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proteins optimization » process optimization (Expand Search), routing optimization (Expand Search), property optimization (Expand Search)
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
based proteins » based protein (Expand Search), based proteomics (Expand Search), capsid proteins (Expand Search)
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Showing 1 - 20 results of 150 for search '(( binary data feature optimization algorithm ) OR ( lines based proteins optimization algorithm ))', query time: 0.38s 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
    Feature selection ratio results for tested algorithms.

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

    Published 2022
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  8. 8
    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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  9. 9
    Numerical example for determining the optimal number of clusters in Algorithm 2.

    Numerical example for determining the optimal number of clusters in Algorithm 2. by Yi Wei Tye (22208735)

    Published 2025
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  10. 10
    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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  11. 11
    Best fitness value results for tested algorithms.

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

    Published 2022
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  12. 12
    Mean fitness value results for tested algorithms.

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

    Published 2022
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  13. 13
    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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  14. 14
    DataSheet1_Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm.docx

    DataSheet1_Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm.docx by Ying Sun (23237)

    Published 2022
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  15. 15
    The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms.

    The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

    Published 2023
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  16. 16
    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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  17. 17
    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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  18. 18
    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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  19. 19
    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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  20. 20
    CEC 2019 benchmark characteristics.

    CEC 2019 benchmark characteristics. by Nebojsa Bacanin (13932723)

    Published 2022
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