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

Zulqurnain Sabir, Salem Ben Said, Juan L.G. Guirao

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Article number975
JournalMathematics
Volume11
Issue number4
DOIs
Publication statusPublished - Feb 2023

Keywords

  • deep neural networks
  • hidden layers
  • monkeypox
  • nonlinear
  • radial basis
  • scale conjugate gradient

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Mathematics
  • Engineering (miscellaneous)

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