Two-dimensional visualization of cell clustering in the Pollen dataset before and after imputation using multiple algorithms. by Siyi Huang (8562174)

Two-dimensional visualization of cell clustering in the Petropoulos dataset before and after imputation using multiple algorithms. by Siyi Huang (8562174)

Two-dimensional visualization of cell clustering in the Guo2 dataset before and after imputation using multiple algorithms. by Siyi Huang (8562174)

Two-dimensional visualization of cell clustering in the iPSC dataset before and after imputation using multiple algorithms. by Siyi Huang (8562174)

Two-dimensional visualization of cell clustering in the CellType dataset before and after imputation using multiple algorithms. by Siyi Huang (8562174)

Predicted sensitivity, specificity, PPV, and NPV of the four algorithms (a, b) and algorithms combinations (c, d) under Model-3 in the primary analysis. by Jia Wei (120974)

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Proportion of positive results in each algorithm and latent prevalences by year. by Jia Wei (120974)

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Distribution of original measurements (blue line) and imputed measurements (red line) for missing values using multiple imputation with chain equation (MICE). by Jia Wei (120974)

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Proportion of positive results in each algorithm and latent prevalences by age (a) and comorbidity (b) groups. by Jia Wei (120974)

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Trace plots of estimated prevalence, sensitivity, and specificity of each algorithm of Model-3 in the primary analysis. by Jia Wei (120974)

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Observed and predicted frequency of each algorithm combination of community-acquired pneumonia (CAP) in Model-3 in the primary analysis. by Jia Wei (120974)

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An illustration of the Deepwalk algorithm operation involves a random walk on the graph, resulting in a “walk” sequence, which is then utilized to construct a graph. by Xiaobing Deng (1622233)

Posterior predicted sensitivity, specificity, PPV, NPV for each algorithm combination under the ‘AND’ rule and ‘OR’ rule in Model-3 in the primary analysis. by Jia Wei (120974)

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Posterior predicted sensitivity, specificity, PPV, NPV for each algorithm combination under the ‘AND’ rule (a) and ‘OR’ rule (b) in Model-3 in the primary analysis. by Jia Wei (120974)

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Distributed and explainable GHSOM for anomaly detection in sensor networks by Paolo Mignone (9760832)

Convergence speed of the improved algorithm. by Qiuping Shen (2562166)

“…<div><p>During the process of detecting gravitational waves in space, addressing noise issues caused by terrestrial vibrations, natural environmental changes, and the factors intrinsic to the detectors, this paper proposes a multiscale variational mode adaptive denoising algorithm based on momentum gradient descent. …”

Data Sheet 1_An individualized risk prediction tool for ectopic pregnancy within the first 10 weeks of gestation based on machine learning algorithms.docx by Xin Du (208780)

“…</p>Conclusion<p>This study employed the CatBoost algorithm to develop an individualized risk prediction model by integrating multiple features from the initial visit. …”

Posterior predicted sensitivity, specificity, PPV, and NPV under Model-3 in subgroup analysis by age (a) and comorbidity (b) groups. by Jia Wei (120974)

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Posterior predicted sensitivity, specificity, PPV, and NPV under Model-3 in sensitivity analyses using different oxygen saturation thresholds. by Jia Wei (120974)

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Posterior predicted sensitivity, specificity, PPV, and NPV under Model-3 in subgroup analysis by year. by Jia Wei (120974)

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