Showing 161 - 180 results of 271 for search '(( significantly improve decrease ) OR ( significantly small decrease ))~', query time: 0.26s Refine Results
  1. 161

    Forest plot for PaCO<sub>2</sub>. by Xuqin Du (8803772)

    Published 2025
    “…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO<sub>2</sub> (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO<sub>2</sub> (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”
  2. 162

    Forest plot for PaO<sub>2</sub>. by Xuqin Du (8803772)

    Published 2025
    “…Compared to standard treatment, QJHTD significantly improved pulmonary function, with increases in FEV1 (MD = 0.32, 95% CI [0.25, 0.38], <i>p </i>= 0.000), FVC (MD = 0.30, 95% CI [0.22, 0.37], <i>p </i>= 0.000), FEV1/FVC (MD = 5.58, 95% CI [4.81, 6.34], <i>p </i>= 0.000), and PaO<sub>2</sub> (MD = 9.62, 95% CI [6.17, 13.08], <i>p </i>= 0.000), and a decrease in PaCO<sub>2</sub> (MD = -9.12, 95% CI [–11.96, –6.28], <i>p </i>= 0.000). …”
  3. 163
  4. 164

    Ablation Experiment GradCAM Heatmap. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  5. 165

    Space-to-depth convolution. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  6. 166

    Data augmentation. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  7. 167

    Side angle tea picking. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  8. 168

    Comparison results of ablation experiments. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  9. 169

    Table of dataset division. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  10. 170

    Striking image. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  11. 171

    Precision, recall, F1-Score curve. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  12. 172

    Model comparison experimental results. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  13. 173

    Slicing aided hyper inference algorithm. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  14. 174

    Loss function variation curve. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  15. 175

    Different model detection results comparison. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  16. 176

    Inner-IoU. by Chunhua Yang (346871)

    Published 2025
    “…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”
  17. 177

    Bimodality of PD distributions. by Max Grogan (20384350)

    Published 2024
    “…Note that model quality continues to improve (decreasing AIC/BIC value) with number of modes, however the most significant decrease occurs between 1 and 2 modes, while remaining improvements are small. …”
  18. 178

    <b>Exploring left lateral decubitus position, and its influence on infant mortality in pregnant women in their third trimester, brought to the emergency through the GVK Ambulance S... by Laggy George (19873020)

    Published 2024
    “…No large studies have been done to adequately understand the immediate and long-term implications of maternal sleep position as a modifiable risk factor, which if addressed may potentially improve infant outcomes like small for gestational babies, stillbirth, and failure-to-thrive. …”
  19. 179

    Supplementary Material for: Effects of wearing hearing aids on gait and cognition: A pilot study by figshare admin karger (2628495)

    Published 2025
    “…Although these results should be interpreted with caution due to the non-randomized controlled trial design and small sample size, the findings suggest that improving hearing acuity among older adults may enhance their overall health status.…”
  20. 180

    DataSheet1_A lightweight MHDI-DETR model for detecting grape leaf diseases.pdf by Zilong Fu (20392494)

    Published 2024
    “…The original residual backbone network was improved using the MobileNetv4 network, significantly reducing the model’s computational requirements and complexity. …”