Showing 41 - 60 results of 1,104 for search '(( algorithm brain function ) OR ( ((algorithm python) OR (algorithm both)) function ))*', query time: 0.49s Refine Results
  1. 41

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

    Published 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. by Pingping Yan (462509)

    Published 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. by Pingping Yan (462509)

    Published 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. by Pingping Yan (462509)

    Published 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. by Pingping Yan (462509)

    Published 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. by Pingping Yan (462509)

    Published 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. by Pingping Yan (462509)

    Published 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. by Pingping Yan (462509)

    Published 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. by Pingping Yan (462509)

    Published 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. by Pingping Yan (462509)

    Published 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. …”
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  15. 55

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

    Published 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. by Huichao Guo (14515171)

    Published 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. …”
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    Comparison of different algorithms. by Dawei Wang (471687)

    Published 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. …”
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