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Total Size:
11.8 MB
Info Hash:
9B480C0460BC8304775278C7D66E07643D44D0D3
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Added:
June 3, 2025, 11:35 a.m.
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(Last updated: June 6, 2025, 3:34 p.m.)
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| ['Raju K. Artificial Intelligence and Machine Learning Techniques...2025.pdf'] | 0 bytes |
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13.6 MB
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2024-07-07
| Uploaded by indexFroggy | Size 13.6 MB | Health [ 12 /3 ] | Added 2024-07-07 |
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11.8 MB
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2025-06-03
| Uploaded by andryold1 | Size 11.8 MB | Health [ 18 /41 ] | Added 2025-06-03 |
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10.9 MB
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2023-07-01
| Uploaded by indexFroggy | Size 10.9 MB | Health [ 35 /4 ] | Added 2023-07-01 |
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2.5 GB
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| Uploaded by krishh1337 | Size 2.5 GB | Health [ 641 /175 ] | Added 2026-03-07 |
NOTE
SOURCE: Raju K. Artificial Intelligence and Machine Learning Techniques...2025
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COVER
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MEDIAINFO
Textbook in PDF format The present book covers various facets of Artificial Intelligence, Machine Learning, and Fuzzy Logic. It includes a brief discussion on performance indicators, Classical and Advanced Machine Learning algorithms, Fuzzy logic-based modelling algorithms, Emerging Research Areas, including Blockchain, recent ML techniques, Evolutionary Algorithms, Large Language Model (LLM)-based Generative AI, the Internet of Things, Big Data, Decision Support Systems, Taguchi design of experiments, data augmentation, and Cross-Validation, and representative case studies. The appendix covers representative AI tools, data sources, books, and journals on AI. The present book can support undergraduate, postgraduate, and Ph.D. students in Artificial Intelligence, Generative Artificial Intelligence, Machine Learning, Data Sciences, Soft Computing, and Fuzzy Logic in Engineering and Management and allied fields. The proposed book has immense value in the interdisciplinary and cross-disciplinary context. The present book consists of seven chapters: (1) an introduction; (2) a description of performance indicators; (3) classical Machine Learning algorithms; (4) advanced Machine Learning algorithms; (5) fuzzy logic-based modelling algorithms; (6) emerging research areas, topics including, Blockchain, recent ML techniques, evolutionary algorithms, AI tools, the Internet of Things, Big Data, decision support systems, Taguchi design of experiments, data augmentation, and cross-validation; (7) representative case studies. The appendix covers representative AI tools, data sources related to AI, books, and journals on AI. The present book can support undergraduate, postgraduate, and Ph.D. students in AI, Data Mining, and Soft Computing in Engineering and Management and allied fields. Introduction Description of Performance Indicators Classical Machine Learning Algorithms Advanced Machine Learning Algorithms Fuzzy-Based Modelling Algorithms Emerging Research Areas Case Studies Appendix A Representative AI Tools and Data Sources Related to AI Appendix B Representative Books and Journals on AI
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