WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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Subject: search.filters.subject.Artificial Neural NetworksWoS Q: search.filters.wosQuartile.Q2

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Now showing 1 - 5 of 5
  • Article
    Citation - WoS: 8
    Citation - Scopus: 9
    A Hybrid Computing Approach To Design the Novel Second Order Singular Perturbed Delay Differential Lane-Emden Model
    (Iop Publishing Ltd, 2022) Baleanu, Dumitru; Raja, Muhammad Asif Zahoor; Hincal, Evren; Sabir, Zulqurnain
    In this study, the mathematical form of the second order perturbed singular delay differential system is presented. The comprehensive features using the singular-point, perturbed factor and pantograph term are provided together with the shape factor of the second order perturbed singular delay differential system. The novel model is simulated numerically through the artificial neural networks (ANNs) based on the global/local optimization procedures, i.e., genetic algorithm (GA) and sequential quadratic programming (SQP). An activation function is constructed by using the differential model based on the second order perturbed singular delay differential system. The optimization of fitness function is performed through the hybrid computing strength of the ANNs-GA-SQP to solve the second order perturbed singular delay differential system. The exactness, substantiation, and authentication of the novel system is observed to solve three different variants of the novel model. The convergence, robustness, correctness, and stability of the numerical approach is performed using the comparison procedures of the available exact solutions. For the reliability, the statistical performances with necessary processes are provided using the ANNs-GA-SQP.
  • Article
    Citation - WoS: 37
    Citation - Scopus: 36
    Fmnsics: Fractional Meyer Neuro-Swarm Intelligent Computing Solver for Nonlinear Fractional Lane-Emden Systems
    (Springer London Ltd, 2022) Raja, Muhammad Asif Zahoor; Umar, Muhammad; Shoaib, Muhammad; Baleanu, Dumitru; Sabir, Zulqurnain
    The fractional neuro-evolution-based intelligent computing has substantial potential to solve fractional order systems represented with Lane-Emden equation arising in astrophysics including Newtonian self-gravitating, spherically symmetric and polytropic fluid. The present study aimed to present a neuro-swarm-based intelligent computing solver for the solution of nonlinear fractional Lane-Emden system (NFLES) using by exploitation of fractional Meyer wavelet artificial neural networks (FMW-ANNs) and global optimization mechanism of particle swarm optimization (PSO) combined with rapid local search of sequential quadratic programming (SQP), i.e., FMW-ANN-PSO-SQP. The motivation for the design of FMW-ANN-PSO-SQP intelligent computing comes with an objective of presenting an accurate, reliable, and viable framworks to deal with stiff nonlinear singular models represented with NFLES involving both fractional and integer derivative terms. The designed algorithm is tested for six different variants of NFLESs. The obtained numerical outcomes obtained by the proposed FMW-ANN-PSO-SQP are compared with the exact results to authenticate the correctness, efficacy, and viability, and these aspects are further endorsed statistical observations.
  • Article
    Citation - WoS: 62
    Citation - Scopus: 53
    Design of Stochastic Numerical Solver for the Solution of Singular Three-Point Second-Order Boundary Value Problems
    (Springer London Ltd, 2021) Baleanu, Dumitru; Shoaib, Muhammad; Raja, Muhammad Asif Zahoor; Sabir, Zulqurnain
    In this paper, a novel meta-heuristic computing solver is presented for solving the singular three-point second-order boundary value problems using artificial neural networks (ANNs) optimized by the combined strength of global and local search ability of genetic algorithms (GAs) and interior point algorithm (IPA), i.e., ANN-GA-IPA. The inspiration for presenting this numerical work comes from the intention of introducing a consistent framework that combines the effective features of neural networks optimized with the contexts of soft computing to handle with such challenging systems. Three numerical variants of singular second-order system have been taken to examine the proficiency, robustness, and stability of the designed approach. The comparison of the proposed results of ANN-GA-IPA from available exact solutions shows the good agreement with 5 to 7 decimal places of the accuracy which established worth of the methodology through performance analyses on a single and multiple executions.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 3
    New Spectral Approaches To the Simultaneous Quantitative Resolution of a Combined Veterinary Formulation by Ann and Pca-Ann Methods
    (Walter de Gruyter & Co, 2011) Baleanu, Dumitru; Sen Koktas, Nigar; Dinc, Erdal; Köktaş, Nigar Şen
    The simultaneous spectral prediction of levamisole (LEV) and triclabendazole (TRI) in combined veterinary formulation was performed by the new chemometric methods, artificial neural network (ANN) and principal component analysis-artificial neural network (PCA-ANN). Despite the overlapping spectra of LEV and TRI in the same wavelength region, the proposed methods do not use any separation procedure for the analysis of the related compounds. Good precision and accuracy were observed for the applications of the proposed artificial neural network models to an independent binary mixture set consisting of the active compounds. These methods were successfully applied for the chemometric quantitation of a veterinary formulation of LEV and TRI.
  • Article
    Citation - WoS: 37
    Citation - Scopus: 43
    Multivariate Analysis of Paracetamol, Propiphenazone, Caffeine and Thiamine in Quaternary Mixtures by Pcr, Pls and Ann Calibrations Applied on Wavelet Transform Data
    (Elsevier, 2008) Baleanu, Dumitru; Ioele, Giuseppina; De Luca, Michele; Ragno, Gaetano; Dinc, Erdal
    The quantitative resolution of a quaternary pharmaceutical mixture consisting of paracetamol, propiphenazone, caffeine and thiamine was performed by the simultaneous use of fractional wavelet transform (FWT) with principal component regression (PCR), partial least squares (PLS) and artificial neural networks (ANN) methods. A calibration set consisting of 22 mixture solutions was prepared by means of an orthogonal experimental design and their absorption spectra were recorded in the spectral range of 210.0-312.3 nm and then transferred into the fractional wavelet domain and processed by FWT. The chemometric calibrations FWT-PCR, FWT-PLS and FWT-ANN were computed by using the relationship between the coefficients provided by FWT method and the concentration data from calibration set. An external validation was carried out by applying the developed methods to the analysis of synthetic mixtures of the related compounds, obtaining successful results. The models were finally used to assay the studied drugs in the commercial pharmaceutical formulations. (c) 2008 Elsevier B.V. All rights reserved.