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Erschienen in:
An Overview of Medical Internet of Things, Artificial Intelligence, and Cloud Computing Employed in Health Care from a Modern Panorama Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

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

verfasst von : S. Shitharth, Gouse Baig Mohammad, K. Sangeetha

Erschienen in: The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care

Verlag: Springer International Publishing

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Abstract

All COVID-19 affected countries putting their efforts to deal with the outspread of this death-dealing disease in terms of infrastructure, economics, medical treatments and many other resources. Nowadays, there are number of coronavirus analysis and prediction models are available to make decisions and to informed, aware people. But, absence of necessary data, these models are not able to show precise values. Based on the datasets, reports and on account of the uniform nature of the coronavirus and variations in its behaviour from place-to-place, this study recommend ML as well as deep learning as worthwhile tool to model the outbreak. To come up with for the well-being of living society, we prefer to utilize the ML and deep learning models with the focus for understanding its everyday exponential behaviour in addition to the prediction graphs of further growth of the COVID-2019 over the world by utilizing the available facts and dataset.

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Vorheriges Kapitel Artificial Intelligence for Disease Identification and Diagnosis
Nächstes Kapitel A Review of Computational Intelligence Technologies for Tackling Covid-19 Pandemic
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Metadaten
Titel
Predicting Epidemic Outbreaks Using IOT, Artificial Intelligence and Cloud
verfasst von
S. Shitharth
Gouse Baig Mohammad
K. Sangeetha
Copyright-Jahr
2021
Buch
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-Jahr: 2021

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

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