
Advances in Artificial Intelligence: Efficiency, Reliability, and Innovations in Machine Learning to Healthcare, and Blockchain
Author(s): Jingfeng Zhang (Editor), Joey Zhou (Editor), Rosa Qi Yue So (Editor), Takaharu Yaguchi (Editor), Kentaroh Toyoda (Editor), Andong Wang (Editor), Peilun Dai (Editor), Haotong Qing (Editor), Xingyu Zheng (Editor)
- Publisher: Springer
- Publication Date: May 15, 2026
- Language: English
- Print length: 205 pages
- ISBN-10: 3032123615
- ISBN-13: 9783032123619
Book Description
Artificial intelligence is transforming the way we live, work, and heal. But true progress depends on more than raw power—it requires systems that are efficient, reliable, and trustworthy.
Advances in Artificial Intelligence: Efficiency, Reliability, and Innovations in Machine Learning, Healthcare, and Blockchain explores cutting-edge breakthroughs across machine learning, healthcare, and blockchain. From interpretable tensor models and life-changing medical applications to secure decentralized learning and safer large language models, this book highlights how innovation can meet responsibility.
Written by leading researchers, this book is an essential resource for anyone looking to understand and shape the next generation of AI.
Editorial Reviews
From the Back Cover
Artificial intelligence is transforming the way we live, work, and heal. But true progress depends on more than raw power—it requires systems that are efficient, reliable, and trustworthy.
Advances in Artificial Intelligence: Efficiency, Reliability, and Innovations in Machine Learning, Healthcare, and Blockchain explores cutting-edge breakthroughs across machine learning, healthcare, and blockchain. From interpretable tensor models and life-changing medical applications to secure decentralized learning and safer large language models, this book highlights how innovation can meet responsibility.
Written by leading researchers, this book is an essential resource for anyone looking to understand and shape the next generation of AI.
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