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Showing 1 - 20 results of 76 for search '(( binary based process selection algorithm ) OR ( binary b wolf optimization algorithm ))*', query time: 0.53s Refine Results
  1. 1
    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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  2. 2
    Feature selection process.

    Feature selection process. by Balraj Preet Kaur (20370832)

    Published 2024
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  3. 3
    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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  4. 4
    Study selection.

    Study selection. by Sebastião Rogério da Silva Neto (11944944)

    Published 2022
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  5. 5
    List of selected mRNAs in the feature selection step.

    List of selected mRNAs in the feature selection step. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  6. 6
    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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  7. 7
    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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  8. 8
    Dataset selection process and exclusion criteria.

    Dataset selection process and exclusion criteria. by Seung Seog Han (4782486)

    Published 2020
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  9. 9
    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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  10. 10
    <i>hi</i>PRS algorithm process flow.

    <i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

    Published 2023
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  11. 11
    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. 12
    Top mRNAs based on repeat count in early-stage and late-stage rules.

    Top mRNAs based on repeat count in early-stage and late-stage rules. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  13. 13
    Twenty of the top early-stage and late-stage rules based on sorted lift value.

    Twenty of the top early-stage and late-stage rules based on sorted lift value. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  14. 14
    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. 15
    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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  16. 16
    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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  17. 17
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.4.

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.4. by Yi-Wen Hsiao (13767916)

    Published 2022
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  18. 18
    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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  19. 19
    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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  20. 20
    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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