Numerical Investigations of a Fractional Nonlinear Dengue Model Using Artificial Neural Networks

Abstract

The aim of this study is to perform the numerical investigations of a fractional nonlinear dengue model using artificial neuron networks (ANNs) along with the Levenberg–Marquardt backpropagation (LMB), i.e. ANNs. The fractional nonlinear dengue model is divided into five classes. The stochastic-based ANNs-LMB scheme is pragmatic on three variants of authentication, training and testing. The data magnitudes for three different variations based on the fractional nonlinear dengue model are selected as 80% for training, 10% for both testing and validation. The numerical procedures of the fractional nonlinear dengue model will be performed through ANNs-LMB and comparative investigations using the reference values that are calculated on the basis of Adams–Bashforth–Moulton scheme. The solution of the fractional nonlinear dengue model is obtained through the ANNs-LMB to reduce the mean square error (MSE). To authenticate the capability and efficiency of the proposed ANNs-LMB, the obtained numerical measures of correlation, MSE results, regression and error histograms (EHs) are provided.

Swarm-optimized numerical investigation of Dengue Fever Model

“…Recently, ANN-based solvers have been exploited for the numerical treatment of the COVID system with its variants [30][31][32][33][34][35][36][37][38][39], HIV infection system [40][41][42], Dange fever [2,[43][44][45][46], hepatitis virus system [47], influenza virus [48][49][50], and HBV virus [7]. The majority of these ANN-based modeling uses log-sigmoid, tan-sigmoid, and radial basis functions as an activation function, however, there is a need to explore other activation functions like the Mexican hat wavelet which has theoretically good approximation capabilities.…”