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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Bibliographic Details
Published in:	The Journal of supercomputing Vol. 78; no. 5; pp. 7078 - 7105
Main Authors:	Kallel, Ameni, Rekik, Molka, Khemakhem, Mahdi
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.04.2022 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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