S1 Syntax - Application of machine learning algorithms to predict early childhood development in children aged 24–59 months across three East African countries حسب Tsion Mulat Tebeje (13955254)

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Risk element category diagram. حسب Yao Hu (3479972)

"…This article extracted features related to risk assessment, such as weather factors, airport facility inspections, and security check results, and conducted qualitative and quantitative analysis on these features to generate a datable risk warning weight table. This article used these data to establish an LSTM model, which trained LSTM to identify potential risks and provide early warning by learning patterns and trends in historical data. …"

The accuracy score change matrix following model development. حسب Uğur Ejder (22683228)

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Supporting data for “The role of forest composition heterogeneity on temperate ecosystem carbon dynamic under climate change" حسب Ziyu Lin (9151064)

"…The process includes (1) harmonizing Landsat 5, 7, 8, and Sentinel-2 data using the HLS algorithm, and (2) filling temporal gaps with an optimized object-based STARFM fusion algorithm. …"

Confusion Matrix for the Hybrid algorithms. حسب Faten Al-hussein (20707521)

"…Medical records from 3,000 King Abdulaziz University Hospital patients containing demographic, lifestyle, and lipid profile data were used to develop the models. For the first time, we utilized recommended machine learning algorithms to develop hybrid prediction models to reduce the number of significant KPIs while enhancing HbA1c prediction accuracy. …"

Data Sheet 3_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Data Sheet 2_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Data Sheet 4_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Data Sheet 6_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.docx حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Data Sheet 1_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.pdf حسب Cong Peng (160287)

"…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …"

Data Sheet 1_Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis.docx حسب Jingtai Ma (21225476)

Mean SHAP value of feature importance of early childhood development. حسب Tsion Mulat Tebeje (13955254)

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Background characteristics and the prevalence of early childhood development in East Africa. حسب Tsion Mulat Tebeje (13955254)

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Receiver operator characteristic (ROC) curves of the machine learning algorithms. حسب Yue Lv (549862)

Detailed information on software packages used for machine learning model development. حسب Jingru Dong (14076094)

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Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx حسب Veera Narayana Balabathina (22518524)

"…This study focuses on developing an efficient classification framework for species-level tree mapping in the Hauz Khas Urban Forest, New Delhi, India, using EO-1 Hyperion hyperspectral imagery.…"

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

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Comparison of mass attenuation coefficient calculated by Geant4 simulation and acquired from NIST data. حسب Dong Hyeok Choi (12562048)

Overview of machine learning framework to predict early childhood development in East Africa. حسب Tsion Mulat Tebeje (13955254)

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Study sample and the prevalence of early childhood development in three East African countries. حسب Tsion Mulat Tebeje (13955254)

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