يعرض 1 - 16 نتائج من 16 نتيجة بحث عن 'final phase process optimization algorithm', وقت الاستعلام: 0.22s تنقيح النتائج
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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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    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. …"
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    Proposed inverted residual parallel block. 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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    Inverted residual bottleneck block. 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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    Sample classes from the HMDB51 dataset. 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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    Sample classes from UCF101 dataset [40]. 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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    Self-attention module for the features learning. 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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    Residual behavior. 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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    Overall framework diagram. حسب Yanhua Xian (21417128)

    منشور في 2025
    "…Secondly, addressing the issue of weight and threshold initialization in BPNN, the Coati Optimization Algorithm (COA) was employed to optimize the network (COA-BPNN). …"
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    <b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) حسب Erola Fenollosa (20977421)

    منشور في 2025
    "…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…"