يعرض 21 - 40 نتائج من 72 نتيجة بحث عن '(( final phase process optimization algorithm ) OR ( binary data global optimization algorithm ))', وقت الاستعلام: 0.67s تنقيح النتائج
  1. 21

    IRBMO vs. feature selection algorithm boxplot. حسب Chenyi Zhu (9383370)

    منشور في 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. …"
  2. 22

    Proposed architecture testing phase. حسب Yasir Khan Jadoon (21433231)

    منشور في 2025
    "…The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. The trained model is employed in the testing phase for the feature extraction from the self-attention layer and passed to the shallow wide neural network classifier for the final classification. …"
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    The structure of the Resnet50. حسب Nour Eldeen Mahmoud Khalifa (19259450)

    منشور في 2024
    "…The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50).…"
  6. 26

    The bottleneck residual block for Resnet50. حسب Nour Eldeen Mahmoud Khalifa (19259450)

    منشور في 2024
    "…The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50).…"
  7. 27

    DeepDate model’s architecture design. حسب Nour Eldeen Mahmoud Khalifa (19259450)

    منشور في 2024
    "…The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50).…"
  8. 28

    IRBMO vs. variant comparison adaptation data. حسب Chenyi Zhu (9383370)

    منشور في 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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    Pseudo Code of RBMO. حسب Chenyi Zhu (9383370)

    منشور في 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. …"
  11. 31

    P-value on CEC-2017(Dim = 30). حسب Chenyi Zhu (9383370)

    منشور في 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. …"
  12. 32

    Memory storage behavior. حسب Chenyi Zhu (9383370)

    منشور في 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. …"
  13. 33

    Elite search behavior. حسب Chenyi Zhu (9383370)

    منشور في 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. …"
  14. 34

    Description of the datasets. حسب Chenyi Zhu (9383370)

    منشور في 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. …"
  15. 35

    S and V shaped transfer functions. حسب Chenyi Zhu (9383370)

    منشور في 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. …"
  16. 36

    S- and V-Type transfer function diagrams. حسب Chenyi Zhu (9383370)

    منشور في 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. …"
  17. 37

    Collaborative hunting behavior. حسب Chenyi Zhu (9383370)

    منشور في 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. …"
  18. 38

    Friedman average rank sum test results. حسب Chenyi Zhu (9383370)

    منشور في 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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