Hybrid-based framework for COVID-19 prediction via federated machine learning models

The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog,...

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Vydané v:	The Journal of supercomputing Ročník 78; číslo 5; s. 7078 - 7105
Hlavní autori:	Kallel, Ameni, Rekik, Molka, Khemakhem, Mahdi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2022 Springer Nature B.V
Predmet:	Algorithms Compilers Computer Science Coronaviruses COVID-19 Data processing Decision making Electronic devices Internet of Things Interpreters Machine learning Network latency Pandemics Prediction models Processor Architectures Prognosis Programming Languages Quality of service architectures Response time (computers) Viral diseases Decision-making Quantitative and qualitative evaluation COVID-19 pandemic Batch/streaming data Hybrid fog-cloud federation Machine learning Real-time prediction IoT devices Federated MLaaS
ISSN:	0920-8542, 1573-0484
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