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  1. 1
    Fuzzy modeling using Bat Algorithm optimization for classification

    Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

    Published 2018
    “…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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  2. 2
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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  3. 3
    Impact learning : A learning method from feature’s impact and competition

    Impact learning : A learning method from feature’s impact and competition by Prottasha, Nusrat Jahan, Murad, Saydul Akbar, Abu Jafar, Md Muzahid, Rana, Masud, Kowsher, Md, Adhikary, Apurba, Biswas, Sujit, Bairagi, Anupam Kumar

    Published 2023
    “…A variety of well-known machine learning algorithms have been developed for use in the field of computer science to analyze data. …”
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  4. 4
    Impact learning: A learning method from feature’s impact and competition

    Impact learning: A learning method from feature’s impact and competition by Prottasha, Nusrat Jahan, Murad, Saydul Akbar, Muzahid, Abu Jafar Md, Rana, Masud, Kowsher, Md, Adhikary, Apurba, Biswas, Sujit, Bairagi, Anupam Kumar

    Published 2023
    “…A variety of well-known machine learning algorithms have been developed for use in the field of computer science to analyze data. …”
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  5. 5
    Impact learning: A learning method from feature's impact and competition

    Impact learning: A learning method from feature's impact and competition by Prottasha, Nusrat Jahan, Murad, Saydul Akbar, Abu Jafar, Md Muzahid, Rana, Masud, Kowsher, Md, Adhikary, Apurba, Biswas, Sujit, Bairagi, Anupam Kumar

    Published 2023
    “…A variety of well-known machine learning algorithms have been developed for use in the field of computer science to analyze data. …”
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  6. 6
    Exploration of machine learning forecasting methods in M4 competition / Muhammad Halim Hamdan and Shuzlina Abdul-Rahman

    Exploration of machine learning forecasting methods in M4 competition / Muhammad Halim Hamdan and Shuzlina Abdul-Rahman by Hamdan, Muhammad Halim, Abdul-Rahman, Shuzlina

    Published 2021
    “…This research aims to compare and contrast the machine learning forecasting techniques that are used in M4 Competition, to get better understanding on each technique and to identify the best technique. …”
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  7. 7
    A random search based effective algorithm for pairwise test data generation

    A random search based effective algorithm for pairwise test data generation by Sabira, Khatun, K. F., Rabbi, Che Yahaya, Yaakub, Klaib, Mohammad F. J.

    Published 2011
    “…This paper proposes an effective random search based pairwise test data generation algorithm named R2Way to optimize the number of test cases. …”
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  8. 8
    Scalable approach for mining association rules from structured XML data

    Scalable approach for mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
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  9. 9
    EasyA: Easy and effective way to generate pairwise test data

    EasyA: Easy and effective way to generate pairwise test data by Rabbi, Khandakar Fazley, Sabira, Khatun, Che Yahaya, Yaakub, Klaib, Mohammad F. J.

    Published 2013
    “…This paper proposes a matrix based calculation for pairwise test data generation algorithm named EasyA to optimize the number of test cases. …”
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  10. 10
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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  11. 11
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  12. 12
    Enhanced bibliographic data retrieval and visualization using query optimization and spectral centrality measure

    Enhanced bibliographic data retrieval and visualization using query optimization and spectral centrality measure by Ramasamy, Chitra, Zolkepli, Maslina

    Published 2019
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  13. 13
    Broadening selection competitive constraint handling algorithm for faster convergence

    Broadening selection competitive constraint handling algorithm for faster convergence by Shaikh, T.A., Hussain, S.S., Tanweer, M.R., Hashmani, M.A.

    Published 2020
    “…The proposed algorithm has been evaluated using 24 benchmark functions. …”
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  14. 14
    Improving the accuracy of AIRS by incorporating real world tournament selection in resource competition phase

    Improving the accuracy of AIRS by incorporating real world tournament selection in resource competition phase by Hormozi, Shahram Golzari, C. Doraisamy, Shyamala, Sulaiman, Md. Nasir, Udzir, Nur Izura

    Published 2009
    “…The combination of cross validation and t test is used as evaluation method. Algorithms tested on benchmark datasets of the UCI machine learning repository show that RWTSAIRS obtained higher accuracy than AIRS in all cases and that the difference between accuracies of two algorithms was significant in majority of cases.…”
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  15. 15
    Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic

    Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic by Yap, Soon Teck

    Published 2004
    “…In CQ Routing Algorithm, the confidence value (C value) can be used to improve the quality of exploration in Q Routing Algorithm. …”
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  16. 16
    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
    “…Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. …”
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  17. 17
    Exploring a Q-learning-based chaotic naked mole rat algorithm for S-box construction and optimization

    Exploring a Q-learning-based chaotic naked mole rat algorithm for S-box construction and optimization by Kamal Zuhairi, Zamli, Din, Fakhrud, Alhadawi, Hussam S.

    Published 2023
    “…This paper introduces a new variant of the metaheuristic algorithm based on the naked mole rat (NMR) algorithm, called the Q-learning naked mole rat algorithm (QL-NMR), for substitution box construction and optimization. …”
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  18. 18
    Performance Comparison of Neural Network Training Algorithms for Modeling Customer Churn Prediction

    Performance Comparison of Neural Network Training Algorithms for Modeling Customer Churn Prediction by Mohd Khalid, Awang, Mohammad Ridwan, Ismail, Mokhairi, Makhtar, M Nordin, A Rahman, Abd Rasid, Mamat

    Published 2017
    “…Predicting customer churn has become the priority of every telecommunication service provider as the market is becoming more saturated and competitive. This paper presents a comparison of neural network learning algorithms for customer churn prediction. …”
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  19. 19
    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System

    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System by Hormozi, Shahram Golzari

    Published 2011
    “…The proposed algorithms have been tested on a variety of datasets from the UCI machine learning repository. …”
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  20. 20
    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy

    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

    Published 2019
    “…The experimental results demonstrate that the proposed algorithm is competitive compared to the state-of-the-art semi-supervised learning algorithms in terms of accuracy. …”
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