A Radial Basis Scale Conjugate Gradient Deep Neural Network for the Monkeypox Transmission System

Zulqurnain Sabir, Salem Ben Said () and Juan L. G. Guirao
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Zulqurnain Sabir: Department of Mathematical Sciences, UAE University, Al Ain P.O. Box 15551, United Arab Emirates
Salem Ben Said: Department of Mathematical Sciences, UAE University, Al Ain P.O. Box 15551, United Arab Emirates
Juan L. G. Guirao: Department of Applied Mathematics and Statistics, Technical University of Cartagena, Hospital de Marina, 30203 Cartagena, Spain

Mathematics, 2023, vol. 11, issue 4, 1-13

Abstract: The motive of this study is to provide the numerical performances of the monkeypox transmission system (MTS) by applying the novel stochastic procedure based on the radial basis scale conjugate gradient deep neural network (RB-SCGDNN). Twelve and twenty numbers of neurons were taken in the deep neural network process in first and second hidden layers. The MTS dynamics were divided into rodent and human, the human was further categorized into susceptible, infectious, exposed, clinically ill, and recovered, whereas the rodent was classified into susceptible, infected, and exposed. The construction of dataset was provided through the Adams method that was refined further by using the training, validation, and testing process with the statics of 0.15, 0.13 and 0.72. The exactness of the RB-SCGDNN is presented by using the comparison of proposed and reference results, which was further updated through the negligible absolute error and different statistical performances to solve the nonlinear MTS.

Keywords: monkeypox; deep neural networks; nonlinear; radial basis; scale conjugate gradient; hidden layers (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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