يعرض 41 - 60 نتائج من 111 نتيجة بحث عن '(( binary a process optimization algorithm ) OR ( primary case bayesian optimization algorithm ))', وقت الاستعلام: 0.56s تنقيح النتائج
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    Related Work Summary. حسب Hend Bayoumi (22693738)

    منشور في 2025
    "…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …"
  3. 43

    Simulation parameters. حسب Hend Bayoumi (22693738)

    منشور في 2025
    "…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …"
  4. 44

    Training losses for N = 10. حسب Hend Bayoumi (22693738)

    منشور في 2025
    "…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …"
  5. 45

    Normalized computation rate for N = 10. حسب Hend Bayoumi (22693738)

    منشور في 2025
    "…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …"
  6. 46

    Summary of Notations Used in this paper. حسب Hend Bayoumi (22693738)

    منشور في 2025
    "…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …"
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    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. …"
  9. 49

    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. …"
  10. 50

    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. …"
  11. 51

    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. …"
  12. 52

    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. …"
  13. 53

    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. …"
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    Proposed method approach. حسب Muhammad Usman Tariq (11022141)

    منشور في 2024
    "…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …"
  16. 56

    LSTM model performance. حسب Muhammad Usman Tariq (11022141)

    منشور في 2024
    "…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …"
  17. 57

    Descriptive statistics. حسب Muhammad Usman Tariq (11022141)

    منشور في 2024
    "…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …"
  18. 58

    CNN-LSTM Model performance. حسب Muhammad Usman Tariq (11022141)

    منشور في 2024
    "…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …"
  19. 59

    MLP Model performance. حسب Muhammad Usman Tariq (11022141)

    منشور في 2024
    "…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …"
  20. 60

    RNN Model performance. حسب Muhammad Usman Tariq (11022141)

    منشور في 2024
    "…The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. …"