sabir, Z.Bano, A.Banaras, M.R.Umar, M.Salahshour, S.2025-12-152025-12-1520261598-586510.1007/s12190-025-02683-x2-s2.0-105021119265https://doi.org/10.1007/s12190-025-02683-xhttps://hdl.handle.net/20.500.14517/8655This 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. © The Author(s) under exclusive licence to Korean Society for Informatics and Computational Applied Mathematics 2025.eninfo:eu-repo/semantics/closedAccessComputer Virus Epidemic SystemDeep Neural NetworksDelayHidden LayersScale Conjugate Gradient SchemeArticleQ1Q2721