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Showing 1 - 20 results of 90 for search '(( binary array design optimization algorithm ) OR ( binary image model detection algorithm ))', query time: 0.53s Refine Results
  1. 1
    Detection result.

    Detection result. by Hailang Li (12557543)

    Published 2022
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  2. 2
    Detection performances and validation losses.

    Detection performances and validation losses. by Hailang Li (12557543)

    Published 2022
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  3. 3
    Number of backbones ensembled and their detection precisions.

    Number of backbones ensembled and their detection precisions. by Hailang Li (12557543)

    Published 2022
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  4. 4
    Detection results of different methods over the rail defect datasets.

    Detection results of different methods over the rail defect datasets. by Hailang Li (12557543)

    Published 2022
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  5. 5
    Urgent versus nonurgent binary classification performance.

    Urgent versus nonurgent binary classification performance. by Kyung Won Kwon (18150416)

    Published 2024
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  6. 6
    Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm

    Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

    Published 2025
    “…The binary GWO algorithm identifies the most relevant features from </p><p dir="ltr">dermatological images, eliminating redundancy and reducing the computational burden. …”
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  7. 7
    Key steps in neurodegeneration detection algorithm.

    Key steps in neurodegeneration detection algorithm. by Andrew S. Clark (16510496)

    Published 2023
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  8. 8
    Effectiveness of image augmentation operations.

    Effectiveness of image augmentation operations. by Hailang Li (12557543)

    Published 2022
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  9. 9
    Image augmentation methods within IAB.

    Image augmentation methods within IAB. by Hailang Li (12557543)

    Published 2022
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  10. 10
    The impact of the combination of image augmentation and feature augmentation operations.

    The impact of the combination of image augmentation and feature augmentation operations. by Hailang Li (12557543)

    Published 2022
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  11. 11
    Performance of the proposed method for binary classification of lung cancer.

    Performance of the proposed method for binary classification of lung cancer. by Md. Sabbir Hossain (9958939)

    Published 2025
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  12. 12
    All data used to train the model is available at https://github.com/sirlis/RailDefect.

    All data used to train the model is available at https://github.com/sirlis/RailDefect. by Hailang Li (12557543)

    Published 2022
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  13. 13
    S1 File -

    S1 File - by Hailang Li (12557543)

    Published 2022
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  14. 14
    Different MBDA structures.

    Different MBDA structures. by Hailang Li (12557543)

    Published 2022
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  15. 15
    Effectiveness of feature augmentation operations.

    Effectiveness of feature augmentation operations. by Hailang Li (12557543)

    Published 2022
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  16. 16
    Feature augmentation methods within FAB.

    Feature augmentation methods within FAB. by Hailang Li (12557543)

    Published 2022
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  17. 17
    Samples of the rail defect dataset with 8 categories.

    Samples of the rail defect dataset with 8 categories. by Hailang Li (12557543)

    Published 2022
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  18. 18
    Structure of the proposed MBDA framework.

    Structure of the proposed MBDA framework. by Hailang Li (12557543)

    Published 2022
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  19. 19
    Graphical comparison of binary class lung cancer classification with different approaches.

    Graphical comparison of binary class lung cancer classification with different approaches. by Md. Sabbir Hossain (9958939)

    Published 2025
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  20. 20
    The illustration depicts our CQ-CNN architecture for binary image classification.

    The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

    Published 2025
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