An advanced heuristic approach for a nonlinear mathematical based medical smoking model

Self Cite

The goal of this research is to introduce the simulation studies of the vector-host disease nonlinear system (VHDNS) along with the numerical treatment of artificial neural networks (ANNs) techniques supported by Levenberg-Marquardt backpropagation (LMQBP), known as ANNs-LMQBP. This mechanism is physically appropriate, where the number of infected people is increasing along with the limited health services. Furthermore, the biological effects have fading memories and exhibit transition behavior. Initially, the model is developed by considering the two and three categories for the humans and the vector species. The VHDNS is constructed with five classes, susceptible humans S h (t), infected humans I h (t), recovered humans R h (t), infected vectors I v (t) , and susceptible vector S v (t) based system of the fractional-order nonlinear ordinary differential equations. To solve the number of variations of the VHDNS, the numerical simulations are performed using the stochastic ANNs-LMQBP. The achieved numerical solutions for solving the VHDNS using the stochastic ANNs-LMQBP have been described for training, verifying, and testing data to decrease the mean square error (MSE). An extensive analysis is provided using the correlation studies, MSE, error histograms (EHs), state transitions (STs), and regression to observe the accuracy, efficiency, expertise, and aptitude of the computing ANNs-LMQBP.

Section: Discussionmentioning

confidence: 99%

Stochastic Investigations for the Fractional Vector-Host Diseased Based Saturated Function of Treatment Model

Botmart¹,

Hiader²,

Sabir³

et al. 2023

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“…Also, ANNs procedures have been used to solve the third kind of Emden-Fowler model in [30] by Sabir et al A new solver based on the Meyer fractional neuro-evolution computing method for solving a doubly singular fractional kind of systems [31]. Few other applications of the stochastic methods can be found [32][33][34][35][36][37] in references therein.…”

Section: Introductionmentioning

confidence: 99%

An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

Weera¹,

Botmart²,

Chantawat³

et al. 2023

The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure. The stochastic procedures mainly depend on the combination of the artificial neural network (ANNs) along with the Levenberg-Marquardt Backpropagation (LMB) i.e., ANNs-LMB technique. The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional order α. The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1. The data proportion is applied as 73%, 15%, and 12% for training, testing, and certification to solve the chaotic fractional system. The acquired results are verified through the comparison of the reference solution, which indicates the proposed technique is efficient and robust. The 4-D chaotic model is numerically solved by using the ANNs-LMB technique to reduce the mean square error (MSE). To authenticate the exactness, and consistency of the technique, the obtained performances are plotted in the figures of correlation measures, error histograms, and regressions. From these figures, it can be witnessed that the provided technique is effective for solving such models to give some new insight into the physical behavior of the model.

“…The purpose of introducing stochastic computing is to solve the nonlinear fractional derivative (NFD) WCM. The stochastic solvers have abundant applications to solve the diversity of applications, like food chain models [39,40], periodic differential models [41,42], thermal explosion theory [43,44], smoking differential models [45,46], corneal shape models [47,48], singular differential systems [49,50], Leptospirosis disease models [51,52] and prevention factor in the HIV systems [53,54]. In this paper, the simulations of the WCM are presented using stochastic procedures.…”

Section: Introductionmentioning

confidence: 99%

A Neural Study of the Fractional Heroin Epidemic Model

Weera¹,

Botmart²,

Zuhra³

et al. 2023

This works intends to provide numerical solutions based on the nonlinear fractional order derivatives of the classical White and Comiskey model (NFD-WCM). The fractional order derivatives have provided authentic and accurate solutions for the NDF-WCM. The solutions of the fractional NFD-WCM are provided using the stochastic computing supervised algorithm named Levenberg-Marquard Backpropagation (LMB) based on neural networks (NNs). This regression approach combines gradient descent and Gauss-Newton iterative methods, which means finding a solution through the sequences of different calculations. WCM is used to demonstrate the heroin epidemics. Heroin has been on-growth world wide, mainly in Asia, Europe, and the USA. It is the fourth foremost cause of death due to taking an overdose in the USA. The nonlinear mathematical system NFD-WCM discusses the overall circumstance of different drug users, such as suspected groups, drug users without treatment, and drug users with treatment. The numerical results of NFD-WCM via LMB-NNs have been substantiated through training, testing, and validation measures. The stability and accuracy are then checked through the statistical tool, such as mean square error (MSE), error histogram, and fitness curves. The suggested methodology's strength is demonstrated by the high convergence between the reference solutions and the solutions generated by adding the efficacy of a constructed solver LMB-NNs, with accuracy levels ranging from 10 −9 to 10 −10 .