Image 2_Construction of a clinical prediction model for osteoporosis in asymptomatic elderly population based on machine learning algorithm.tif by Jiaming Wang (2637667)

“…</p>Method<p>In this study, a robust and accurate prediction model for osteoporosis was developed and validated based on machine learning and SHAP techniques. We validated the model using ROC, calibration, and DCA curves. …”

Table 1_Construction of a clinical prediction model for osteoporosis in asymptomatic elderly population based on machine learning algorithm.docx by Jiaming Wang (2637667)

“…</p>Method<p>In this study, a robust and accurate prediction model for osteoporosis was developed and validated based on machine learning and SHAP techniques. We validated the model using ROC, calibration, and DCA curves. …”

Image 1_Construction of a clinical prediction model for osteoporosis in asymptomatic elderly population based on machine learning algorithm.tif by Jiaming Wang (2637667)

“…</p>Method<p>In this study, a robust and accurate prediction model for osteoporosis was developed and validated based on machine learning and SHAP techniques. We validated the model using ROC, calibration, and DCA curves. …”

Quantum Descriptor-Based Machine-Learning Modeling of Thermal Hazard of Cyclic Sulfamidates by Michal Dabros (10343989)

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DGA and Legitimate Domain names samples for malicious DGA detection experiments with transformer-based embeddings by Suleiman Yerima (4821276)

Image 1_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.tif by Xuehui Fan (10762662)

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

Screening of palmitoylation genes based on machine learning algorithms. by Qijun Wo (12992781)

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Accuracy of the support vector machines for different proportion of test samples and different length of nodes used for training. by Abeer Aljohani (18497914)

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Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

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The schematic diagram of the iForest algorithm. by Zhibo Xie (6790775)

“…Experiments have shown that as the dimensionality of the data increases, the performance indicators of the new scheme, such as <i>P</i> (precision), <i>R</i> (recall), <i>F1</i> score, and AUC (area under the curve), become increasingly superior to commonly used support vector machines (SVM), local outlier factors (LOF), and iForests. …”

Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks by Balázs Mészáros (16890225)

“…<p dir="ltr">The data in this repository accompanies the paper 'Efficient Event-based Delay Learning in Spiking Neural Networks'</p><p dir="ltr">The data relates to 4 benchmarks:</p><ol><li>Spiking Heidelberg Digits (SHD).…”

Confusion matrix for Random Forest algorithm (3000 Number of trees) highlights the classification accuracy across different classes. by Sadia Nazim (21440115)

Performance evaluation of ML regression models using RMSE. by Marwah Salman (22593380)

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ML regression methods and corresponding regressors used in LPA. by Marwah Salman (22593380)

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Data Sheet 1_Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms.xls by Chongkang Yue (19815378)

Figure 5 from DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways by Zhaoxiang Cai (20445441)

Figure 3 from DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways by Zhaoxiang Cai (20445441)

Figure 1 from DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways by Zhaoxiang Cai (20445441)

Figure 4 from DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways by Zhaoxiang Cai (20445441)