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Showing 21 - 40 results of 12,389 for search '(( algorithm i function ) OR ((( algorithm python function ) OR ( algorithm etc function ))))*', query time: 0.57s Refine Results
  1. 21
    Constructing the dynamical law.

    Constructing the dynamical law. by Robert Marsland (8616483)

    Published 2020
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  2. 22
    S1 File -

    S1 File - by Yuh-Chin T. Huang (17867207)

    Published 2024
    Subjects:
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  3. 23
    S1 Dataset -

    S1 Dataset - by Yuh-Chin T. Huang (17867207)

    Published 2024
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  4. 24
    Algorithm membership function.

    Algorithm membership function. by Mohamed Raef Smaoui (9865830)

    Published 2022
    “…<p>(Top) Input Membership Function. The algorithm classifies glucose input into 4 sets: low, medium, high, and ex_high. …”
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  5. 25
    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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  6. 26
    <b>Algorithm Pseudocode</b>

    <b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

    Published 2025
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  7. 27
    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, the temporal molecular states <i>n</i> at specific time <i>t</i> will be generated for further visualization.

    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

    Published 2025
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  8. 28
    Results obtained by different algorithms on 23 benchmark functions.

    Results obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

    Published 2025
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  9. 29
    Comparison of algorithms in two cases.

    Comparison of algorithms in two cases. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  10. 30
    Flow of the NSGA-II algorithm.

    Flow of the NSGA-II algorithm. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  11. 31
    A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data

    A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

    Published 2022
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  12. 32
    Effect of <i>|Δd</i><sub><i>n</i></sub><i>|</i> perturbation on 3D modeling.

    Effect of <i>|Δd</i><sub><i>n</i></sub><i>|</i> perturbation on 3D modeling. by Giacomo Janson (8138517)

    Published 2019
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  13. 33
    Convergence curves obtained by different algorithms on 23 benchmark functions.

    Convergence curves obtained by different algorithms on 23 benchmark functions. by Yuqi Xiong (12343771)

    Published 2025
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  14. 34
    Different algorithms in 23 benchmark functions to obtain partial boxplot.

    Different algorithms in 23 benchmark functions to obtain partial boxplot. by Yuqi Xiong (12343771)

    Published 2025
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  15. 35
    Results obtained by different algorithms on CEC2022 benchmark functions.

    Results obtained by different algorithms on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

    Published 2025
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  16. 36
    Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways.

    Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. by Alexis M. Thornton (11068403)

    Published 2021
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  17. 37
    Performance of the Algorithm 1.

    Performance of the Algorithm 1. by Bogumił Kamiński (7827794)

    Published 2019
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  18. 38
    Modeling with uniform <i>σ</i> values.

    Modeling with uniform <i>σ</i> values. by Giacomo Janson (8138517)

    Published 2019
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  19. 39
    The workflow used to analyze imbalanced and balanced sequences.

    The workflow used to analyze imbalanced and balanced sequences. by Solomon Shiferaw Beyene (9143178)

    Published 2020
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  20. 40
    Performances of Naïve Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN).

    Performances of Naïve Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN). by Solomon Shiferaw Beyene (9143178)

    Published 2020
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