Path analysis diagram illustrating the associations between alexithymia, social support, anticipated stigma and patient satisfaction, with their respective correlation coefficients... by Keya Bibikar (22386306)

Comparison of the results of our model and other methods. by Weijia Zhu (65481)

Fig 6 - by Samuel Hylander (331771)

Out-of-sample Test Results of the Drop-off Decision Models: (a) ROC Curve of the First Forced Drop-off Decision Models; (b) ROC Curve for Second and More Forced Parking and Drop-of... by Fangyi Yang (18867853)

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P values of Kruskal–Wallis test at 95% significance level. by Fahd Saeed Alakbari (10701871)

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Synthetic feasibility scores of 20 molecular fragments. by Bin Pan (742525)

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The scatter plot of the target variable solubility dataset. by Bin Pan (742525)

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Samples with the highest solubility after high-throughput screening. by Bin Pan (742525)

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Assessment values of machine learning models. by Bin Pan (742525)

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List of datasets in AqSolDB. by Bin Pan (742525)

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Schematic workflow diagram of the StackBoost model framework. by Bin Pan (742525)

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Comparison of training and predicted values of compound water solubility for different machine learning models: (A) AdaBoost, (B) GBDT, (C) LGBM, (D) RF, (E) XGBoost, (F) RF, and (... by Bin Pan (742525)

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Pearson correlation analysis between pairs of feature parameters. by Bin Pan (742525)

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Comparison of actual vs predicted values after transfer. by Bin Pan (742525)

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List of available information in each column of AqSolDB. by Bin Pan (742525)

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3D Scatter plot of features related to water-soluble materials. by Bin Pan (742525)

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Workflow diagram of the prediction algorithm framework. by Bin Pan (742525)

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Feature importance derived from SHAP analysis. by Bin Pan (742525)

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Results of longitudinal analysis of mental health in HCWs. by A. H. Ayesha Lavell (16734002)

Comparison of Model Five-classification Results. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”