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Showing 21 - 40 results of 66 for search '(( binary state driven optimization algorithm ) OR ( lines based work optimization algorithm ))', query time: 0.62s Refine Results
  1. 21
    Comparison of performance with different trip time.

    Comparison of performance with different trip time. by Yunhu Huang (21402795)

    Published 2025
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  2. 22
    If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions.

    If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

    Published 2024
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  3. 23
    Algorithm 1: Cap inhibitory densities to number of neurons.

    Algorithm 1: Cap inhibitory densities to number of neurons. by Dimitri Rodarie (10148838)

    Published 2022
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  4. 24
    PSO algorithms flowchart.

    PSO algorithms flowchart. by Mohammed F. Elnaggar (18297954)

    Published 2024
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  5. 25
    Buffer allocation problem in production flow lines: A new Benders-decomposition-based exact solution approach

    Buffer allocation problem in production flow lines: A new Benders-decomposition-based exact solution approach by Mengyi Zhang (7454231)

    Published 2021
    “…This work proposes a new Mixed-Integer Linear Programming (MILP) formulation for exact solution of sample-based BAP. …”
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  6. 26
    QA vs GA for 10 full-length proteins.

    QA vs GA for 10 full-length proteins. by Dillion M. Fox (4043159)

    Published 2021
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  7. 27
    Eigenvalues measured by QA vs GA scores.

    Eigenvalues measured by QA vs GA scores. by Dillion M. Fox (4043159)

    Published 2021
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  8. 28
    Table2_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table2_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  9. 29
    Table1_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table1_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  10. 30
    Table3_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table3_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  11. 31
    Table4_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table4_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  12. 32
    Classification baseline performance.

    Classification baseline performance. by Doaa Sami Khafaga (21463870)

    Published 2025
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  13. 33
    Feature selection results.

    Feature selection results. by Doaa Sami Khafaga (21463870)

    Published 2025
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  14. 34
    ANOVA test result.

    ANOVA test result. by Doaa Sami Khafaga (21463870)

    Published 2025
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  15. 35
    Summary of literature review.

    Summary of literature review. by Doaa Sami Khafaga (21463870)

    Published 2025
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  16. 36
    The profile log-likelihood of data simulated from four distinct MA(1) models, demonstrating a few examples of multimodal likelihood surfaces.

    The profile log-likelihood of data simulated from four distinct MA(1) models, demonstrating a few examples of multimodal likelihood surfaces. by Jesse Wheeler (18462457)

    Published 2025
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  17. 37
    Evidence for an AR(1) model for the Lake Michigan-Huron data.

    Evidence for an AR(1) model for the Lake Michigan-Huron data. by Jesse Wheeler (18462457)

    Published 2025
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  18. 38
    Proportion of models that achieved nominal coverage of Bonferroni adjusted 95% confidence intervals.

    Proportion of models that achieved nominal coverage of Bonferroni adjusted 95% confidence intervals. by Jesse Wheeler (18462457)

    Published 2025
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  19. 39
    Identifying the groups becomes harder when the degradation chain is long, especially for groups catalyzing upstream reactions.

    Identifying the groups becomes harder when the degradation chain is long, especially for groups catalyzing upstream reactions. by Yuanchen Zhao (12905580)

    Published 2024
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  20. 40
    Effect of “nonfunctional group” on function.

    Effect of “nonfunctional group” on function. by Yuanchen Zhao (12905580)

    Published 2024
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