يعرض 81 - 100 نتائج من 145 نتيجة بحث عن '(( binary base model optimization algorithm ) OR ( binary based complex optimization algorithm ))*', وقت الاستعلام: 1.12s تنقيح النتائج
  1. 81

    Collaborative hunting behavior. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"
  2. 82

    Friedman average rank sum test results. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"
  3. 83

    IRBMO vs. variant comparison adaptation data. حسب Chenyi Zhu (9383370)

    منشور في 2025
    "…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"
  4. 84
  5. 85

    ROC curves for the test set of four models. حسب Meng Cao (105914)

    منشور في 2025
    الموضوعات:
  6. 86
  7. 87

    Flowchart scheme of the ML-based model. حسب Noshaba Qasmi (20405009)

    منشور في 2024
    "…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …"
  8. 88

    SHAP bar plot. حسب Meng Cao (105914)

    منشور في 2025
    الموضوعات:
  9. 89

    Display of the web prediction interface. حسب Meng Cao (105914)

    منشور في 2025
    الموضوعات:
  10. 90

    Sample screening flowchart. حسب Meng Cao (105914)

    منشور في 2025
    الموضوعات:
  11. 91

    Descriptive statistics for variables. حسب Meng Cao (105914)

    منشور في 2025
    الموضوعات:
  12. 92

    SHAP summary plot. حسب Meng Cao (105914)

    منشور في 2025
    الموضوعات:
  13. 93

    A* Path-Finding Algorithm to Determine Cell Connections حسب Max Weng (22327159)

    منشور في 2025
    "…Future work aims to generalize this algorithm for broader biological applications by training additional Cellpose models and adapting the A* framework.…"
  14. 94

    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment حسب Jianfang Cao (1881379)

    منشور في 2019
    "…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …"
  15. 95
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  17. 97

    Summary of existing CNN models. حسب Nicodemus Songose Awarayi (18414494)

    منشور في 2024
    "…The model further showed superior results on binary classification compared with existing methods. …"
  18. 98
  19. 99

    An Example of a WPT-MEC Network. حسب Hend Bayoumi (22693738)

    منشور في 2025
    "…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"
  20. 100

    Related Work Summary. حسب Hend Bayoumi (22693738)

    منشور في 2025
    "…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"