A Reliable Deep Neural Network Using the Radial Basis for the Spreading Virus in Computers with Kill Signals

Abstract

Purpose: The purpose of this work is to provide a reliable neural network process for the spreading virus in computers with kill signals. The mathematical model shows susceptible, exposed, infected individuals to form the virus inactive, and kill signals classes. Method: A structure of deep neural network (DNN) is designed by using two different hidden layers having radial basis activation functions in both layers, optimization through the Bayesian regularization, twenty and thirty numbers of neurons in primary and secondary hidden layers for the spreading virus in computers with kill signals. The stochastic DNN framework is presented to solve the spreading virus in computers with kill signals by selecting the data for training as 70 %, and 15 %, 15 % for both validation and testing. Results: The accuracy of the scheme is observed through the overlapping of the solutions along with negligible absolute error for solving the model. The consistency of the solver is observed through the process of error histogram, regression, and state transition. Novelty: The proposed DNN structure having radial basis activation function has never been applied for the spreading virus in computers with kill signals.