Parcel-scale crop planting structure extraction combining time-series of Sentinel-1 and Sentinel-2 data based on a semantic edge-aware multi-task neural network by Zhiguang Tang, Xiangdong Wang, Qin Jiang, Haizhu Pan, Gang Deng, He Chen, Yuanhong You, Sijia Li, Haiyan Hou

“…Ultimately, a parcel-scale random forest (RF) model was developed to enable accurate crop type classification and spatial delineation of crop planting structures. …”
Get full text

Combined influence of crushed brick powder and recycled concrete aggregate on the mechanical, durability and microstructural properties of eco-concrete: An experimental and machine... by Md. Habibur Rahman Sobuz, Mahmudur Hossain Khan, Md. Rakibul Islam, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi, M Jameel, Md. Munir Hayet Khan

“…Additionally, the study evaluates machine learning algorithms such as extreme gradient boosting (XG Boost), random forest (RF), and bagging model (BAG) for predicting the mechanical strength of concrete specimens. …”
Get full text

Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

“…Based on the 7-year (2018–2024) in situ measurements from Beijing, Nanjing, and Shanghai, validation results reveal that AngleNet achieves substantial improvements, with an average R2 of 0.71 and a root mean square error (RMSE) of 10.39%, surpassing conventional models such as LGBM (light gradient boosting machine) and RF (random forest) by over 10% in both metrics, and demonstrating a remarkable 41% increase in R2 and a 10% reduction in RMSE compared to the previous BRNN method (batch normalization and robust neural network). …”
Get full text

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The Gradient Boosting model achieved the lowest error rate of 27%, indicating robust predictive capability. …”
Get full text

A Data-Driven Approach to Engineering Instruction: Exploring Learning Styles, Study Habits, and Machine Learning by Lauren Genith Isaza Dominguez, Antonio Robles-Gomez, Rafael Pastor-Vargas

“…Several machine learning models, including Random Forest and Voting Ensemble, were tested to predict academic performance using study behavior data. …”
Get full text

Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study by Tran Thi Ngan, Tran Thi Ngan, Dang Huong Tra, Ngo Thi Quynh Mai, Hoang Van Dung, Nguyen Van Khai, Pham Van Linh, Nguyen Thi Thu Phuong, Nguyen Thi Thu Phuong

“…Other ensemble models, including Random Forest and Gradient Boosting, also showed strong performance (R² > 80%). …”
Get full text

An integrated method of selecting environmental covariates for predictive soil depth mapping by Yuan-yuan LU, Feng LIU, Yu-guo ZHAO, Xiao-dong SONG, Gan-lin ZHANG

“…Second, three conventional methods (Pearson correlation analysis (PsCA), generalized additive models (GAMs), and Random Forest (RF)) were used to generate optimal combinations of environmental covariates. …”
Get full text

Predicting student retention: A comparative study of machine learning approach utilizing sociodemographic and academic factors by Reymark D. Deleña, Norniña J. Dia, Redeemtor R. Sacayan, Joseph C. Sieras, Suhaina A. Khalid, Amer Hussien T. Macatotong, Sacaria B. Gulam

“…Results indicate that XGBoost outperformed all other models, achieving the highest cross-validated accuracy (90.66 %), F1 Score (90.72), and one of the lowest error values (Mean Square Error (MSE) = 9.34, Log Loss = 0.26). …”
Get full text

Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data by Donato Romano, Michele Magarelli, Pierfrancesco Novielli, Domenico Diacono, Pierpaolo Di Bitonto, Nicola Amoroso, Alfonso Monaco, Roberto Bellotti, Sabina Tangaro

“…This study investigated the predictive performance of three regression models—Gradient Boosting (GB), Random Forest (RF), and XGBoost—in forecasting mortality due to endocrine, nutritional, and metabolic diseases across Italian provinces. …”
Get full text

Rapid retrieval of soil moisture using a novel portable L-band radiometer in the Hulunbeier Prairie, China by Shaoning Lv, Derek Houtz, Shiyuan Li, Yin Hu, Jing Zhang, Dongli Wu, Lei Jin, Jun Wen

Get full text

Ganoderma Disease in Oil Palm Trees Using Hyperspectral Imaging and Machine Learning by Chee Seng Kwang, Siti Fatimah Abdul Razak, Sumendra Yogarayan, M. Z. Adli Zahisham, Tze Huey Tam, M. K. Anuar Mohd Noor, Haryati Abidin

“…In this paper, four different classifiers, such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Decision Tree (DT), were trained on a tabular dataset generated by clustering spectral signatures of each pixel in the hyperspectral image. …”
Get full text

A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
Get full text

Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features by Kennette James Basco, Alana Singh, Daniel Nasef, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Traditional interpretation methods are manual, time-consuming, and prone to error. Machine learning offers a promising alternative for automating the classification of electrocardiogram abnormalities. …”
Get full text

Winter wheat plant density determination: Robust predictions across varied agronomic conditions using multiscale RGB imaging by Jara Jauregui-Besó, Adrian Gracia-Romero, Constanza S. Carrera, Marta da Silva Lopes, José Luis Araus, Shawn Carlisle Kefauver

Get full text

Integrating image segmentation and auxiliary data for efficient estimation of FVC and AGB by Xifeng Zhang, Lu Xu, Yaxiao Li, Ying Yang, Jianguo Li, Hongyuan Ma

“…Two predictor sets are used to train five inversion models (ridge regression, k-nearest neighbors, support vector regression, random forest, and partial least squares regression): one using only the vegetation pixel ratio, and the other combining the vegetation pixel ratio with vegetation density and mean height. …”
Get full text

Using satellite-derived attributes as proxies for soil carbon cycling to map carbon stocks in alpine grassland soils by Ren-Min Yang, Lai-Ming Huang, Zhifeng Yan, Xin Zhang, Shao-Jun Yan

“…We evaluated the effectiveness of these indices in predicting SOC stocks, we compared PLS-SEM and quantile regression forest (QRF) models across different variable combinations, including static, intra-annual, and inter-annual information. …”
Get full text

Feature-Driven Density Prediction of Maraging Steel Additively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learning by Rajesh Kumar Balaraman, Shaista Hussain, John Kgee Ong, Qing Yang Tan, Nagarajan Raghavan

“…With these three sets of input features, the study investigates the effectiveness of four supervised machine learning (ML) regression models: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP), for predicting the density of printed samples. …”
Get full text

Modelling flood losses of micro-businesses in Ho Chi Minh City, Vietnam by A. Buch, A. Buch, A. Buch, D. Paprotny, K. Rafiezadeh Shahi, K. Rafiezadeh Shahi, H. Kreibich, N. Sairam

“…This study aims to derive the drivers of flood losses [%] for micro-businesses by applying a conditional random forest to survey data (relative business content losses: <span class="inline-formula"><i>n</i>=317</span>; relative business interruption losses: <span class="inline-formula"><i>n</i>=361</span>) collected from micro-businesses in HCMC. …”
Get full text

Toward Real-Time Discharge Volume Predictions in Multisite Health Care Systems: Longitudinal Observational Study by Fernando Acosta-Perez, Justin Boutilier, Gabriel Zayas-Caban, Sabrina Adelaine, Frank Liao, Brian Patterson

“…In experiment 2, the mean absolute percentage error increased from 11% to 16% when predicting 4 hours in advance relative to zero hours in Hospital 1 and from 24% to 35% in Hospital 2. …”
Get full text

NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region by Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nadia Ouaadi, Nicolas Baghdadi, Mehrez Zribi

“…Low root mean square error (RMSE) values characterize NDVI estimation during the first period. …”
Get full text