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Showing 41 - 60 results of 258 for search '(( binary data process optimization algorithm ) OR ( binary a based optimization algorithm ))*', query time: 0.53s Refine Results
  1. 41
    The result of the best fitness value, mean fitness value, standard deviation of fitness value and feature selection ratio of algorithms on the COVID-19 dataset.

    The result of the best fitness value, mean fitness value, standard deviation of fitness value and feature selection ratio of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

    Published 2022
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  2. 42
    An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA.

    An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. by Olaide N. Oyelade (14047002)

    Published 2023
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  3. 43
    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>.

    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>. by Benjamin Gallois (10131683)

    Published 2021
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  4. 44
    CEC 2019 benchmark characteristics.

    CEC 2019 benchmark characteristics. by Nebojsa Bacanin (13932723)

    Published 2022
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  5. 45
    Results of Holm’s step-down procedure.

    Results of Holm’s step-down procedure. by Nebojsa Bacanin (13932723)

    Published 2022
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  6. 46
    Friedman ranks of tested methods on CEC2019 benchmark functions.

    Friedman ranks of tested methods on CEC2019 benchmark functions. by Nebojsa Bacanin (13932723)

    Published 2022
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  7. 47
    V-shaped transfer functions.

    V-shaped transfer functions. by Nebojsa Bacanin (13932723)

    Published 2022
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  8. 48
    Resilts of Friedman and Iman-Davenport tests (<i>α</i> = 0.05).

    Resilts of Friedman and Iman-Davenport tests (<i>α</i> = 0.05). by Nebojsa Bacanin (13932723)

    Published 2022
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  9. 49
    Mean fitness and standard deviation results of compared approaches on CEC2019 benchmark functions.

    Mean fitness and standard deviation results of compared approaches on CEC2019 benchmark functions. by Nebojsa Bacanin (13932723)

    Published 2022
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  10. 50
    Error rate convergence graphs.

    Error rate convergence graphs. by Nebojsa Bacanin (13932723)

    Published 2022
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  11. 51
    COVID-19 dataset description.

    COVID-19 dataset description. by Nebojsa Bacanin (13932723)

    Published 2022
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  12. 52
    Control parameter settings for comparative feature selection methods.

    Control parameter settings for comparative feature selection methods. by Nebojsa Bacanin (13932723)

    Published 2022
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  13. 53
    The result of the Wilcoxon test of presented COFFO against compared methods.

    The result of the Wilcoxon test of presented COFFO against compared methods. by Nebojsa Bacanin (13932723)

    Published 2022
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  14. 54
    COFFO control parameters.

    COFFO control parameters. by Nebojsa Bacanin (13932723)

    Published 2022
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  15. 55
    Convergence graphs for ten CEC 2019 benchmark functions and direct comparison between COFFO and FFO.

    Convergence graphs for ten CEC 2019 benchmark functions and direct comparison between COFFO and FFO. by Nebojsa Bacanin (13932723)

    Published 2022
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  16. 56
    Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19.

    Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

    Published 2021
    “…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …”
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  17. 57
    Confusion matrix for a binary outcome (adapted from Fawcett [30]).

    Confusion matrix for a binary outcome (adapted from Fawcett [30]). by João Albuquerque (12936572)

    Published 2022
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  18. 58
    RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values .

    RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

    Published 2023
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  19. 59
    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results

    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

    Published 2025
    “…A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. …”
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  20. 60
    Schematic representation of semi-automated algorithm for organoids analyzing.

    Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

    Published 2025
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