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dc.contributor.authorHosseini, Eghbal
dc.contributor.authorGhafoor, Kayhan Zrar
dc.contributor.authorSadiq, Ali Safaa
dc.contributor.authorGuizani, Mohsen
dc.contributor.authorEmrouznejad, Ali
dc.date.accessioned2020-08-18T09:28:47Z
dc.date.available2020-08-18T09:28:47Z
dc.date.issued2020-07-28
dc.identifier.citationHosseini, E., Ghafoor, K.Z., Sadiq, A.S., Guizani, M. and Emrouznejad, A. (2020) COVID-19 optimizer algorithm, modeling and controlling of coronavirus distribution process, IEEE Journal of Biomedical and Health Informatics, 24 (10), pp. 2765-2775 DOI 10.1109/JBHI.2020.3012487en
dc.identifier.issn2168-2194en
dc.identifier.doi10.1109/JBHI.2020.3012487en
dc.identifier.urihttp://hdl.handle.net/2436/623495
dc.descriptionThis is an accepted manuscript of an article published by IEEE in IEEE Journal of Biomedical and Health Informatics on 28/07/2020, available online: https://ieeexplore.ieee.org/document/9151280 The accepted version of the publication may differ from the final published version.en
dc.description.abstractThe emergence of novel COVID-19 causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and prevent the infection rate.It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. Further, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems.This paper first proposes a novel COVID19 optimizer Algorithm (CVA) to cover almost all feasible regions of the optimization problems. We also simulate the coronavirus distribution process in several countries around the globe. Then, we model a coronavirus distribution process as an optimization problem to minimize the number of COVID-19 infected countries and henceslow down the epidemic spread.Furthermore, we propose three scenarios to solve the optimization problem using most effective factors in the distribution process. Simulation results show one of the controlling scenarios outperform the others.Extensive simulations using several optimization problems show that the CVA technique performs best with up to 15%, 37%, 53% and 59% increase compared with Volcano Eruption Algorithm (VEA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttps://ieeexplore.ieee.org/document/9151280en
dc.subjectCovid-19en
dc.subjectCoronavirus Distribution Processen
dc.subjectCoronavirus Simulated Algorithmen
dc.subjectControlling Coronavirus Distributionen
dc.titleCOVID-19 optimizer algorithm, modeling and controlling of coronavirus distribution processen
dc.typeJournal articleen
dc.identifier.journalIEEE Journal of Biomedical and Health Informaticsen
dc.date.updated2020-07-31T12:32:46Z
dc.date.accepted2020-07-18
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW18082020ASen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2020-08-18en
dc.source.volume24
dc.source.issue10
dc.source.beginpage2765
dc.source.endpage2775
refterms.dateFCD2020-08-18T08:58:57Z
refterms.versionFCDAM
refterms.dateFOA2020-08-18T09:28:48Z


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