Showing 1,141 - 1,160 results of 14,223 for search '(( significant increase decrease ) OR ( significant ((inter decrease) OR (mean decrease)) ))*', query time: 0.58s Refine Results
  1. 1141
  2. 1142

    Flow of participants through the study. by Sedighe Esmaeilzadeh (21100668)

    Published 2025
    “…Furthermore, melatonin significantly decreased sleep latency, exhibiting a large effect size, and contributed to a medium reduction in the use of sleep medications. …”
  3. 1143

    Demographic Characteristics of Participants. by Sedighe Esmaeilzadeh (21100668)

    Published 2025
    “…Furthermore, melatonin significantly decreased sleep latency, exhibiting a large effect size, and contributed to a medium reduction in the use of sleep medications. …”
  4. 1144
  5. 1145
  6. 1146
  7. 1147
  8. 1148
  9. 1149
  10. 1150
  11. 1151
  12. 1152

    Increased <i>Huwe1</i> after induction of Sox2 in mouse ES cells. by Yen-Fu Cheng (5819855)

    Published 2025
    “…<i>Atoh1</i> expression increased with <i>Sox2</i> at low levels but decreased at higher <i>Sox2</i> level. …”
  13. 1153

    PCA-CGAN model parameter settings. by Chao Tang (10925)

    Published 2025
    “…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
  14. 1154

    MIT-BIH dataset proportion analysis chart. by Chao Tang (10925)

    Published 2025
    “…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
  15. 1155

    Wavelet transform preprocessing results. by Chao Tang (10925)

    Published 2025
    “…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
  16. 1156

    PCAECG_GAN. by Chao Tang (10925)

    Published 2025
    “…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
  17. 1157

    MIT dataset expansion quantities and Proportions. by Chao Tang (10925)

    Published 2025
    “…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
  18. 1158

    Experimental hardware and software environment. by Chao Tang (10925)

    Published 2025
    “…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
  19. 1159

    PCA-CGAN K-fold experiment table. by Chao Tang (10925)

    Published 2025
    “…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”
  20. 1160

    Classification model parameter settings. by Chao Tang (10925)

    Published 2025
    “…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”