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probes optimization » process optimization (Expand Search), robust optimization (Expand Search), property optimization (Expand Search)
selection algorithm » detection algorithm (Expand Search), detection algorithms (Expand Search)
process selection » process reflection (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based probes » based protein (Expand Search)
data process » data processing (Expand Search), damage process (Expand Search), data access (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
Showing 21 - 40 results of 92 for search '(( binary based probes optimization algorithm ) OR ( binary data process selection algorithm ))', query time: 0.64s Refine Results
  1. 21
    V-shaped transfer functions.

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

    Published 2022
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  2. 22
    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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  3. 23
    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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  4. 24
    Error rate convergence graphs.

    Error rate convergence graphs. by Nebojsa Bacanin (13932723)

    Published 2022
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  5. 25
    COVID-19 dataset description.

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

    Published 2022
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  6. 26
    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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  7. 27
    COFFO control parameters.

    COFFO control parameters. by Nebojsa Bacanin (13932723)

    Published 2022
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  8. 28
    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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  9. 29
    The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE under various classification tasks, including: the multiclass classification for distinguishing each individual PAH patient group (Control <i>vs</i>. IPAH <i>vs</i>. HPAH <i>vs</i>. UMC), binary classification of the healthy control <i>vs</i>. the combination of HPAH and IPAH, and the binary classification of HPAH <i>vs</i>. UMC.

    The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE... by Song Cui (553627)

    Published 2019
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  10. 30
    Flowchart of the proposed Margin Weighted Robust Discriminant Score (MW-RDS) algorithm.

    Flowchart of the proposed Margin Weighted Robust Discriminant Score (MW-RDS) algorithm. by Sheema Gul (21519289)

    Published 2025
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  11. 31
    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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  12. 32
    The performance of classifiers based on 60 selected mRNAs.

    The performance of classifiers based on 60 selected mRNAs. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  13. 33
    Selected mRNAs based on association rule mining analysis, for early and late stages of PTC.

    Selected mRNAs based on association rule mining analysis, for early and late stages of PTC. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  14. 34
    Performance of the ceRNAR package using synthetic data with randomly selected correlation values from 0.8 to 0.9.

    Performance of the ceRNAR package using synthetic data with randomly selected correlation values from 0.8 to 0.9. by Yi-Wen Hsiao (13767916)

    Published 2022
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  15. 35
    The performance of the SVM classifier based on 60 (30 plus 30-zeroed) selected mRNAs for GSE 60542 dataset.

    The performance of the SVM classifier based on 60 (30 plus 30-zeroed) selected mRNAs for GSE 60542 dataset. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  16. 36
    Medium-scale dataset comparative analysis using the number of features selected.

    Medium-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

    Published 2023
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  17. 37
    Large-scale dataset comparative analysis using the number of features selected.

    Large-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

    Published 2023
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  18. 38
    Small-scale dataset comparative analysis using the number of features selected.

    Small-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

    Published 2023
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  19. 39
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.9.

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.9. by Yi-Wen Hsiao (13767916)

    Published 2022
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  20. 40
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.5 to 0.7.

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.5 to 0.7. by Yi-Wen Hsiao (13767916)

    Published 2022
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