Forecast of COVID-19 New Cases Using an MLP Regressor
|Over 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.
|Artificial neural networks (ANN)
|Forecast of COVID-19 New Cases Using an MLP Regressor
|SUNY Polytechnic Institute
|Department of Computer and Information Sciences
|The 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.
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SUNY Polytechnic Institute College of Engineering
This collection contains master's theses, capstone projects, and other student and faculty work from programs within the Department of Engineering, including computer science and network security.