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  1. 1
    PMT : opposition based learning technique for enhancing metaheuristic algorithms performance

    PMT : opposition based learning technique for enhancing metaheuristic algorithms performance by Hammoudeh, S. Alamri

    Published 2020
    “…Metaheuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. …”
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  2. 2
    Bi-Directional Monte Carlo Tree Search

    Bi-Directional Monte Carlo Tree Search by Spoerer, Kristian

    Published 2021
    “…Furthermore, Bi-Directional Search has been applied to a Reinforcement Learning algorithm. …”
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  3. 3
    A direct ensemble classifier for learning imbalanced multiclass data

    A direct ensemble classifier for learning imbalanced multiclass data by Samry @ Mohd Shamrie Sainin

    Published 2013
    “…The learning framework consists of ensemble learning and decision combiner model with general supervised learning algorithms as base learner. …”
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  4. 4
    Fast and efficient sequential learning algorithms using direct-link RBF networks

    Fast and efficient sequential learning algorithms using direct-link RBF networks by Asirvadam , Vijanth Sagayan, McLoone, Sean, Irwin, George

    Published 2003
    “…Novel fast and efficient sequential learning algorithms are proposed for direct-link radial basis function (DRBF) networks. …”
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  5. 5
    A direct ensemble classifier for imbalanced multiclass learning

    A direct ensemble classifier for imbalanced multiclass learning by Sainin, Mohd Shamrie, Alfred, Rayner

    Published 2012
    “…Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks.Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain.Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers.In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. …”
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  6. 6
    Computationally efficient sequential learning algorithms for direct link resource-allocating networks

    Computationally efficient sequential learning algorithms for direct link resource-allocating networks by Asirvadam , Vijanth Sagayan, McLoone, Sean, Irwin, George W

    Published 2005
    “…Computationally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). …”
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  7. 7
    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
    “…The class is known, but it is hidden from the learning model. Unlike supervised, unsupervised directly build the learning model for unlabeled example. …”
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  8. 8
    A new variant of black hole algorithm based on multi population and levy flight for clustering problem

    A new variant of black hole algorithm based on multi population and levy flight for clustering problem by Haneen Abdul Wahab, Abdul Raheem

    Published 2020
    “…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems. …”
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  9. 9
    Deep reinforcement learning approaches for multi-objective problem in Recommender Systems

    Deep reinforcement learning approaches for multi-objective problem in Recommender Systems by Ee, Yeo Keat

    Published 2022
    “…This is because the reinforcement learning agent is able to predict items directly by capture user latent information and explore large sparsity state space effectively. …”
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  10. 10
    Traffic signal control with deep reinforcement learning

    Traffic signal control with deep reinforcement learning by Lau, Joseph Yi Zhe

    Published 2025
    “…The project restudies the nature of the problem, and therefore, propose a new formulation of Markov decision process (MDP) and framework in TSC to improve efficiency and generalizability of the algorithm in various scenario. …”
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  11. 11
    A novel framework for identifying twitter spam data using machine learning algorithms

    A novel framework for identifying twitter spam data using machine learning algorithms by Maziku, Susana Boniphace, Abdul Rahiman, Amir Rizaan, Muhammed, Abdullah, Abdullah @ Selimun, Mohd Taufik

    Published 2020
    “…The research results contribute significantly to the field of cyber-security by forming a real-time system using machine learning algorithms.…”
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  12. 12
    Support directional shifting vector: A direction based machine learning classifier

    Support directional shifting vector: A direction based machine learning classifier by Kowsher, Md., Hossen, Imran, Tahabilder, Anik, Prottasha, Nusrat Jahan, Habib, Kaiser, Zafril Rizal, M Azmi

    Published 2021
    “…Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. …”
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  13. 13
    Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

    Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction by Al-Himyari, Bayadir Abbas

    Published 2017
    “…The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. …”
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  14. 14
    The effect of adaptive parameters on the performance of back propagation

    The effect of adaptive parameters on the performance of back propagation by Abdul Hamid, Norhamreeza

    Published 2012
    “…The results show that the proposed algorithm extensively improves the learning process of conventional Back Propagation algorithm.…”
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  15. 15
    Towards Software Product Lines Optimization Using Evolutionary Algorithms

    Towards Software Product Lines Optimization Using Evolutionary Algorithms by Jamil, Muhammad Abid, K Nour, Mohamed, Alhindi, Ahmed Hasan, Awang Abu Bakar, Normi Sham, Arif, Muhammad, Muhammad Aljabri, Tareq

    Published 2019
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    Proceeding Paper
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  16. 16
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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  17. 17
    Reactive approach for automating exploration and exploitation in ant colony optimization

    Reactive approach for automating exploration and exploitation in ant colony optimization by Allwawi, Rafid Sagban Abbood

    Published 2016
    “…Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. …”
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  18. 18
    PMT: opposition-based learning technique for enhancing meta-heuristic performance

    PMT: opposition-based learning technique for enhancing meta-heuristic performance by Alamri, Hammoudeh S., Kamal Z., Zamli

    Published 2019
    “…Meta-heuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. …”
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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
    “…Finally, the remaining unresolved problems and some directions of SKF research were discussed. …”
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  20. 20
    Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid

    Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid by Wahid, Muhammad Hazim

    Published 2022
    “…One of the major challenges when applying machine learning is to identify the best algorithm from a variety of algorithms to solve a problem. …”
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