Showing 1 - 17 results of 17 for search '(( binary care access optimization algorithm ) OR ( primary data wolf optimization algorithm ))', query time: 0.49s Refine Results
  1. 1

    S1 Data - by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  2. 2

    Parameter settings for algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  3. 3

    Parameter settings for algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  4. 4

    Average runtime of different algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  5. 5

    Average runtime of different algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  6. 6

    Flowchart of GJO-GWO algorithm. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  7. 7

    Detailed information of benchmark functions. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  8. 8

    Evaluation metrics of the models’ performance. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  9. 9

    Detailed information of datasets. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  10. 10

    Friedman test results. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  11. 11

    Average number of selected features. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  12. 12

    Wilcoxon rank sum test results. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  13. 13

    Wilcoxon rank sum test results. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  14. 14

    Average number of selected features. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
  15. 15
  16. 16
  17. 17

    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

    Published 2025
    “…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”