WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Article Citation - WoS: 1Citation - Scopus: 1Design and Experimental Verification of a Posture Correction System: Development of an Artificial Neural Network To Predict the Effectiveness of the Developed System To Correct Poor Posture(Taylor & Francis inc, 2024) Yildiz, Eren; Das, MemikThis research aims to address designing an experiment to evaluate the impact of a developed posture correction system. Also, the correct posture learning habits of users can be estimated with an artificial neural network (ANN) structure that predicts the poor posture count (PPC) in the last session of the experiment using the information received from the users and the developed system. The developed system aims to collect data from different individuals about their sitting posture information. An ANN analysis tool is developed to predict the individuals' habits of learning the correct posture. This setup is based on a flex sensor and has the capability of collecting posture information data and warning the user when the posture is not correct. A three-session experiment was conducted on 12 healthy participants to investigate his/her posture habits. The data was analyzed to determine the average PPC value. It was observed that PPC decreased by 56.27% from session one to session three, and the average improvement evaluation (IE) value after each session was found to be positive. In addition to experimental analysis, the collected posture data was used to train and validate an ANN architecture capable of predicting PPC values. The developed device is effective in improving posture habits and has the potential to predict PPC values with the ANN architecture.Article Citation - WoS: 12Citation - Scopus: 17Dynamics of Multi-Point Singular Fifth-Order Lane-Emden System With Neuro-Evolution Heuristics(Springer Heidelberg, 2022) Ali, Mohamed R.; Fathurrochman, Irwan; Raja, Muhammad Asif Zahoor; Sadat, R.; Baleanu, Dumitru; Sabir, ZulqurnainThe objective of the presented communication is to examine and analyze the solutions of nonlinear multi-singular fifth-order Lane-Emden (LE) system for different scenarios by variation of shape factors settled on the equivalent design of the LE equations. The neuro-evolution based stochastic computing is explored for the numerical measures using the artificial neural networks (ANNs) models for the appropriate continuous mapping, while the learning of decision variables is conducted using the integrated meta-heuristic global search of genetic algorithms (GA) hybrid with the local search efficiency of active-set (AS) i.e., ANN-GA-AS scheme. The numerical approach ANN-GA-AS is applied efficiently for the fifth kind of nonlinear LE model and statistical calculations further validate the accuracy, robustness as well as convergence.Article Citation - WoS: 60Citation - Scopus: 68Fractional 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, ZulqurnainThis 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: 37Citation - Scopus: 36Fmnsics: 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, ZulqurnainThe 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: 62Citation - Scopus: 53Design 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, ZulqurnainIn 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: 2Citation - Scopus: 3New 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 ŞenThe 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.