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10.1109/JBHI.2020.3012487 http://scihub22266oqcxt.onion/10.1109/JBHI.2020.3012487 32750974!8545158!32750974     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2020 ; 24 (10): 2765-2775 Nephropedia Template TP {{tp|p=32750974|t=2020. COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process |pdf=|usr=}}{{32750974}} gab.com Text COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=32750974 #FOAvip The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. So, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems. This article first proposes a novel COVID-19 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 hence slow 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 outperforms the others. Extensive simulations using several optimization schemes 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. Twit Text FOAVip COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=32750974 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32750974 #FOAvip English Wikipedia <ref>{{Cite pmid|32750974}}</ref> COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process #MMPMID32750974 Hosseini E; Ghafoor KZ; Sadiq AS; Guizani M; Emrouznejad A IEEE J Biomed Health Inform 2020[Oct]; 24 (10): 2765-2775 PMID32750974show ga The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. So, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems. This article first proposes a novel COVID-19 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 hence slow 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 outperforms the others. Extensive simulations using several optimization schemes 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. |*Algorithms[MESH] |*Betacoronavirus[MESH] |*Models, Biological[MESH] |COVID-19[MESH] |Computational Biology[MESH] |Computer Simulation[MESH] |Coronavirus Infections/epidemiology/*prevention & control/transmission[MESH] |Humans[MESH] |Pandemics/*prevention & control/statistics & numerical data[MESH] |Pneumonia, Viral/epidemiology/*prevention & control/transmission[MESH]COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus (epmc).pdf DeepDyve Pubget Overpricing