Ahmad Iftikhar

• PhD
• Professor (Associate) at University of Gujrat

Techniques based on artificial intelligence have gained popularity recently as a means of solving challenges across a vast array of industries. The optimization of heat transfer systems and fluid flow design is directed by the measurement of irreversibility and inefficiency in thermodynamic processes through entropy generation in fluids. The purpos...

In the present work a unified description of the early negative pressure dark energy during inflation and the late-time dark energy is posited within the framework of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setl...

In the current paper, an analysis of magnetohydrodynamic Williamson nanofluid boundary layer flow is presented, with multiple slips in a porous medium, using a newly designed human-brain-inspired Ricker wavelet neural network solver. The solver employs a hybrid approach that combines genetic algorithms, serving as a global search method, with seque...

The current research is a revolution in the field of neural computation as a quite new stochastic technique based on Ricker wavelet neural networks (RWNNs) is developed to analyze the Maxwell fluid (Max-F) boundary layer flow (BLF) with heat and mass transfer effects over an elongating surface. The global and local search solvers used with RWNNs ar...

Measles continues to be a significant contributor to child mortality worldwide, causing thousands of deaths each year, even though a safe and effective vaccine is available. In recent years, global measles cases have risen significantly, with the majority of infections occurring in children under 5 years old and immunocompromised adults. The presen...

In the current work, the deterministic hepatitis B virus epidemic (DHBVE) model and the stochastic hepatitis B virus epidemic (SHBVE) model are two nonlinear mathematical models that serve as the framework to illustrate and predict the dynamic virus behavior of hepatitis B. We employ an approximation based on the outcomes of the deterministic model...

Cholera is mainly spread by the ingestion of contaminated food or water, especially in areas where poor sanitation is prevalent. The bacteria responsible for cholera, Vibrio cholerae, are observed to multiply in environments lacking proper water treatment and sewage management systems. A novel exploration of machine learning solutions is presented...

This study is the application of a recurrent neural networks with Bayesian regularization optimizer (RNNs-BRO) to analyze the effect of various physical parameters on fluid velocity, temperature, and mass concentration profiles in the Darcy–Forchheimer flow of propylene glycol mixed with carbon nanotubes model across a stretched cylinder. This mode...

In this research-oriented study, dual diffusive Casson nanofluid stretching flow embedded in a Darcy-Forchheimer-type porous medium is scrutinized. A quite new computerized neuro-heuristic optimization technique based on Ricker wavelet neural networks fabricated through global and local solvers namely genetic algorithms and sequential quadratic pro...

The primary subject of this article is the study of the viscous flow of nanofluids consisting of copper-methanol and water in the presence of a three-dimensional stretched sheet, which is subjected to magnetohydrodynamic effects (3D-MHD-NF) by employing artificial recurrent neural networks that are optimized using a Bayesian regularization techniqu...

In this research, a novel design stochastic numerical technique is presented to investigate the unsteady form magnetohydrodynamic (MHD) slip flow along the boundary layer to analyze the transportation and heat transfer in a solar collector through nano liquids which is a revolution in the field of neurocomputing. Thermal conductivity in variable fo...

Comprehending the latent and incubation phases is crucial for the proliferation of infectious viruses and serves as a foundation for developing measures to prevent and govern outbreaks. This communication exploits the novel application of feedforward neural network optimizing with the Levenberg–Marquardt scheme (NN-LMQS) for analyzing the dynamics...

Real-time forecasting of infectious diseases is crucial for effective public health management, particularly during outbreaks. When infectious disease predictions are based on mechanistic models, they can guide resource allocation and help evaluate the potential effects of different interventions. However, accurately parametrizing these models in r...

In this research-oriented study, dual diffusive Casson nanofluid stretching flow embedded in a Darcy-Forchheimer type porous medium is scrutinized using a quite new computerized neuro-heuristic optimization technique based on Ricker wavelet neural networks fabricated through global and local solvers namely genetic algorithms and sequential quadrati...

In this study, an intelligent computing framework is presented to explore the in°uence of unpredictable environmental factors on the spread of infections. A stochastic non-linear SIS epidemic model (SISEM) is examined that incorporates a non-linear incidence rate, and impact of random°uctuations on the disease transmission. The knacks of arti¯cial...

The improvement of thermal exchange is of utmost interest in a wide range of engineering areas. The current study focuses on thermal evaluation involving natural radiation and convection in a fractionally arranged moving longitudinal fin model placed under a magnetic field. We implement the Levenberg Marquardt backpropagation (LMB) algorithm for in...

This study aims to develop a supervised learning artificial recurrent neural network algorithm supported by Bayesian regularization called (ARNN‐BR) to analyze the impact of physical parameters, including radius of curvature (κ$\kappa $), Casson parameter (β$\beta $), heat generation parameter (λ$\lambda $) and radiation parameter (Rd${{R}_d}$) on...

In recent years, the integration of stochastic techniques, especially those based on artificial neural networks, has emerged as a pivotal advancement in the field of computational fluid dynamics. These techniques offer a powerful framework for the analysis of complex fluid flow phenomena and address the uncertainties inherent in fluid dynamics syst...

In this study, an integrated computational intelligence algorithm is implemented for the numerical treatment of the two-point boundary value problems that arise in the nonlinear corneal shape (NCS) model through the exploitation of wavelet neural networks including Mexican-Hat (MHWNNs) and Gaussian-wavelet (GWNNs) through global genetic algorithms...

The current investigative exploration exemplifies the conceptualization of a novel design intelligent computing paradigm based on artificial neural networks (ANNs) by utilizing radial basis function (RBF) to analyze mag-netohydrodynamic (MHD) Williamson nanofluid two-dimensional flow along a stretchable sheet under the effect of chemical reaction a...

This chapter presents an analysis of the influence of mixed convection and variable viscosity under the impact of a transverse magnetic field on a stretching surface. Nanofluid viscidness is supposed to be reliant on temperature. The influence of variable viscidity on the transversal magnetic field and hybrid convection can be seen by using Reynold...

In this research, stochastic computing techniques based on artificial neural networks are applied to the proposed singular nonlinear differential equation to explain thermoregulation in the human dermal region problem to investigate and predict the effect of bioheat temperature on human skin at different atmospheric temperatures with different case...

In this research, stochastic computing techniques based on artificial neural networks are applied to the proposed singular nonlinear differential equation to explain thermoregulation in the human dermal region problem to investigate and predict the effect of bioheat temperature on human skin at different atmospheric temperatures with different case...

Alcohol abuse is a substantial cause of various health and societal issues, as well as a significant factor in global disease. Once alcohol is consumed in the gastrointestinal tract, it undergoes metabolism in the liver and lungs. In this investigation, the nonlinear deterministic and stochastic differential frameworks are analyzed numerically to p...

The aim of this study is to present a novel application of Levenberg–Marquardt backpropagation (LMB) to investigate numerically the solution of functional differential equations (FDE) arising in quantum calculus models (QCMs). The various types of discrete versions of FDM in QCMs are always found to be stiff to solve due to involvement of delay and...

Using an artificial neural network and the Bayesian Regularization Technique (NNs-BRT), the stochastic method’s strength is used to analyze the differential system, illustrating a nonlinear smoke epidemic differential model (NSED). This allows for a more precise, dependable, cost-effective, dynamic calculating approach. In addition to experiments u...

In this communication, a new stochastic numerical paradigm is introduced for thorough scrutinization of the magnetohydrodynamic (MHD) boundary layer flow of Prandtl–Eyring fluidic model along a stretched sheet impact on thermal radiation as well as convective heating scenarios by exploiting the accurate approximation knacks of ANNs (artificial‐neur...

In this present investigative mode of study, a biological innovational approach is adopted in the form of an intelligent computing paradigm to investigate the properties of flow in the case of incompressible magneto nano-polymeric Casson nanofluid using a stochastic numerical technique named artificial neural networks based on the hybridization of...

The current research work focuses on the thermal effectiveness and distribution of an internal heat-generating longitudinal rectangular fin that is dependent on temperature thermal conductivity that varies exponentially. Additionally, the thermal dispersion of a longitudinal fin is studied for thermal conductivities that vary exponentially with tem...

In this present investigative mode of study, the most fascinating biological innovational approach is adopted in the form of an intelligent computing paradigm (ICP) to investigate the properties of flow in the case of incompressible magneto nano-polymeric Casson nanofluid (MNP-CNF). The computational part of the current research study is completed...

In this investigative study, the electro-magneto hydrodynamic (EMHD) infuence on a nano viscous fuid model is scrutinized by designing an artifcial neural network (ANN) paradigm using a neuro-heuristic approach (NHA) through the combination of GAs (genetic algorithms) and one of the most efcient locally searching solver SQP (sequential quadratic pr...

In this study, a novel neuro heuristic approach is designed to investigate the flow properties of magnetohydrodynamic (MHD) nanofluid along an exponentially extending sheet with a permeable medium with the impact of radiation as well as fluctuating heat source/sink. The designed scheme to handle the suggested problem is established through the well...

Over the past two decades, avian influenza viruses have rapidly spread in poultry around the world. Both humans and industrial poultry are at risk from avian influenza virus infection because of their high levels of zoonotic transmission and pandemic potential. The goal of this study is to explore the implementation of a soft computing paradigm tha...

The most important factor for increasing crop production is pest and pathogen resistance, which has a major impact on global food security. Pest management also emphasizes the need for farming awareness. A high crop yield is ultimately achieved by protecting crops from pests and raising public awareness of the devastation caused by pests. In this r...

Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation. Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and dual function of immune stimulations. This work aims to design a soft computing approach using stupendous knacks of neural networks...

The purpose of this study is to explain the design and analysis of a differential system representing a non-linear smoking mathematical (NSM) model by leveraging the strength of the stochastic method via an artificial Neural Network with Levenberg Marquardt technique (NNs-LMBT), which allows for a more accurate, reliable, and efficient calculation...

This research uses the Sinc Collocation method to numerically examine the Degasperis–Procesi and Benjamin–Bona–Mahony equations, achieving a high level of precision and accuracy on computational grounds with a variety of mesh points. The proposed technique involves global collocation using Sinc bases function (SBF) as an activation function. Initia...

In this article, we analyze the dynamics of the non-linear tumor-immune delayed (TID) model illustrating the interaction among tumor cells and the immune system (cytotoxic T lymphocytes, T helper cells), where the delays portray the times required for molecule formation, cell growth, segregation, and transportation, among other factors by exploitin...

The proposed research utilizes a computational approach to attain a numerical solution for the singularly perturbed delay differential equation (SPDDE) problem arising in the neuronal variability model through artificial neural networks (ANNs) with different solvers. The log-sigmoid function is used to construct the fitness function. The implementa...

The proposed research work analyzes the bio-inspired problem through artificial neural networks with a feed-forward approach utilized to approximate the numerical results for singular nonlinear bio-heat equation (BHE) with boundary conditions based on four different scenarios created on the variation of environmental temperature to illustrate the e...

A complete shape factor investigation of water‐based mixture type hybrid nanofluid in a permeable boundary with the impact of magnetic field, thick dissemination, and warm radiation is presented in this article. A computational convection analysis of an inverted semi vertical cone with a porous surface in the form of SiO2$$ {\mathrm{SiO}}_2 $$/wate...

This study is designed to analyze the fuzzy dynamical model of vibrating mass system by using neural networks (NNs). Stochastic numerical solvers are implemented with Levenberg–Marquardt backpropagation (LMB) algorithm. The proposed solvers handle the uncertainties of fuzzy differential system and minimize the error consistently. Validation, consta...

A complete shape factor investigation of water-based mixture type hybrid nano-fluid in a permeable boundary with the impact of magnetic field, thick dissemination, and warm radiation is presented in this article. A computational convection analysis of an inverted semi vertical cone with a porous surface in the form of S i O 2 / w a t e r nano-fluid...

The present research-oriented study provides the application of a renowned numerical-based neuro-evolution heuristic technique by the utilization of feed-forward neural networks (FFNNs) trained with a hybrid composition of genetic algorithms (GAs) and sequential quadratic programming (SQP) i.e. FFNNs-GAs-SQP to provide a descriptive analysis on the...

The dynamics of viral infection within-plant hosts are of critical importance for characterizing the prevalence and impact of plant diseases. However, few mathematical modeling efforts have been made to characterize viral dynamics within plants. In this study, the dynamics of the vector-borne plant epidemic (VBPE) model are modeled with two nonline...

The current study explores a new direction of research in which the magnetohydrodynamic (MHD) effects on two-dimensional nanofluid boundary layer flow under the influence of radiation are investigated in a porous medium using inverse multiquadric (IMQ) radial basis neural networks (RBNNs) i.e. IMQ-RBNNs. A quite new computational approach is used h...

In this investigation, intelligent predictive stochastic computing is presented by exploitation of artificial neural networks Levenberg-Marquardt approach (ANNs-LMA) to analyze the dynamics of a nonlinear differential delay computer virus (DCV) model. The governing differential delay system with four classes representation with nonlinear delayed or...

The main objective of this research is to design a numerical computational solver-based two-layers structure of Levenberg–Marquardt backpropagation neural networks, i.e. LMB-NNs for the analyses of the heat transfer phenomenon and velocities structure in the MHD incompressible hybrid nanofluidic flow (MHD-IHNF) model with thermal slip and heat abso...

In this research-based study, the electro-magneto impacts are numerically observed during Darcy-Forchheimer (DF) viscous fluid flow having nonlinear form of thermal radiation. This two-dimensional fluid flow over a stretchable sheet is exposed to joule heating, thermal convection and viscous dissipation. The entire computational work is successfull...

Wire coating is a commercial method to insulate wires for mechanical intensity and environmental protection. In this experimental study, the technique of computational intelligence is used for nonlinear wire coating analysis by soaking the wires in Oldroyd 8-constant fluid under a constant pressure gradient with the help of feed forward artificial...

A stochastic computing approach is implemented in the present work to solve the nonlinear nanofluidics system that occurs in the model of atomic physics. The process converts the partial differential nanofluidics system with suitable level of similarities transformation into nonlinear systems of differential equations. For the construction of datas...

A repeatedly infected person is one of the most important barriers to malaria disease eradication in the population. In this article, the effects of recurring malaria re-infection and decline in the spread dynamics of the disease are investigated through a supervised learning based neural networks model for the system of non-linear ordinary differe...

The objective of this work is to explore the flow features and thermal radiation properties of the 2-D Magnetohydrodynamic (MHD) Carreau nanofluid model over an impenetrable stretching surface by utilizing the supervised learning strength of Levenberg–Marquardt backpropagation neural networking technique (LMBNNT). The mathematical formulation for M...

The differential equations having delays take paramount interest in the research community due to their fundamental role to interpret and analyze the mathematical models arising in biological studies. This study deals with the exploitation of knack of artificial intelligence-based computing paradigm for numerical treatment of the functional delay d...

This paper portrays the exploitation/exploration of artificial intelligence (AI) inspired computing to study the behavior of the multi-delay differential systems that revealed the impact of latent period and the dynamics of the a susceptible, vaccinated, exposed, infectious and recovered (SVEIR) epidemic model involving vaccination by means of the...

The presented study deals with the exploitation of the artificial intelligence knacks-based stochastic paradigm for the numerical treatment of the nonlinear delay differential system for dynamics of plant virus propagation with the impact of seasonality and delays (PVP-SD) model by implementing neural networks backpropagation with Bayesian regulari...

The main objective of this study is to numerically investigate the dynamical behav-13 ior of nonlinear Fitzhugh-Nagumo and Bateman-Burger systems through the Sinc colloca-14 tion method by means of the θ-weighted scheme on various grid points of time-dependent 15 evolutionary one spatial dimension in open quantum flow field model. The proposed tech...

The main objective of this study is to numerically investigate the dynamical behavior of nonlinear Fitzhugh–Nagumo and Bateman–Burger systems through the Sinc collocation method by means of the θ-weighted scheme on various grid points of time-dependent evolutionary one spatial dimension in open quantum flow field model. The proposed technique based...

In this research paper, an innovative bio-inspired algorithm based on evolutionary cubic splines method (CSM) has been utilized to estimate the numerical results of nonlinear ordinary differential equation Painlevé-I. The computational mechanism is used to support the proposed technique CSM and optimize the obtained results with global search techn...

Nowadays the increasing demand for highly effective cooling devices involving radiative-convective porous fin heat sink with functionally graded material (FGM) has gained intense research attention due to their extensive use in industrial, commercial and strategic types of equipment. The present study aims to introduce a novel application of stocha...

In this study, numerical treatment of liquid crystal model described through Hunter-Saxton equation (HSE) has been presented by sinc collocation technique through theta weighted scheme due to its enormous applications including, defects, phase diagrams, self-assembly, rheology, phase transitions, interfaces, and integrated biological applications i...

The aim of study is to investigate the mass and heat transfer phenomena in hybrid hydro-nanofluidic system involving Al2O3–Cu–H2O over the rotating disk in porous medium with viscous dissolution and Joule heating through the stochastic solver by way of Levenberg-Marquardt backpropagation neural networks. The mathematical model in system of PDEs des...

A novel design of stochastic numerical computing method is introduced for computational fluid dynamics problem governed with nonlinear thin film flow (TFF) system by exploiting the competency of polynomial splines for discretization and optimization with evolutionary computing aided with brilliance of local search. The TFF model of second grade flu...

Recent progress seems to suggest that the use of Sinc collocation method for the numerical treatment of partial differential equations, as a great level of precision and accuracy has been obtained on computational grounds. Sinc functions based on collocation points, provide us, the approximation of functions over all types of problems containing si...

This study carries the novel applications of the Sinc collocation method to investigate the numerical computing paradigm of Schrödinger wave equation and Transport equation as a great level of accuracy and precision. A global collocation-based Sinc function is embedded with a cardinal expansion to discretize initially time derivatives by finite dif...

In this study, bio-inspired computational techniques have been exploited to get the numerical solution of a nonlinear two-point boundary value problem arising in the modelling of the corneal shape. The computational process of modelling and optimization makes enormously straightforward to obtain accurate approximate solutions of the corneal shape m...

In this study, a design of integrated computational intelligent paradigm has been presented for numerical treatment of the one-dimensional boundary value problems represented with Falkner-Skan equations (FSE) by exploitation of Gaussian wavelet neural networks (GWNNs), genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., GWNN-...

The aim of this study is to analysis the mass and heat transfer in radiative three dimensional flow of hybrid nanofluid over the stretchable sheet by exploiting the strength of integrated computational intelligent algorithm by utilization of Gaussian wavelet neural networks (GWNNs) trained with the genetic algorithms (GAs) based global search suppo...

The porous media transport theories are thoroughly operative to analyse transferral phe�nomenon in reducing the bio-convective flow instabilities and biological tissues. The present study is designed to investigate the heat transfer phenomena in nanofluidic sys�tem involving Cu � H2O over the stretched porous media with the strength of stochastic s...

The objective of the current investigation is to examine the influence of variable viscosity and transverse magnetic field on mixed convection fluid model through stretching sheet based on copper and silver nanoparticles by exploiting the strength of numerical computing via Lobatto IIIA solver. The nonlinear partial differential equations are chang...

A novel numerical computing framework through Lobatto IIIA method is presented for the dynamical investigation of nanofluidic problem with Williamson fluid flow on a stretching sheet by considering the thermal slip and velocity. The impact of thermophoresis and brownian motion on phenomena of heat transfer are explored by using Buongiorno model. Th...

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 s...

The present study aims to provide an innovative stochastic numerical solver’s application by the use of neural networks with Levenberg-Marquardt backpropagation to examine the dynamics of hydrogen possessions and variable viscosity in the fluidic system of electrically conducting copper and silver nanoparticles with mixed convection. The system of...

The main theme of the present communication is to analyze the flow of micropolar Casson fluid with the influence of dissipation effects and inclined magnetic field upon a stretching surface by incorporating the Lobatto IIIA based numerical solver. In the presented study, the Navier–Stokes theory is exploited to model the governing fluidic system an...

This work is conducted to investigate the flow of viscoplastic fluid on a stretchable surface submerged in permeable medium and the effect of joule heating, micro-rotation and inclined magnetic field (MF) has been examined numerically. The Cartesian coordinate system has been used for mathematical formulation of the governing equations in PDEs and...

This article presents a methodology to solve a one-dimensional steady-state nonlinear reactive transport model (RTM) that is meant for fluid and solute transport model of soft tissues and microvessels. The methodology integrates the artificial neural network (ANN), genetic algorithms (GAs), and pattern search (PS) aided by active-set technique (AST...

The importance of rectangular porous fins for the transformation of heat through the system is well-recognized to analyze the physical characteristics of material in practical applications. In this study, a neuro-computing based stochastic numerical paradigm has been designed to study the dynamics of temperature distribution in porous fin model by...