Browsing by Author "Shoaib, Muhammad"

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Citation Count: Sabir, Zulqurnain...et al. (2021). "Design of Gudermannian Neuroswarming to solve the singular Emden-Fowler nonlinear model numerically", Nonlinear Dynamics, Vol. 106, No. 4, pp. 3199-3214.
Design of Gudermannian Neuroswarming to solve the singular Emden-Fowler nonlinear model numerically
(2021) Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Cengiz, Korhan; Shoaib, Muhammad; 56389
The current investigation is related to the design of novel integrated neuroswarming heuristic paradigm using Gudermannian artificial neural networks (GANNs) optimized with particle swarm optimization (PSO) aid with active-set (AS) algorithm, i.e., GANN-PSOAS, for solving the nonlinear third-order Emden-Fowler model (NTO-EFM) involving single as well as multiple singularities. The Gudermannian activation function is exploited to construct the GANNs-based differential mapping for NTO-EFMs, and these networks are arbitrary integrated to formulate the fitness function of the system. An objective function is optimized using hybrid heuristics of PSO with AS, i.e., PSOAS, for finding the weights of GANN. The correctness, effectiveness and robustness of the designed GANN-PSOAS are verified through comparison with the exact solutions on three problems of NTO-EFMs. The assessments on statistical observations demonstrate the performance on different measures for the accuracy, consistency and stability of the proposed GANN-PSOAS solver.
Citation Count: Sabir, Zulqurnain...et al. (2020). "Design of stochastic numerical solver for the solution of singular three-point second-order boundary value problems", Neural Computing & Application.
Design of stochastic numerical solver for the solution of singular three-point second-order boundary value problems
(2020) Sabir, Zulqurnain; Baleanu, Dumitru; Shoaib, Muhammad; Raja, Muhammad Asif Zahoo; 56389
In this paper, a novel meta-heuristic computing solver is presented for solving the singular three-point second-order boundary value problems using artificial neural networks (ANNs) optimized by the combined strength of global and local search ability of genetic algorithms (GAs) and interior point algorithm (IPA), i.e., ANN-GA-IPA. The inspiration for presenting this numerical work comes from the intention of introducing a consistent framework that combines the effective features of neural networks optimized with the contexts of soft computing to handle with such challenging systems. Three numerical variants of singular second-order system have been taken to examine the proficiency, robustness, and stability of the designed approach. The comparison of the proposed results of ANN-GA-IPA from available exact solutions shows the good agreement with 5 to 7 decimal places of the accuracy which established worth of the methodology through performance analyses on a single and multiple executions.
Citation Count: Shoaib, Muhammad...et al. (2019). "Fixed Point Theorems for Multi-Valued Contractions in b-Metric Spaces With Applications to Fractional Differential and Integral Equations", IEEE Access, Vol. 7, pp. 127373-127383.
Fixed Point Theorems for Multi-Valued Contractions in b-Metric Spaces With Applications to Fractional Differential and Integral Equations
(IEEE-INST Electrical Electronics Engineers INC, 2019) Shoaib, Muhammad; Abdeljawad, Thabet; Jarad, Fahd; Sarwar, Muhammad; 234808
The aim of this manuscript is to establish common fixed points results for multi-valued mappings via generalized rational type contractions in complete b-metric spaces. Using the derived results, existence of solutions to certain integral equations and fractional differential equations in the frame of Caputo fractional derivative are studied. Examples are provided for the authenticity of the presented work.
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: Jamshed, Wasim...et.al. (2021). "The improved thermal efficiency of Prandtl–Eyring hybrid nanofluid via classical Keller box technique", Scientific Reports, Vol.11, No.1, pp.1-24.
The improved thermal efficiency of Prandtl–Eyring hybrid nanofluid via classical Keller box technique
(2021) Jamshed, Wasim; Baleanu, Dumitru; Nasir, Nor Ain Azeany Moh; Shahzad, Faisal; Nisar, Kottakkaran Sooppy; Shoaib, Muhammad; Ahmad, Sohail; Ismail, Khadiga Ahmed; 56389
Prandtl–Eyring hybrid nanofluid (P-EHNF) heat transfer and entropy generation were studied in this article. A slippery heated surface is used to test the flow and thermal transport properties of P-EHNF nanofluid. This investigation will also examine the effects of nano solid tubes morphologies, porosity materials, Cattaneo–Christov heat flow, and radiative flux. Predominant flow equations are written as partial differential equations (PDE). To find the solution, the PDEs were transformed into ordinary differential equations (ODEs), then the Keller box numerical approach was used to solve the ODEs. Single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) using Engine Oil (EO) as a base fluid are studied in this work. The flow, temperature, drag force, Nusselt amount, and entropy measurement visually show significant findings for various variables. Notably, the comparison of P-EHNF's (MWCNT-SWCNT/EO) heat transfer rate with conventional nanofluid (SWCNT-EO) results in ever more significant upsurges. Spherical-shaped nano solid particles have the highest heat transport, whereas lamina-shaped nano solid particles exhibit the lowest heat transport. The model's entropy increases as the size of the nanoparticles get larger. A similar effect is seen when the radiative flow and the Prandtl–Eyring variable-II are improved.