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Showing 81 - 100 results of 2,179 for search '(( algorithm python function ) OR ((( algorithm within function ) OR ( algorithm cl functions ))))', query time: 0.58s Refine Results
  1. 81
    Boxplots obtained by testing ch150 for 50 times by each algorithm.

    Boxplots obtained by testing ch150 for 50 times by each algorithm. by Jin Zhang (53297)

    Published 2023
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  2. 82
    Table of relationships between algorithms and five strategies for two experimental groups.

    Table of relationships between algorithms and five strategies for two experimental groups. by Jin Zhang (53297)

    Published 2023
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  3. 83
    Boxplots obtained by testing fl1400 for 10 times by each algorithm.

    Boxplots obtained by testing fl1400 for 10 times by each algorithm. by Jin Zhang (53297)

    Published 2023
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  4. 84
    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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  5. 85
    Frequency of frameshift mutations in tumor suppressor genes within TCGA and DFCI cohorts.

    Frequency of frameshift mutations in tumor suppressor genes within TCGA and DFCI cohorts. by Alexis M. Thornton (11068403)

    Published 2021
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  6. 86
    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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  7. 87
    The topology of the NYN.

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

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  8. 88
    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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  9. 89
    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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  10. 90
    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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  11. 91
    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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  12. 92
    The topology of HN.

    The topology of HN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  13. 93
    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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  14. 94
    GD and soft loss function (Loss<sub>S</sub>) parameters are optimized through the hyperparameter tuning process within their search spaces.

    GD and soft loss function (Loss<sub>S</sub>) parameters are optimized through the hyperparameter tuning process within their search spaces. by Burcu Ozek (16949357)

    Published 2024
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  15. 95
    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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  16. 96
    Comparison of algorithms across metrics in test problems.

    Comparison of algorithms across metrics in test problems. by Jihui Hu (19812736)

    Published 2024
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  17. 97
    Results of all algorithms according to evaluation matrices.

    Results of all algorithms according to evaluation matrices. by Ahmed Bahaa (12887063)

    Published 2022
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  18. 98
    Significantly enriched Gene Ontology (GO) terms among the 512 genes showing significant results in the all ancestry TWAS. Enrichment analyses were conducted using the multi-query in g:Profiler [16]. Columns include source, term name, GO id, and adjusted p-values across each phenotype definition. P-values were adjusted to maintain an experiment-wide type 1 error rate of 0.05 using the g:SCS algorithm which accounts for the dependent structure of functionally annotated gene sets.

    Significantly enriched Gene Ontology (GO) terms among the 512 genes showing significant results in the all ancestry TWAS. Enrichment analyses were conducted using the multi-query i... by Jenna C. Carlson (3950024)

    Published 2025
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  19. 99
    Performance comparison of different mainstream algorithms.

    Performance comparison of different mainstream algorithms. by Haisong Xu (141729)

    Published 2025
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  20. 100
    Distribution of cross correlations in functional connectivity in ABIDE sample.

    Distribution of cross correlations in functional connectivity in ABIDE sample. by Alexandria M. Jensen (6494957)

    Published 2024
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