An Artificial Deep Neural Network Approach for the Computer Epidemic Virus Model

Abstract

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.