A numerical simulation of the fractional order Leptospirosis model using the supervise neural network

Mukdasai, Kanit; Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Sadat, R.; Ali, Mohamed R.; Singkibud, Peerapongpat

doi:10.1016/j.aej.2022.06.013

Cited by 51 publications

(8 citation statements)

References 22 publications

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“…2 . The regression-based knacks of AI-SCG-based supervised neural networks are incorporated to study the dynamics of fractional SIQ system while reference dataset for the implementation of the regressive networks is formulated using Adams numerical procedures for the solution of fractional differential equations [ 36 – 38 ]. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing.…”

Section: Methodology: Proposed Ai-scg Proceduresmentioning

confidence: 99%

A computational supervised neural network procedure for the fractional SIQ mathematical model

Mukdasai

Sabir

Raja

et al. 2023

Eur. Phys. J. Spec. Top.

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The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. Supplementary Information The online version contains supplementary material available at 10.1140/epjs/s11734-022-00738-9.

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Section: Methodology: Proposed Ai-scg Proceduresmentioning

confidence: 99%

A computational supervised neural network procedure for the fractional SIQ mathematical model

Mukdasai

Sabir

Raja

et al. 2023

Eur. Phys. J. Spec. Top.

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“…For numerical computation, the time step is chosen as

\delta \tau \le {\mathsf{0.25}}\times \delta {\xi }^{{\mathsf{2}}}.

which is derived from the Courant‐Friedrichs‐Lewy condition of numerical stability. It is to be mentioned here that, one may solve the above nonlinear equations (25)–(28) by the various numerical techniques 52 A.…”

Section: Numerical Techniquesmentioning

confidence: 99%

“…It is to be mentioned here that, one may solve the above nonlinear equations ( 25)-( 28) by the various numerical techniques. 52 The algorithm related to the above numerical scheme is provided in Appendix A. The numerical computations in Figures 2-15 were performed with the assumption that C τ C ( ) = 0 (i.e., for the constant stretching speed of the cylinder).…”

Section: Mathematical Formulationmentioning

confidence: 99%

Thermocapillary thin film flow upon a porous heated stretchable cylinder

Gogoi,

Maity

2023

Heat Trans

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Earlier research work related to the unsteady flow of thin liquid film on a heated stretching cylinder has considered the boundary‐layer approximation under the assumption that the film thickness is thick. In this article, we have considered the full set of Navier–Stokes equations to study the unsteady film development over a porous heated stretching cylinder with the assumption that film height either coincides or lies within the boundary‐layer thickness. The effects of magnetic field, suction/injection, and cooling or heating of the cylinder have been considered for investigation. The governing set of coupled nonlinear partial differential equations is solved numerically by the implicit finite difference scheme (Crank–Nicolson). It was found that the film thinning rate diminishes with rising values of the porosity parameter and Hartmann number. It is also noted that the film thickness is enhanced for injection whereas opposite results are observed for suction through the porous cylinder. The thermocapillary effect showed that the thinning rate of the liquid film decreases or increases according to the surface of the cylinder being heated or cooled. It is observed that continuously increasing the stretching speed of the cylinder produces faster thinning of the liquid film. The temperature of the liquid film is the maximum at the surface of the cylinder and it decreases toward the free surface when the cylinder is heated. But the opposite phenomenon occurs when the cylinder is cooled.

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“…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

Computers, Materials &Amp; Continua

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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 .

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A numerical simulation of the fractional order Leptospirosis model using the supervise neural network

Cited by 51 publications

References 22 publications

A computational supervised neural network procedure for the fractional SIQ mathematical model

A computational supervised neural network procedure for the fractional SIQ mathematical model

Thermocapillary thin film flow upon a porous heated stretchable cylinder

A Neural Study of the Fractional Heroin Epidemic Model

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