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. 2023 Apr 1:528:160-177.
doi: 10.1016/j.neucom.2023.01.007. Epub 2023 Jan 12.

Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19

Rita Zgheib  1 , Ghazar Chahbandarian  2 , Firuz Kamalov  3 , Haythem El Messiry  4   5 , Ahmed Al-Gindy  3
Affiliations

Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19

Rita Zgheib et al. Neurocomputing (Amst). .

Abstract

The connection between humans and digital technologies has been documented extensively in the past decades but needs to be evaluated through the current global pandemic. Artificial Intelligence(AI), with its two strands, Machine Learning (ML) and Semantic Reasoning, has proven to be a great solution to provide efficient ways to prevent, diagnose and limit the spread of COVID-19. IoT solutions have been widely proposed for COVID-19 disease monitoring, infection geolocation, and social applications. In this paper, we investigate the usage of the three technologies for handling the COVID-19 pandemic. For this purpose, we surveyed the existing ML applications and algorithms proposed during the pandemic to detect COVID-19 disease using symptom factors and image processing. The survey includes existing approaches including semantic technologies and IoT systems for COVID-19. Based on the survey result, we classified the main challenges and the solutions that could solve them. The study proposes a conceptual framework for pandemic management and discusses challenges and trends for future research.

Keywords: COVID-19; Cloud architecture; Internet of things; Machine learning; Ontologies; Survey.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

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Fig. 1
Paper distribution in this review by year.
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Fig. 2
Machine Learning steps.
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Fig. 3
Neural Network structure.
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Fig. 4
Fuzzification Eq. (1) DeFuzzification Eq. (2).
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Ontology learning cake.
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The disease and symptoms ontological model for the gastroenteritis disease..
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Fig. 7
a–d, CT scans of the chest with Bilateral focal consolidation, lobar consolidation and patchy consolidation lower lung. e, CXR shows Bilateral diffuse patchy and fuzzy shadows for the same patient after 5 days from CT.
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Fig. 8
The design pattern for CIDO.
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Fig. 9
The Cloud computing an IoT ecosystem. Adapted from .
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Fig. 10
The ML-Based semantic IoT Framework.
See this image and copyright information in PMC

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