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Showing 1 - 20 results of 1,056 for search '(((( complement pass algorithm ) OR ( relevant data algorithm ))) OR ( level according algorithm ))', query time: 0.53s Refine Results
  1. 1
    The relevant data file for this article.

    The relevant data file for this article. by Linsha Liu (20378293)

    Published 2024
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  2. 2
    The proportion of neutrophils in SIC according to different algorithms.

    The proportion of neutrophils in SIC according to different algorithms. by Wan-Jing Zhen (20427813)

    Published 2024
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  3. 3
    Relevant computed image data.

    Relevant computed image data. by Zhaoyu Ku (20660546)

    Published 2025
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  4. 4
    Summary of relevant works.

    Summary of relevant works. by Bashir Olaniyi Sadiq (22478011)

    Published 2025
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  5. 5
    The relevant code in the manuscript can be found in the supporting information data file.

    The relevant code in the manuscript can be found in the supporting information data file. by Xini Fang (20861990)

    Published 2025
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  6. 6
    Model-relevant variable data for external patients.

    Model-relevant variable data for external patients. by Lei Gao (6566)

    Published 2025
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  7. 7
    Relevant data underlying the findings described in manuscript.

    Relevant data underlying the findings described in manuscript. by Lin Qi (128224)

    Published 2025
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  8. 8
    Data generation algorithm.

    Data generation algorithm. by Aluwani Ramalata (21262977)

    Published 2025
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  9. 9
    The flowchart of Algorithm 2.

    The flowchart of Algorithm 2. by Jing Xu (15337)

    Published 2024
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  10. 10
    Relevant Parameters.

    Relevant Parameters. by Dongyan Wang (195482)

    Published 2025
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  11. 11
    Data Sheet 1_Algorithmic esthetics meets human intuition: the synergy of innovation and relevance shapes sustained attention.pdf

    Data Sheet 1_Algorithmic esthetics meets human intuition: the synergy of innovation and relevance shapes sustained attention.pdf by Wen Guan (5612195)

    Published 2025
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  12. 12
    Comparison of the EODA algorithm with existing algorithms in terms of recall.

    Comparison of the EODA algorithm with existing algorithms in terms of recall. by Sunil Kumar (102321)

    Published 2025
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  13. 13
    Comparison of the EODA algorithm with existing algorithms in terms of precision.

    Comparison of the EODA algorithm with existing algorithms in terms of precision. by Sunil Kumar (102321)

    Published 2025
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  14. 14
    STIs suspected according to the syndromic approach using the management algorithm in FSW, Lomé, 2023, N = 119.

    STIs suspected according to the syndromic approach using the management algorithm in FSW, Lomé, 2023, N = 119. by Oumarou I. Wone Adama (22634046)

    Published 2025
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  15. 15
    Distribution of patients in different Lp(a) cutoffs according to lipid fraction levels.

    Distribution of patients in different Lp(a) cutoffs according to lipid fraction levels. by Maria Helane Costa Gurgel Castelo (13861631)

    Published 2025
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  16. 16
    Definitions of relevant SSA parameters.

    Definitions of relevant SSA parameters. by Yeping Shi (14279118)

    Published 2024
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  17. 17
    Comparison of the EODA algorithm with existing algorithms in terms of F1-Score.

    Comparison of the EODA algorithm with existing algorithms in terms of F1-Score. by Sunil Kumar (102321)

    Published 2025
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  18. 18
    Algorithm for optimizing the model using transformer.

    Algorithm for optimizing the model using transformer. by Weiling Liu (1465480)

    Published 2025
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  19. 19
    Multiclass comparison of algorithms on test data for KDDCup 99.

    Multiclass comparison of algorithms on test data for KDDCup 99. by Arati Behera (19864844)

    Published 2024
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  20. 20
    Data Sheet 2_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.zip

    Data Sheet 2_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.zip by Rui Zhang (13940)

    Published 2025
    “…The model for potential pregnancy loss was also developed using five machine learning algorithms and was based on 7 indicators. According to the results obtained from the testing set, the sensitivity was higher than 92.02%, the specificity was higher than 95.18%, the accuracy was higher than 94.34%, and the AUC was higher than 0.972.…”
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