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Showing 1 - 20 results of 60 for search '(( binary data wolf optimization algorithm ) OR ( history data process optimization algorithm ))*', query time: 1.12s Refine Results
  1. 1
    History of Process Transformation

    History of Process Transformation by de Wit Coetsee (21380672)

    Published 2025
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  2. 2
    Overview of the cohort definition process.

    Overview of the cohort definition process. by Nicola Lazzarini (3528197)

    Published 2022
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  3. 3
    ROC area for the ML algorithms applied on Dataset 3.

    ROC area for the ML algorithms applied on Dataset 3. by Murad Al-Rajab (10661360)

    Published 2023
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  4. 4
    Classification accuracy for the ML algorithms applied to Dataset 3.

    Classification accuracy for the ML algorithms applied to Dataset 3. by Murad Al-Rajab (10661360)

    Published 2023
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  5. 5
    Classification accuracy for the ML algorithms applied to Dataset 2.

    Classification accuracy for the ML algorithms applied to Dataset 2. by Murad Al-Rajab (10661360)

    Published 2023
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  6. 6
    ROC area for the ML algorithms applied to Dataset 1.

    ROC area for the ML algorithms applied to Dataset 1. by Murad Al-Rajab (10661360)

    Published 2023
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  7. 7
    ROC area for the ML algorithms applied to Dataset 2.

    ROC area for the ML algorithms applied to Dataset 2. by Murad Al-Rajab (10661360)

    Published 2023
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  8. 8
    Confusion matrix for the ML algorithms applied to Dataset 2.

    Confusion matrix for the ML algorithms applied to Dataset 2. by Murad Al-Rajab (10661360)

    Published 2023
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  9. 9
    Classification accuracy for the ML algorithms applied to Dataset 1.

    Classification accuracy for the ML algorithms applied to Dataset 1. by Murad Al-Rajab (10661360)

    Published 2023
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  10. 10
    ML confusion matrix for the ML algorithms applied to Dataset 1.

    ML confusion matrix for the ML algorithms applied to Dataset 1. by Murad Al-Rajab (10661360)

    Published 2023
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  11. 11
    ML confusion matrix for the ML algorithms applied to Dataset 3.

    ML confusion matrix for the ML algorithms applied to Dataset 3. by Murad Al-Rajab (10661360)

    Published 2023
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  12. 12
    Summary of ROC area for the ML algorithms applied to all datasets.

    Summary of ROC area for the ML algorithms applied to all datasets. by Murad Al-Rajab (10661360)

    Published 2023
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  13. 13
    Robustness of the optimization process in a real dataset.

    Robustness of the optimization process in a real dataset. by Neta Ravid Tannenbaum (2940351)

    Published 2023
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  14. 14
    History of the total number of births to U.S. residents per year and the adoption growth of the <i>2003 Revised Birth Certificate</i>.

    History of the total number of births to U.S. residents per year and the adoption growth of the <i>2003 Revised Birth Certificate</i>. by Karl W. Schulz (14309461)

    Published 2022
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  15. 15
    Predictive model-building process.

    Predictive model-building process. by Mukhtar Ijaiya (18935122)

    Published 2025
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  16. 16
    Comparative analysis of model performance indicators and diagnostic visualizations for full-featured and simplified models across ten algorithms.

    Comparative analysis of model performance indicators and diagnostic visualizations for full-featured and simplified models across ten algorithms. by Aref Andishgar (18155608)

    Published 2024
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  17. 17
    The flowchart of the proposed algorithm.

    The flowchart of the proposed algorithm. by Muhammad Ayyaz Sheikh (18610943)

    Published 2024
    “…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”
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  18. 18
    PCA for exploring the data structure of the datasets.

    PCA for exploring the data structure of the datasets. by Murad Al-Rajab (10661360)

    Published 2023
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  19. 19
    Parameter Settings for the Proposed Hybrid Model. The Particle Swarm Optimization (PSO) parameters (swarm size, iterations, inertia weight, cognitive and social coefficient) were empirically determined. The weighting coefficients (β₁, β₂, β₄) for the input vectors were tuned to reflect the relative importance of medical imaging, clinical data, and medical history, respectively. genomic data (β₃) was weighted as 0 in this study due to its unavailability in dataset.The regularization parameter (C) and the kernel parameter (γ) of the SVM are not predetermined but their values are dynamically determined by the PSO optimization process.

    Parameter Settings for the Proposed Hybrid Model. The Particle Swarm Optimization (PSO) parameters (swarm size, iterations, inertia weight, cognitive and social coefficient) were e... by Medhat A. Tawfeek (22522087)

    Published 2025
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  20. 20
    Classification of characteristics.

    Classification of characteristics. by Mario A. Quiroz-Juárez (11457140)

    Published 2021
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