Modeling typhoid dynamics using recurrent neural networks with Bayesian regularization.

Journal: Computational biology and chemistry
Published Date: Nov 21, 2025

Abstract

The current research provides the numerical investigation of the epidemic typhoid model by using the stochastic artificial intelligence-based recurrent neural network. The typhoid system has five different categories: the susceptible, the carrier, the infected, the recovery and the bacterial population. The epidemic typhoid model is numerically handled by using the Bayesian regularization neural network. A Runge-Kutta procedure is used to get the dataset of the model, which is further trained through the Bayesian regularization. The separation of the dataset is performed through training 75 %, testing 15 % and validation 10 %. The precision of obtained performances of the epidemic typhoid model is perceived through the valuation of achieved and reference outcomes along with the absolute error calculated as 10-06 to 10-07. The attained actions of the model are specified to lessen the mean square error in input 10-08-10-10. To observe the efficiency of recurrent neural networks based on Bayesian regularization training, some tests like histogram error, state transitions and correlation indexes have been presented.

Authors

  • Zulqurnain Sabir
    Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
  • M A Abdelkawy
    Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.
  • Muhammad Athar Mehmood
    Department of Mathematics, University of Gujrat, Pakistan. Electronic address: [email protected].
  • Mustafa Bayram
    Department of Computer Engineering, Biruni University, Istanbul 34010, Turkey. Electronic address: [email protected].

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