Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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Author: search.filters.author.Sabir, ZulqurnainPublisher: search.filters.publisher.World Scientific Publ Co Pte Ltd

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Now showing 1 - 5 of 5
  • Article
    Citation - WoS: 21
    Citation - Scopus: 34
    Applications of Gudermannian Neural Network for Solving the Sitr Fractal System
    (World Scientific Publ Co Pte Ltd, 2021) Umar, Muhammad; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Sabir, Zulqurnain
    This study is related to explore the Gudermannian neural network (GNN) for solving a nonlinear SITR COVID-19 fractal system by using the optimization efficiencies of a genetic algorithm (GA), a global search technique and sequential quadratic programming (SQP) and a quick local search scheme, i.e. GNN-GA-SQP. The nonlinear SITR COVID-19 fractal system is dependent on four collections: "susceptible", "infected", "treatment" and "recovered". For the optimization procedures through the GNN-GA-SQP, a merit function is constructed using the nonlinear SITR COVID-19 fractal system and its corresponding initial conditions. The description of each collection of the nonlinear SITR COVID-19 fractal system is provided along with comprehensive detail. The comparison of the achieved numerical result performances of each collection of the nonlinear SITR COVID-19 fractal system is performed with the Adams results to verify the exactness of the designed computational GNN-GA-SQP. The statistical processes based on different operators are presented for 30 independent trials using 5 neurons to authenticate the consistency of the designed computational GNN-GA-SQP. Moreover, the graphs of absolute error (AE), performance indices, and convergence measures along with the boxplots and histograms are also plotted to check the stability, exactness and reliability of the designed computational GNN-GA-SQP.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    Meyer Wavelet Neural Networks Procedures To Investigate the Numerical Performances of the Computer Virus Spread With Kill Signals
    (World Scientific Publ Co Pte Ltd, 2023) Baleanu, Dumitru; Raja, Muhammad Asif Zahoor; Alshomrani, Ali S. S.; Hincal, Evren; Sabir, Zulqurnain
    This study shows the design of the Meyer wavelet neural networks (WNNs) to perform the numerical solutions of the spread of computer virus with kill signals, i.e. SEIR-KS system. The optimization of the SEIR-KS system is performed by the Meyer WNNs together with the optimization through the genetic algorithm (GA) and sequential quadratic (SQ) programming, i.e. Meyer WNNs-GASQ programming. A sigmoidal-based log-sigmoid function is implemented as an activation function, while 10 numbers of neurons work with 120 variables throughout this study. The correctness of the proposed Meyer WNNs-GASQP programming is observed through the comparison of the obtained and reference numerical solutions. For the consistency and reliability of the Meyer WNNs-GASQ programming, an analysis based on different statistical procedures is performed using 40 numbers of independent executions. Moreover, the use of different statistical operators like mean, median, minimum, standard deviation and semi-interquartile range further validates the correctness of the Meyer WNNs-GASQ programming for solving the SEIR-KS system.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    Computational Performances of Morlet Wavelet Neural Network for Solving a Nonlinear Dynamic Based on the Mathematical Model of the Affection of Layla and Majnun
    (World Scientific Publ Co Pte Ltd, 2023) Baleanu, Dumitru; Raja, Muhammad Asif Zahoor; Alshomrani, Ali S.; Hincal, Evren; Sabir, Zulqurnain
    The aim of this study is to design a novel stochastic solver through the Morlet wavelet neural networks (MWNNs) for solving the mathematical Layla and Majnun (LM) system. The numerical representations of the mathematical LM system have been presented by using the MWNNs along with the optimization is performed through the hybridization of the global and local search schemes. The local active-set (AS) and global genetic algorithm (GA) operators have been used to optimize an error-based merit function using the differential LM model and its initial conditions. The correctness of the MWNNs using the local and global operators is observed through the comparison of the obtained solutions and the Adams scheme, which is used as a reference solution. For the stability of the MWNNs using the global and local operators, the statistical performances with different operators have been provided using the multiple executions to solve the nonlinear LM system.
  • Article
    Citation - WoS: 60
    Citation - Scopus: 68
    Fractional Mayer Neuro-Swarm Heuristic Solver for Multi-Fractional Order Doubly Singular Model Based on Lane-Emden Equation
    (World Scientific Publ Co Pte Ltd, 2021) Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Sabir, Zulqurnain
    This research is related to present a novel fractional Mayer neuro-swarming intelligent solver for the numerical treatment of multi-fractional order doubly singular Lane-Emden (LE) equation using combined investigations of the Mayer wavelet (MW) neural networks (NNs) optimized by the global search effectiveness of particle swarm optimization (PSO) and interior-point (IP) method, i.e. MW-NN-PSOIP. The design of novel fractional Mayer neuro-swarming intelligent solver for multi-fractional order doubly singular LE equation is derived from the standard LE model and the shape factors; fractional order terms along with singular points are examined. The modeling based on the MW-NN strength is implemented to signify the multi-fractional order doubly singular LE model using the ability of mean squared error in terms of the merit function and the networks are optimized with the integrated capability of PSOIP scheme. The perfection, verification and validation of the fractional Mayer neuro-swarming intelligent solver for three different cases of the multi-fractional order doubly singular LE equation are recognized through comparative investigations from the reference results on different measures based on the convergence, robustness, stability and accuracy. Furthermore, the statics interpretations further validate the performance of the proposed fractional Mayer neuro-swarming intelligent solvers.
  • Article
    Citation - WoS: 29
    Citation - Scopus: 32
    Design of Neuro-Swarming Heuristic Solver for Multi-Pantograph Singular Delay Differential Equation
    (World Scientific Publ Co Pte Ltd, 2021) Baleanu, Dumitru; Raja, Muhammad Asif Zahoor; Guirao, Juan L. G.; Sabir, Zulqurnain
    This research work is to design a neural-swarming heuristic procedure for numerical investigations of Singular Multi-Pantograph Delay Differential (SMP-DD) equation by applying the function approximation aptitude of Artificial Neural Networks (ANNs) optimized efficient swarming mechanism based on Particle Swarm Optimization (PSO) integrated with convex optimization with Active Set (AS) algorithm for rapid refinements, named as ANN-PSO-AS. A merit function (MF) on mean squared error sense is designed by using the differential ANN models and boundary condition. The optimization of this MF is executed with the global PSO and local search AS approaches. The planned ANN-PSO-AS approach is instigated for three different SMP-DD model-based equations. The assessment with available standard results relieved the effectiveness, robustness and precision that is further authenticated through statistical investigations of Variance Account For, Root Mean Squared Error, Semi-Interquartile Range and Theil's inequality coefficient performances.