يعرض 81 - 100 نتائج من 157 نتيجة بحث عن '(( binary based design optimization algorithm ) OR ( binary its process optimization algorithm ))', وقت الاستعلام: 0.40s تنقيح النتائج
  1. 81

    Before upsampling. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  2. 82

    Results of gradient boosting classifier. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  3. 83

    Results of Decision tree. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  4. 84

    Adaboost classifier results. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  5. 85

    Results of Lightbgm. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  6. 86

    Results of Lightbgm. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  7. 87

    Results of KNN. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  8. 88

    After upsampling. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  9. 89

    Results of Extra tree. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  10. 90

    Gradient boosting classifier results. حسب Balraj Preet Kaur (20370832)

    منشور في 2024
    "…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …"
  11. 91

    the functioning of BRPSO. حسب Hossein Jarrahi (22530251)

    منشور في 2025
    "…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …"
  12. 92

    Characteristic of 6- and 10-story SMRF [99,98]. حسب Hossein Jarrahi (22530251)

    منشور في 2025
    "…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …"
  13. 93

    The RFD’s behavior mechanism (2002). حسب Hossein Jarrahi (22530251)

    منشور في 2025
    "…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …"
  14. 94
  15. 95

    Wilcoxon test results for feature selection. حسب Amal H. Alharbi (21755906)

    منشور في 2025
    "…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …"
  16. 96

    Feature selection metrics and their definitions. حسب Amal H. Alharbi (21755906)

    منشور في 2025
    "…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …"
  17. 97

    Statistical summary of all models. حسب Amal H. Alharbi (21755906)

    منشور في 2025
    "…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …"
  18. 98

    Feature selection results. حسب Amal H. Alharbi (21755906)

    منشور في 2025
    "…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …"
  19. 99

    ANOVA test for feature selection. حسب Amal H. Alharbi (21755906)

    منشور في 2025
    "…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …"
  20. 100

    Classification performance of ML and DL models. حسب Amal H. Alharbi (21755906)

    منشور في 2025
    "…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …"