Showing 61 - 80 results of 210 for search '(( binary a based optimization algorithm ) OR ( binary based design optimization algorithm ))', query time: 0.37s Refine Results
  1. 61

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

    Published 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. 62

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

    Published 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. 63

    Adaboost classifier results. by Balraj Preet Kaur (20370832)

    Published 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. 64

    Results of Lightbgm. by Balraj Preet Kaur (20370832)

    Published 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. 65

    Results of Lightbgm. by Balraj Preet Kaur (20370832)

    Published 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. 66

    Feature selection process. by Balraj Preet Kaur (20370832)

    Published 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. 67

    Results of KNN. by Balraj Preet Kaur (20370832)

    Published 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. 68

    After upsampling. by Balraj Preet Kaur (20370832)

    Published 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. 69

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

    Published 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. 70

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

    Published 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. 71

    Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

    Published 2021
    “…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …”
  12. 72
  13. 73

    Analysis and design of algorithms for the manufacturing process of integrated circuits by Sonia Fleytas (16856403)

    Published 2023
    “…There is a binary integer programming model for this problem in the literature, from which its authors proposed a genetic algorithm to obtain approximate solutions. …”
  14. 74

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

    Published 2025
    “…Numerical results validate the superiority of the proposed approach, demonstrating significant gains in computation performance and time efficiency compared to conventional techniques, making real-time and optimal offloading design truly viable even in a fast-fading environment.…”
  15. 75
  16. 76

    Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data by Fei Xue (24567)

    Published 2021
    “…In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. …”
  17. 77

    the functioning of BRPSO. by Hossein Jarrahi (22530251)

    Published 2025
    “…The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
  18. 78

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

    Published 2025
    “…The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
  19. 79

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

    Published 2025
    “…The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
  20. 80

    Related Work Summary. by Hend Bayoumi (22693738)

    Published 2025
    “…Numerical results validate the superiority of the proposed approach, demonstrating significant gains in computation performance and time efficiency compared to conventional techniques, making real-time and optimal offloading design truly viable even in a fast-fading environment.…”