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  1. 1
    Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function

    Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function by Hammash, Nayif Mohammed

    Published 2012
    “…This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. …”
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  2. 2
    Energy-aware scheduling optimization in hybrid flow shops using artificial bee colony algorithm

    Energy-aware scheduling optimization in hybrid flow shops using artificial bee colony algorithm by Mohd Abdul Hadi, Osman, Mohd Fadzil Faisae, Ab Rashid, Nik Mohd Zuki, Nik Mohamed, Muhammad Ammar, Nik Mu’tasim

    Published 2024
    “…Through an extensive computational experiment involving a well-known benchmark suite, the ABC algorithm demonstrated remarkable performance, consistently outperforming several popular metaheuristic algorithms, including Genetic Algorithms, Particle Swarm Optimization, Memetic Algorithms, and Whale Optimization Algorithm in 75% of the problems. …”
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  3. 3
    A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System

    A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System by Wong, Ze-Hao

    Published 2020
    “…Finally, surface defects are segmented from an anomaly heat map which is generated based on histogram distance functions. Results show that the proposed algorithm required a learning dataset size as small as 5 samples and was resistant to learning labelling error up to 50%.…”
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  4. 4
    Restoration planning strategy of transmission system based on optimal energizing time of sectionalizing islands / Dian Najihah Abu Talib

    Restoration planning strategy of transmission system based on optimal energizing time of sectionalizing islands / Dian Najihah Abu Talib by Dian Najihah , Abu Talib

    Published 2019
    “…There are two discrete optimization techniques used in this work, which are the Artificial Bee Colony algorithm and Evolutionary Programming. …”
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  5. 5
    Improving the exploration strategy of an automated android GUI testing tool based on the Q-Learning algorithm by selecting potential actions

    Improving the exploration strategy of an automated android GUI testing tool based on the Q-Learning algorithm by selecting potential actions by Goh, Kwang Yi, Baharom, Salmi, Din, Jamilah

    Published 2022
    “…Furthermore, the proposed techniques based on the Q-Learning algorithm do not consider context-based actions. …”
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  6. 6
    Enhanced Q-Learning algorithm for potential actions selection in automated graphical user interface testing

    Enhanced Q-Learning algorithm for potential actions selection in automated graphical user interface testing by Goh, Kwang Yi

    Published 2023
    “…To overcome this limitation, the Q-Learning algorithm was proposed by several researchers to minimise randomness. …”
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  7. 7
    A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

    A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem by Kamal Z., Zamli, Fakhrud, Din, Ahmed, Bestoun S., Bures, Miroslav

    Published 2018
    “…In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). …”
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  8. 8
    Analysis of parallel flow type internally cooled membrane-based liquid desiccant dehumidifier using a neural networks approach

    Analysis of parallel flow type internally cooled membrane-based liquid desiccant dehumidifier using a neural networks approach by Quadros, Jaimon Dennis, Khan, Sher Afghan, T., Prashanth

    Published 2021
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  9. 9
    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets

    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

    Published 2019
    “…Thereafter, a multi-objective hybrid algorithm (MOHA), an extension of the self-adaptive hybrid algorithm is proposed and tested on the established multi-objective (MO) test functions. …”
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  10. 10
    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm by Shahrizal, Saat, Mohd Ashraf, Ahmad, Mohd Riduwan, Ghazali

    Published 2025
    “…The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. …”
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  11. 11
    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm by Shahrizal, Saat, Mohd Ashraf, Ahmad, Mohd Riduwan, Ghazali

    Published 2025
    “…The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. …”
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  12. 12
    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems

    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

    Published 2022
    “…The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. …”
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  13. 13
    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model

    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…Expectation maximization (EM) is one of the representatives clustering algorithms which have broadly applied in solving classification problems by improving the density of data using the probability density function. …”
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  14. 14
    Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification

    Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification by Wong S.Y., Yap K.S.

    Published 2023
    Conference Paper
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  15. 15
    Air pollution forecasting in Kuala Terengganu using Artificial Neural Network (ANN) / Nur Raudzah Abdullah

    Air pollution forecasting in Kuala Terengganu using Artificial Neural Network (ANN) / Nur Raudzah Abdullah by Abdullah, Nur Raudzah

    Published 2020
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  16. 16
    Joint optimization of resources allocation for quality of service aware next-generation heterogeneous cellular networks / Hayder Faeq Rasool Alhashimi

    Joint optimization of resources allocation for quality of service aware next-generation heterogeneous cellular networks / Hayder Faeq Rasool Alhashimi by Hayder Faeq Rasool , Alhashimi

    Published 2025
    “…Finally, a State-Action-Reward-State-Action (SARSA) algorithm, which is a reinforcement learning approach, is proposed to solve the power allocation optimization problem. …”
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  17. 17
    Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain

    Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain by Mohamad Zain, Muhammad Asyraf

    Published 2020
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  18. 18
    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks

    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

    Published 2025
    thesis::doctoral thesis
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  19. 19
    New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification

    New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification by Balakrishnan, Malarvili, Ting, Chee Ming, Shaikh Salleh, Sheikh Hussain

    Published 2009
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  20. 20
    A hyper-heuristic based strategy for image segmentation using multilevel thresholding

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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