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Engr. Dr. Wasim Ullah Khan received B.E(E) Degree in Electronic Enigneering, MS in Electrical Engineering and Ph.D in Information and Communication Engineering from University of Science and Technology of China (USTC), Hefei, China. Currently he is doing Post Doctorate from Wuhan University, China. His Current research interests are signal processing, speech enhancement, Audio Signal Processing, Direction of arrival and Image Processing
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.
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 StyleWasiq 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 StyleWasiq 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.
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.
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 StyleAdiqa 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 StyleAdiqa 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.
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.
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 StyleAdiqa 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 StyleAdiqa 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.
This research concerns the heat transfer and entropy generation analysis in the MHD axisymmetric flow of Al2O3-Cu/H2O hybrid nanofluid. The magnetic induction effect is considered for large magnetic Reynolds number. The influences of thermal radiations, viscous dissipation and convective temperature conditions over flow are studied. The problem is modeled using boundary layer theory, Maxwell’s equations and Fourier’s conduction law along with defined physical factors. Similarity transformations are utilized for model simplification which is analytically solved with the homotopy analysis method. The h-curves up to 20th order for solutions establishes the stability and convergence of the adopted computational method. Rheological impacts of involved parameters on flow variables and entropy generation number are demonstrated via graphs and tables. The study reveals that entropy in system of hybrid nanofluid affected by magnetic induction declines for β while it enhances for Bi, R and λ. Moreover, heat transfer rate elevates for large Bi with convective conditions at surface.
Nabeela Parveen; Muhammad Awais; Saeed Awan; Wasim Khan; Yigang He; Muhammad Malik. Entropy Generation Analysis and Radiated Heat Transfer in MHD (Al2O3-Cu/Water) Hybrid Nanofluid Flow. Micromachines 2021, 12, 887 .
AMA StyleNabeela Parveen, Muhammad Awais, Saeed Awan, Wasim Khan, Yigang He, Muhammad Malik. Entropy Generation Analysis and Radiated Heat Transfer in MHD (Al2O3-Cu/Water) Hybrid Nanofluid Flow. Micromachines. 2021; 12 (8):887.
Chicago/Turabian StyleNabeela Parveen; Muhammad Awais; Saeed Awan; Wasim Khan; Yigang He; Muhammad Malik. 2021. "Entropy Generation Analysis and Radiated Heat Transfer in MHD (Al2O3-Cu/Water) Hybrid Nanofluid Flow." Micromachines 12, no. 8: 887.
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.
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 StyleMuhammad 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 StyleMuhammad 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.
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.
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 StyleWasiq 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 StyleWasiq 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.
The current study is an attempt to analytically characterize the second law analysis and mixed convective rheology of the (Al2O3–Ag/H2O) hybrid nanofluid flow influenced by magnetic induction effects towards a stretching sheet. Viscous dissipation and internal heat generation effects are encountered in the analysis as well. The mathematical model of partial differential equations is fabricated by employing boundary-layer approximation. The transformed system of nonlinear ordinary differential equations is solved using the homotopy analysis method. The entropy generation number is formulated in terms of fluid friction, heat transfer and Joule heating. The effects of dimensionless parameters on flow variables and entropy generation number are examined using graphs and tables. Further, the convergence of HAM solutions is examined in terms of defined physical quantities up to 20th iterations, and confirmed. It is observed that large
Wasim Khan; Muhammad Awais; Nabeela Parveen; Aamir Ali; Saeed Awan; Muhammad Malik; Yigang He. Analytical Assessment of (Al2O3–Ag/H2O) Hybrid Nanofluid Influenced by Induced Magnetic Field for Second Law Analysis with Mixed Convection, Viscous Dissipation and Heat Generation. Coatings 2021, 11, 498 .
AMA StyleWasim Khan, Muhammad Awais, Nabeela Parveen, Aamir Ali, Saeed Awan, Muhammad Malik, Yigang He. Analytical Assessment of (Al2O3–Ag/H2O) Hybrid Nanofluid Influenced by Induced Magnetic Field for Second Law Analysis with Mixed Convection, Viscous Dissipation and Heat Generation. Coatings. 2021; 11 (5):498.
Chicago/Turabian StyleWasim Khan; Muhammad Awais; Nabeela Parveen; Aamir Ali; Saeed Awan; Muhammad Malik; Yigang He. 2021. "Analytical Assessment of (Al2O3–Ag/H2O) Hybrid Nanofluid Influenced by Induced Magnetic Field for Second Law Analysis with Mixed Convection, Viscous Dissipation and Heat Generation." Coatings 11, no. 5: 498.
The presented communication provides the analysis of entropy generation and heat transport rate in peristalsis of hybrid nanofluid induced by metachronal ciliary beating under magnetic environment for sufficiently large magnetic Reynolds number. Nanoparticles of Cu and Al2O3 are suspended in water. Features of their structures are determined by using long-wavelength approximation with zero Reynolds number. Adams Bashforth method has been applied to compute the results of the flow variables as well as entropy generation number from the formulated differential system which are then interpreted graphically to establish physical significance for different values of physical interest. This investigation reveals that thermal performance of fluid can be boosted by utilizing hybrid nanomaterial about the strength of a wall for stability. Irreversibility analysis ensures that entropy reduced for strong magnetic field while thermal heat generation results in an increase in temperature causing an enhancement in entropy of the system. Error analysis has been performed with reasonably accurate tolerance level. The comparative outcomes of both numerical approaches are presented with plentiful graphical as well as numerical demonstrations which demonstrate the importance in terms of robustness, accuracy and stability.
Saeed Ehsan Awan; Muhammad Awais; Muhammad Asif Zahoor Raja; Nabeela Parveen; Hafiz Muhammad Ali; Wasim Ullah Khan; Yigang He. Numerical Treatment for Dynamics of Second Law Analysis and Magnetic Induction Effects on Ciliary Induced Peristaltic Transport of Hybrid Nanomaterial. Frontiers in Physics 2021, 9, 1 .
AMA StyleSaeed Ehsan Awan, Muhammad Awais, Muhammad Asif Zahoor Raja, Nabeela Parveen, Hafiz Muhammad Ali, Wasim Ullah Khan, Yigang He. Numerical Treatment for Dynamics of Second Law Analysis and Magnetic Induction Effects on Ciliary Induced Peristaltic Transport of Hybrid Nanomaterial. Frontiers in Physics. 2021; 9 ():1.
Chicago/Turabian StyleSaeed Ehsan Awan; Muhammad Awais; Muhammad Asif Zahoor Raja; Nabeela Parveen; Hafiz Muhammad Ali; Wasim Ullah Khan; Yigang He. 2021. "Numerical Treatment for Dynamics of Second Law Analysis and Magnetic Induction Effects on Ciliary Induced Peristaltic Transport of Hybrid Nanomaterial." Frontiers in Physics 9, no. : 1.
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.
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 StyleMuhammad 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 StyleMuhammad 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.
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.
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 StyleMuhammad 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 StyleMuhammad 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.
Wasim Ullah Khan; Yigang He; Muhammad Asif Zahoor Raja; Naveed Ishtiaq Chaudhary; Zeshan Aslam Khan; Syed Muslim Shah. Flower Pollination Heuristics for Nonlinear Active Noise Control Systems. Computers, Materials & Continua 2021, 67, 815 -834.
AMA StyleWasim Ullah Khan, Yigang He, Muhammad Asif Zahoor Raja, Naveed Ishtiaq Chaudhary, Zeshan Aslam Khan, Syed Muslim Shah. Flower Pollination Heuristics for Nonlinear Active Noise Control Systems. Computers, Materials & Continua. 2021; 67 (1):815-834.
Chicago/Turabian StyleWasim Ullah Khan; Yigang He; Muhammad Asif Zahoor Raja; Naveed Ishtiaq Chaudhary; Zeshan Aslam Khan; Syed Muslim Shah. 2021. "Flower Pollination Heuristics for Nonlinear Active Noise Control Systems." Computers, Materials & Continua 67, no. 1: 815-834.
Object tracking is still an intriguing task as the target undergoes significant appearance changes due to illumination, fast motion, occlusion and shape deformation. Background clutter and numerous other environmental factors are other major constraints which remain a riveting challenge to develop a robust and effective tracking algorithm. In the present study, an adaptive Spatio-temporal context (STC)-based algorithm for online tracking is proposed by combining the context-aware formulation, Kalman filter, and adaptive model learning rate. For the enhancement of seminal STC-based tracking performance, different contributions were made in the proposed study. Firstly, a context-aware formulation was incorporated in the STC framework to make it computationally less expensive while achieving better performance. Afterwards, accurate tracking was made by employing the Kalman filter when the target undergoes occlusion. Finally, an adaptive update scheme was incorporated in the model to make it more robust by coping with the changes of the environment. The state of an object in the tracking process depends on the maximum value of the response map between consecutive frames. Then, Kalman filter prediction can be updated as an object position in the next frame. The average difference between consecutive frames is used to update the target model adaptively. Experimental results on image sequences taken from Template Color (TC)-128, OTB2013, and OTB2015 datasets indicate that the proposed algorithm performs better than various algorithms, both qualitatively and quantitatively.
Khizer Mehmood; Abdul Jalil; Ahmad Ali; Baber Khan; Maria Murad; Wasim Ullah Khan; Yigang He. Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking. Electronics 2020, 10, 43 .
AMA StyleKhizer Mehmood, Abdul Jalil, Ahmad Ali, Baber Khan, Maria Murad, Wasim Ullah Khan, Yigang He. Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking. Electronics. 2020; 10 (1):43.
Chicago/Turabian StyleKhizer Mehmood; Abdul Jalil; Ahmad Ali; Baber Khan; Maria Murad; Wasim Ullah Khan; Yigang He. 2020. "Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking." Electronics 10, no. 1: 43.