Artificial intelligence : (Record no. 240885)
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| fixed length control field | 08263nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260211153645.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260211b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9788126519934 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | AIMIT LIRARY |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | 1 |
| Classification number | 006.3 |
| Item number | GOEL |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Goel, Lavika. |
| 9 (RLIN) | 254454 |
| 245 ## - TITLE STATEMENT | |
| Title | Artificial intelligence : |
| Remainder of title | concepts and application / |
| Statement of responsibility, etc. | By Lavika Goel. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi : |
| Name of publisher, distributor, etc. | Wiley , |
| Date of publication, distribution, etc. | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxi,755p. ; |
| Other physical details | PB |
| Dimensions | 28.2 cm. |
| 500 ## - GENERAL NOTE | |
| General note | Artificial Intelligence: Concepts and Applications is a comprehensive discourse on the fundamental principles and concepts that lead to building artificially intelligent programs. It details the wide range of possible application areas where artificial intelligence can be used. The concepts of heuristic search and development of meta-heuristic algorithms has led a far way towards the development of computational intelligence algorithms and nature inspired algorithms that have been used in a variety of problem solving methods.<br/><br/> |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Preface<br/><br/>Acknowledgments<br/><br/>About the Author<br/><br/>List of Video Content<br/><br/>PART I Foundations of Artificial Intelligence<br/><br/>Chapter 1 Basics of Artificial Intelligence<br/><br/>1.1 What is Artificial Intelligence?<br/><br/>1.2 Definition of Artificial Intelligence Through Problems<br/><br/>1.3 History of Artificial Intelligence<br/><br/>1.4 Artificial Intelligence – Problems and Techniques<br/><br/>1.5 Production Systems<br/><br/>1.6 Shift in Focus of AI Towards Providing Smarter Solutions<br/><br/>Chapter 2 Problem Solving Methods in Artificial Intelligence<br/><br/>2.1 Introduction<br/><br/>2.2 State Space Search<br/><br/>2.3 Production System<br/><br/>2.4 Problem Characteristics<br/><br/>2.5 Control Strategy<br/><br/>2.6 Issues in the Design of Search Programs<br/><br/>2.7 Search Strategies<br/><br/>2.8 Advanced Problems<br/><br/>Chapter 3 Informed and Uninformed Search Strategies<br/><br/>3.1 Introduction<br/><br/>3.2 Generate-and-Test Method<br/><br/>3.3 Hill Climbing Method<br/><br/>3.4 Best First Search and A* Search<br/><br/>3.5 Means End Analysis<br/><br/>3.6 Intelligent Agents and Environment<br/><br/>3.7 Problem Reduction, AO* Algorithm<br/><br/>3.8 Constraint Satisfaction with Inference, Backtracking, and Local Search<br/><br/>3.9 Local Search Algorithms and Optimization Problems<br/><br/>3.10 Local Search in Continuous Spaces<br/><br/>Chapter 4 Knowledge Representation<br/><br/>4.1 Introduction<br/><br/>4.2 Ontologies, Objects, and Events<br/><br/>4.3 Representations and Mappings<br/><br/>4.4 Approaches to Knowledge Representation<br/><br/>4.5 Forward versus Backward Chaining<br/><br/>4.6 Matching and Control Knowledge<br/><br/>4.7 Slot and Filler Structures<br/><br/>4.8 Issues in Knowledge Representation<br/><br/>4.9 Developments in the Field of Knowledge Representation<br/><br/>PART II Basics of Machine Learning<br/><br/>Chapter 5 Neural Networks and Applications<br/><br/>5.1 Introduction<br/><br/>5.2 Learning in Neural Networks<br/><br/>5.3 Choosing Cost Function<br/><br/>5.4 Types of Learning<br/><br/>5.5 Recurrent Neural Network<br/><br/>5.6 Back-propagation<br/><br/>5.7 Convolutional Neural Networks and Deep Neural Networks<br/><br/>5.8 Applications of Neural Networks<br/><br/>5.9 Challenges in Neural Networks<br/><br/>Chapter 6 Fuzzy Logic and Applications<br/><br/>6.1 Introduction<br/><br/>6.2 Set Theory<br/><br/>6.3 Fuzzy Set Theory<br/><br/>6.4 Terminology Associated with Fuzzy Sets<br/><br/>6.5 Fuzzification and Defuzzification<br/><br/>6.6 Formation of Fuzzy Rules<br/><br/>6.7 Fuzzy Logic Inference System<br/><br/>6.8 Fuzzy Database and Queries<br/><br/>6.9 Fuzzy Logic Control System<br/><br/>6.10 Fuzzy Inference Processing: Mamdani and Sugeno<br/><br/>6.11 Adaptive Neuro-Fuzzy Inference System<br/><br/>6.12 Applications<br/><br/>Chapter 7 Statistical Machine Learning<br/><br/>7.1 Introduction<br/><br/>7.2 Probability Axioms<br/><br/>7.3 Bayes’ Rule<br/><br/>7.4 Bayesian Network<br/><br/>7.5 Dynamic Bayesian Networks<br/><br/>7.6 Hidden Markov Model<br/><br/>7.7 Probabilistic Reasoning<br/><br/>7.8 Certainty Factor Theory<br/><br/>7.9 Dempster–Shafer Theory<br/><br/>Chapter 8 Decision Processes and Reinforcement Learning<br/><br/>8.1 What is Learning?<br/><br/>8.2 Forms of Learning<br/><br/>8.3 Learning Decision Trees<br/><br/>8.4 Theory of Learning<br/><br/>8.5 Learning by Examples<br/><br/>8.6 Inductive Learning<br/><br/>8.7 Explanation-Based Learning<br/><br/>8.8 Regression and Classification with Linear Models<br/><br/>8.9 Artificial Neural Networks<br/><br/>8.10 Parametric Models<br/><br/>8.11 Non-Parametric Models<br/><br/>8.12 Support Vector Machines<br/><br/>8.13 Ensemble Learning<br/><br/>8.14 Statistical Learning<br/><br/>8.15 Reinforcement Learning<br/><br/>8.16 Applications of Reinforcement Learning<br/><br/>Chapter 9 Classification Problems in Machine Learning<br/><br/>9.1 Utility Theory<br/><br/>9.2 Multi-Attribute Utility Function<br/><br/>9.3 Decision Network<br/><br/>9.4 Value of Information<br/><br/>9.5 Decision-Theoretic Expert Systems<br/><br/>9.6 Sequential Decision Problems<br/><br/>9.7 Multiple Agent Solution: Game Theory<br/><br/>9.8 Mechanism Design<br/><br/>9.9 Modern Approaches to Classification<br/><br/>PART III Applications of Artificial Intelligence<br/><br/>Chapter 10 Game Playing<br/><br/>10.1 Introduction<br/><br/>10.2 Minimax Search Procedure<br/><br/>10.3 Alpha–Beta Cutoff<br/><br/>10.4 Imperfect Real-Time Decisions<br/><br/>10.5 Stochastic Games<br/><br/>10.6 State-of-the-Art Game Programs<br/><br/>10.7 Modern Examples<br/><br/>Chapter 11 Text Analysis and Mining<br/><br/>11.1 Introduction<br/><br/>11.2 Language Models<br/><br/>11.3 Text Classification<br/><br/>11.4 Information Retrieval<br/><br/>11.5 Information Extraction<br/><br/>11.6 Phrase Structure Grammar<br/><br/>11.7 Syntactic Processing<br/><br/>11.8 Augmented Grammars and Semantic Analysis<br/><br/>11.9 Discourse and Pragmatic Processing<br/><br/>11.10 Statistical Natural Language Processing<br/><br/>11.11 Cross-Lingual Natural Language Processing<br/><br/>11.12 Spell Checking<br/><br/>11.13 Speech Recognition<br/><br/>11.14 Use of Python’s NLTK Library in Modern Text Mining Applications<br/><br/>11.15 Case Study: Sentiment Analysis of User Comments on Social Networking Website Twitter using Machine Learning<br/><br/>Chapter 12 Expert Systems and Applications<br/><br/>12.1 Expert System<br/><br/>12.2 Knowledge Representation<br/><br/>12.3 Expert System Shells<br/><br/>12.4 Knowledge Acquisition of an Expert System<br/><br/>12.5 Applications of Expert Systems<br/><br/>12.6 Examples of Expert Systems<br/><br/>12.7 Problem Solving Examples<br/><br/>PART IV Logic in Artificial Intelligence<br/><br/>Chapter 13 First-Order Logic<br/><br/>13.1 Introduction<br/><br/>13.2 Propositional Logic<br/><br/>13.3 First-Order Logic<br/><br/>Chapter 14 Prolog<br/><br/>14.1 Introduction<br/><br/>14.2 Logic Programming: Symbolic Logic, Clausal Form<br/><br/>14.3 Converting English to Prolog Facts and Rules<br/><br/>14.4 Prolog Terminology<br/><br/>14.5 Variables and Arithmetic Operators<br/><br/>14.6 Inference Process of Prolog<br/><br/>14.7 Tracing Model of Execution<br/><br/>14.8 List Structures<br/><br/>14.9 Operations on List<br/><br/>14.10 Drawbacks of Prolog<br/><br/>14.11 Applications of Logic Programming<br/><br/>Chapter 15 Modern Artificial Intelligence Languages and Tools<br/><br/>15.1 Python<br/><br/>15.2 MATLAB<br/><br/>15.3 R<br/><br/>PART V Trends in Machine Learning<br/><br/>Chapter 16 Concepts in Machine Learning<br/><br/>16.1 Introduction<br/><br/>16.2 Approaches to Machine Learning<br/><br/>16.3 Building Efficient Machine Learning Systems<br/><br/>16.4 Reasons for Sudden Spurt in Use of Machine Learning<br/><br/>16.5 Artificial Intelligence versus Machine Learning<br/><br/>16.6 Taxonomy of Machine Learning Based Techniques<br/><br/>16.7 List of Machine Learning Softwares<br/><br/>Chapter 17 Advanced Topics in Machine Learning<br/><br/>17.1 Introduction<br/><br/>17.2 Artificial Immune System<br/><br/>17.3 Swarm Intelligence<br/><br/>17.4 Geoscience-Based Techniques<br/><br/>17.5 Selection of Suitable Technique Based on Problem Characteristics<br/><br/>17.6 Performance Validation of Intelligent Systems Using Statistics<br/><br/>17.7 Applied Machine Learning<br/><br/>Appendix A Project Work<br/><br/>Appendix B Multiple-Choice Questions and Answers<br/><br/>Appendix C Interview Questions and Answers<br/><br/>Appendix D Bibliography<br/><br/>Index |
| Statement of responsibility | About the Author<br/>Dr. Lavika Goel is currently an Assistant Professor in the Department of Computer Science and Engineering at the Malaviya National Institute of Technology (NIT), Jaipur, Rajasthan, India. She earlier worked at the Birla Institute of Technology and Science (BITS), Pilani, for about five years. She also holds a corporate experience working at Oracle India Private Ltd. She received the prestigious Young Scientist Award by VIFRA International Foundation on 19 December, 2015 during the Annual Research Meet held in Chennai |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Informedg and uniformed search strategies |
| 9 (RLIN) | 254455 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Fuzzy logic and applications |
| 9 (RLIN) | 254456 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Source of heading or term | Advanced topics in machine learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Edition | 1st |
| Call number prefix | 006.3 GOEL |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Total Checkouts | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | MCA | St Aloysius Institute of Management & Information Technology | St Aloysius Institute of Management & Information Technology | Artificial intelligence | 02/03/2026 | KL Book House | 949.00 | Bill.no:1288; Bill.dt:2026/01/23 | 006.3 GOEL | MCA17349 | 05/23/2026 | 711.75 | 02/11/2026 | Book |
