بدائل البحث:
process optimization » robust optimization (توسيع البحث), model optimization (توسيع البحث), policy optimization (توسيع البحث)
phase process » whole process (توسيع البحث)
process optimization » robust optimization (توسيع البحث), model optimization (توسيع البحث), policy optimization (توسيع البحث)
phase process » whole process (توسيع البحث)
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Proposed architecture testing phase.
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.
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.
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.
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.
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].
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.
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.
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.
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)
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.…"