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processes optimization » process optimization (Expand Search), process optimisation (Expand Search), property optimization (Expand Search)
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
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Showing 1 - 20 results of 210 for search '(( binary b feature optimization algorithm ) OR ( genes based processes optimization algorithm ))', query time: 0.69s Refine Results
  1. 1
    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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  2. 2
    Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO.

    Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

    Published 2025
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  3. 3
    Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO.

    Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

    Published 2025
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  4. 4
    Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm.

    Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm. by Ahmed M. Elshewey (21463867)

    Published 2025
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  5. 5
    Illustration of the BRIDES algorithm and the selection process.

    Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

    Published 2022
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  6. 6
    Feature selection results.

    Feature selection results. by Doaa Sami Khafaga (21463870)

    Published 2025
    “…Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”
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  7. 7
    Optimal ALARM BN discovered by CausNet.

    Optimal ALARM BN discovered by CausNet. by Nand Sharma (21519845)

    Published 2025
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  8. 8
    Genetic Algorithm (GA) and CAGE-based personalization block diagrams.

    Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

    Published 2020
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  9. 9
    Expression values and standard errors of genes within each patient group under the optimal feature filtering method (ALOGA < 128) used for (blue bars) distinguishing each individual PAH patient group (Control <i>vs</i>. IPAH <i>vs</i>. HPAH <i>vs</i>. UMC) with an accuracy value of 0.97; (red bars) distinguishing the healthy control <i>vs</i>. the combination of HPAH and IPAH with a perfect accuracy; and (green bars) distinguishing the HPAH <i>vs</i>. UMC with a perfect accuracy.

    Expression values and standard errors of genes within each patient group under the optimal feature filtering method (ALOGA < 128) used for (blue bars) distinguishing each individua... by Song Cui (553627)

    Published 2019
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  10. 10
    The feature selection process of the IG-GPSO.

    The feature selection process of the IG-GPSO. by Fangyuan Yang (2567629)

    Published 2024
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  11. 11
    Optimal ovarian cancer BNs learned by CausNet and CausNet-partial.

    Optimal ovarian cancer BNs learned by CausNet and CausNet-partial. by Nand Sharma (21519845)

    Published 2025
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  12. 12
    Fig 4 -

    Fig 4 - by Olaide N. Oyelade (14047002)

    Published 2023
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  13. 13
    Fig 7 -

    Fig 7 - by Olaide N. Oyelade (14047002)

    Published 2023
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  14. 14
    Fig 2 -

    Fig 2 - by Olaide N. Oyelade (14047002)

    Published 2023
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  15. 15
    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using... by Olaide N. Oyelade (14047002)

    Published 2023
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  16. 16
    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50... by Olaide N. Oyelade (14047002)

    Published 2023
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  17. 17
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets usi... by Olaide N. Oyelade (14047002)

    Published 2023
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  18. 18
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional data... by Olaide N. Oyelade (14047002)

    Published 2023
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  19. 19
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... by Olaide N. Oyelade (14047002)

    Published 2023
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  20. 20
    The feature ranking grouping process of the IG-GPSO.

    The feature ranking grouping process of the IG-GPSO. by Fangyuan Yang (2567629)

    Published 2024
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