Showing 181 - 200 results of 956 for search '(( algorithm fibrin function ) OR ( ((algorithm python) OR (algorithm both)) function ))', query time: 0.45s Refine Results
  1. 181

    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. …”
  2. 182

    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. …”
  3. 183

    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. …”
  4. 184

    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. …”
  5. 185

    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. …”
  6. 186

    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. …”
  7. 187

    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. …”
  8. 188

    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. …”
  9. 189

    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. …”
  10. 190

    Data Sheet 1_IGSA-SAC: a novel approach for intrusion detection using improved gravitational search algorithm and soft actor-critic.docx by Lizhong Jin (20991293)

    Published 2025
    “…On the AWID dataset, IGSA-SAC surpasses 98.9% in both accuracy and F1-score, outperforming existing intrusion detection algorithms.…”
  11. 191

    Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

    Published 2025
    “…With the assistance of a pattern recognition algorithm , the early diagnosis of CKD was achieved by the sensor, with PCA being used due to sensor’s cross-sensitivity to CKD biomarkers. …”
  12. 192

    Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

    Published 2025
    “…With the assistance of a pattern recognition algorithm , the early diagnosis of CKD was achieved by the sensor, with PCA being used due to sensor’s cross-sensitivity to CKD biomarkers. …”
  13. 193

    Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

    Published 2025
    “…With the assistance of a pattern recognition algorithm , the early diagnosis of CKD was achieved by the sensor, with PCA being used due to sensor’s cross-sensitivity to CKD biomarkers. …”
  14. 194

    Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

    Published 2025
    “…With the assistance of a pattern recognition algorithm , the early diagnosis of CKD was achieved by the sensor, with PCA being used due to sensor’s cross-sensitivity to CKD biomarkers. …”
  15. 195

    Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

    Published 2025
    “…With the assistance of a pattern recognition algorithm , the early diagnosis of CKD was achieved by the sensor, with PCA being used due to sensor’s cross-sensitivity to CKD biomarkers. …”
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  18. 198

    Hyperparameters of different datasets. by GaoXiang Zhao (21499525)

    Published 2025
    “…Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. …”
  19. 199

    Results of different models. by GaoXiang Zhao (21499525)

    Published 2025
    “…Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. …”
  20. 200

    Impact of class imbalance. by GaoXiang Zhao (21499525)

    Published 2025
    “…Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. …”