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For detailed syllabus of all the other subjects of AI&DS 6th Sem, 2021 curriculum do visit AI&DS 6th Sem subject syllabuses for 2021 regulation. For Artificial Intelligence & Data Science 6th Sem semesters scheme and subjects, refer to AI&DS 6th Sem 2021 regulation scheme. The scheme details of Professional Elective-VII for AI&DS 6th Sem is as follows.

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For updated syllabus of Artificial Intelligence & Data Science (AI&DS) 2021, kindly visit AI&DS updated syllabus.

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For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&DS Professional Elective-VII syllabus scheme. The detailed syllabus of ethics and ai is as follows.

Course Objectives:

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Unit I

INTRODUCTION
Definition of morality and ethics in AI-Impact on society-Impact on human psychology-Impact on the legal system-Impact on the environment and the planet-Impact on trust

Unit II

ETHICAL INITIATIVES IN AI
International ethical initiatives-Ethical harms and concerns-Case study: healthcare Autonomous Vehicles , Warfare and weaponization.robots,

Unit III

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Unit IV

ROBOETHICS: SOCIAL AND ETHICAL IMPLICATION OF ROBOTICS
Robot-Roboethics- Ethics and Morality- Moral Theories-Ethics in Science and Technology – Ethical Issues in an ICT Society- Harmonization of Principles- Ethics and Professional Responsibility-Roboethics Taxonomy.

Unit V

AI AND ETHICS- CHALLENGES AND OPPORTUNITIES
Challenges – Opportunities- ethical issues in artificial intelligence- Societal Issues Concerning the Application of Artificial Intelligence in Medicine- decision-making role in industries-National and International Strategies on AI.

Course Outcomes:

On completion of the course, the students will be able to

1. Learn about morality and ethics in AI
2. Acquire the knowledge of real time application ethics, issues and its challenges.
3. Understand the ethical harms and ethical initiatives in AI
4. Learn about AI standards and Regulations like AI Agent, Safe Design of Autonomous and Semi-Autonomous Systems
5. Understand the concepts of Roboethics and Morality with professional responsibilities.
6. Learn about the societal issues in AI with National and International Strategies on AI

Practical Exercises

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Text Books:

1. y. Eleanor Bird, Jasmin Fox-Skelly, Nicola Jenner, Ruth Larbey, Emma Weitkamp and Alan Winfield ,”The ethics of artificial intelligence: Issues and initiatives”, EPRS | European Parliamentary Research Service Scientific Foresight Unit (STOA) PE 634.452 – March 2020
2. Patrick Lin, Keith Abney, George A Bekey,” Robot Ethics: The Ethical and Social Implications of Robotics”, The MIT Press- January 2014.

Reference Books:

1. Towards a Code of Ethics for Artificial Intelligence (Artificial Intelligence: Foundations, Theory, and Algorithms) by Paula Boddington, November 2017
2. Mark Coeckelbergh,” AI Ethics”, The MIT Press Essential Knowledge series, April 2020

Web Link

1. https://sci-hub.mksa.top/10.1007/978-3-540-30301-5_65
2. https://www.scu.edu/ethics/all-about-ethics/artificial-intelligence-and-ethics-sixteen-challenges-and-opportunities/
3. https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/
4. https://sci-hub.mksa.top/10.1159/000492428

For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.

For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&DS Professional Elective-VII syllabus scheme. The detailed syllabus of cognitive science is as follows.

Course Objectives:

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Unit I

PHILOSOPHY, PSYCHOLOGY AND NEUROSCIENCE
Philosophy: Mental-physical Relation – From Materialism to Mental Science – Logic and the Sciences of the Mind – Psychology: Place of Psychology within Cognitive Science – Science of Information Processing -Cognitive Neuroscience – Perception – Decision – Learning and Memory – Language Understanding and Processing.

Unit II

COMPUTATIONAL INTELLIGENCE
Machines and Cognition – Artificial Intelligence – Architectures of Cognition – Knowledge Based Systems – Logical Representation and Reasoning – Logical Decision Making -Learning -Language – Vision.

Unit III

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Unit IV

INFERENCE MODELS OF COGNITION
Generative Models – Conditioning – Causal and statistical dependence – Conditional dependence – Data Analysis – Algorithms for Inference.

Unit V

LEARNING MODELS OF COGNITION
Learning as Conditional Inference – Learning with a Language of Thought – Hierarchical Models-Learning (Deep) Continuous Functions – Mixture Models.

Practical Exercises

1. Demonstration of Mathematical functions using WebPPL.
2. Implementation of reasoning algorithms.
3. Developing an Application system using generative model.
4. Developing an Application using conditional inference learning model.
5. Application development using hierarchical model.
6. Application development using Mixture model.

Course Outcomes:

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Text Books:

1. Vijay V Raghavan,Venkat N.Gudivada, VenuGovindaraju, C.R. Rao, Cognitive Computing: Theory and Applications: (Handbook of Statistics 35), Elsevier publications, 2016
2. Judith Hurwitz, Marcia Kaufman, Adrian Bowles, Cognitive Computing and Big Data Analytics, Wiley Publications, 2015
3. Robert A. Wilson, Frank C. Keil, “The MIT Encyclopedia of the Cognitive Sciences”,The MIT Press, 1999.
4. Jose Luis Bermudez, Cognitive Science -An Introduction to the Science of the Mind, Cambridge University Press 2020

Reference Books:

1. Noah D. Goodman, Andreas Stuhlmuller, “The Design and Implementation of Probabilistic Programming Languages”, Electronic version of book
• https://dippl.org/.
2. Noah D. Goodman, Joshua B. Tenenbaum, The ProbMods Contributors, “Probabilistic Models of Cognition”, Second Edition, 2016
• https://probmods.org/.

For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.

For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&DS Professional Elective-VII syllabus scheme. The detailed syllabus of game theory is as follows.

Course Objectives:

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Unit I

INTRODUCTION
Introduction — Making rational choices: basics of Games — strategy — preferences — payoffs — Mathematical basics — Game theory — Rational Choice — Basic solution concepts-non-cooperative versus cooperative games — Basic computational issues — finding equilibria and learning in games- Typical application areas for game theory (e.g. Google’s sponsored search, eBay auctions, electricity trading markets).

Unit II

GAMES WITH PERFECT INFORMATION
Games with Perfect Information — Strategic games — prisoner’s dilemma, matching pennies -Nash equilibria —mixed strategy equilibrium — zero-sum games

Unit III

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Unit IV

NON-COOPERATIVE GAME THEORY
Non-cooperative Game Theory — Self-interested agents — Games in normal form — Analyzing games: from optimality to equilibrium — Computing Solution Concepts of Normal — Form Games— Computing Nash equilibria of two-player, zero-sum games —Computing Nash equilibria of two-player, general- sum games — Identifying dominated strategies

Unit V

MECHANISM DESIGN
Aggregating Preferences — Social Choice — Formal Model — Voting — Existence of social functions — Ranking systems — Protocols for Strategic Agents: Mechanism Design — Mechanism design with unrestricted preferences

Course Outcomes:

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

1. Discuss the notion of a strategic game and equilibria and identify the characteristics of main applications of these concepts.
2. Discuss the use of Nash Equilibrium for other problems.
3. Identify key strategic aspects and based on these be able to connect them to appropriate game theoretic concepts given a real world situation.
4. Identify some applications that need aspects of Bayesian Games.
5. Implement a typical Virtual Business scenario using Game theory.

Laboratory Exercises

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Text Books:

1. M. J. Osborne, An Introduction to Game Theory. Oxford University Press, 2012.
2. M. Machler, E. Solan, S. Zamir, Game Theory, Cambridge University Press, 2013.
3. N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani, Algorithmic Game Theory. Cambridge University Press, 2007.
4. A.Dixit and S. Skeath, Games of Strategy, Second Edition. W W Norton & Co Inc, 2004.
5. YoavShoham, Kevin Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press 2008.
6. Zhu Han, DusitNiyato, WalidSaad, TamerBasar and Are Hjorungnes, “Game Theory in Wireless and Communication Networks”, Cambridge University Press, 2012.
7. Y.Narahari, “Game Theory and Mechanism Design”, IISC Press, World Scientific.
8. William Spaniel, “Game Theory 101: The Complete Textbook”, CreateSpace Independent Publishing, 2011.

For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.

For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&DS Professional Elective-VII syllabus scheme. The detailed syllabus of optimization techniques is as follows.

Course Objectives:

Download the iStudy App for all syllabus and other updates.

Unit I

INTRODUCTION
Optimization Techniques: Introduction to Optimization Problems – Single and Muti- objective Optimization – Classical Techniques – Overview of various Optimization methods – Evolutionary Computing: Genetic Algorithm and Genetic Programming: Basic concept – encoding -representation – fitness function – Reproduction – differences between GA and Traditional optimization methods – Applications – Bio- inspired Computing (BIC): Motivation – Overview of BIC – usage of BIC – merits and demerits of BIC.

Unit II

SWARM INTELLIGENCE
Introduction – Biological foundations of Swarm Intelligence – Swarm Intelligence in Optimization -Ant Colonies: Ant Foraging Behavior – Towards Artificial Ants – Ant Colony Optimization (ACO) -S-ACO – Ant Colony Optimization Metaheuristic: Combinatorial Optimization – ACO Metaheuristic – Problem solving using ACO – Other Metaheuristics – Simulated annealing – Tabu Search -Local search methods – Scope of ACO algorithms.

Unit III

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Unit IV

SWARM ROBOTICS
Foraging for food – Clustering of objects – Collective Prey retrieval – Scope of Swarm Robotics -Social Adaptation of Knowledge: Particle Swarm – Particle Swarm Optimization (PSO) – Particle 164
Swarms for Dynamic Optimization Problems – Artificial Bee Colony (ABC) Optimization biologically inspired algorithms in engineering.

Unit V

CASE STUDIES
Other Swarm Intelligence algorithms: Fish Swarm – Bacteria foraging – Intelligent Water Drop Algorithms – Applications of biologically inspired algorithms in engineering. Case Studies: ACO and PSO for NP-hard problems – Routing problems – Assignment problems – Scheduling problems – Subset problems – Machine Learning Problems – Travelling Salesman problem.

Course Outcomes:

1. Familiarity with the basics of several biologically inspired optimization techniques.
2. Familiarity with the basics of several biologically inspired computing paradigms.
3. Ability to select an appropriate bio-inspired computing method and implement for any application and data set.
4. Theoretical understanding of the differences between the major bio-inspired computing methods.
5. Learn Other Swarm Intelligence algorithms and implement the Bio-inspired technique with other traditional algorithms.

Text Books:

Download the iStudy App for all syllabus and other updates.

Reference Books:

1. Eric Bonabeau, Marco Dorigo, Guy Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems”, Oxford University press, 2000.
2. Christian Blum, Daniel Merkle (Eds.), “Swarm Intelligence: Introduction and Applications”, Springer Verlag, 2008.
3. Leandro N De Castro, Fernando J Von Zuben, “Recent Developments in Biologically Inspired Computing”, Idea Group Inc., 2005.
4. Albert Y.Zomaya, “Handbook of Nature-Inspired and Innovative Computing”, Springer, 2006.
5. C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001.

For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.

For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&DS Professional Elective-VII syllabus scheme. The detailed syllabus of cyber security is as follows.

Course Objectives:

Download the iStudy App for all syllabus and other updates.

Unit I

INTRODUCTION
Cyber Security – History of Internet – Impact of Internet – CIA Triad; Reason for Cyber Crime -Need for Cyber Security – History of Cyber Crime; Cybercriminals – Classification of Cybercrimes – A Global Perspective on Cyber Crimes; Cyber Laws – The Indian IT Act – Cybercrime and Punishment.

Unit II

ATTACKS AND COUNTERMEASURES
OSWAP; Malicious Attack Threats and Vulnerabilities: Scope of Cyber-Attacks – Security Breach -Types of Malicious Attacks – Malicious Software – Common Attack Vectors – Social engineering Attack – Wireless Network Attack – Web Application Attack – Attack Tools – Countermeasures.

Unit III

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Unit IV

INTRUSION DETECTION
Host -Based Intrusion Detection – Network -Based Intrusion Detection – Distributed or Hybrid Intrusion Detection – Intrusion Detection Exchange Format – Honeypots – Example System Snort.

Unit V

INTRUSION PREVENTION
Firewalls and Intrusion Prevention Systems: Need for Firewalls – Firewall Characteristics and Access Policy – Types of Firewalls – Firewall Basing – Firewall Location and Configurations -Intrusion Prevention Systems – Example Unified Threat Management Products.

Practical Exercises:

1. Install Kali Linux on Virtual box
2. Explore Kali Linux and bash scripting
3. Perform open source intelligence gathering using Netcraft, Whois Lookups, DNS Reconnaissance, Harvester and Maltego
4. Understand the nmap command d and scan a target using nmap
5. Install metasploitable2 on the virtual box and search for unpatched vulnerabilities
6. Use Metasploit to exploit an unpatched vulnerability
7. Install Linus server on the virtual box and install ssh
8. Use Fail2banto scan log files and ban Ips that show the malicious signs
9. Launch brute-force attacks on the Linux server using Hydra.
10. Perform real-time network traffic analysis and data pocket logging using Snort

Course Outcomes:

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Text Books:

1. Anand Shinde, “Introduction to Cyber Security Guide to the World of Cyber Security”, Notion Press, 2021 (Unit 1)
2. Nina Godbole, Sunit Belapure, “Cyber Security: Understanding Cyber Crimes, Computer Forensics and Legal Perspectives”, Wiley Publishers, 2011 (Unit 1)
3. https://owasp.org/www-project-top-ten/

Reference Books:

1. David Kim, Michael G. Solomon, “Fundamentals of Information Systems Security”, Jones & Bartlett Learning Publishers, 2013 (Unit 2)
2. Patrick Engebretson, “The Basics of Hacking and Penetration Testing: Ethical Hacking and Penetration Testing Made easy”, Elsevier, 2011 (Unit 3)
3. Kimberly Graves, “CEH Official Certified Ethical hacker Review Guide”, Wiley Publishers, 2007 (Unit 3)
4. William Stallings, Lawrie Brown, “Computer Security Principles and Practice”, Third Edition, Pearson Education, 2015 (Units 4 and 5)
5. Georgia Weidman, “Penetration Testing: A Hands-On Introduction to Hacking”, No Starch Press, 2014 (Lab)

For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.

For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&DS Professional Elective-VII syllabus scheme. The detailed syllabus of health care analytics is as follows.

Course Objectives:

Download the iStudy App for all syllabus and other updates.

Unit I

INTRODUCTION TO HEALTHCARE ANALYSIS
Overview – History of Healthcare Analysis Parameters on medical care systems- Health care policy- Standardized code sets – Data Formats – Machine Learning Foundations: Tree Like reasoning , Probabilistic reasoning and Bayes Theorem, Weighted sum approach.

Unit II

ANALYTICS ON MACHINE LEARNING
Machine Learning Pipeline – Pre-processing -Visualization – Feature Selection – Training model parameter – Evaluation model : Sensitivity , Specificity , PPV ,NPV, FPR ,Accuracy , ROC , Precision Recall Curves , Valued target variables -Python: Variables and types, Data Structures and containers , Pandas Data Frame Operations – Scikit -Learn : Pre-processing , Feature Selection.

Unit III

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Unit IV

HEALTHCARE AND DEEP LEARNING
Introduction on Deep Learning – DFF network CNN- RNN for Sequences – Biomedical Image and Signal Analysis – Natural Language Processing and Data Mining for Clinical Data – Mobile Imaging and Analytics – Clinical Decision Support System.

Unit V

CASE STUDIES
Predicting Mortality for cardiology Practice -Smart Ambulance System using IOT -Hospital Acquired Conditions (HAC) program- Healthcare and Emerging Technologies – ECG Data Analysis.

Course Outcomes:

1. Use machine learning and deep learning algorithms for health data analysis
2. Apply the data management techniques for healthcare data
3. Evaluate the need of healthcare data analysis in e-healthcare, telemedicine and other critical care applications
4. Design health data analytics for real time applications
5. Design emergency care system using health data analysis

Reference Books:

Download the iStudy App for all syllabus and other updates.

For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.

For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&DS Professional Elective-VII syllabus scheme. The detailed syllabus of app development is as follows.

Course Objectives:

Download the iStudy App for all syllabus and other updates.

Unit I

FUNDAMENTALS OF MOBILE & WEB APPLICATION DEVELOPMENT 6
Basics of Web and Mobile application development, Native App, Hybrid App, Cross-platform App, What is Progressive Web App, Responsive Web design,

Unit II

NATIVE APP DEVELOPMENT USING JAVA
Native Web App, Benefits of Native App, Scenarios to create Native App, Tools for creating Native App, Cons of Native App, Popular Native App Dev elopment Frameworks, Java & Kotlin for Android, Swift & Objective-C for iOS, Basics of React Native, Native Components, JSX, State, Props

Unit III

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Unit IV

CROSS-PLATFORM APP DEVELOPMENT USING REACT-NATIVE
What is Cross-platform App, Benefits of Cross-platform App, Criteria for creating Cross-platform App, Tools for creating Cross-platform App, Cons of Cross-platform App, Popular Cross- platform App Development Frameworks, Flutter, Xamarin, React-Native, Basics of React Native, Native Components, JSX, State, Props

Unit V

NON-FUNCTIONAL CHARACTERISTICS OF APP FRAMEWORKS
Comparison of different Ap p frameworks, Build Performance, App Performance, Debugging capabilities, Time to Market, Maintainability, Ease of Development, UI/UX, Reusability

Course Outcomes:

1. Develop Native applications with GUI Components.
2. Develop hybrid applications with basic event handling.
3. Implement cross-platform applications with location and data storage capabilities.
4. Implement cross platform applications with basic GUI and event handling.
5. Develop web applications with cloud database access.

Practical Exercises:

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Text Books:

1. Head First Android Development, Dawn Griffiths, O’Reilly, 1st edition
2. Apache Cordova in Action, Raymond K. Camden, Manning. 2015
3. Full Stack React Native: Create beautiful mobile apps with JavaScript and React Native, Anthony Accomazzo, Houssein Djirdeh, Sophia Shoemaker, Devin Abbott, FullStack publishing

Reference Books:

1. Android Programming for Beginners, John Horton, Packt Publishing, 2nd Edition
2. Native Mobile Development by Shaun Lewis, Mike Dunn
3. Building Cross-Platform Mobile and Web Apps for Engineers and Scientists: An Active Learning Approach, Pawan Lingras, Matt Triff, Rucha Lingras
4. Apache Cordova 4 Programming, John M Wargo, 2015
5. React Native Cookbook, Daniel Ward, Packt Publishing, 2nd Edition

For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.

For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&DS Professional Elective-VII syllabus scheme. The detailed syllabus of bio-inspired optimization techniques is as follows.

Course Objectives:

Download the iStudy App for all syllabus and other updates.

Unit I

INTRODUCTION
Optimization Techniques: Introduction to Optimization Problems – Single and Muti- objective Optimization – Classical Techniques – Overview of various Optimization methods – Evolutionary Computing: Genetic Algorithm and Genetic Programming: Basic concept – encoding -representation – fitness function – Reproduction – differences between GA and Traditional optimization methods – Applications – Bio- inspired Computing (BIC): Motivation – Overview of BIC – usage of BIC – merits and demerits of BIC.

Unit II

SWARM INTELLIGENCE
Introduction – Biological foundations of Swarm Intelligence – Swarm Intelligence in Optimization -Ant Colonies: Ant Foraging Behavior – Towards Artificial Ants – Ant Colony Optimization (ACO) -S-ACO – Ant Colony Optimization Metaheuristic: Combinatorial Optimization – ACO Metaheuristic – Problem solving using ACO – Other Metaheuristics – Simulated annealing – Tabu Search -Local search methods – Scope of ACO algorithms.

Unit III

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Unit IV

SWARM ROBOTICS
Foraging for food – Clustering of objects – Collective Prey retrieval – Scope of Swarm Robotics -Social Adaptation of Knowledge: Particle Swarm – Particle Swarm Optimization (PSO) – Particle 164
Swarms for Dynamic Optimization Problems – Artificial Bee Colony (ABC) Optimization biologically inspired algorithms in engineering.

Unit V

CASE STUDIES
Other Swarm Intelligence algorithms: Fish Swarm – Bacteria foraging – Intelligent Water Drop Algorithms – Applications of biologically inspired algorithms in engineering. Case Studies: ACO and PSO for NP-hard problems – Routing problems – Assignment problems – Scheduling problems – Subset problems – Machine Learning Problems – Travelling Salesman problem.

Course Outcomes:

1. Familiarity with the basics of several biologically inspired optimization techniques.
2. Familiarity with the basics of several biologically inspired computing paradigms.
3. Ability to select an appropriate bio-inspired computing method and implement for any application and data set.
4. Theoretical understanding of the differences between the major bio-inspired computing methods.
5. Learn Other Swarm Intelligence algorithms and implement the Bio-inspired technique with other traditional algorithms.

Text Books:

Download the iStudy App for all syllabus and other updates.

Reference Books:

1. Eric Bonabeau, Marco Dorigo, Guy Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems”, Oxford University press, 2000.
2. Christian Blum, Daniel Merkle (Eds.), “Swarm Intelligence: Introduction and Applications”, Springer Verlag, 2008.
3. Leandro N De Castro, Fernando J Von Zuben, “Recent Developments in Biologically Inspired Computing”, Idea Group Inc., 2005.
4. Albert Y.Zomaya, “Handbook of Nature-Inspired and Innovative Computing”, Springer, 2006.
5. C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001.

For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.

Download the iStudy App for all syllabus and other updates.

For detailed syllabus of all the other subjects of AI&DS 6th Sem, 2021 curriculum do visit AI&DS 6th Sem subject syllabuses for 2021 regulation. For Artificial Intelligence & Data Science 6th Sem semesters scheme and subjects, refer to AI&DS 6th Sem 2021 regulation scheme. The scheme details of Professional Elective-VI for AI&DS 6th Sem is as follows.

Don’t forget to download iStudy App for the latest syllabus, results, class timetable and many more features. In case of questions, don’t feel shy to leave a comment or leave feedback in the iStudy app for faster response.

For the results of Artificial Intelligence & Data Science 6th Sem, kindly visit AI&DS 6th Sem direct results link.

For exam time table of Artificial Intelligence & Data Science (AI&DS), kindly visit Anna University exam timetables.

For Artificial Intelligence & Data Science (AI&DS) notices, kindly visit Anna University notices.

Download the iStudy App for all syllabus and other updates.

For updated syllabus of Artificial Intelligence & Data Science (AI&DS) 2021, kindly visit AI&DS updated syllabus.

Wishing you great luck ahead.