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Showing 1 - 20 results of 24 for search '(( primary data wolf optimization algorithm ) OR ( binary image design optimization algorithm ))', query time: 1.54s Refine Results
  1. 1
    S1 Data -

    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). …”
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  2. 2
    Parameter settings for algorithms.

    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). …”
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  3. 3
    Parameter settings for algorithms.

    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). …”
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  4. 4
    Average runtime of different algorithms.

    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). …”
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  5. 5
    Average runtime of different algorithms.

    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). …”
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  6. 6
    Flowchart of GJO-GWO algorithm.

    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). …”
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  7. 7
    Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale.

    Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

    Published 2023
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  8. 8
    Detailed information of benchmark functions.

    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). …”
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  9. 9
    Evaluation metrics of the models’ performance.

    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). …”
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  10. 10
    Detailed information of datasets.

    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). …”
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  11. 11
    Friedman test results.

    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). …”
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  12. 12
    Average number of selected features.

    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). …”
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  13. 13
    Wilcoxon rank sum test results.

    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). …”
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  14. 14
    Wilcoxon rank sum test results.

    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). …”
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  15. 15
    Average number of selected features.

    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). …”
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  16. 16
    Proposed IDS architecture.

    Proposed IDS architecture. by Saad Hammood Mohammed (20623506)

    Published 2025
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  17. 17
    Sample image for illustration.

    Sample image for illustration. by Indhumathi S. (19173013)

    Published 2024
    “…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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  18. 18
    Quadratic polynomial in 2D image plane.

    Quadratic polynomial in 2D image plane. by Indhumathi S. (19173013)

    Published 2024
    “…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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  19. 19
    Comparison analysis of computation time.

    Comparison analysis of computation time. by Indhumathi S. (19173013)

    Published 2024
    “…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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  20. 20
    Process flow diagram of CBFD.

    Process flow diagram of CBFD. by Indhumathi S. (19173013)

    Published 2024
    “…Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …”
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