يعرض 2,221 - 2,240 نتائج من 4,254 نتيجة بحث عن 'significant ((((gap decrease) OR (((step decrease) OR (greatest decrease))))) OR (mean decrease))', وقت الاستعلام: 0.52s تنقيح النتائج
  1. 2221
  2. 2222

    Prediction of transition readiness. حسب Sharon Barak (4803966)

    منشور في 2025
    "…In most transition domains, help needed did not decrease with age and was not affected by function. …"
  3. 2223

    Algorithm training accuracy experiments. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  4. 2224

    Repeat the detection experiment. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  5. 2225

    Detection network structure with IRAU [34]. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  6. 2226

    Ablation experiments of various block. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  7. 2227

    Kappa coefficients for different algorithms. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  8. 2228

    The structure of ASPP+ block. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  9. 2229

    The structure of attention gate block [31]. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  10. 2230

    DSC block and its application network structure. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  11. 2231

    The structure of multi-scale residual block [30]. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  12. 2232

    The structure of IRAU and Res2Net+ block [22]. حسب Yingying Liu (360782)

    منشور في 2025
    "…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"
  13. 2233
  14. 2234
  15. 2235
  16. 2236

    Dataset visualization diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  17. 2237

    Dataset sample images. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  18. 2238

    Performance comparison of different models. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  19. 2239

    C2f and BC2f module structure diagrams. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  20. 2240

    YOLOv8n detection results diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"