Performance of machine learning algorithms. by Wenxue Liu (6877754)

Confusion Matrix for Subspace algorithm (3000 Learning Cycles) providing insight into the performance evaluation of Subspace algorithm. by Sadia Nazim (21440115)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Visualization of mean absolute SHAP values for conditional algorithm for each class. by Sadia Nazim (21440115)

Visualization of mean absolute SHAP values for Interventional algorithm for each class. by Sadia Nazim (21440115)

Supplementary Material for: The British-Israeli Project for Algorithm-Based Management of Age-related Macular Degeneration: Deep Learning Integration for Real- World Data Management and Analysis by figshare admin karger (2628495)

“…Purpose: To describe the development of an integrative dataset, combining clinical and optical coherence tomography (OCT) imaging data by applying a deep learning algorithm for automated, objective, and comprehensive quantification of OCT scans to two large real-world datasets of eyes with neovascular age-related macular degeneration (nAMD). …”

Comparison of information on predicting survival outcomes or metastasis of cancer patients using machine learning algorithms in the past. by Jingru Dong (14076094)

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Confusion matrix for RUSBoost algorithm (3000 Learning Cycles) highlights the classification accuracy across different classes. by Sadia Nazim (21440115)

Structure and the optimization technique of the algorithms adopted in the study. by M. A. Habib (16839161)

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Data Sheet 1_A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm.pdf by Tianli Wang (1438498)

Visualization of mean absolute SHAP values for conditional algorithm across all classes. by Sadia Nazim (21440115)

Visualization of mean absolute SHAP values for Interventional algorithm across all classes. by Sadia Nazim (21440115)

Relative prediction error of candidate methods relative to the PNLR with a threshold of 0.05. by Lulu Pan (8977979)

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Relative prediction error of candidate methods relative to the PNLR with a threshold of 0.1. by Lulu Pan (8977979)

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Relative prediction error of candidate methods relative to the PNLR with a threshold of 0.15. by Lulu Pan (8977979)

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Feature Importance Analysis of the Algorithms expressed by SHAP indices. by M. A. Habib (16839161)

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Statistical analysis of the prediction results from the ML algorithms. by M. A. Habib (16839161)

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Relative prediction error of PtLR, Naive Trans-PNLR, Naive Trans-PtLR, Trans-PNLR, and Trans-PtLR relative to the PNLR. by Lulu Pan (8977979)

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The goodness of fit criteria results in all algorithms for optimal hyperparameter values. by Sevtap Tırınk (22394716)

PLS-SEM algorithm. by Ernest Nyamekye (18451455)