Browsing by Author "Umar, Muhammad"

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Citation Count: Raja, Muhammad Asif Zahoor; Umar, Muhammad; Sabir, Zulqurnain, "A new stochastic computing paradigm for the dynamics of nonlinear singular heat conduction model of the human head", European Physical Journal Plus, Vol. 133, No. 3, (2018)
A New Stochastic Computing Paradigm for the Dynamics of Nonlinear Singular Heat Conduction Model of the Human Head
(Springer Heidelberg, 2018) Raja, Muhammad Asif Zahoor; Umar, Muhammad; Sabir, Zulqurnain; Khan, Junaid Ali; Baleanu, Dumitru; 56389
Bio-inspired computing approaches are effective to solve a variety of dynamical problems. The strength of these stochastic solvers is exploited for the numerical treatment of a nonlinear heat conduction model of the human head using artificial neural networks (ANNs), genetic algorithms (GAs), active-set technique (AST), and their hybrids. The universal function approximation competencies of unsupervised ANNs are utilized in constructing the mathematical model of the problem by defining an error function in the mean squared sense. The training of the design parameters of ANN models is made with global search brilliance of GAs, viably local search with AST and hybrid approach GA-AST. The results of the proposed schemes are determined in terms of temperature profiles by considering variants of the problem with different Biot numbers, metabolic thermogenesis slope parameters and thermogenesis heat production factors. The correctness, effectiveness and convergence of the proposed approaches are also ascertained through statistics.
Citation Count: Sabir, Zulqurnain;...et.al. (2021). "Applications Of Gudermannian Neural Network For Solving The Sitr Fractal System", Fractals, Vol.29, No.1.
Applications Of Gudermannian Neural Network For Solving The Sitr Fractal System
(2021) Sabir, Zulqurnain; Umar, Muhammad; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; 56389
This study is related to explore the Gudermannian neural network (GNN) for solving a nonlinear SITR COVID-19 fractal system by using the optimization efficiencies of a genetic algorithm (GA), a global search technique and sequential quadratic programming (SQP) and a quick local search scheme, i.e. GNN-GA-SQP. The nonlinear SITR COVID-19 fractal system is dependent on four collections: "susceptible", "infected", "treatment"and "recovered". For the optimization procedures through the GNN-GA-SQP, a merit function is constructed using the nonlinear SITR COVID-19 fractal system and its corresponding initial conditions. The description of each collection of the nonlinear SITR COVID-19 fractal system is provided along with comprehensive detail. The comparison of the achieved numerical result performances of each collection of the nonlinear SITR COVID-19 fractal system is performed with the Adams results to verify the exactness of the designed computational GNN-GA-SQP. The statistical processes based on different operators are presented for 30 independent trials using 5 neurons to authenticate the consistency of the designed computational GNN-GA-SQP. Moreover, the graphs of absolute error (AE), performance indices, and convergence measures along with the boxplots and histograms are also plotted to check the stability, exactness and reliability of the designed computational GNN-GA-SQP.
Citation Count: Sabir, Zulqurnain...et al. (2022). "FMNSICS: Fractional Meyer neuro-swarm intelligent computing solver for nonlinear fractional Lane–Emden systems", Neural Computing and Applications, Vol. 34, No. 6, pp. 4193-4206.
FMNSICS: Fractional Meyer neuro-swarm intelligent computing solver for nonlinear fractional Lane–Emden systems
(2022) Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Umar, Muhammad; Shoaib, Muhammad; Baleanu, Dumitru; 56389
The fractional neuro-evolution-based intelligent computing has substantial potential to solve fractional order systems represented with Lane–Emden equation arising in astrophysics including Newtonian self-gravitating, spherically symmetric and polytropic fluid. The present study aimed to present a neuro-swarm-based intelligent computing solver for the solution of nonlinear fractional Lane–Emden system (NFLES) using by exploitation of fractional Meyer wavelet artificial neural networks (FMW-ANNs) and global optimization mechanism of particle swarm optimization (PSO) combined with rapid local search of sequential quadratic programming (SQP), i.e., FMW-ANN-PSO-SQP. The motivation for the design of FMW-ANN-PSO-SQP intelligent computing comes with an objective of presenting an accurate, reliable, and viable framworks to deal with stiff nonlinear singular models represented with NFLES involving both fractional and integer derivative terms. The designed algorithm is tested for six different variants of NFLESs. The obtained numerical outcomes obtained by the proposed FMW-ANN-PSO-SQP are compared with the exact results to authenticate the correctness, efficacy, and viability, and these aspects are further endorsed statistical observations. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Citation Count: Sabir, Zulqurnain...et al. (2021). "Numerical solutions of a novel designed prevention class in the HIV nonlinear model", CMES - Computer Modeling in Engineering and Sciences, Vol. 129, No. 1, pp. 227-251.
Numerical solutions of a novel designed prevention class in the HIV nonlinear model
(2021) Sabir, Zulqurnain; Umar, Muhammad; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; 56389
The presented research aims to design a new prevention class (P) in the HIV nonlinear system, i.e., the HIPV model. Then numerical treatment of the newly formulated HIPV model is portrayed handled by using the strength of stochastic procedure based numerical computing schemes exploiting the artificial neural networks (ANNs) modeling legacy together with the optimization competence of the hybrid of global and local search schemes via genetic algorithms (GAs) and active-set approach (ASA), i.e., GA-ASA. The optimization performances through GA-ASA are accessed by presenting an error-based fitness function designed for all the classes of the HIPV model and its corresponding initial conditions represented with nonlinear systems of ODEs. To check the exactness of the proposed stochastic scheme, the comparison of the obtained results and Adams numerical results is performed. For the convergence measures, the learning curves are presented based on the different contact rate values. Moreover, the statistical performances through different operators indicate the stability and reliability of the proposed stochastic scheme to solve the novel designed HIPV model. © 2021 Tech Science Press. All rights reserved.
Citation Count: Umar, Muhammad...et al. (2019). "Unsupervised constrained neural network modeling of boundary value corneal model for eye surgery", Applied Soft Computing, Vol. 85.
Unsupervised constrained neural network modeling of boundary value corneal model for eye surgery
(2019) Umar, Muhammad; Amin, Fazli; Wahab, Hafiz Abdul; Baleanu, Dumitru; 56389
In this article, a numerical computing technique is developed for solving the nonlinear second order corneal shape model (CSM) using feed-forward artificial neural networks, optimized with particle swarm optimization (PSO) and active-set algorithms (ASA). The design parameter is approved initially with PSO known as global search, while for further prompt local refinements ASA is used. The performance of the design structure is scrutinized by solving a number of variants of CSM. The typical Adams numerical results are used for comparison of the proposed results, which establish the worth of the scheme in terms of convergence and accuracy. For more satisfaction, the present results are also compared with radial basis function (RBF) results. Moreover, statistical analysis based on mean absolute deviation, Theil's inequality coefficient and Nash Sutcliffe efficiency is presented (C) 2019 Published by Elsevier B.V.