Sustainable Applications for Machine Learning (Hardcover)
Language: English
Published by Mdpi AG, 2026
- Hardcover
- New
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
AbeBooks seller since June 22, 2007
Condition: New
£ 85.35
Quantity: 1 available
Add to basketItem description from seller
Hardcover. 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. 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.…
Seller Inventory # 9783725871766
- Title
- Sustainable Applications for Machine Learning (Hardcover)
- Author
- Danial Javaheri
- Publisher
- Mdpi AG
- Publication year
- 2026
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3725871760
- ISBN 13
- 9783725871766
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.
AussieBookSeller
Truganina, VIC, Australia
AbeBooks seller since June 22, 2007
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