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Showing 1 - 20 results of 19,396 for search '(((( algorithm three function ) OR ( algorithm _ function ))) OR ( algorithm body function ))', query time: 1.12s Refine Results
  1. 1
    Comparison of the curves of the DCIOU loss function and the CIOU loss function.

    Comparison of the curves of the DCIOU loss function and the CIOU loss function. by Xunqian Xu (21261899)

    Published 2025
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  2. 2
    Three-dimensional function image for testing algorithm performance.

    Three-dimensional function image for testing algorithm performance. by Jianpeng Zhang (528185)

    Published 2024
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  3. 3
    Distribution of objective function values solved by three algorithms.

    Distribution of objective function values solved by three algorithms. by Meilin Zhu (688698)

    Published 2025
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  4. 4
    Average function evaluation times of the three optimization algorithms.

    Average function evaluation times of the three optimization algorithms. by Shuhui Yu (1535500)

    Published 2025
    “…<p>Average function evaluation times of the three optimization algorithms.…”
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  5. 5
    Example of the algorithm function as an excel template.

    Example of the algorithm function as an excel template. by Thomas Nickolay (18121412)

    Published 2024
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  6. 6
    Patient-reported function, pain interference, mood, and sleep vary by cluster membership.

    Patient-reported function, pain interference, mood, and sleep vary by cluster membership. by Benedict J. Alter (9745466)

    Published 2021
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  7. 7
    Convergence curves of three algorithms under different test functions.

    Convergence curves of three algorithms under different test functions. by Jianpeng Zhang (528185)

    Published 2024
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  8. 8
    PR curves of three algorithms under different test functions.

    PR curves of three algorithms under different test functions. by Jianpeng Zhang (528185)

    Published 2024
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  9. 9
    The details of the Scelestial algorithm.

    The details of the Scelestial algorithm. by Mohammad-Hadi Foroughmand-Araabi (6658772)

    Published 2022
    “…The edge lengths represent the cost of the edge according to the Scelestial’s cost function (see Section 3.5.5). In step 2 an example of a subset of sequences for <i>K</i> is highlighted in the picture. …”
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  10. 10
    Algorithm results based on experimental likelihood functions.

    Algorithm results based on experimental likelihood functions. by Nicolas Herzig (9230425)

    Published 2020
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  11. 11
    Optimization Performance Comparison of Metaheuristic Algorithms.

    Optimization Performance Comparison of Metaheuristic Algorithms. by Xie Qiu (13243770)

    Published 2025
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  12. 12
    Datasheet1_Gait analysis comparison between manual marking, 2D pose estimation algorithms, and 3D marker-based system.pdf

    Datasheet1_Gait analysis comparison between manual marking, 2D pose estimation algorithms, and 3D marker-based system.pdf by Dimitrios Menychtas (16935831)

    Published 2023
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  13. 13
    Comparison between different PSO algorithms on function 1–3.

    Comparison between different PSO algorithms on function 1–3. by Lai Xu (1828831)

    Published 2019
    “…<p>Comparison between different PSO algorithms on function 1–3.</p>…”
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  14. 14
    Flowchart of the algorithm.

    Flowchart of the algorithm. by Xiaowen Zhang (83669)

    Published 2023
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  15. 15
    The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs.

    The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

    Published 2025
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  16. 16
    The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs.

    The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

    Published 2025
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  17. 17
    The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs.

    The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

    Published 2025
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  18. 18
    The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs.

    The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

    Published 2025
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  19. 19
    Algorithm results based on FE simulated likelihood functions.

    Algorithm results based on FE simulated likelihood functions. by Nicolas Herzig (9230425)

    Published 2020
    “…<p>(A) Nodule depth estimation by the algorithm with the likelihood functions obtained by FEM simulation. …”
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  20. 20
    Comparison of assembly results of three algorithms.

    Comparison of assembly results of three algorithms. by Guohua Cao (697580)

    Published 2025
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