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Showing 1 - 20 results of 46 for search 'binary search models optimization algorithm', query time: 0.39s Refine Results
  1. 1
    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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  2. 2
    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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  3. 3
    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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  4. 4
    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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  5. 5
    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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  6. 6
    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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  7. 7
    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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  8. 8
    Hyperparameter settings for the optimization algorithms.

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

    Published 2025
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  9. 9
    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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  10. 10
    Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm.

    Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

    Published 2025
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  11. 11
    Performance of the proposed AD-PSO-Guided WOA-LSTM algorithm compared with another algorithm.

    Performance of the proposed AD-PSO-Guided WOA-LSTM algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

    Published 2025
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  12. 12
    Histograms of various water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Organic Carbon, Trihalomethanes, Turbidity, and a binary portability check.

    Histograms of various water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Organic Carbon, Trihalomethanes, Turbidity, and a binary portability check. by Amal H. Alharbi (21755906)

    Published 2025
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  13. 13
    The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms.

    The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

    Published 2025
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  14. 14
    Total computational cost of the proposed AD-PSO-Guided WOA-LSTM algorithm compared with another algorithm.

    Total computational cost of the proposed AD-PSO-Guided WOA-LSTM algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

    Published 2025
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  15. 15
    Adaptive dynamic algorithm for Australia rainfall.

    Adaptive dynamic algorithm for Australia rainfall. by Ahmed M. Elshewey (21463867)

    Published 2025
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  16. 16
    Prediction results of individual models.

    Prediction results of individual models. by Ahmed M. Elshewey (21463867)

    Published 2025
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  17. 17
    Hyperparameters of the LSTM Model.

    Hyperparameters of the LSTM Model. by Ahmed M. Elshewey (21463867)

    Published 2025
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  18. 18
    Metrics for evaluating the performance of the proposed model.

    Metrics for evaluating the performance of the proposed model. by Ahmed M. Elshewey (21463867)

    Published 2025
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  19. 19
    Elite search behavior.

    Elite search behavior. by Chenyi Zhu (9383370)

    Published 2025
    “…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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  20. 20
    The Pseudo-Code of the IRBMO Algorithm.

    The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

    Published 2025
    “…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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