Isbn: 9783032286710 - sustainable ai techniques for real-time risk monitoring (the springer series in applied machine learning) (9 resultados)

Hardcover. Condición: new. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Hardcover. Condición: Brand New. 354 pages. 6.14x0.81x9.21 inches. In Stock.

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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 341 pp. Englisch.…

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Nature Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, Heidelberg 356 pp. Englisch.…

Hardcover. Condición: new. 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condición: new. 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…