Muhammad Sulaiman

Muhammad Sulaiman
Abdul Wali Khan University Mardan · Department of Mathematics

PhD in Mathematics (University of Essex UK)
Soft Computing Techniques, Differential Equations, Heuristics, Artificial Neural Networks, Optimization Problems.

About

70
Publications
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Introduction
Muhammad Sulaiman received his B.Sc. degree from the University of Peshawar in 2004, M.Sc., and MPhil degrees in mathematics from the Quaid-e-Azam University Islamabad Pakistan, in 2007, and 2009 respectively. The PhD degree in mathematics from the University of Essex UK, in April 2015. He is an Associate Professor of Mathematics with the Abdul Wali Khan University Mardan, Pakistan. For more information please visit: www.msulaiman.org
Additional affiliations
August 2021 - present
Abdul Wali Khan University Mardan
Position
  • Professor (Associate)
February 2016 - August 2021
Abdul Wali Khan University Mardan
Position
  • Professor (Assistant)
October 2011 - April 2015
University of Essex
Position
  • Research Assistant
Education
October 2011 - April 2015
University of Essex
Field of study
  • Applied Mathematics

Publications

Publications (70)
Article
The steady-state reactive transport model (RTM) is a generalization of the nonlinear reaction-diffusion model in porous catalysts. The RTM is expressed as a non-linear ordinary differential equation of second-order with boundary conditions. Artificial neural network (ANN), Particle swarm optimization (PSO), and hybrid of PSO-SQP (Sequential Quadrat...
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In this study, we provide a discretized system of a continuous dynamical model for enhancing crop production in the presence of insecticides and insects. Crops are assumed to grow logistically but are limited by an insect population that entirely depends on agriculture. To protect crops from insects, farmers use insecticides, and their overmuch use...
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Heat transfer has a vital role in material selection, machinery efficacy, and energy consumption. The notion of heat transfer is essential in understanding many phenomena related to several engineering fields. Particularly, Mechanical, civil and chemical engineering. The presentation of the heat transfer model in this manuscript is a dedication to...
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Little is known about the rising impacts of Coriolis force and volume fraction of nanoparticles in industrial, mechanical, and biological domains, with an emphasis on water conveying 47 nm nanoparticles of alumina nanoparticles. We explored the impact of the volume fraction and rotation parameter on water conveying 47 nm of alumina nanoparticles ac...
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In the present research work, we designed a hybrid stochastic numerical solver to investigate nonlinear singular two-point boundary value problems with Neumann and Robin boundary conditions arising in various physical models. In this method, we hybridized Harris Hawks Optimizer with Interior Point Algorithm named HHO-IPA. We construct artificial ne...
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This study investigated the steady two-phase flow of a nanofluid in a permeable duct with thermal radiation, a magnetic field, and external forces. The basic continuity and momentum equations were considered along with the Buongiorno model to formulate the governing mathematical model of the problem. Furthermore, the intelligent computational stren...
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is paper analyzed the three-dimensional (3D) condensation film problem over an inclined rotating disk. e mathematical model of the problem is governed by nonlinear partial differential equations (NPDE's), which are reduced to the system of nonlinear ordinary differential equations (NODE's) using a similarity transformation. Furthermore, the system...
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In this paper, the mathematical models for flow and heat-transfer analysis of a non-Newtonian fluid with axisymmetric channels and porous walls are analyzed. The governing equations of the problem are derived by using the basic concepts of continuity and momentum equations. Furthermore, artificial intelligence-based feedforward neural networks (ANN...
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In this study, the intelligent computational strength of neural networks (NNs) based on the backpropagated Levenberg-Marquardt (BLM) algorithm is utilized to investigate the numerical solution of nonlinear multiorder fractional differential equations (FDEs). The reference data set for the design of the BLM-NN algorithm for different examples of FDE...
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In this paper, a mathematical model for the rolling motion of ships in random beam seas has been investigated. The ships’ steady-state rolling motion with a nonlinear restoring moment and damping effect is modeled by the nonlinear second-order differential equation. Furthermore, an artificial neural network (NN)-based, backpropagated Levenberg-Marq...
Article
In recent studies, several chaos-based secure communication (CBSC) systems are developed and are assumed to be effective and promising tools for balancing communications in wireless applications. The reliability of a wireless communication system is dependent on the synchronization between receiving and transmitting nodes. Therefore, it is crucial...
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In this paper, a novel soft computing technique is designed to analyze the mathematical model of the steady thin film flow of Johnson–Segalman fluid on the surface of an infinitely long vertical cylinder used in the drainage system by using artificial neural networks (ANNs). The approximate series solutions are constructed by Legendre polynomials a...
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In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A data set for different p...
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In this paper, a hybrid metaheuristic of Particle Swarm Optimization (PSO) and the Interior Point Algorithm (IPA) is used to analyze and find better solutions to the nonlinear magneto-hydrodynamic Jeffery Hamel (MHD-JHF) problem modeling the arterial blood flow in humans. The nonlinear magnetohydrodynamic Jeffery-Hamel partial differential equation...
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In this paper, a mathematical model for large deformation of a cantilever beam subjected to tip-concentrated load is presented. The model is governed by nonlinear differential equations. Large deformation of a cantilever beam has number of applications is structural engineering. Since finding an exact solution to such nonlinear models is difficult...
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In this study, we have investigated the mathematical model of an immobilized enzyme system that follows the Michaelis–Menten (MM) kinetics for a micro-disk biosensor. The film reaction model under steady state conditions is transformed into a couple differential equations which are based on dimensionless concentration of hydrogen peroxide with enzy...
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A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number (Es), the electrical Reynolds number (ReE), and el...
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This paper analyzes the mathematical model of electrohydrodynamic (EHD) fluid flow in a circular cylindrical conduit with an ion drag configuration. The phenomenon was modelled as a nonlinear differential equation. Furthermore, an application of artificial neural networks (ANNs) with a generalized normal distribution optimization algorithm (GNDO) a...
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In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner–Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heurist...
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In this paper, we analyzed the mass transfer model with chemical reactions during the absorption of carbon dioxide (CO2) into phenyl glycidyl ether (PGE) solution. The mathematical model of the phenomenon is governed by a coupled nonlinear differential equation that corresponds to the reaction kinetics and diffusion. The system of differential equa...
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In this paper, one dimensional mathematical model of convective-conductive-radiative fins is presented with thermal conductivity depending on temperature. The temperature field with insulated tip is determined for a fin in convective, conductive and radiative environments. Moreover, an intelligent soft computing paradigm named as the LeNN-WOA-NM al...
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In the present article, mathematical analysis of drilling system with reverse air circulation is presented by a novel hybrid technique of feed forward artificial neural network (ANN) and biogeography based cuckoo search (BHCS) algorithm. A series solution is constructed with unknown weights for the differential equations representing the drilling p...
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In this paper, the problem of temperature distribution for convective straight fins with constant and temperature-dependent thermal conductivity is solved by using artificial neural networks trained by the biogeography-based heterogeneous cuckoo search (BHCS) algorithm. We have solved the integer and noninteger order energy balance equation in orde...
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In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynom...
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In this paper, a novel soft computing algorithm is designed for the numerical solution of third-order nonlinear multi-singular Emden–Fowler equation (TONMS-EFE) using the strength of universal approximation capabilities of Legendre polynomials based Legendre neural networks supported with optimization power of the Whale Optimization Algorithm (WOA)...
Article
In this paper, a mathematical model for wire coating in the presence of pressure type die along with the bath of Oldroyd 8-constant fluid is presented. The model is governed by a partial differential equation, transformed into a nonlinear ordinary differential equation in dimensionless form through similarity transformations. We have designed a nov...
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Design problems in structural engineering are often modeled as differential equations. These problems are posed as initial or boundary value problems with several possible variations in structural designs. In this paper, we have derived a mathematical model that represents different structures of beam-columns by varying axial load with or without i...
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In this research, we have suggested a combined strategy to calculate and determine the solutions for problems originating in combustion theory and heat transfer. We know these problems as Bratu differential equations. We aim to suggest and test a soft computing technique using an efficient meta-heuristic the Symbiotic organism search (SOS) algorith...
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This research paper deals with a problem related to the damped materials contained in structural dynamics. The problem dealt with here involves a fractional-order damping coefficient in the form of fractional derivatives that present a better mathematical model of the vibration systems. Fractional derivatives are widely used to characterize the vis...
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The flow of fluids in multi-phase porous media results due to many interesting natural phenomena. The counter-current water imbibition phenomena, that occur during oil extraction through a cylindrical well is an interesting problem in petroleum engineering. During the secondary oil recovery process, water is injected into a porous media having hete...
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Ensemble methods based on k-NN models minimise the effect of outliers in a training dataset by searching groups of the k closest data points to estimate the response of an unseen observation. However, traditional k-NN based ensemble methods use the arithmetic mean of the training points’ responses for estimation which has several weaknesses. Tradit...
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In this work, we present a new semi-analytic approximating procedure called optimal homotopy asymptotic method for solving three-dimensional Volterra integral equations of the second kind. The efficiency of the proposed method is confirmed via some numerical examples. Results of the proposed method have been compared with Shifted Chebyshev polynomi...
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In this research, we have investigated doubly singular ordinary differential equations and a real application problem of studying the temperature profile in a porous fin model. We have suggested a novel soft computing strategy for the training of unknown weights involved in the feed-forward artificial neural networks (ANNs). Our neuroevolutionary a...
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Robust modeling of a multimodal dynamic system is a challenging and fast-growing area of research. In this study, an integrated bi-modal computing paradigm based on Nonlinear Autoregressive Radial Basis Functions (NAR-RBFs) neural network model, a new family of deep learning with the strength of hybrid artificial neural network, is presented for th...
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Real application problems in physics, engineering, economics, and other disciplines are often modeled as differential equations. Classical numerical techniques are computationally expensive when we require solutions to our mathematical problems with no prior information. Hence, researchers are more interested in developing numerical methods that ca...
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The object of this study was to demonstrate the ability of machine learning (ML) methods for the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) retinal fundus (RF) images were used. The datasets of DR—that is, the mild, moderate, non-proliferative, proliferative, and normal human eye ones—were acquired from 500 p...
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This paper aims at the analysis of the VdP heartbeat mathematical model. We have analysed the conditionality of a mathematical model which represents the oscillatory behaviour of the heart. A novel neuroevolutionary approach is chosen to analyse the mathematical model. The characteristics of the cardiac pulse of the heart are examined by considerin...
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In modern and large scale power distribution topologies, directional relays play an important role in the operation of an electrical system. These relays must be coordinated optimally so that their overall operating time is reduced to a minimum. They are sensor protection devices for the power systems and must be coordinated properly. The present w...
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In this research, a soft computing approach based on a Nature-inspired technique, the Fractional-Order Darwinian Particle Swarm Optimization (FO-DPSO) algorithm, is hybridized with feed-forward artificial neural network (FF-ANN) to suggest and calculate better solutions for non-linear second-order ordinary differential equation (ODE) representing t...
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Forecasting of fast fluctuated and high-frequency financial data is always a challenging problem in the field of economics and modelling. In this study, a novel hybrid model with the strength of fractional order derivative is presented with their dynamical features of deep learning, long-short term memory (LSTM) networks, to predict the abrupt stoc...
Article
In this paper, we have designed a new optimization technique, which is named as the Improved Multi-verse Algorithm with Levy Flights (ILFMVO) algorithm. The quality of the population is an important factor that can directly or indirectly affect the strength of an algorithm in searching for the given search space for an optimal solution. Also, havin...
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In this research, a soft computing approach based on a Nature-inspired technique, the Fractional-Order Darwinian Particle Swarm Optimization (FO-DPSO) algorithm, is hybridized with feed-forward arti cial neural network (FF-ANN) to suggest and calculate better solutions for non-linear second-order ordinary differential equation (ODE) representing th...
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For analysis of physical properties of different materials, rectangular porous fins are used to examine the heat transformation through a system. In this paper, a metaheuristic is combined with neural computing modelling to study the effects of temperature changes in a porous fin model. Cuckoo search algorithm is used as an efficient optimization t...
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Research community has a growing interest in neural networks because of their practical applications in many fields for accurate modeling and prediction of the complex behavior of systems arising from engineering, economics, business, financial and metrological fields. Artificial neural networks (ANN) are very flexible function approximations tool...
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Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution with optional external archive (JADE) and self-adaptive differential evolution (SaDE) are two well-known self-adaptive versions of differential evolution (DE). They are both unconstrained searc...
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Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization proble...
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The costs of different fuels are increasing gradually, for operation of power production units. Thus new optimization techniques are needed to tackle the complex problems of Economic Load Dispatch (ELD). Metaheuristics are very helpful for policy and decision makers in achieving the best results by minimizing the cost function. In this paper, we ha...
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The ongoing load-shedding and energy crises due to mismanagement of energy produced by different sources in Pakistan, and increasing dependency on those sources which produce energy using expensive fuels have contributed to rise in load shedding and price of energy per kilo watt hour. In this paper, we have presented the linear programming model of...