يعرض 1 - 20 نتائج من 43 نتيجة بحث عن '(( binary ptv wolf optimization algorithm ) OR ( final phase process optimization algorithm ))', وقت الاستعلام: 0.58s تنقيح النتائج
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    Flow chart of particle swarm algorithm. 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).…"
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    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).…"
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    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).…"
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    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).…"
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    Comparison with existing SOTA techniques. 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. …"