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

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https://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?article=1637&context=beer
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http://hdl.handle.net/11603/26456
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Date
2022-11
Type of Work
1 page
Text
conference papers and proceedings
presentations (communicative events)
Citation of Original Publication
Ahmad, 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=beer
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Subjects
COVID-19
Multilayer Perceptron
Residual Neural Network
Optimization
Mesh Adaptive Direct Search Algorithm
Abstract
This 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 Turkey