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<p>This research’s goal is to demonstrate the numerical computing capabilities of the epidemic computer virus delay differential model (ECV-DDM) by relating the deep neural network along with a scale conjugate gradient scheme (DNNP-SCGS). The deep neural network process is implemented by taking 20 and 35 neurons and log-sigmoid transfer function in hidden layers. The mathematical form of the ECV-DDM is divided into uninfected <i>S</i>(<i>u</i>), latently infected <i>L</i>(<i>u</i>), breaking-out <i>B</i>(<i>u</i>), and the antivirus aptitude <i>R</i>(<i>u</i>). The framework based on stochastic computing is accessible for the ECV-DDM by using the data selection as 12%, 14%, and 74% for training, testing and authentication. The comparison of obtained and Runge-Kutta method is presented for the precision of the DNNP-SCGS. The approach’s reliability is authenticated by presenting the state transitions, regression values, correlation, and error histograms.</p>

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An artificial deep neural network approach for the computer epidemic virus model

  • 期刊论文

  • 发表日期：

    2025年11月7日

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  • Zulqurnain Sabir,
  • Ambreen Bano,
  • Muhammad Rehan Banaras,
  • Muhammad Umar,
  • Soheil Salahshour

摘要

This research’s goal is to demonstrate the numerical computing capabilities of the epidemic computer virus delay differential model (ECV-DDM) by relating the deep neural network along with a scale conjugate gradient scheme (DNNP-SCGS). The deep neural network process is implemented by taking 20 and 35 neurons and log-sigmoid transfer function in hidden layers. The mathematical form of the ECV-DDM is divided into uninfected S(u), latently infected L(u), breaking-out B(u), and the antivirus aptitude R(u). The framework based on stochastic computing is accessible for the ECV-DDM by using the data selection as 12%, 14%, and 74% for training, testing and authentication. The comparison of obtained and Runge-Kutta method is presented for the precision of the DNNP-SCGS. The approach’s reliability is authenticated by presenting the state transitions, regression values, correlation, and error histograms.