Solving Mathematical Epidemiology Models Via Neural Nets Tuned By Mesh Adaptive Direct Search

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dc.contributor.authorAhmad, Muhammad Jalil
dc.contributor.authorGünel, Korhan
dc.date.accessioned2022-12-14T18:36:36Z
dc.date.available2022-12-14T18:36:36Z
dc.date.issued2022-11
dc.description.abstractThis study was carried out with the aim of developing an artificial neural network model that will predict the rate of positive cases, infected and recovered individuals in the population with respect to the COVID-19 pandemic in Turkeyen_US
dc.description.urihttps://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?article=1637&context=beeren_US
dc.format.extent1 pageen_US
dc.genreconference papers and proceedingsen_US
dc.genrepresentations (communicative events)en_US
dc.identifierdoi:10.13016/m2jc0b-5uqv
dc.identifier.citationAhmad, Muhammad Jalil, & Korhan Gunel. "Solving Mathematical Epidemiology Models Via Neural Nets Tuned By Mesh Adaptive Direct Search." In Proceedings of the Symposium on BEER (November 2022). https://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?article=1637&context=beeren_US
dc.identifier.urihttp://hdl.handle.net/11603/26456
dc.language.isoen_USen_US
dc.publisherIllinois State Universityen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.subjectCOVID-19en_US
dc.subjectMultilayer Perceptronen_US
dc.subjectResidual Neural Networken_US
dc.subjectOptimizationen_US
dc.subjectMesh Adaptive Direct Search Algorithmen_US
dc.titleSolving Mathematical Epidemiology Models Via Neural Nets Tuned By Mesh Adaptive Direct Searchen_US
dc.typeTexten_US

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