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Showing 1 - 20 results of 311 for search '(( binary _ features optimization algorithm ) OR ( binary _ learning optimization algorithm ))', query time: 0.53s Refine Results
  1. 1
    Number of features selected for IRBMO vs. other binary algorithms.

    Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  2. 2
    Sensitivity values of IRBMO vs. other binary algorithms.

    Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  3. 3
    Specificity values of IRBMO vs. other binary algorithms.

    Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  4. 4
    Fitness values of IRBMO vs. other binary algorithms.

    Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  5. 5
    Classification accuracy values of IRBMO vs. other binary algorithms.

    Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  6. 6
    F-score values of IRBMO vs. other binary algorithms.

    F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  7. 7
    Initial parameter settings for the optimization algorithms.

    Initial parameter settings for the optimization algorithms. by Doaa Sami Khafaga (21463870)

    Published 2025
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  8. 8
    Hyperparameter settings for the optimization algorithms.

    Hyperparameter settings for the optimization algorithms. by Ahmed M. Elshewey (21463867)

    Published 2025
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  9. 9
    Feature selection ratio results for tested algorithms.

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

    Published 2022
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  10. 10
    Descriptive analysis of the outcomes by the optimized LSTM using several optimization algorithms.

    Descriptive analysis of the outcomes by the optimized LSTM using several optimization algorithms. by Ahmed M. Elshewey (21463867)

    Published 2025
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  11. 11
    Bar chart comparing the average fitness values of various feature selection algorithms.

    Bar chart comparing the average fitness values of various feature selection algorithms. by Doaa Sami Khafaga (21463870)

    Published 2025
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  12. 12
    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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  13. 13
    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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  14. 14
    Best fitness value results for tested algorithms.

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

    Published 2022
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  15. 15
    Mean fitness value results for tested algorithms.

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

    Published 2022
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  16. 16
    Box plots with swarm overlays for six feature selection algorithms, annotated with mean and standard deviation values.

    Box plots with swarm overlays for six feature selection algorithms, annotated with mean and standard deviation values. by Doaa Sami Khafaga (21463870)

    Published 2025
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  17. 17
    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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  18. 18
    Flowchart depicting the structure of the BDMO algorithm.

    Flowchart depicting the structure of the BDMO algorithm. by Olatunji A. Akinola (13919696)

    Published 2022
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  19. 19
    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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  20. 20
    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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