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feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
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Showing 1 - 20 results of 84 for search '(( history data feature optimization algorithm ) OR ( binary data driven optimization algorithm ))', query time: 0.50s Refine Results
  1. 1
    Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things

    Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

    Published 2025
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  2. 2
    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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  3. 3
    History of Process Transformation

    History of Process Transformation by de Wit Coetsee (21380672)

    Published 2025
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  4. 4
    Input features.

    Input features. by Nicola Lazzarini (3528197)

    Published 2022
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  5. 5
    Age feature importance.

    Age feature importance. by Nicola Lazzarini (3528197)

    Published 2022
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  6. 6
    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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  7. 7
    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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  8. 8
    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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  9. 9
    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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  10. 10
    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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  11. 11
    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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  12. 12
    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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  13. 13
    Most important predictive features.

    Most important predictive features. by Nicola Lazzarini (3528197)

    Published 2022
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  14. 14
    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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  15. 15
    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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  16. 16
    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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  17. 17
    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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  18. 18
    The number of relevant genes (features) selected by the proposed model on each phase.

    The number of relevant genes (features) selected by the proposed model on each phase. by Murad Al-Rajab (10661360)

    Published 2023
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  19. 19
    Event-driven data flow processing.

    Event-driven data flow processing. by Yixian Wen (12201388)

    Published 2025
    “…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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  20. 20
    Description, feature counts, and data partitioning of the MIMIC-III clinical database (v1.4) used in this study. The table categorizes patient data into three main relevant modalities: clinical data, medical history, and medical imaging. The approximate number of instances of each type of feature is recorded on the Count column: it is an indicator of the scale and variety of the dataset. Datas were separated into training (75 percent), validation (10 percent) and test (15 percent) sets to maintain patient level separation to prevent data leakage.

    Description, feature counts, and data partitioning of the MIMIC-III clinical database (v1.4) used in this study. The table categorizes patient data into three main relevant modalit... by Medhat A. Tawfeek (22522087)

    Published 2025
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