Utvidet returrett til 31. januar 2025

System Design for Epidemics Using Machine Learning and Deep Learning

Om System Design for Epidemics Using Machine Learning and Deep Learning

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9783031197543
  • Bindende:
  • Paperback
  • Sider:
  • 348
  • Utgitt:
  • 2. februar 2024
  • Utgave:
  • 24001
  • Dimensjoner:
  • 155x19x235 mm.
  • Vekt:
  • 528 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 20. desember 2024
Utvidet returrett til 31. januar 2025

Beskrivelse av System Design for Epidemics Using Machine Learning and Deep Learning

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.

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