Hardcover. Sustainable AI Techniques for Real-Time Risk Monitoring offers a comprehensive examination of energy-efficient artificial intelligence approaches for hazard detection in smart environments. The book begins by identifying the limitations of traditional AI models particularly their high computational and energy demands and introduces the concept of Green AI as a sustainable alternative. It systematically presents key methodologies, including lightweight deep learning architectures, model optimization techniques, and the integration of edge and fog computing. In addition, it explores advanced paradigms such as federated learning and bio-inspired computing to enable scalable and resource-efficient real-time monitoring systems.The book further elaborates on practical applications across diverse domains, including fire hazard detection, industrial safety, environmental monitoring, and smart healthcare systems. It also examines how secure and decentralized technologiessuch as blockchainenhance the reliability of IoT-based hazard detection frameworks. The concluding section outlines future research directions, emphasizing renewable-powered IoT infrastructures and the ethical, legal, and societal implications of Green AI.Overall, this book serves as a valuable resource for academics, researchers, and practitioners striving to develop sustainable, reliable, and energy-conscious intelligent safety systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Sustainable AI Techniques for Real-Time Risk Monitoring (Hardcover)
Editor: Springer Nature Switzerland AG, Cham
Fecha de publicaci�n: 2026
Idioma: Ingl�s
ISBN 10: 3032286719
ISBN 13: 9783032286710
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

Sustainable AI Techniques for Real-Time Risk Monitoring offers a comprehensive examination of energy-efficient artificial intelligence approaches for hazard detection in smart environments. The book begins by identifying the limitations of traditional AI models ―particularly their high computational and energy demands ― and introduces the concept of Green AI as a sustainable alternative. It systematically presents key methodologies, including lightweight deep learning architectures, model optimization techniques, and the integration of edge and fog computing. In addition, it explores advanced paradigms such as federated learning and bio-inspired computing to enable scalable and resource-efficient real-time monitoring systems.

The book further elaborates on practical applications across diverse domains, including fire hazard detection, industrial safety, environmental monitoring, and smart healthcare systems. It also examines how secure and decentralized technologies―such as blockchain―enhance the reliability of IoT-based hazard detection frameworks. The concluding section outlines future research directions, emphasizing renewable-powered IoT infrastructures and the ethical, legal, and societal implications of Green AI.

Overall, this book serves as a valuable resource for academics, researchers, and practitioners striving to develop sustainable, reliable, and energy-conscious intelligent safety systems.

Acerca del autor

Dr. Abu Sarwar Zamani is an Assistant Professor at Prince Sattam bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia, and an Honorary Research Fellow at INTI International University, Malaysia. Previously, he worked as a Senior Lecturer at Shaqra University and King Saud University, Kingdom of Saudi Arabia. He received his Postdoctoral Fellowship from the Kulliyyah of Engineering, International Islamic University Malaysia, Gombak, Malaysia; his Ph.D. in Computer Science from the Pacific Academy of Higher Education and Research University, India; and his M.Sc. in Computer Science from Hamdard University, New Delhi, India, in 2007.

His research interests include Artificial Intelligence (AI), Machine Learning (ML), the Internet of Things (IoT), Health Informatics, and Big Data. With more than 15 years of experience in teaching, research, and industry, Dr. Zamani has published over 100 research papers. He also serves as an Academic Editor, Associate Editor, and Guest Editor for leading journals published by Springer, Elsevier, and MDPI.

Professor (Dr.) Aisha Hassan Abdalla Hashim is a Full Professor in the Department of Electrical and Computer Engineering at the International Islamic University Malaysia (IIUM), Kuala Lumpur, Malaysia. She received her Ph.D. in Computer Engineering (2007), M.Sc. in Computer Science (1996), and B.Sc. in Electronics Engineering (1990).

Professor Aisha has served as an external examiner, visiting professor, and adjunct professor at several universities. She has published more than 200 journal and conference papers and supervised or co-supervised over 40 Ph.D. and Masters students. She was appointed the IIUM Internationalization Ambassador to Sudan in October 2014 and has played a key role in initiating several memoranda of understanding (MoUs) and promoting Ph.D. student mobility between IIUM and Sudanese universities. In addition to her academic work, she has served as a member of the Board of Studies at the International Islamic School, Malaysia.

Dr. Hazra Imran is an Associate Teaching Professor in the Khoury College of Computer Sciences at Northeastern University, based in Vancouver, Canada. With more than 18 years of experience in academia, Dr. Imran teaches courses in database systems, capstone projects, web development, and the Align program. Beyond teaching, she engages in research collaborations and technological innovations in education, regularly presenting her work at international conferences.

Dr. Imran has served on numerous university and journal committees and maintains an active record of scholarly publications. She holds a Ph.D. in Computer Science and completed a Postdoctoral Fellowship at Athabasca University, Canada, where her research focused on advanced adaptivity and personalization in learning systems.

Dr. Padmaja Savaram is an Assistant Professor in the Department of Computer Science at the College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia. Previously, she served as an Associate Professor in the Department of Computer Science and Engineering at Keshav Memorial Institute of Technology (KMIT) beginning in 2017 and as Head of the Department from 2019 to 2023.

With over 22 years of teaching and research experience, her research expertise lies in Sentiment Analysis, which has resulted in patents, books, research articles, and a monograph on embedded systems. She serves on the editorial board of Data Science and Big Data Analytics and reviews manuscripts for several international journals. Her professional contributions include delivering invited lectures, conducting workshops, and presenting research at leading institutions such as defense laboratories and educational organizations.

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