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dc.contributor.authorAlarcon, Hector
dc.date.accessioned2023-02-16T17:12:21Z
dc.date.available2023-02-16T17:12:21Z
dc.date.issued2022-12
dc.identifier.urihttp://hdl.handle.net/20.500.12648/8378
dc.description.abstractOver the past year the COVID-19 pandemic has overwhelmed healthcare systems and government institutions worldwide, creating the need for accurate prediction of confirmed cases. Current research focuses on prediction methodologies aiming at mitigating economical anxiety and aiding in detection, preemption and forecasting of the pandemic. Modeling using artificial neural networks (ANN) is an important component of this effort and this research aims to contribute with a methodology to forecast occurrence of new cases of COVID-19 1, 2 and 7 days ahead by using historical case data. These approaches were compared and contrasted with some published results in terms of network architecture and neural network of use. Three regressors were developed and showcased in this document with their accuracy evaluated using the root mean squared error (RMSE) from 1179 to 2806. The quality evaluation supports the conclusion that these regressors compete with those of current architectures.en_US
dc.language.isoen_USen_US
dc.subjectCOVID-19en_US
dc.subjectPrediction methodologiesen_US
dc.subjectArtificial neural networks (ANN)en_US
dc.titleForecast of COVID-19 New Cases Using an MLP Regressoren_US
dc.typeMasters Projecten_US
dc.description.versionNAen_US
refterms.dateFOA2023-02-16T17:12:22Z
dc.description.institutionSUNY Polytechnic Instituteen_US
dc.description.departmentDepartment of Computer and Information Sciencesen_US
dc.description.degreelevelMSen_US
dc.description.advisorHashem, Sherif
dc.description.advisorAndriamanalimanana, Bruno
dc.description.advisorSengupta, Saumendra
dc.description.advisorCarpenter, Michael
dc.date.semesterFall 2022en_US
dc.accessibility.statementThe files associated with this item have been scanned and run through optical character recognition, a process that turns an image into a text-searchable file.en_US


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