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Artificial intelligence and machine learning techniques in engineering and management

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence and machine learning techniques in engineering and management/ by Komaragiri Srinivasa Raju, Dasika Nagesh Kumar.
Author:	Srinivasa Raju, K.
other author:	Nagesh Kumar, D.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxv, 266 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Description of Performance Indicators -- Chapter 3. Classical Machine Learning Algorithms -- Chapter 4. Advanced Machine Learning Algorithms -- Chapter 5. Fuzzy-based Modelling techniques -- Chapter 6. Emerging Research Areas -- Chapter 7. Case Studies.
Contained By:	Springer Nature eBook
Subject:	Engineering - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-96-2621-2
ISBN:	9789819626212

Artificial intelligence and machine learning techniques in engineering and management
Srinivasa Raju, K.

Artificial intelligence and machine learning techniques in engineering and management[electronic resource] /by Komaragiri Srinivasa Raju, Dasika Nagesh Kumar. - Singapore :Springer Nature Singapore :2025. - xxv, 266 p. :ill., digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Description of Performance Indicators -- Chapter 3. Classical Machine Learning Algorithms -- Chapter 4. Advanced Machine Learning Algorithms -- Chapter 5. Fuzzy-based Modelling techniques -- Chapter 6. Emerging Research Areas -- Chapter 7. Case Studies.

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.

ISBN: 9789819626212

Standard No.: 10.1007/978-981-96-2621-2doiSubjects--Topical Terms:

570384
Engineering
--Data processing.

LC Class. No.: TA347.A78

Dewey Class. No.: 620.0028563

Artificial intelligence and machine learning techniques in engineering and management
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245 1 0 $a Artificial intelligence and machine learning techniques in engineering and management $h [electronic resource] / $c by Komaragiri Srinivasa Raju, Dasika Nagesh Kumar.
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300 $a xxv, 266 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1. Introduction -- Chapter 2. Description of Performance Indicators -- Chapter 3. Classical Machine Learning Algorithms -- Chapter 4. Advanced Machine Learning Algorithms -- Chapter 5. Fuzzy-based Modelling techniques -- Chapter 6. Emerging Research Areas -- Chapter 7. Case Studies.
520 $a 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.
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650 2 4 $a Computer Science. $3 626642
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