A novel computational approach-based hyperbolic tangent sigmoid deep neural network for the hepatitis B virus model

The structure of a novel computational hyperbolic tangent sigmoid deep neural network (HTS-DNN) is presented for the numerical solutions of the hepatitis B virus model, which is based on the antibody immune response. The mathematical model is categorized as healthy and hepatocytes, capsids, antibodies and free viruses. A novel process based on the HTS-DNN is exploited by using two hidden layers with 20 and 30 numbers of neurons. The optimization is performed through Bayesian regularization, which is one of the reliable procedures used in the optimization of various problems. A dataset is obtained through the Runge–Kutta solver, which is used to reduce the mean square error by dividing the training, testing and verification data as 70%, 16% and 14%. Moreover, the statistical representations in the sense of error histogram, regression, and state transitions also approve the accuracy of the scheme.