Sustainable Applications for Machine Learning
Language: English
Published by MDPI AG, 2026
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 126.29
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - This Reprint brings together recent research on the sustainability of machine learning across critical domains. As AI and smart systems grow, vast data and complex decision-making raise concerns around reliability, efficiency, security, privacy, and societal impact. Contributions examine sustainability from both theoretical and practical perspectives-beyond computational efficiency to include dependability, robustness, ethics, and governance. The works present approaches for trustworthy and resilient ML in real-world settings.Topics include dependable learning, deep neural network optimization and acceleration, privacy-preserving and federated learning, security in generative models, ethical AI, and sustainability of NLP systems. Applied studies may span healthcare, smart cities, Industry 4.0, sustainable supply chains, and circular economy. Combining methodological and application-driven research, this Reprint offers a valuable reference for researchers, practitioners, and decision-makers focused on sustainable ML in high-impact and mission-critical contexts.…
Seller Inventory # 9783725871766
- Title
- Sustainable Applications for Machine Learning
- Publisher
- MDPI AG
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3725871760
- ISBN 13
- 9783725871766
- Item weight
- 885 grams
- Dimensions
- 250x175x22 mm
This Reprint brings together recent research on the sustainability of machine learning across critical domains. As AI and smart systems grow, vast data and complex decision-making raise concerns around reliability, efficiency, security, privacy, and societal impact. Contributions examine sustainability from both theoretical and practical perspectives-beyond computational efficiency to include dependability, robustness, ethics, and governance. The works present approaches for trustworthy and resilient ML in real-world settings.
Topics include dependable learning, deep neural network optimization and acceleration, privacy-preserving and federated learning, security in generative models, ethical AI, and sustainability of NLP systems. Applied studies may span healthcare, smart cities, Industry 4.0, sustainable supply chains, and circular economy. Combining methodological and application-driven research, this Reprint offers a valuable reference for researchers, practitioners, and decision-makers focused on sustainable ML in high-impact and mission-critical contexts.
"Synopsis" may belong to another edition of this title.
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