بدائل البحث:
significant processes » significant progress (توسيع البحث), significant promise (توسيع البحث), significant increases (توسيع البحث)
processes decrease » progressive decrease (توسيع البحث)
lower decrease » larger decrease (توسيع البحث), linear decrease (توسيع البحث), teer decrease (توسيع البحث)
we decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), nn decrease (توسيع البحث)
significant processes » significant progress (توسيع البحث), significant promise (توسيع البحث), significant increases (توسيع البحث)
processes decrease » progressive decrease (توسيع البحث)
lower decrease » larger decrease (توسيع البحث), linear decrease (توسيع البحث), teer decrease (توسيع البحث)
we decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), nn decrease (توسيع البحث)
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Recruitment flow diagram of the current study.
منشور في 2025"…</p><p>Conclusions</p><p>Clinicians should assess and address patients’ recovery expectations early in the care process, as these may significantly influence long-term HRQoL outcomes. …"
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Literature comparison.
منشور في 2025"…To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. …"
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Flow chart of proposed system.
منشور في 2025"…To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. …"
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Literature review.
منشور في 2025"…To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. …"
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IMU data and video synchronization.
منشور في 2025"…To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. …"
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Confusion matrix-punch classification.
منشور في 2025"…To reduce labeling effort, we apply a Query by Committee-based active learning technique, significantly decreasing the required labeling effort by one-sixth. …"
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S1 File -
منشور في 2025"…Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. …"
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Confusion matrix for ClinicalBERT model.
منشور في 2025"…Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. …"
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Confusion matrix for LastBERT model.
منشور في 2025"…Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. …"
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Student model architecture.
منشور في 2025"…Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. …"
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Configuration of the LastBERT model.
منشور في 2025"…Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. …"
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Confusion matrix for DistilBERT model.
منشور في 2025"…Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. …"
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ROC curve for LastBERT model.
منشور في 2025"…Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. …"
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Sample Posts from the ADHD dataset.
منشور في 2025"…Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. …"
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Top-level overview for ADHD classification study.
منشور في 2025"…Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. …"
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Some examples of selected Chinese characters.
منشور في 2025"…Our model shows clear enhancements in structural accuracy (SSIM improved to 0.91), pixel-level fidelity (RMSE reduced to 2.68), perceptual quality aligned with human vision (LPIPS reduced to 0.07), and stylistic realism (FID decreased to 13.87). It reduces the model size to 100M parameters, cuts training time to just 1.3 hours, and lowers inference time to only 21 minutes. …"