EXPLORING THE ACCURACY OF AN OPTIMIZATION-FREE NEURAL NETWORK FORECASTING MODEL IN MATHEMATICAL EPIDEMIOLOGY: A CASE STUDY IN TÜRKİYE

Thumbnail Image

Files

Ahmad_Cunel.pdf (558.88 KB)

Links to Files

http://jomardpublishing.com/UploadFiles/Files/journals/JTME/V8N1/Ahmad_Cunel.pdf

Permanent Link

http://hdl.handle.net/11603/28159

Collections

UMBC Mathematics and Statistics Department
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0003-1694-3567

Date

2023-04-30

Type of Work

journal articles
Text

Department

Program

Citation of Original Publication

Ahmad, Muhammad Jalil and Korhan G¨unel. "EXPLORING THE ACCURACY OF AN OPTIMIZATION-FREE NEURAL NETWORK FORECASTING MODEL IN MATHEMATICAL EPIDEMIOLOGY: A CASE STUDY IN TURK˙IYE." Journal of Modern Technology and Engineering 8, no.1 (30 April 2023): 63-71. http://jomardpublishing.com/UploadFiles/Files/journals/JTME/V8N1/Ahmad_Cunel.pdf.

Rights

This 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.
Attribution 4.0 International (CC BY 4.0)

Subjects

Abstract

In this study, we explore the use of mathematical epidemiology models in predicting COVID-19 cases in Turkey. Our approach employs a Feed-Forward Neural Network solver, which is designed to quickly converge and make accurate predictions. To eliminate the need for time-intensive optimization procedures, the network weights are calculated using the Extreme Learning Machine algorithm, ensuring adherence to the initial conditions set by the epidemiology models. We examine the performance of both the Susceptible-Infected (SI) and Susceptible-Infected-Susceptible (SIS) models using this approach and evaluate their accuracy.