoOLAP Fact Retrieval
Framework.

UNIT IV BI TECHNOLOGIES 9
Successful BI – LOFT Effect – Importance of BI Tools – BI standardization - Creating business value
through location based intelligence – Technologies enabling BI – technologies for information integration -
Building effective BI Systems – Strategic, Tactical, Operational and Financial Intelligence.



UNIT V FUTURE OF BI 9
Knowledge Discovery for BI – Markov Logic Networks – BI Search and Text Analytics –
AdvancedVisualization – Semantic Web Technologies for building BI - Service oriented BI – Collaborative
BI - Evaluating BI – Stakeholder model of BI.

TOTAL : 45PERIODS

















Curriculum and Syllabus | M.E Software Engineering | R2017 Page 53

Department of IT | REC





OUTCOMES:
At the end of this course, the students should be able to:
1. Assess the business intelligence potential of today‘s data rich environment
2. Plan how to decide when to use which technique
3. Outline how to implement major techniques using Excel add-ins
4. Gain the intellectual capital required to provide business analytics services.
5. Studied about the Gain competences in information systems, web science, decision science,
software engineering, and innovation and entrepreneurship.

REFERENCES:
1. Cindi Howson,"Successful Business Intelligence‖, Tata McGraw-Hill Education,2007
2. DavidLoshin,‖BusinessIntelligence:TheSavvyManager'sGuide‖,MorganKaufmann,2ndEdition,
Newnes Publishers,2012

3. ElizabethVitt,Michael Luckevich, StaciaMisner,―Business Intelligence‖,O'Reilly Media,Inc.,2010.
4. LarissaTerpelukMoss,S.Atre,‖BusinessIntelligenceRoadmap:TheCompleteProjectLifecycle for
Decision-Support Applications, Addison-Wesley Information Technology Series‖, illustrated edition,
Addison-Wesley Professional,2003
5. Marie -AudeAufaure, Esteban Zimány,― Business Intelligence ,First European Summer School
eBISS,2011.
6. Murugan Anandarajan, Asokan Anandarajan, Cadambi A. Srinivasan, Business Intelligence
Techniques: A Perspective from Accounting and Finance‖, illustrated Springer,2003.
7. Rajiv Sabherwal, Irma Becerra-Fernandez,―Business Intelligence ,illustrated Edition,JohnWiley&
Sons,2010.









































Curriculum and Syllabus | M.E Software Engineering | R2017 Page 54