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process optimization » model optimization (Expand Search)
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Showing 1 - 11 results of 11 for search '(( binary damage process optimization algorithms ) OR ( binary based web optimization algorithm ))', query time: 0.40s Refine Results
  1. 1
    Display of the web prediction interface.

    Display of the web prediction interface. by Meng Cao (105914)

    Published 2025
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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
    Calibration curves for the test set of four models.

    Calibration curves for the test set of four models. by Meng Cao (105914)

    Published 2025
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  4. 4
    SHAP bar plot.

    SHAP bar plot. by Meng Cao (105914)

    Published 2025
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  5. 5
    ROC curves for the test set of four models.

    ROC curves for the test set of four models. by Meng Cao (105914)

    Published 2025
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  6. 6
    Sample screening flowchart.

    Sample screening flowchart. by Meng Cao (105914)

    Published 2025
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  7. 7
    Descriptive statistics for variables.

    Descriptive statistics for variables. by Meng Cao (105914)

    Published 2025
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  8. 8
    SHAP summary plot.

    SHAP summary plot. by Meng Cao (105914)

    Published 2025
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  9. 9
    Performance metrics of the models on the training and test set.

    Performance metrics of the models on the training and test set. by Meng Cao (105914)

    Published 2025
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  10. 10
    Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx

    Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx by Yuan Liu (88411)

    Published 2020
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  11. 11
    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles

    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

    Published 2025
    “…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”
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