Showing 101 - 120 results of 364 for search '(( binary from based optimization algorithm ) OR ( primary data models optimization algorithm ))', query time: 0.45s Refine Results
  1. 101
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    SHAP bar plot. by Meng Cao (105914)

    Published 2025
    “…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
  3. 103

    Sample screening flowchart. by Meng Cao (105914)

    Published 2025
    “…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
  4. 104

    Descriptive statistics for variables. by Meng Cao (105914)

    Published 2025
    “…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
  5. 105

    SHAP summary plot. by Meng Cao (105914)

    Published 2025
    “…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
  6. 106

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

    Published 2025
    “…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
  7. 107

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

    Published 2025
    “…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
  8. 108

    Proposed model specificity and DSC outcomes. by Subathra Gunasekaran (19492680)

    Published 2024
    “…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …”
  9. 109

    Bi-directional LSTM Model performance. by Muhammad Usman Tariq (11022141)

    Published 2024
    “…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …”
  10. 110

    Prediction results of different models. by Qinghua Li (398885)

    Published 2024
    “…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …”
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    S1 Data - by Gaosha Li (20570760)

    Published 2025
    “…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”
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