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Showing 81 - 100 results of 15,779 for search '(((( algorithm b function ) OR ( algorithm a functions ))) OR ( algorithm python function ))', query time: 1.26s Refine Results
  1. 81
    The SSIM for the different algorithms.

    The SSIM for the different algorithms. by Bingbing Li (461702)

    Published 2024
    “…Unlike traditional threshold functions, the improved threshold function is a continuous function that can avoid the pseudo Gibbs effect after image denoising and improve image quality. …”
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  2. 82
    Convergence curve of shift function.

    Convergence curve of shift function. by Yong Gao (29929)

    Published 2023
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  3. 83
    Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen reference.

    Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... by Ross J. Burton (10941910)

    Published 2021
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  4. 84
    Flowchart of the general algorithm for one trial.

    Flowchart of the general algorithm for one trial. by Junying Wang (509009)

    Published 2025
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  5. 85
    Reward function related parameters.

    Reward function related parameters. by Honglei Pang (22693724)

    Published 2025
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  6. 86
    Linear relationship between ln(methadone concentration) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function.

    Linear relationship between ln(methadone concentration) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

    Published 2020
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  7. 87
    Linear relationship between ln(methadone metabolism) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function.

    Linear relationship between ln(methadone metabolism) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

    Published 2020
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  8. 88
    Comparison of different algorithms.

    Comparison of different algorithms. by Jiandong Qiu (20389944)

    Published 2025
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  9. 89
    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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  10. 90
    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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  11. 91
    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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  12. 92
    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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  13. 93
    Visual depiction of the MSA algorithm.

    Visual depiction of the MSA algorithm. by Kayson Fakhar (12890364)

    Published 2022
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  14. 94
    Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.

    Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. by Panfei Li (22379086)

    Published 2025
    “…<p>Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.…”
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  15. 95
    The eVIP algorithm uses RNA-seq data to predict the function of somatic mutations.

    The eVIP algorithm uses RNA-seq data to predict the function of somatic mutations. by Alexis M. Thornton (11068403)

    Published 2021
    “…<p>(A) Overview of the experimental approach (B) Schematic of the eVIP decision tree-based eVIP algorithm. …”
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  16. 96
    Population diversity comparison on all function (a) NMO-SARA vs MS-GEP-A (b) GEP vs MS-GEP-I (c) FF-GEP vs MS-GEP.

    Population diversity comparison on all function (a) NMO-SARA vs MS-GEP-A (b) GEP vs MS-GEP-I (c) FF-GEP vs MS-GEP. by Jin Ding (11522)

    Published 2023
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  17. 97
    Limitation of the conventional DBT reconstruction algorithm.

    Limitation of the conventional DBT reconstruction algorithm. by Yunsu Choi (11993484)

    Published 2022
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  18. 98
    Performance comparison in CP-EE model (a) SVR model (b) BPNN model (c) RF model (d) MS-GEP model.

    Performance comparison in CP-EE model (a) SVR model (b) BPNN model (c) RF model (d) MS-GEP model. by Jin Ding (11522)

    Published 2023
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  19. 99
    Results of the SNMR kernel functions for different typical geological models.

    Results of the SNMR kernel functions for different typical geological models. by Hanbo Chen (722038)

    Published 2022
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  20. 100
    GELU function.

    GELU function. by Yuanzhou Zheng (15195249)

    Published 2023
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