Sabir, ZulqurnainBano, AmbreenBanaras, Muhammad RehanUmar, MuhammadSalahshour, Soheil2025-12-152025-12-1520251598-58651865-208510.1007/s12190-025-02683-x2-s2.0-105021119265https://doi.org/10.1007/s12190-025-02683-xThis 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.eninfo:eu-repo/semantics/closedAccessDeep Neural NetworksDelayComputer Virus Epidemic SystemScale Conjugate Gradient SchemeHidden LayersArticle