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  1. 1

    Application Of Genetic Algorithms For Robust Parameter Optimization by Belavendram, N.

    Published 2010
    “…In this paper, a genetic algorithm is used to study a seven factor non-linear equation for a Wheatstone bridge as the equation to be optimized. …”
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  2. 2

    Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

    Published 2021
    “…In this research, a novel algorithm (Herschel Bulkley Network) is introduced to simulate the non-Newtonian fluid flow in a pipe using data redundant deep neural network (DNN) for fully developed, laminar, and incompressible flow conditions. …”
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  3. 3

    Modified Sumudu Transform Analytical Approximate Methods For Solving Boundary Value Problems by Al-Nemrat, Asem Mustafa Moh’ad

    Published 2019
    “…In this algorithm, the convolution theorem has been used to find an optimal Lagrange multiplier.…”
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  4. 4

    Modified Sumudu Transform Analytical Approximate Methods For Solving Boundary Value Problems by Al-Nemrat, Asem Mustafa Moh’ad

    Published 2019
    “…In this algorithm, the convolution theorem has been used to find an optimal Lagrange multiplier.…”
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    Thesis
  5. 5

    Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications by Kumar, A., Ridha, S., Ilyas, S.U.

    Published 2020
    “…In the present study, the solution for the initial condition and boundary value problems in ordinary and partial differential equation by deep learning method have been analyzed. …”
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  6. 6
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    A Preliminary Report on the Utilization of PSO for Solving the Hamiltonian Systems by Irianto, Irianto

    Published 2014
    “…This method utilizes an algorithm called Particle Swarm Optimization or PSO. …”
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  10. 10

    Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak by Emily Sing Kiang, Siew

    Published 2025
    “…A constructive algorithm was developed to generate an initial feasible solution, which was subsequently refined using two primary approaches to evaluate their efficiency: Iterative Threshold Pipe Variable Neighborhood Descent (IT-PVND), and a modified Whale Optimization Algorithm (WOA). …”
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  11. 11

    Static voltage stability analysis using generalized regression neural network by Mirzaei, Maryam, Jasni, Jasronita, Hizam, Hashim, Abdul Wahab, Noor Izzri, Moazami, Ehsan

    Published 2013
    “…The focus of this paper is on voltage stability monitoring using the generalized regression neural network by improving algorithm. …”
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  12. 12

    Optimization driven model-space approach for gas clouds using full waveform inversion by Prajapati, S., Ghosh, D.P., Yousoff, W.I.B.W., Iskandar, B.S., Perak, T.

    Published 2014
    “…An alternative simultaneous-source encoding inversion technique is proposed. With the use of proposed algorithm, successfully recoverd the true geological information though inversion, however, the use of simultaneous source introduces the crosstalk artifacts which are efficient at higher frequencies and minimum at low frequencies, which we can clearly see in the model fit at top but it has minimal effect on whole dataset. …”
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  13. 13

    Operational matrix based on orthogonal polynomials and artificial neural networks methods for solving fractal-fractional differential equations by Shloof, Aml Melad Asan

    Published 2024
    “…This study provided some new methods to solve initial value problems (IVPs) and boundary value problems (BVPs) of fractal-fractional differential equations (FFDEs) using operational matrix (OM) and artificial neural networks (ANNs). …”
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  14. 14

    An advanced scheme based on Artificial Intelligence technique for solving nonlinear Riccati systems by Admon, Mohd Rashid, Senu, Norazak, Ahmadian, Ali, Abdul Majid, Zanariah

    Published 2024
    “…This work aims to implement a feedforward ANN with two hidden layers to solve nonlinear systems based on the fractional Riccati differential equation (FRDE). The network parameters are trained using the Adam optimization method with the aid of automatic differentiation. …”
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  15. 15

    Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network by Mohamad Afiq, Mohd Asrul

    Published 2023
    “…The model reliability describes the second sub-objective, which is to determine the feasible operating window of the MEC using multiple-objective optimization based on the nonlinear convex method using gradient descent algorithm as the objective function in maximizing the percentage efficiency of the MEC after validating the mathematical model. …”
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    Enhancing Autonomous Guided Vehicles with Red-Black TOR Iterative Method by A’Qilah Ahmad Dahalan, Azali Saudi, Jumat Sulaiman

    Published 2023
    “…By carefully choosing the optimal parameter values, the suggested B-MTOR improved the computational execution of the algorithm. …”
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  18. 18

    Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model by Zulqurnain Sabir, Ag. Asri Ag. Ibrahim, Muhammad Asif Zahoor Raja, Kashif Nisar, Muhammad Umar, Joel J. P. C. Rodrigues, Samy R. Mahmoud

    Published 2021
    “…For the solutions of these classes based on influenza disease system, the design of an objective function is presented using these differential system equations and its corresponding initial conditions. …”
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  19. 19

    Study on numerical solution of a variable order fractional differential equation based on symmetric algorithm by Liu, Jingrui, Pan, Dongyang

    Published 2019
    “…Numerical solution algorithm for a class of fractional differential equations with transformed arrays based on the proposed symmetry algorithm. …”
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  20. 20

    A new algorithm for system of integral equations by Rasulov, Abdujabar, Kilicman, Adem, Eshkuvatov, Zainidin K., Raimova, Gulnora

    Published 2014
    “…Using the new algorithm it is also possible to solve an initial boundary value problem for system of parabolic equations. …”
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