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Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications [Paperback]

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  • Category: Books (Technology & Engineering)
  • ISBN-10:  1032127643
  • ISBN-10:  1032127643
  • ISBN-13:  9781032127644
  • ISBN-13:  9781032127644
  • Publisher:  CRC Press
  • Publisher:  CRC Press
  • Pages:  292
  • Pages:  292
  • Binding:  Paperback
  • Binding:  Paperback
  • SKU:  1032127643-11-MPOD
  • SKU:  1032127643-11-MPOD
  • Item ID: 107093127
  • Seller: ShopSpell
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  • Notes: Brand New Item. Not shipped to AK, HI, APO, FPO, AE.

The book incorporates the many facets of computational intelligence, such as machine learning and deep learning, to provide groundbreaking developments in healthcare applications. It discusses theory, analytical methods, numerical simulation, scientific techniques, analytical outcomes, and computational structuring.

Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.

Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at School of Engineering and Technology, Sharda University, Greater Noida, India.

Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.

This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applicatil/

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