يعرض 41 - 60 نتائج من 1,104 نتيجة بحث عن '(((( algorithm python function ) OR ( algorithm both function ))) OR ( algorithm brain function ))', وقت الاستعلام: 0.49s تنقيح النتائج
  1. 41

    Ablation study on the RSOD dataset. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  2. 42

    Structure and working principle of LI-YOLOv8. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  3. 43

    C2f-E improvement process. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  4. 44

    Structure of Detect and GP-Detect. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  5. 45

    YOLOv8 structure and working principle. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  6. 46

    Improvement of CBS to CBR process. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  7. 47

    EMA attention mechanism working principle. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  8. 48

    Ablation study on the NWPU VHR-10 dataset. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  9. 49

    GSConv working principle. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  10. 50

    PR comparison on NWPU VHR-10 dataset. حسب Pingping Yan (462509)

    منشور في 2025
    "…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
  11. 51
  12. 52
  13. 53
  14. 54
  15. 55

    The pseudocode for the NAFPSO algorithm. حسب Huichao Guo (14515171)

    منشور في 2025
    "…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"
  16. 56

    PSO algorithm flowchart. حسب Huichao Guo (14515171)

    منشور في 2025
    "…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"
  17. 57
  18. 58
  19. 59

    Comparison of different algorithms. حسب Dawei Wang (471687)

    منشور في 2025
    "…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …"
  20. 60