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Muhammad Asif Zahoor Raja
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Yunlin 64002, Taiwan

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Neural Networks
Genetic Algorithms
particle swarm optimization (PSO)
Signal Processing
fractional calculus
Fractional differential equations

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Journal article
Published: 29 August 2021 in Entropy
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In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.

ACS Style

Wasiq Ali; Yaan Li; Muhammad Asif Zahoor Raja; Wasim Ullah Khan; Yigang He. State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing. Entropy 2021, 23, 1124 .

AMA Style

Wasiq Ali, Yaan Li, Muhammad Asif Zahoor Raja, Wasim Ullah Khan, Yigang He. State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing. Entropy. 2021; 23 (9):1124.

Chicago/Turabian Style

Wasiq Ali; Yaan Li; Muhammad Asif Zahoor Raja; Wasim Ullah Khan; Yigang He. 2021. "State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing." Entropy 23, no. 9: 1124.

Journal article
Published: 25 August 2021 in Sustainability
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Agricultural diversification efforts towards sustainable agriculture generates environmental and economic benefits. Climate change and agricultural production are characterized by a complex cause-effect relationship. In the present study, the primary dataset is collected through an interview-based survey from 410 farmers in 3 districts located in different agro-ecological zones of Punjab, Pakistan. Detailed analysis is conducted by employing the Gaussian treatment effects approach. Results of the study show that the farmers who adopted agricultural diversification to mitigate the impact of climate change were less and insignificantly benefited e.g., on an average of RS 95,260 (US $635) per annum whereas non-adopted farmers lost their farm income on an average of RS 115,750 (US $772) per annum if they had practiced the agricultural diversification. Moreover, determinants of agricultural diversification such as demographic and institutional indicators were significant and larger effects to adopt as compared to social indicators. This study suggests that policies should be designed in the regional context particularly related to the improvement in demographic characteristics and institutional factors such as providing subsidies, training, and awareness to the farmers, particularly to those who practice agricultural diversification. These measures will help to raise the farmers’ adaptive capacity for the adoption of agricultural diversification, and it will enable them to generate tangible benefits by increasing income through adopting sustainable agricultural livelihood.

ACS Style

Adiqa Kausar Kiani; Asif Sardar; Wasim Ullah Khan; Yigang He; Abdulbaki Bilgic; Yasemin Kuslu; Muhammad Asif Zahoor Raja. Role of Agricultural Diversification in Improving Resilience to Climate Change: An Empirical Analysis with Gaussian Paradigm. Sustainability 2021, 13, 9539 .

AMA Style

Adiqa Kausar Kiani, Asif Sardar, Wasim Ullah Khan, Yigang He, Abdulbaki Bilgic, Yasemin Kuslu, Muhammad Asif Zahoor Raja. Role of Agricultural Diversification in Improving Resilience to Climate Change: An Empirical Analysis with Gaussian Paradigm. Sustainability. 2021; 13 (17):9539.

Chicago/Turabian Style

Adiqa Kausar Kiani; Asif Sardar; Wasim Ullah Khan; Yigang He; Abdulbaki Bilgic; Yasemin Kuslu; Muhammad Asif Zahoor Raja. 2021. "Role of Agricultural Diversification in Improving Resilience to Climate Change: An Empirical Analysis with Gaussian Paradigm." Sustainability 13, no. 17: 9539.

Journal article
Published: 25 August 2021 in Sustainability
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The research community of environmental economics has had a growing interest for the exploration of artificial intelligence (AI)-based systems to provide enriched efficiencies and strengthened human knacks in daily live maneuvers, business stratagems, and society evolution. In this investigation, AI-based intelligent backpropagation networks of Bayesian regularization (IBNs-BR) were exploited for the numerical treatment of mathematical models representing environmental economic systems (EESs). The governing relations of EESs were presented in the form of differential models representing their fundamental compartments or indicators for economic and environmental parameters. The reference datasets of EESs were assembled using the Adams numerical solver for different EES scenarios and were used as targets of IBNs-BR to find the approximate solutions. Comparative studies based on convergence curves on the mean square error (MSE) and absolute deviation from the reference results were used to verify the correctness of IBNs-BR for solving EESs, i.e., MSE of around 10−9 to 10−10 and absolute error close to 10−5 to 10−7. The endorsement of results was further validated through performance evaluation by means of error histogram analysis, the regression index, and the mean squared deviation-based figure of merit for each EES scenario.

ACS Style

Adiqa Kausar Kiani; Wasim Ullah Khan; Muhammad Asif Zahoor Raja; Yigang He; Zulqurnain Sabir; Muhammad Shoaib. Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems. Sustainability 2021, 13, 9537 .

AMA Style

Adiqa Kausar Kiani, Wasim Ullah Khan, Muhammad Asif Zahoor Raja, Yigang He, Zulqurnain Sabir, Muhammad Shoaib. Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems. Sustainability. 2021; 13 (17):9537.

Chicago/Turabian Style

Adiqa Kausar Kiani; Wasim Ullah Khan; Muhammad Asif Zahoor Raja; Yigang He; Zulqurnain Sabir; Muhammad Shoaib. 2021. "Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems." Sustainability 13, no. 17: 9537.

Journal article
Published: 16 July 2021 in Applied Acoustics
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This paper describes the designing and optimization of tonpilz type transducers based on number of layers, width and thicknesses and type of active materials, dimensions of head and tail mass by exploiting finite element models for their effective and optimized usage in underwater SONAR applications. Geometry parameters of tonpilz transducers have been studied in terms of active piezoelectric stack and associated components comprised of head and tail masses and optimized structure is explored. Piezoelectric stack including piezoceramic material (PZT-4) and single crystal (PMNPT), is utilized for underwater acoustic generation, while Aluminum is taken as head mass and highly attenuated material stainless steel is used for tail mass. Performance of tonpilz transducer has been investigated and evaluated in terms of total radiating power (TRP), transmitting voltage response (TVR), directivity index and specific acoustic impedance for all designed structures. Comparison is carried out for the optimized structure that provides high sensitive frequency region in form of constant TVR and maximum sound directivity used for accurate under water detection applications. TVR in the sensitive region using PMNPT as the stack material shows enhanced results as compared to PZT-4. Sound intensity level in terms of TRP peaks also rises with a greater number of layers for both PMNPT and PZT-4 driving stack materials. Furthermore, it has been observed that Driving stack material is more effective than the other components of Tonpilz transducer because it effects the flat response and also enhances the TVR response. Secondly, head mass of the Tonpilz transducer is more effective than the remaining because resonance and flexural frequency depend on it.

ACS Style

Zeeshan Abdullah; Sidra Naz; Muhammad Asif Zahoor Raja; Aneela Zameer. Design of wideband tonpilz transducers for underwater SONAR applications with finite element model. Applied Acoustics 2021, 183, 108293 .

AMA Style

Zeeshan Abdullah, Sidra Naz, Muhammad Asif Zahoor Raja, Aneela Zameer. Design of wideband tonpilz transducers for underwater SONAR applications with finite element model. Applied Acoustics. 2021; 183 ():108293.

Chicago/Turabian Style

Zeeshan Abdullah; Sidra Naz; Muhammad Asif Zahoor Raja; Aneela Zameer. 2021. "Design of wideband tonpilz transducers for underwater SONAR applications with finite element model." Applied Acoustics 183, no. : 108293.

Journal article
Published: 08 July 2021 in Surfaces and Interfaces
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The current research article is intended to examine the nonlinear input-output fitting with two-layer feed forward neural network for numerical treatment of heat transfer effects in Bodewadt flow over a permeable disk. Estimation parameters in Bodewadt flow model include wall suction parameter (1.7 ≤ A ≤ 6.7), magnetic field (0.1 ≤ M ≤ 1.0), Prandtl number (0.2 ≤ Pr ≤ 5.0) and variable viscosity (2.0 ≤ θe ≤ 20.0). The influences of magnetic field, Joule heating, internal heat generation/absorption, wall suction, viscous dissipation along with variable fluid properties are also contemplated as well. The energy equation in Bodewadt flow for permeable disk governing heat transfer and fluid motion are transformed into self-similar dimensionless differential equation by using Von-Karman variables. The temperature and velocity of the fluid about the disk by taking various values of the physical parameter are solved by Adams Bashforth method to determine the reference computational results of Bodewadt flow model. The values of skin friction co-efficient and Nusselt number are also calculated and physically interpreted for the assorted parameters. Further, the obtained experimental dataset of the system is used to authenticate the artificial neural network modeling with optimization of Levenberg-Marquardt backpropagation. Fitting data precision is examined on mean squared error based cost function for the system and the outputs of intelligent networks are demonstrated using performance parameter, error histograms, regression and fitting plots. Least mean square error trained at decreasing gradient with optimized weights having strong correlation R = 1 between target and network output and a consistent convergence further certified the worth of methodology.

ACS Style

Muhammad Awais; Murium Bibi; Muhammad Asif Zahoor Raja; Saeed Ehsan Awan; Muhammad Yousaf Malik. Intelligent numerical computing paradigm for heat transfer effects in a Bodewadt flow. Surfaces and Interfaces 2021, 26, 101321 .

AMA Style

Muhammad Awais, Murium Bibi, Muhammad Asif Zahoor Raja, Saeed Ehsan Awan, Muhammad Yousaf Malik. Intelligent numerical computing paradigm for heat transfer effects in a Bodewadt flow. Surfaces and Interfaces. 2021; 26 ():101321.

Chicago/Turabian Style

Muhammad Awais; Murium Bibi; Muhammad Asif Zahoor Raja; Saeed Ehsan Awan; Muhammad Yousaf Malik. 2021. "Intelligent numerical computing paradigm for heat transfer effects in a Bodewadt flow." Surfaces and Interfaces 26, no. : 101321.

Journal article
Published: 29 June 2021 in Coatings
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The present study introduced the unsteady squeezing flow of two-dimensional viscous fluid with nanoparticles between two disks by using the Levenberg–Marquardt backpropagated neural network (LMB-NN). Conversion of the partial differential equations (PDEs) into equivalent ordinary differential equations (ODEs) is performed by suitable similarity transformation. The data collection for suggested (LMB-NN) is made for various magnetohydrodynamic squeezing flow (MHDSF) scenarios in terms of the squeezing parameter, Prandtl number, Brownian motion parameter, and the thermophoresis parameter by employing the Runge–Kutta technique with the help of Mathematica software. The worth of the proposed methodology has been established for the proposed solver (LMB-NN) with different scenarios and cases, and the outcomes are compared through the effectiveness and reliability of mean square error (MSE) for the squeezing flow problem MHDSF. Moreover, the state transition, Fitness outline, histogram error, and regression presentation also endorse the strength and reliability of the solver LMB-NN. The high convergence between the reference solutions and the solutions obtained by incorporating the efficacy of a designed solver LMB-NN indicates the strength of the proposed methodology, where the accuracy level is achieved in the ranges from 106 to 1012.

ACS Style

Maryam Almalki; Eman Alaidarous; Muhammad Raja; Dalal Maturi; Muhammad Shoaib. Optimization through the Levenberg—Marquardt Backpropagation Method for a Magnetohydrodynamic Squeezing Flow System. Coatings 2021, 11, 779 .

AMA Style

Maryam Almalki, Eman Alaidarous, Muhammad Raja, Dalal Maturi, Muhammad Shoaib. Optimization through the Levenberg—Marquardt Backpropagation Method for a Magnetohydrodynamic Squeezing Flow System. Coatings. 2021; 11 (7):779.

Chicago/Turabian Style

Maryam Almalki; Eman Alaidarous; Muhammad Raja; Dalal Maturi; Muhammad Shoaib. 2021. "Optimization through the Levenberg—Marquardt Backpropagation Method for a Magnetohydrodynamic Squeezing Flow System." Coatings 11, no. 7: 779.

Journal article
Published: 24 June 2021 in Arabian Journal for Science and Engineering
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In physical science, nonlinear singular Lane–Emden and pantograph delay differential equations (LE–PDDEs) have abundant applications and thus are of great interest for the researchers. The presented investigation is related to the development of a new application of intelligent computing for the solution of the LE–PDDEs-based system introduced recently by merging the essence of delay differential equation of Pantograph type and standard second-order Lane–Emden equation. Intelligent computing is exploited through Levenberg–Marquardt backpropagation networks (LMBNs) and Bayesian regularization backpropagation networks (BRBNs) to provide the solutions to nonlinear second-order LE–PDDEs. The performance of design LMBNs and BRBNs is substantiated on three different case studies through comparative analysis from known exact/explicit solutions. The correctness of the designed solvers for LE–PDDEs is further certified by accomplishing through assessment on error histograms, regression measures and index of mean squared error.

ACS Style

Imtiaz Khan; Muhammad Asif Zahoor Raja; Muhammad Abdul Rehman Khan; Muhammad Shoaib; Saeed Islam; Zahir Shah. Design of Backpropagated Intelligent Networks for Nonlinear Second-Order Lane–Emden Pantograph Delay Differential Systems. Arabian Journal for Science and Engineering 2021, 1 -14.

AMA Style

Imtiaz Khan, Muhammad Asif Zahoor Raja, Muhammad Abdul Rehman Khan, Muhammad Shoaib, Saeed Islam, Zahir Shah. Design of Backpropagated Intelligent Networks for Nonlinear Second-Order Lane–Emden Pantograph Delay Differential Systems. Arabian Journal for Science and Engineering. 2021; ():1-14.

Chicago/Turabian Style

Imtiaz Khan; Muhammad Asif Zahoor Raja; Muhammad Abdul Rehman Khan; Muhammad Shoaib; Saeed Islam; Zahir Shah. 2021. "Design of Backpropagated Intelligent Networks for Nonlinear Second-Order Lane–Emden Pantograph Delay Differential Systems." Arabian Journal for Science and Engineering , no. : 1-14.

Short communication
Published: 18 June 2021 in Case Studies in Thermal Engineering
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Recently, the applications of artificial intelligence through soft computing and machine learning algorithms have become the focal point of researcher's consideration on account of their convenience for accurate modelling, ease in simulation and effective assessment. This article endorses soft computing based backpropagated neural networks (BNNs) with Levenberg Marquardt technique (LMT), i.e., BNN-LMT, over a novel mathematical model based on biconvection, second grade combine convection nanofluid (BSCCN) flow associated with Cattaneo-Christove (CC) heat flux model for thermal transportation and viscous dissipation, Darct-Forhheimer (DF) law for permeable medium and Hall (H) current for high intensity electric conductive on flow motion model, i.e., BSCCN-CCDFH flow model. Self-similar transformations are used to reduce the multivariable function model to mathematical system of a single variable. The assessment of thermal buoyancy parameter, Hall parameter, porosity parameter, thermophoresis factor, Lewis number and Peclet number over the flow rate dynamics, energy, nanofluid concentration and microorganism concentration profiles is made through dataset based on Adam numerical solver for different physical quantity based scenarios. The results of exhaustive numerical simulation studies show that the proposed technique BNN-LMT is an efficient, reliable, accurate and rapid convergent stochastic numerical solver exploited viably for the BSCCN-CCDFH flow model having number of physical variations.

ACS Style

Muhammad Asif Zahoor Raja; Zeeshan Khan; Samina Zuhra; Naveed Ishtiaq Chaudhary; Wasim Ullah Khan; Yigang He; Saeed Islam; Muhammad Shoaib. Cattaneo-christov heat flux model of 3D hall current involving biconvection nanofluidic flow with Darcy-Forchheimer law effect: Backpropagation neural networks approach. Case Studies in Thermal Engineering 2021, 26, 101168 .

AMA Style

Muhammad Asif Zahoor Raja, Zeeshan Khan, Samina Zuhra, Naveed Ishtiaq Chaudhary, Wasim Ullah Khan, Yigang He, Saeed Islam, Muhammad Shoaib. Cattaneo-christov heat flux model of 3D hall current involving biconvection nanofluidic flow with Darcy-Forchheimer law effect: Backpropagation neural networks approach. Case Studies in Thermal Engineering. 2021; 26 ():101168.

Chicago/Turabian Style

Muhammad Asif Zahoor Raja; Zeeshan Khan; Samina Zuhra; Naveed Ishtiaq Chaudhary; Wasim Ullah Khan; Yigang He; Saeed Islam; Muhammad Shoaib. 2021. "Cattaneo-christov heat flux model of 3D hall current involving biconvection nanofluidic flow with Darcy-Forchheimer law effect: Backpropagation neural networks approach." Case Studies in Thermal Engineering 26, no. : 101168.

Journal article
Published: 05 June 2021 in Chinese Journal of Physics
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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-GA-SQP. The GWNNs is used for mathematical modeling of the problem by constructing mean squared error based objective function while optimization of the cost function is initially conducted with efficacy of GAs as a global search and while fine tuning is performed with efficiency local search with SQP. The numerical results are obtained by proposed GWNN-GA-SQP for different FSEs arising in nonlinear regimes of computation fluid mechanics studies. A comparison of the results of proposed GWNN-GA-SQP stochastic numerical solver with reference state of the art solutions of Adams method establishes the accuracy, convergence and stability, which further endorsed through statistics on multiples runs. The T-Paired test is also applied to validate the effectiveness of the proposed GWNN-GA-SQP algorithm for solving nonlinear FSEs.

ACS Style

Hira Ilyas; Muhammad Asif Zahoor Raja; Iftikhar Ahmad; Muhammad Shoaib. A novel design of Gaussian Wavelet Neural Networks for nonlinear Falkner-Skan systems in fluid dynamics. Chinese Journal of Physics 2021, 72, 386 -402.

AMA Style

Hira Ilyas, Muhammad Asif Zahoor Raja, Iftikhar Ahmad, Muhammad Shoaib. A novel design of Gaussian Wavelet Neural Networks for nonlinear Falkner-Skan systems in fluid dynamics. Chinese Journal of Physics. 2021; 72 ():386-402.

Chicago/Turabian Style

Hira Ilyas; Muhammad Asif Zahoor Raja; Iftikhar Ahmad; Muhammad Shoaib. 2021. "A novel design of Gaussian Wavelet Neural Networks for nonlinear Falkner-Skan systems in fluid dynamics." Chinese Journal of Physics 72, no. : 386-402.

Journal article
Published: 31 May 2021 in Surfaces and Interfaces
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In this research article, artificial neural networks back-propagated with Levenberg Marquardt scheme (ANN-BLMS) is presented to analyze the entropy generation of carbon nanotubes (CNTs) between two rotating stretching discs under the influence of thermal radiation and magneto-hydrodynamic nano-fluid flow model. (MHD-NFM). The fluid flow is initially represented by system of PDEs is then transformed into system of ODEs. A set of data for proposed ANN-BLMS is generated for various scenarios by variation of stretching parameters of lower and upper disks A1 and A2 respectively, suction injection parameter (Ws), rotational parameter (Ω), the radiation parameter (R), the Eckert number (Ec), the Hartmann number (M) by using Adams Numerical method. The approximate solution of different cases is determined by testing, training and validation process of ANN-BLMS and comparison for verification of correctness of proposed model. Later on, regression analysis, mean square error and histogram studies endorse the performance of proposed ANN-BLMS.

ACS Style

Muhammad Shoaib; Muhammad Asif Zahoor Raja; Muhammad Abdul Rehman Khan; Imrana Farhat; Saeed Ehsan Awan. Neuro-computing networks for entropy generation under the influence of MHD and thermal radiation. Surfaces and Interfaces 2021, 25, 101243 .

AMA Style

Muhammad Shoaib, Muhammad Asif Zahoor Raja, Muhammad Abdul Rehman Khan, Imrana Farhat, Saeed Ehsan Awan. Neuro-computing networks for entropy generation under the influence of MHD and thermal radiation. Surfaces and Interfaces. 2021; 25 ():101243.

Chicago/Turabian Style

Muhammad Shoaib; Muhammad Asif Zahoor Raja; Muhammad Abdul Rehman Khan; Imrana Farhat; Saeed Ehsan Awan. 2021. "Neuro-computing networks for entropy generation under the influence of MHD and thermal radiation." Surfaces and Interfaces 25, no. : 101243.

Journal article
Published: 21 May 2021 in Applied Sciences
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In this work, a new heuristic computing design is presented with an artificial intelligence approach to exploit the models with feed-forward (FF) Gudermannian neural networks (GNN) accomplished with global search capability of genetic algorithms (GA) combined with local convergence aptitude of active-set method (ASM), i.e., FF-GNN-GAASM to solve the second kind of Lane–Emden nonlinear singular models (LE-NSM). The proposed method based on the computing intelligent Gudermannian kernel is incorporated with the hidden layer configuration of FF-GNN models of differential operatives of the LE-NSM, which are arbitrarily associated with presenting an error-based objective function that is used to optimize by the hybrid heuristics of GAASM. Three LE-NSM-based examples are numerically solved to authenticate the effectiveness, accurateness, and efficiency of the suggested FF-GNN-GAASM. The reliability of the scheme via statistical valuations is verified in order to authenticate the stability, accuracy, and convergence.

ACS Style

Kashif Nisar; Zulqurnain Sabir; Muhammad Zahoor Raja; Ag. Ag. Ibrahim; Joel Rodrigues; Adnan Shahid Khan; Manoj Gupta; Aldawoud Kamal; Danda Rawat. Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models. Applied Sciences 2021, 11, 4725 .

AMA Style

Kashif Nisar, Zulqurnain Sabir, Muhammad Zahoor Raja, Ag. Ag. Ibrahim, Joel Rodrigues, Adnan Shahid Khan, Manoj Gupta, Aldawoud Kamal, Danda Rawat. Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models. Applied Sciences. 2021; 11 (11):4725.

Chicago/Turabian Style

Kashif Nisar; Zulqurnain Sabir; Muhammad Zahoor Raja; Ag. Ag. Ibrahim; Joel Rodrigues; Adnan Shahid Khan; Manoj Gupta; Aldawoud Kamal; Danda Rawat. 2021. "Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models." Applied Sciences 11, no. 11: 4725.

Journal article
Published: 15 May 2021 in Ain Shams Engineering Journal
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In the artificial neural networks domain, the Levenberg-Marquardt technique is novel with convergent stability and generates a numerical solution of the wire coating system for Sisko fluid flow (WCS-SFF) through regression plots, histogram representations, state transition measures, and means squared errors. In this paper, the analysis of fluid flow problem based on WCS-SFF is studied with a new application of intelligent computing system via supervised learning mechanism using the efficacy of neural networks trained by Levenberg-Marquardt algorithm (NN-TLMA). The original mathematical formulation in terms of PDEs for WCS-SFF is converted into dimensionless nonlinear ODEs. The data collection for the projected NN-TLMA is produced for parameters associated with the system model WCS-SFF influencing the velocity using the explicit Runge-Kutta technique. The training, validation, and testing processes of NN-TLMA are utilized to evaluate the obtained results of WCS-SFF for various cases, and a comparison of the obtained results is performed with reference data set to check the accuracy and effectiveness of the proposed algorithm NN-TLMA for the analysis of non-Newtonian fluid problem-related WCS-SFF. The proposed NN-TLMA for solving the WCS-SFF is effectively confirmed through state transition dynamics, mean square error, regression analyses, and error histogram studies. The powerful consistency of suggested outcomes with reference solutions indicates the validity of the framework, and the accuracy of 10-8 to 10-6 is also achieved.

ACS Style

Jawaher Lafi Aljohani; Eman Salem Alaidarous; Muhammad Asif Zahoor Raja; Muhammed Shabab Alhothuali; Muhammad Shoaib. Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid. Ain Shams Engineering Journal 2021, 1 .

AMA Style

Jawaher Lafi Aljohani, Eman Salem Alaidarous, Muhammad Asif Zahoor Raja, Muhammed Shabab Alhothuali, Muhammad Shoaib. Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid. Ain Shams Engineering Journal. 2021; ():1.

Chicago/Turabian Style

Jawaher Lafi Aljohani; Eman Salem Alaidarous; Muhammad Asif Zahoor Raja; Muhammed Shabab Alhothuali; Muhammad Shoaib. 2021. "Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid." Ain Shams Engineering Journal , no. : 1.

Original article
Published: 07 May 2021 in Complex & Intelligent Systems
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The current study is related to present a novel neuro-swarming intelligent heuristic for nonlinear second-order Lane–Emden multi-pantograph delay differential (NSO-LE-MPDD) model by applying the approximation proficiency of artificial neural networks (ANNs) and local/global search capabilities of particle swarm optimization (PSO) together with efficient/quick interior-point (IP) approach, i.e., ANN-PSOIP scheme. In the designed ANN-PSOIP scheme, a merit function is proposed by using the mean square error sense along with continuous mapping of ANNs for the NSO-LE-MPDD model. The training of these nets is capable of using the integrated competence of PSO and IP scheme. The inspiration of the ANN-PSOIP approach instigates to present a reliable, steadfast, and consistent arrangement relates the ANNs strength for the soft computing optimization to handle with such inspiring classifications. Furthermore, the statistical soundings using the different operators certify the convergence, accurateness, and precision of the ANN-PSOIP scheme.

ACS Style

Zulqurnain Sabir; Muhammad Asif Zahoor Raja; Dac-Nhuong Le; Ayman A. Aly. A neuro-swarming intelligent heuristic for second-order nonlinear Lane–Emden multi-pantograph delay differential system. Complex & Intelligent Systems 2021, 1 -14.

AMA Style

Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Dac-Nhuong Le, Ayman A. Aly. A neuro-swarming intelligent heuristic for second-order nonlinear Lane–Emden multi-pantograph delay differential system. Complex & Intelligent Systems. 2021; ():1-14.

Chicago/Turabian Style

Zulqurnain Sabir; Muhammad Asif Zahoor Raja; Dac-Nhuong Le; Ayman A. Aly. 2021. "A neuro-swarming intelligent heuristic for second-order nonlinear Lane–Emden multi-pantograph delay differential system." Complex & Intelligent Systems , no. : 1-14.

Journal article
Published: 29 April 2021 in Entropy
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In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.

ACS Style

Wasiq Ali; Wasim Khan; Muhammad Raja; Yigang He; Yaan Li. Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target. Entropy 2021, 23, 550 .

AMA Style

Wasiq Ali, Wasim Khan, Muhammad Raja, Yigang He, Yaan Li. Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target. Entropy. 2021; 23 (5):550.

Chicago/Turabian Style

Wasiq Ali; Wasim Khan; Muhammad Raja; Yigang He; Yaan Li. 2021. "Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target." Entropy 23, no. 5: 550.

Regular article
Published: 13 April 2021 in The European Physical Journal Plus
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Piezoelectric stage has become promising actuator for wide applications of micro-/nano-positioning systems represented mathematically with Bouc–Wen hysteresis model to examine the efficiency. In this investigation, the numerical study of piezostage actuator based on nonlinear Bouc–Wen hysteresis model is presented by neurocomputing intelligence via Levenberg–Marquardt backpropagated neural networks (LMB-NNs). Numerical computing strength of Adams method is implemented to generate a dataset of LMB-NNs for training, testing and validation process based on different scenarios of input voltage signals to piezostage actuator model. The performance of LMB-NNs of nano-positioning system model is validated through accuracy measures on means square error, histogram illustrations and regression analysis.

ACS Style

Sidra Naz; Muhammad Asif Zahoor Raja; Ammara Mehmood; Aneela Zameer; Muhammad Shoaib. Neuro-intelligent networks for Bouc–Wen hysteresis model for piezostage actuator. The European Physical Journal Plus 2021, 136, 1 -20.

AMA Style

Sidra Naz, Muhammad Asif Zahoor Raja, Ammara Mehmood, Aneela Zameer, Muhammad Shoaib. Neuro-intelligent networks for Bouc–Wen hysteresis model for piezostage actuator. The European Physical Journal Plus. 2021; 136 (4):1-20.

Chicago/Turabian Style

Sidra Naz; Muhammad Asif Zahoor Raja; Ammara Mehmood; Aneela Zameer; Muhammad Shoaib. 2021. "Neuro-intelligent networks for Bouc–Wen hysteresis model for piezostage actuator." The European Physical Journal Plus 136, no. 4: 1-20.

Journal article
Published: 07 April 2021 in Symmetry
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The current study aims to design an integrated numerical computing-based scheme by applying the Levenberg–Marquardt backpropagation (LMB) neural network to solve the nonlinear susceptible (S), infected (I) and recovered (R) (SIR) system of differential equations, representing the spreading of infection along with its treatment. The solutions of both the categories of spreading infection and its treatment are presented by taking six different cases of SIR models using the designed LMB neural network. A reference dataset of the designed LMB neural network is established with the Adam numerical scheme for each case of the spreading infection and its treatment. The approximate outcomes of the SIR system based on the spreading infection and its treatment are presented in the training, authentication and testing procedures to adapt the neural network by reducing the mean square error (MSE) function using the LMB. Studies based on the proportional performance and inquiries based on correlation, error histograms, regression and MSE results establish the efficiency, correctness and effectiveness of the proposed LMB neural network scheme.

ACS Style

Muhammad Umar; Zulqurnain Sabir; Muhammad Zahoor Raja; Manoj Gupta; Dac-Nhuong Le; Ayman Aly; Yolanda Guerrero-Sánchez. Computational Intelligent Paradigms to Solve the Nonlinear SIR System for Spreading Infection and Treatment Using Levenberg–Marquardt Backpropagation. Symmetry 2021, 13, 618 .

AMA Style

Muhammad Umar, Zulqurnain Sabir, Muhammad Zahoor Raja, Manoj Gupta, Dac-Nhuong Le, Ayman Aly, Yolanda Guerrero-Sánchez. Computational Intelligent Paradigms to Solve the Nonlinear SIR System for Spreading Infection and Treatment Using Levenberg–Marquardt Backpropagation. Symmetry. 2021; 13 (4):618.

Chicago/Turabian Style

Muhammad Umar; Zulqurnain Sabir; Muhammad Zahoor Raja; Manoj Gupta; Dac-Nhuong Le; Ayman Aly; Yolanda Guerrero-Sánchez. 2021. "Computational Intelligent Paradigms to Solve the Nonlinear SIR System for Spreading Infection and Treatment Using Levenberg–Marquardt Backpropagation." Symmetry 13, no. 4: 618.

Journal article
Published: 04 April 2021 in Coatings
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Novel nonlinear power-law flux models were utilized to model the heat transport phe-nomenon in nano-micropolar fluid over a flexible surface. The nonlinear conservation laws (mass, momentum, energy, mass transport and angular momentum) and KKL cor-relations for nanomaterial under novel flux model were solved numerically. Computed results were used to study the shear-thinning and shear-thickening nature of nano pol-ymer suspension by considering n-diffusion theory. Normalized velocity, temperature and micro-rotation profiles were investigated under the variation of physical parame-ters. Shear stresses at the wall for nanoparticles (CuO and Al2O3 ) were recorded and dis-played in the table. Error analyses for different physical parameters were prepared for various parameters to validate the obtained results.

ACS Style

Muhammad Awais; Saeed Ehsan Awan; Muhammad Raja; Muhammad Nawaz; Wasim Khan; Muhammad Yousaf Malik; Yigang He. Heat Transfer in Nanomaterial Suspension (CuO and Al2O3) Using KKL Model. Coatings 2021, 11, 417 .

AMA Style

Muhammad Awais, Saeed Ehsan Awan, Muhammad Raja, Muhammad Nawaz, Wasim Khan, Muhammad Yousaf Malik, Yigang He. Heat Transfer in Nanomaterial Suspension (CuO and Al2O3) Using KKL Model. Coatings. 2021; 11 (4):417.

Chicago/Turabian Style

Muhammad Awais; Saeed Ehsan Awan; Muhammad Raja; Muhammad Nawaz; Wasim Khan; Muhammad Yousaf Malik; Yigang He. 2021. "Heat Transfer in Nanomaterial Suspension (CuO and Al2O3) Using KKL Model." Coatings 11, no. 4: 417.

Journal article
Published: 01 April 2021 in Mathematics and Computers in Simulation
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The present research work is to puts forth the numerical solutions of the nonlinear second-order Lane-Emden-pantograph (LEP) delay differential equation by using the approximation competency of the artificial neural networks (ANNs) trained with the combined strengths of global/local search exploitation of genetic algorithm (GA) and active-set (AS) method, i.e., ANNGAAS. In the proposed ANNGAAS, the objective function is designed by using the mean square error function with continuous mappings of ANNs for the LEP delay differential equation. The training of these constructed networks is conducted proficiently using the integrated capability of global search with GA and assisted local search along with AS approach. The performance of design computing paradigm ANNGAAS is evaluated effectively on variants of LEP delay differential models, while the statistical investigations based on different operators further validate the accuracy and convergence.

ACS Style

Zulqurnain Sabir; Muhammad Asif Zahoor Raja; Hafiz Abdul Wahab; Gilder Cieza Altamirano; Yu-Dong Zhang; Dac-Nhuong Le. Integrated intelligence of neuro-evolution with sequential quadratic programming for second-order Lane–Emden pantograph models. Mathematics and Computers in Simulation 2021, 188, 87 -101.

AMA Style

Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Hafiz Abdul Wahab, Gilder Cieza Altamirano, Yu-Dong Zhang, Dac-Nhuong Le. Integrated intelligence of neuro-evolution with sequential quadratic programming for second-order Lane–Emden pantograph models. Mathematics and Computers in Simulation. 2021; 188 ():87-101.

Chicago/Turabian Style

Zulqurnain Sabir; Muhammad Asif Zahoor Raja; Hafiz Abdul Wahab; Gilder Cieza Altamirano; Yu-Dong Zhang; Dac-Nhuong Le. 2021. "Integrated intelligence of neuro-evolution with sequential quadratic programming for second-order Lane–Emden pantograph models." Mathematics and Computers in Simulation 188, no. : 87-101.

Journal article
Published: 18 February 2021 in Applied Soft Computing
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In this investigation, nature-inspired heuristic strategy exploiting moth flame optimization (MFO) algorithm combined with active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) are presented to take care of the enhancement issues of economic load dispatch (ELD) problem involving valve point loading effect (VPLE) and stochastic wind (SW). The strength of MFO algorithm is used as a global search mechanism that explore and exploit the entire search space while ASA, IPA and SQP are responsible for refinement of local optimum. The performance of the design system is based on 40 generating units including 37 thermal and 3 wind power units and is evaluated to verify the effectiveness of the scheme. The worth of the design integrated heuristic of MFO algorithm is endorsed through outcomes of the state of the art counterpart solvers in case of ELD problems integrated with wind power units in terms of cost minimization and computational complexity parameters.

ACS Style

Babar Sattar Khan; Muhammad Asif Zahoor Raja; Affaq Qamar; Naveed Ishtiaq Chaudhary. Design of moth flame optimization heuristics for integrated power plant system containing stochastic wind. Applied Soft Computing 2021, 104, 107193 .

AMA Style

Babar Sattar Khan, Muhammad Asif Zahoor Raja, Affaq Qamar, Naveed Ishtiaq Chaudhary. Design of moth flame optimization heuristics for integrated power plant system containing stochastic wind. Applied Soft Computing. 2021; 104 ():107193.

Chicago/Turabian Style

Babar Sattar Khan; Muhammad Asif Zahoor Raja; Affaq Qamar; Naveed Ishtiaq Chaudhary. 2021. "Design of moth flame optimization heuristics for integrated power plant system containing stochastic wind." Applied Soft Computing 104, no. : 107193.

Journal article
Published: 15 February 2021 in Coatings
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Rheology of MHD bioconvective nanofluid containing motile microorganisms is inspected numerically in order to analyze heat and mass transfer characteristics. Bioconvection is implemented by combined effects of magnetic field and buoyancy force. Gyrotactic microorganisms enhance the heat and transfer as well as perk up the nanomaterials’ stability. Variable transport properties along with assisting and opposing flow situations are taken into account. The significant influences of thermophoresis and Brownian motion have also been taken by employing Buongiorno’s model of nanofluid. Lie group analysis approach is utilized in order to compute the absolute invariants for the system of differential equations, which are solved numerically using Adams-Bashforth technique. Validity of results is confirmed by performing error analysis. Graphical and numerical illustrations are prepared in order to get the physical insight of the considered analysis. It is observed that for controlling parameters corresponding to variable transport properties c2, c4, c6, and c8, the velocity, temperature, concentration, and bioconvection density distributions accelerates, respectively. While heat and mass transfer rates increases for convection parameter and bioconvection Rayleigh number, respectively.

ACS Style

Muhammad Awais; Saeed Ehsan Awan; Muhammad Asif Zahoor Raja; Nabeela Parveen; Wasim Ullah Khan; Muhammad Yousaf Malik; Yigang He. Effects of Variable Transport Properties on Heat and Mass Transfer in MHD Bioconvective Nanofluid Rheology with Gyrotactic Microorganisms: Numerical Approach. Coatings 2021, 11, 231 .

AMA Style

Muhammad Awais, Saeed Ehsan Awan, Muhammad Asif Zahoor Raja, Nabeela Parveen, Wasim Ullah Khan, Muhammad Yousaf Malik, Yigang He. Effects of Variable Transport Properties on Heat and Mass Transfer in MHD Bioconvective Nanofluid Rheology with Gyrotactic Microorganisms: Numerical Approach. Coatings. 2021; 11 (2):231.

Chicago/Turabian Style

Muhammad Awais; Saeed Ehsan Awan; Muhammad Asif Zahoor Raja; Nabeela Parveen; Wasim Ullah Khan; Muhammad Yousaf Malik; Yigang He. 2021. "Effects of Variable Transport Properties on Heat and Mass Transfer in MHD Bioconvective Nanofluid Rheology with Gyrotactic Microorganisms: Numerical Approach." Coatings 11, no. 2: 231.