Showing 181 - 200 results of 287 for search '(((( complement control algorithm ) OR ( elements wt algorithm ))) OR ( level coding algorithm ))', query time: 0.26s Refine Results
  1. 181

    <b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> by BRISC Dataset (22559540)

    Published 2025
    “…It provides high-quality, physician-validated pixel-level masks and a balanced multi-class classification split, suitable for benchmarking segmentation and classification algorithms as well as multi-task learning research.…”
  2. 182
  3. 183
  4. 184

    Range of point clouds. by Xinpeng Yao (18882573)

    Published 2025
    “…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”
  5. 185

    Results of ablation experiment. by Xinpeng Yao (18882573)

    Published 2025
    “…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”
  6. 186

    Transformer Encoder network structure. by Xinpeng Yao (18882573)

    Published 2025
    “…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”
  7. 187

    Line chart of frame rate. by Xinpeng Yao (18882573)

    Published 2025
    “…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”
  8. 188

    The total loss and three-component loss. by Xinpeng Yao (18882573)

    Published 2025
    “…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”
  9. 189

    Improved upsampling module based on Transformer. by Xinpeng Yao (18882573)

    Published 2025
    “…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …”
  10. 190
  11. 191
  12. 192
  13. 193
  14. 194
  15. 195
  16. 196
  17. 197

    Overall framework design. by Matthew Yit Hang Yeow (20721206)

    Published 2025
    “…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …”
  18. 198

    Gamma distribution of reuse. by Matthew Yit Hang Yeow (20721206)

    Published 2025
    “…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …”
  19. 199

    Top 5 correlated features based on reuse. by Matthew Yit Hang Yeow (20721206)

    Published 2025
    “…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …”
  20. 200

    Features with the top importance score. by Matthew Yit Hang Yeow (20721206)

    Published 2025
    “…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …”