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  1. 1
    An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications.

    An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. by S. Ahmed, Bestoun, Enoiu, Eduard, Afzal, Wasif, Kamal Z., Zamli

    Published 2020
    “…The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including modified choice function, Tabu high-level hyperheuristic, teaching learning-based optimization, sine cosine algorithm, and symbiotic optimization search in clustering quality within comparable execution time.…”
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  2. 2
    Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network

    Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network by Kang, Miew How

    Published 2016
    “…First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. …”
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  3. 3
    Functional link neural network with modified bee-firefly learning algorithm for classification task

    Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

    Published 2016
    “…This work proposed the implementation of modified Artificial Bee Colony with Firefly algorithm for training the FLNN network to overcome the drawback of BP-learning algorithm. …”
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  4. 4
    An approach to improve functional link neural network training using modified artificial bee colony for classification task

    An approach to improve functional link neural network training using modified artificial bee colony for classification task by Yana Mazwin Mohmad Hassim, Rozaida Ghazali

    Published 2013
    “…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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  5. 5
    An approach to improve functional link neural network training using modified artificial bee colony for classification task

    An approach to improve functional link neural network training using modified artificial bee colony for classification task by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

    Published 2012
    “…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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  6. 6
    An approach to improve functional link neural network training using modified artificial bee colony for classification task

    An approach to improve functional link neural network training using modified artificial bee colony for classification task by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

    Published 2012
    “…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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  7. 7
    An ensemble of neural network and modified grey wolf optimizer for stock prediction

    An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

    Published 2019
    “…The research implements stock prediction analysis as a case study for training the neural network by adopting MGWO algorithm. …”
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  8. 8
    Control algorithm for two-tank system using multiparametric programming

    Control algorithm for two-tank system using multiparametric programming by Zakaria, A., Mid, E.C., Mohamed, M.F., Hussin, M.H.M., Shaari, A.S., Ruslan, Eliyana, Hadi, Dayanasari, Masri, M.

    Published 2023
    “…In conclusion, the implementation of multiparametric programming is able to estimate the value of the output for the control algorithm of the two-tank system.…”
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  9. 9
    Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives

    Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives by Jegatheesan N., Ibrahim M.R., Ahmed A.N., Koting S., El-Shafie A., Katman H.Y.B.

    Published 2025
    “…This study aimed to develop models assessing 26 machine-learning algorithms in regression analysis to predict the properties of terminal blend crumb rubber-modified bitumen (TB-CRMB) made with crosslinking additives. …”
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  10. 10
    Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter

    Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter by Azhar, Nur Huwaina

    Published 2019
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  11. 11
    Implementation of AES algorithm into information investigation autopsy (IIA) / Ahmad Wafiy Hamad Zaki

    Implementation of AES algorithm into information investigation autopsy (IIA) / Ahmad Wafiy Hamad Zaki by Hamad Zaki, Ahmad Wafiy Hamad Zaki

    Published 2013
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  12. 12
    Needleman wunsch implementation for SPAM/UCE inline filter

    Needleman wunsch implementation for SPAM/UCE inline filter by Tamil E.M., Idris M.Y.I., Thong C.M., Jali M.Z., Madihah Mohd Saudi

    Published 2024
    Conference Paper
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  13. 13
    Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom

    Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom by Masrom, Suraya

    Published 2015
    “…However, in many cases, implementing the suitable hybrid algorithms for a given optimization problem is a considerably difficult, which in most cases, is time consuming. …”
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  14. 14
    An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators

    An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Muhammad Ikram, Mohd Rashid

    Published 2023
    “…This paper proposes the implementation of metaheuristic algorithm namely, teaching–learning-based optimization (TLBO) algorithm to solve optimal power flow (OPF) problem. …”
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  15. 15
    Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom

    Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom by Masrom, Suraya

    Published 2015
    “…However, in many cases, implementing the suitable hybrid algorithms for a given optimization problem is a considerably difficult, which in most cases, is time consuming. …”
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  16. 16
    A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition

    A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition by Koh J.S., Tan R.H.G., Lim W.H., Tan N.M.L.

    Published 2024
    “…A modified initialization scheme that leverages grid partitioning and oppositional-based learning is incorporated to produce an evenly distributed initial population across P-V curve. …”
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  17. 17
    Parallel Implementation of Two Level Barotropic Models Applied to the Weather Prediction Problem

    Parallel Implementation of Two Level Barotropic Models Applied to the Weather Prediction Problem by Bahri, Susila

    Published 2004
    “…To process the data collected from British Atmospheric Data Centre (BADC), the sequential programs in row and columnwise fashions are developed and implemented. …”
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  18. 18
    A genetic algorithm for solving single level lotsizing problems

    A genetic algorithm for solving single level lotsizing problems by Zenon, Nasaruddin, Ahmad, Ab. Rahman, Ali, Rosmah

    Published 2003
    “…In this paper a genetic algorithm for solving single level lot-sizing problems is proposed and the results of applying the algorithm toexample problems are discussed. …”
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  19. 19
    Simulated Kalman Filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem

    Simulated Kalman Filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem by Suhazri Amrin, Rahmad, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

    Published 2022
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  20. 20
    Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers

    Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

    Published 2022
    “…These algorithms are selected from the different metaheuristics classification groups, where the implementation of these algorithms into the said problems will be tested on the modified IEEE 14-bus system. …”
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