Artificial Intelligence and Machine Learning Techniques in Engineering and Management - Softcover

Srinivasa Raju, Komaragiri; Nagesh Kumar, Dasika

Softcover

ISBN 10: 9819626234 ISBN 13: 9789819626236

Publisher: Springer, 2026

Synopsis

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.

About this title

About the Author

Dr. K. Srinivasa Raju is Senior Professor at the Department of Civil Engineering, BITS Pilani - Hyderabad Campus, India. He completed Ph.D. from the Indian Institute of Technology Kharagpur.  His main research interests include artificial intelligence, machine learning, the impact of climate change, and multicriterion decision-making. He has authored three books along with Prof. D. Nagesh Kumar, IISc, Bangalore, namely, Fluid Mechanics: Problem-solving using MATLAB (2020); Impact of Climate Change on Water Resources (2018); and Multicriterion Analysis in Engineering and Management (2010). He published more than 58 journal papers, 12 book chapters, and 88 conference papers. He edited five books and was a guest editor for three special issues of journals. He has been a reviewer for more than 45 international journals. Presently, he is Managing Editor of the Journal of Water and Climate Change and Associate Editor of ISH Journal of Hydraulic Engineering. He is also Editorial Board Member of Scientific Reports-Springer Nature.

Dr. D. Nagesh Kumar is Professor at the Department of Civil Engineering at the Indian Institute of Science (IISc), Bangalore, India. He completed Ph.D. from IISc Bangalore. He is presently working as Edward M Curtis Visiting Professor, Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, USA, on sabbatical from IISc. He is Fellow of the Indian Academy of Sciences, Indian National Science Academy, and National Academy of Sciences. He held the Prof. Satish Dhawan Chair Professor position from 2018-21. He was the Chairman, Centre for Earth Sciences, IISc, during 2014-20. He was a Boyscast Fellow with Utah Water Research Laboratory, Utah State University, Logan, UT, USA, in 1999 and Visiting Professor in EMSE, St. Etienne, France in 2012. His research interests include climate hydrology, climate change, water resource systems, deep learning, evolutionary algorithms, fuzzy logic, MCDM, and Remote sensing & GIS applications in water resource engineering. He has supervised 10 Post-Docs and 22 Ph.D.s. He is co-author of 8 books and published more than 240 papers. He has received funding support of more than INR 50 crores for sponsored research. He is Editor-in-chief of Journal of Water and Climate Change and Associate Editor of ASCE Journal of Hydrologic Engineering.

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2026
• Language: English
• ISBN 10: 9819626234
• ISBN 13: 9789819626236
• Binding: Paperback
• Number of pages: 291