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System Design for Epidemics Using Machine Learning and Deep Learning

About 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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  • Language:
  • English
  • ISBN:
  • 9783031197512
  • Binding:
  • Hardback
  • Pages:
  • 348
  • Published:
  • February 1, 2023
  • Edition:
  • 23001
  • Dimensions:
  • 160x25x241 mm.
  • Weight:
  • 688 g.
Delivery: 2-3 weeks
Expected delivery: November 13, 2024

Description of 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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