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Showing 61 - 80 results of 2,164 for search '(((( algorithm co function ) OR ( algorithm pre functions ))) OR ( algorithm python function ))', query time: 0.62s Refine Results
  1. 61
    “Integration and prediction” panel for CoV-human protein-protein interaction network.

    “Integration and prediction” panel for CoV-human protein-protein interaction network. by Paolo Perlasca (9909695)

    Published 2020
    Subjects:
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  2. 62
    Performance of ENQUIRE’s gene normalization algorithm.

    Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

    Published 2025
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  3. 63
    Benchmark functions.

    Benchmark functions. by Peiwen Zhang (1863202)

    Published 2025
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  4. 64
    Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration.

    Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. by Raul Castañeda (13932971)

    Published 2022
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  5. 65
    Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation"

    Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

    Published 2022
    “…</p> <p><br></p> <p>3. Algorithm</p> <p>Below is the main body of the test data generator program:</p> <p>  </p> <p>the main body of a Python program to generate test data for Python functions.…”
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  6. 66
    3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER.

    3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

    Published 2019
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  7. 67
    WGCNA and machine learning algorithms.

    WGCNA and machine learning algorithms. by Shushu Wang (6870191)

    Published 2024
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  8. 68
    3D modeling qualities of the AM multiple-template models built by including DOPE in the objective function of MODELLER.

    3D modeling qualities of the AM multiple-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

    Published 2019
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  9. 69
    Information of nodes and pipes of NYN.

    Information of nodes and pipes of NYN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  10. 70
    The topology of the NYN.

    The topology of the NYN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  11. 71
    The result of Wilcoxon signed-rand test.

    The result of Wilcoxon signed-rand test. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  12. 72
    The Simulation and optimization process of pipe diameter selection.

    The Simulation and optimization process of pipe diameter selection. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  13. 73
    Optional pipe diameter and unit price of NYN.

    Optional pipe diameter and unit price of NYN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  14. 74
    Optional pipe diameter and unit price of HN.

    Optional pipe diameter and unit price of HN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  15. 75
    The topology of HN.

    The topology of HN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  16. 76
    Information of nodes and pipes of HN.

    Information of nodes and pipes of HN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  17. 77
    Functional enrichment analysis.

    Functional enrichment analysis. by Benafsh Husain (7140098)

    Published 2019
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  18. 78
    Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic

    Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic by NELSON L. DIAS (8078003)

    Published 2019
    “…With these practical rules, simple summation algorithms provide sufficiently robust results for the calculation of the density function and its definite integrals. …”
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  19. 79
    Sets of best performing parameters obtained after gridsearching through different values for each classification algorithm.

    Sets of best performing parameters obtained after gridsearching through different values for each classification algorithm. by Adhiraj Nath (17118269)

    Published 2023
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  20. 80
    The final values of the cost function, the selected features number, and the implementation time for the introduced meta-heuristic algorithms.

    The final values of the cost function, the selected features number, and the implementation time for the introduced meta-heuristic algorithms. by Hamed Khodadadi (21446172)

    Published 2025
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