ArticleIEEE access : practical innovations, open solutions2020
Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT: A Joint Graph Theory and Reinforcement Learning Approach.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.Frontiers in public health · 2022Pooled it
- A Novel Framework for Data Assessment That Uses Edge Technology to Improve the Detection of Communicable Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture.PloS one · 2023Article
- COVID-19 spread algorithm in the international airport network-DetArpds.PeerJ. Computer science · 2023Article
- Vehicular networks for combating a worldwide pandemic: Preventing the spread of COVID-19.Smart health (Amsterdam, Netherlands) · 2022Article
- A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.Frontiers in artificial intelligence · 2022Review
- Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach.BioMed research international · 2022Article
- Internet of Things (IoT) Adoption Model for Early Identification and Monitoring of COVID-19 Cases: A Systematic Review.International journal of preventive medicine · 2022Review
- Review
- Applications of artificial intelligence in battling against covid-19: A literature review.Chaos, solitons, and fractals · 2021Review
- Biochemical and phylogenetic networks-I: hypertrees and corona products.Journal of mathematical chemistry · 2021Article
- Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction.Frontiers in public health · 2021Article
- Development of minimal basic data set to report COVID-19.Medical journal of the Islamic Republic of Iran · 2020Article
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5 authors.
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Abstract
The recent outbreak of the coronavirus disease 2019 (COVID-19) has rapidly become a pandemic, which calls for prompt action in identifying suspected cases at an early stage through risk prediction. To suppress its further spread, we exploit the social relationships between mobile devices in the Social Internet of Things (SIoT) to help control its propagation by allocating the limited protective resources to the influential so-called high-degree individuals to stem the tide of precipitated spreading. By exploiting the so-called differential contact intensity and the infectious rate in susceptible-exposed-infected-removed (SEIR) epidemic model, the resultant optimization problem can be transformed into the minimum weight vertex cover (MWVC) problem of graph theory. To solve this problem in a high-dynamic random network topology, we propose an adaptive scheme by relying on the graph embedding technique during the state representation and reinforcement learning in the training phase. By relying on a pair of real-life datasets, the results demonstrate that our scheme can beneficially reduce the epidemiological reproduction rate of the infection. This technique has the potential of assisting in the early identification of COVID-19 cases.
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