يعرض 1,741 - 1,760 نتائج من 18,354 نتيجة بحث عن 'significantly ((((((we decrease) OR (a decrease))) OR (linear decrease))) OR (greater decrease))', وقت الاستعلام: 0.52s تنقيح النتائج
  1. 1741

    YOLOV8. حسب Junyan Wang (4738518)

    منشور في 2025
    "…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"
  2. 1742

    Faster-RCNN. حسب Junyan Wang (4738518)

    منشور في 2025
    "…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"
  3. 1743

    Results of ablation experiments. حسب Junyan Wang (4738518)

    منشور في 2025
    "…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"
  4. 1744

    Structure diagram of SPDConv. حسب Junyan Wang (4738518)

    منشور في 2025
    "…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"
  5. 1745

    Wise-IOU regression diagram. حسب Junyan Wang (4738518)

    منشور في 2025
    "…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"
  6. 1746

    Visualization of detection results. حسب Junyan Wang (4738518)

    منشور في 2025
    "…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"
  7. 1747

    Structure diagram of the SE attention mechanism. حسب Junyan Wang (4738518)

    منشور في 2025
    "…However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. …"
  8. 1748
  9. 1749
  10. 1750
  11. 1751
  12. 1752
  13. 1753
  14. 1754
  15. 1755
  16. 1756

    Top 10 significant functional annotations of up-regulated DEGs. حسب Meitner Cadena (22216261)

    منشور في 2025
    "…Functional annotations are ordered by decreasing significance, with color indicating significance according to the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…"
  17. 1757

    Top 10 significant functional annotations of down-regulated DEGs. حسب Meitner Cadena (22216261)

    منشور في 2025
    "…Functional annotations are ordered by decreasing significance, with color indicating significance level based on the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…"
  18. 1758
  19. 1759

    Major hyperparameters of RF-SVR. حسب Jintao Li (448681)

    منشور في 2024
    "…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …"
  20. 1760

    Pseudo code for coupling model execution process. حسب Jintao Li (448681)

    منشور في 2024
    "…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R<sup>2</sup> value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …"