VIF analysis results for hazard-causing factors. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”

Benchmark function information. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”

Geographical distribution of the study area. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”

Results for model hyperparameter values. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”

Flow chart of this study. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”

Stability analysis of each model. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”

Robustness Analysis of each model. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”

Data_Sheet_1_Robust optimization for casualty scheduling considering injury deterioration and point-edge mixed failures in early stage of post-earthquake relief.docx by Yufeng Zhou (3702208)

Table_1_Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma.docx by Jiang Zhu (40858)

Image_1_Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma.TIFF by Jiang Zhu (40858)

Supplementary file 1_A study on a real-world data-based VTE risk prediction model for lymphoma patients.docx by Changli He (22424818)

“…</p>Conclusion<p>This study established and validated a machine learning model for predicting VTE risk in lymphoma patients, with the optimal model demonstrating excellent discriminatory ability (AUC = 0.954). …”

Table 1_Association between pro-inflammatory diet and fecal incontinence: a large population-based study.pdf by Haiyang Wang (22389)

“…We also identified total saturated fat, polyunsaturated fatty acid, vitamin A, β carotene, vitamin B2, and iron are the primary dietary factors associated with FI. Based on these dietary factors, we developed a novel FI risk prediction model. …”

Image 1_Association between pro-inflammatory diet and fecal incontinence: a large population-based study.pdf by Haiyang Wang (22389)

“…We also identified total saturated fat, polyunsaturated fatty acid, vitamin A, β carotene, vitamin B2, and iron are the primary dietary factors associated with FI. Based on these dietary factors, we developed a novel FI risk prediction model. …”

Table_1_Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.XLSX by Qin-Yu Zhao (10014626)

Table_2_Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.DOCX by Qin-Yu Zhao (10014626)

DataSheet1_Primary sarcopenia is associated with elevated spontaneous NET formation.pdf by Irina Balazs (18102253)

Data_Sheet_1_Adaptive restraint design for a diverse population through machine learning.pdf by Wenbo Sun (531308)

Data_Sheet_1_A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer.docx by Tingting Ma (228102)

DataSheet_1_A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn... by Gaosen Zhang (539619)

Table_1_Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units.DOCX by Ximing Nie (10008344)

“…The calibration, discrimination, and risk classification of predicted hospital mortality based on machine learning algorithms were assessed. …”