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10. Predicting Epidemic Outbreaks Using IOT, Artificial Intelligence and Cloud

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
S. Shitharth
Gouse Baig Mohammad
K. Sangeetha
Published in
The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care
Publisher
Springer International Publishing

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Abstract

The chapter delves into the application of IoT, AI, and cloud technologies to predict epidemic outbreaks, specifically focusing on the COVID-19 pandemic. It begins with an overview of the rapid spread of the virus and the challenges faced by healthcare systems. The text then explores the use of machine learning, deep learning, and big data analytics to understand and predict the virus's behavior. Methods such as supervised and unsupervised learning are discussed, along with practical examples of how these technologies can be applied to track and forecast the spread of the virus. The chapter also includes visualizations and analyses of COVID-19 data from various countries, highlighting the importance of data-driven decision-making in combating the pandemic. Throughout, the text emphasizes the potential of advanced technologies in enhancing our understanding and response to epidemic outbreaks.
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previous chapter Artificial Intelligence for Disease Identification and Diagnosis
next chapter A Review of Computational Intelligence Technologies for Tackling Covid-19 Pandemic
Title
Predicting Epidemic Outbreaks Using IOT, Artificial Intelligence and Cloud
Authors
S. Shitharth
Gouse Baig Mohammad
K. Sangeetha
Copyright Year
2021
Book
The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care

Print ISBN: 978-3-030-75219-4

Electronic ISBN: 978-3-030-75220-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-75220-0_10
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