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Showing 1 - 20 results of 74 for search '(( binary state processes optimization algorithm ) OR ( binary based ai optimization algorithm ))', query time: 0.55s Refine Results
  1. 1
    The result of the accuracy of tested algorithms.

    The result of the accuracy of tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  2. 2
    Feature selection ratio results for tested algorithms.

    Feature selection ratio results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  3. 3
    Best fitness value results for tested algorithms.

    Best fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  4. 4
    Mean fitness value results for tested algorithms.

    Mean fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  5. 5
    Standard deviation of fitness value results for tested algorithms.

    Standard deviation of fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  6. 6
    a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset.

    a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

    Published 2022
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  7. 7
    Boxplots analysis of the tested algorithms using average error rate across 21 datasets.

    Boxplots analysis of the tested algorithms using average error rate across 21 datasets. by Nebojsa Bacanin (13932723)

    Published 2022
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  8. 8
    Algorithm for generating hyperparameter.

    Algorithm for generating hyperparameter. by Balraj Preet Kaur (20370832)

    Published 2024
    “…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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  9. 9
    Results of machine learning algorithm.

    Results of machine learning algorithm. by Balraj Preet Kaur (20370832)

    Published 2024
    “…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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  10. 10
    S1 Dataset - Novel chaotic oppositional fruit fly optimization algorithm for feature selection applied on COVID 19 patients’ health prediction

    S1 Dataset - Novel chaotic oppositional fruit fly optimization algorithm for feature selection applied on COVID 19 patients’ health prediction by Nebojsa Bacanin (13932723)

    Published 2022
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  11. 11
    ROC comparison of machine learning algorithm.

    ROC comparison of machine learning algorithm. by Balraj Preet Kaur (20370832)

    Published 2024
    “…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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  12. 12
    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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  13. 13
    CEC 2019 benchmark characteristics.

    CEC 2019 benchmark characteristics. by Nebojsa Bacanin (13932723)

    Published 2022
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  14. 14
    Results of Holm’s step-down procedure.

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

    Published 2022
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  15. 15
    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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  16. 16
    V-shaped transfer functions.

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

    Published 2022
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  17. 17
    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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  18. 18
    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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  19. 19
    Error rate convergence graphs.

    Error rate convergence graphs. by Nebojsa Bacanin (13932723)

    Published 2022
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  20. 20
    COVID-19 dataset description.

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

    Published 2022
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