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Showing 1 - 20 results of 226 for search '(( binary _ features optimization algorithm ) OR ( binary aware learning optimization algorithm ))', query time: 0.65s Refine Results
  1. 1
    <i>hi</i>PRS algorithm process flow.

    <i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

    Published 2023
    “…<b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …”
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  2. 2
    Feature selection convergence time comparison of various binary metaheuristic algorithms.

    Feature selection convergence time comparison of various binary metaheuristic algorithms. by Amal H. Alharbi (21755906)

    Published 2025
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  3. 3
    Number of features selected for IRBMO vs. other binary algorithms.

    Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  4. 4
    Sensitivity values of IRBMO vs. other binary algorithms.

    Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  5. 5
    Specificity values of IRBMO vs. other binary algorithms.

    Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  6. 6
    Fitness values of IRBMO vs. other binary algorithms.

    Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  7. 7
    Feature selection ratio results for tested algorithms.

    Feature selection ratio results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  8. 8
    Classification accuracy values of IRBMO vs. other binary algorithms.

    Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  9. 9
    F-score values of IRBMO vs. other binary algorithms.

    F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  10. 10
    Hyperparameter settings for the optimization algorithms.

    Hyperparameter settings for the optimization algorithms. by Ahmed M. Elshewey (21463867)

    Published 2025
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  11. 11
    The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms.

    The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

    Published 2023
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  12. 12
    Bar chart comparing the average fitness values of various feature selection algorithms.

    Bar chart comparing the average fitness values of various feature selection algorithms. by Doaa Sami Khafaga (21463870)

    Published 2025
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  13. 13
    a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset.

    a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

    Published 2022
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  14. 14
    Initial parameter settings for the optimization algorithms.

    Initial parameter settings for the optimization algorithms. by Doaa Sami Khafaga (21463870)

    Published 2025
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  15. 15
    Descriptive analysis of the outcomes by the optimized LSTM using several optimization algorithms.

    Descriptive analysis of the outcomes by the optimized LSTM using several optimization algorithms. by Ahmed M. Elshewey (21463867)

    Published 2025
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  16. 16
    The result of the accuracy of tested algorithms.

    The result of the accuracy of tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  17. 17
    Numerical example for determining the optimal number of clusters in Algorithm 2.

    Numerical example for determining the optimal number of clusters in Algorithm 2. by Yi Wei Tye (22208735)

    Published 2025
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  18. 18
    Box plots with swarm overlays for six feature selection algorithms, annotated with mean and standard deviation values.

    Box plots with swarm overlays for six feature selection algorithms, annotated with mean and standard deviation values. by Doaa Sami Khafaga (21463870)

    Published 2025
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  19. 19
    Control parameter settings for comparative feature selection methods.

    Control parameter settings for comparative feature selection methods. by Nebojsa Bacanin (13932723)

    Published 2022
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  20. 20
    Best fitness value results for tested algorithms.

    Best fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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