A stochastic computational intelligent solver for numerical treatment of mosquito dispersal model in a heterogeneous environment

Umar, Muhammad; Raja, Muhammad Asif Zahoor; Sabir, Zulqurnain; Alwabli, Afaf S.; Shoaib, Mohammed

doi:10.1140/epjp/s13360-020-00557-8

Cited by 140 publications

(46 citation statements)

References 66 publications

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“…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].…”

Section: Resultsmentioning

confidence: 99%

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

Guirao

Sabir

Saeed

2020

Mathematical Problems in Engineering

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In this study, the design of a novel model based on nonlinear third-order Emden–Fowler delay differential (EF-DD) equations is presented along with two types using the sense of delay differential and standard form of the second-order EF equation. The singularity at ξ = 0 at single or multiple points of each type of the designed EF-DD model are discussed. The detail of shape factors and delayed points is provided for both types of the designed third-order EF-DD model. For the verification and validation of the model, two numerical examples are presented of each case and numerical results have been performed using the artificial neural network along with the hybrid of global and local capabilities. The comparison of the obtained numerical results with the exact solutions shows the perfection and correctness of the designed third-order EF-DD model.

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Section: Resultsmentioning

confidence: 99%

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

Guirao

Sabir

Saeed

2020

Mathematical Problems in Engineering

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“…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].…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2020

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The aim of this work is to design an intelligent computing paradigm through Levenberg-Marquardt artificial neural networks (LMANNs) for solving the mathematical model of Corona virus disease 19 (COVID-19) propagation via human to human interaction. The model is represented with systems of nonlinear ordinary differential equations represented with susceptible, exposed, symptomatic and infectious, super spreaders, infection but asymptomatic, hospitalized, recovery and fatality classes, and reference dataset of the COVID-19 model is generated by exploiting the strength of explicit Runge-Kutta numerical method for metropolitans of China and Pakistan including Wuhan, Karachi, Lahore, Rawalpindi and Faisalabad. The created dataset is arbitrary used for training, validation and testing processes for each cyclic update in Levenberg-Marquardt backpropagation for numerical treatment of the dynamics of COVID-19 model. The effectiveness and reliable performance of the design LMANNs are endorsed on the basis of assessments of achieved accuracy in terms of mean squared error based merit functions, error histograms and regression studies.

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“…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] .…”

Section: Introductionmentioning

confidence: 99%

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

Umar

Sabir

Raja

et al. 2021

Alexandria Engineering Journal

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A stochastic computational intelligent solver for numerical treatment of mosquito dispersal model in a heterogeneous environment

Cited by 140 publications

References 66 publications

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

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

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

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

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