Stochastic numerical technique for solving HIV infection model of CD4+ T cells

Abstract

The intension of the present work is to present the stochastic numerical approach for solving human immunodeficiency virus (HIV) infection model of cluster of differentiation 4 of T-cells, i.e., CD4 + T cells. A reliable integrated intelligent computing framework using layered structure of neural network with different neurons and their optimization with efficacy of global search by genetic algorithms supported with rapid local search methodology of active-set method, i.e., hybrid of GA-ASM, is used for solving the HIV infection model of CD4 + T cells. A comparison between the present results for different neurons-based models and the numerical values of the Runge-Kutta method reveals that the present intelligent computing techniques is trustworthy, convergent and robust. Statistics-based observation on different performance indices further demonstrates the applicability, effectiveness and convergence of the present schemes.

Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control

“…On the basis of above numerical study and investigation, following key findings of SEIPAHRF model for COVID-19 can be observed. In future, one may implement proposed LMANN for solving the systems representing computer virus models [35,36], prediction studies [37][38][39][40][41], nonlinear fractional differential equation [42,43], bioinformatics models [44][45][46] and financial modeling [30,47].…”

Design and Numerical Solutions of a Novel Third-Order Nonlinear Emden–Fowler Delay Differential Model

“…In the future, the proposed scheme ANN-GA-ASM can be applied as an accurate and efficient stochastic numerical solver for nonlinear singular models [42][43][44], computational models of fluid dynamics [45][46][47][48], fractional models [49][50][51][52], and biological models [53][54][55][56][57].…”

Integrated neuro-swarm heuristic with interior-point for nonlinear SITR model for dynamics of novel COVID-19

“…The considerable prospective of neuro-swarm computing solvers is to exploit the stiff systems by operating the collective approximation capability of artificial neural networks (ANNs) together with the optimization of global particle swarm optimization (PSO) along with the local search interior-point approach (IPA), i.e., ANN-PSOIPA [3] , [4] , [5] , [6] , [7] . Recently, the stochastic numerical solvers have been used to exploit nonlinear prey-predator models [8] , financial market forecasting [9] , nonlinear functional differential model [10] , [11] , computing approached contain nonlinear optics [12] , Thomas-Fermi model [13] , summer precipitation forecast for meteorological positions [14] , higher order nonlinear multi-singular model [15] , nonlinear Troesch’s problem [16] , singular periodic boundary value problems [17] , doubly stochastic differential equation [18] , HIV infection model of CD4+ T cells [19] , fourth-order nonlinear singular model [20] , corneal shape model [21] , nonlinear mosquito dispersal model [22] and heat distribution human head model [23] .…”