A natural language processing approach to support biomedical data harmonization: Leveraging large language models. by Zexu Li, Suraj P Prabhu, Zachary T Popp, Shubhi S Jain, Vijetha Balakundi, Ting Fang Alvin Ang, Rhoda Au, Jinying Chen

“…LLM-derived features contributed most to RF's performance. One major cause of errors in automatic variable matching was ambiguous variable definitions.…”
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Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning by Jinhang Liu, Wenying Zhang, Yongfeng Wu, Juncheng Ma, Yulin Zhang, Binhui Liu

“…Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. …”
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Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning by Renata Retkute, Kathleen S. Crew, John E. Thomas, Christopher A. Gilligan

“…The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. …”
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Predicting the compressive strength of concrete incorporating waste powders exposed to elevated temperatures utilizing machine learning by Islam N. Fathy, Hany A. Dahish, Mohammed K. Alkharisi, Alaa A. Mahmoud, Hala Emad Elden Fouad

“…The XGB model demonstrated the highest R2 of 0.9989, alongside the lowest prediction errors: MAE of 0.1351 MPa, RMSE of 0.1842 MPa, and MAPE of 0.48%. …”
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Retrieval of non-optical active water quality parameters in complex Lake environments using a novel zoning-based ensemble modeling strategy by Cheng Cai, Linlin Liu, Ziming Wang, Wei Pang, Congshuo Bai, Huanxue Zhang

“…Within the sampling area, the model achieved R2 values of 0.56, 0.54, and 0.57 and root mean square errors (RMSE) of 0.04 mg/L, 4.56 mg/L, and 1.87 mg/L for retrieval of NH3-N, COD, and DO, respectively. …”
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STREAM: a system for tracing radiative transfer and energy balance in heterogeneous surfaces by Tengyuan Fan, Zunjian Bian, Jean-Louis Roujean, Huaguo Huang, Hua Li, Junhua Bai, Biao Cao, Yongming Du, Qing Xiao, Qinhuo Liu

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Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data by Yilin Bao, Xiangtian Meng, Weimin Ruan, Huanjun Liu, Mingchang Wang, Abdul Mounem Mouazen

“…Results indicate that (1) the proposed paradigm achieves optimal SOC content prediction accuracy in humid regions, with a root mean square error (RMSE) of 3.58 g kg−1, a coefficient of determination (R2) of 0.76, a ratio of performance to interquartile distance (RPIQ) of 2.26, and a mean absolute error (MAE) of 4.73 g kg−1. …”
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Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model by Qianchuan Mi, Zhiguo Huo, Meixuan Li, Lei Zhang, Rui Kong, Fengyin Zhang, Yi Wang, Yuxin Huo

“…Initially, we employed the Random Forest (RF) regression model that integrated multi-source environmental factors to estimate soil moisture prior to the sowing of winter wheat, achieving an average coefficient of determination (R²) of 0.8618, root mean square error (RMSE) of 0.0182 m³ m⁻³, and mean absolute error (MAE) of 0.0148 m³ m⁻³ across eight soil depths. …”
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Application of Artificial Intelligence Techniques for the Estimation of Basal Insulin in Patients with Type I Diabetes by Guillermo Edinson Guzman Gómez, Luis Eduardo Burbano Agredo, Veline Martínez, Oscar Fernando Bedoya Leiva

“…We used neural networks (NNs), Bayesian networks (BNs), support vector machines (SVMs), and random forests (RF). We then evaluated the agreement between predicted and actual values using several statistical error measurements: mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), Pearson’s correlation coefficient (R), and determination coefficient (R2). …”
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A study on predicting the risk of coronary artery disease in OSAHS patients based on a four-variable screening tool potential predictive model and its correlation with the severity... by Yanli Yao, Yu Li, Yulan Chen, Xuan Qiu, Gulimire Aimaiti, Ayiguzaili Maimaitimin

“…A comprehensive analysis utilizing the random forest machine learning algorithm demonstrated that the AHI exhibits the highest predictive value. …”
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Analysis of factors influencing clinical pregnancy rates in frozen-thawed embryo transfer cycles by Junqiang Wang, Zexing Yang, Ying Chen, Ying Chen, Fengchen Gao, Wenxiu Zhao, Shuxuan Cao, Yixi Li, Limei He, Limei He

“…The top-ranked predictors with the lowest average out-of-bag (OOB) error rates were incorporated into a multivariate logistic regression model to determine independent predictors of clinical pregnancy in FET cycles.ResultsThe overall clinical pregnancy rate (CPR) was 46.08%. …”
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Evaluating war-induced damage to agricultural land in the Gaza Strip since October 2023 using PlanetScope and SkySat imagery by He Yin, Lina Eklund, Dimah Habash, Mazin B. Qumsiyeh, Jamon Van Den Hoek

“…Third, we assessed the damage to greenhouses by classifying PlanetScope imagery using a random forest model. We performed accuracy assessments on a generated tree crop fields damage map using 1,200 randomly sampled 3 × 3-m areas, and we generated error-adjusted area estimates with a 95% confidence interval. …”
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Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions by Khaled M. Salem, Javier M. Rey-Hernández, A. O. Elgharib, Francisco J. Rey-Martínez

“…The performances of both models are calculated using the Root Mean Square Percentage Error (RMSPE), Root Mean Square Relative Percentage Error (RMSRPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Relative Percentage Error (MARPE), Kling–Gupta Efficiency (KGE), and also the coefficient of determination, R². …”
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Do empirically-derived personality subtypes relate to cognitive inflexibility in anorexia nervosa and bulimia nervosa? by Lauren M. Schaefer, Glen Forester, Elizabeth N. Dougherty, Angeline R. Bottera, Erika E. Forbes, Jennifer E. Wildes

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Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning by Dthenifer Cordeiro Santana, Rafael Felipe Ratke, Fabio Luiz Zanatta, Cid Naudi Silva Campos, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Natielly Pereira da Silva, Gabriela Souza Oliveira, Regimar Garcia dos Santos, Rita de Cássia Félix Alvarez, Carlos Antonio da Silva Junior, Matildes Blanco, Paulo Eduardo Teodoro

“…Each database was subjected to different machine learning models: artificial neural networks (ANNs), decision tree (DT), linear regression (LR), M5P, and random forest (RF) algorithms. Pearson’s correlation coefficient, mean absolute error, and root mean square error were tested as model accuracy metrics. …”
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Phenological piecewise modelling is more conducive than whole-season modelling to winter wheat yield estimation based on remote sensing data by Xin Huang, Wenquan Zhu, Cenliang Zhao, Zhiying Xie, Hui Zhang

“…Compared with the whole-season models, the R2 for the phenological piecewise models improved by 1.4% to 7.6%, the root mean square error (RMSE) decreased by 1.1% to 8.2% among four regression methods . …”
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A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas by Ju Wang, Ju Wang, Ju Wang, Bo-Hui Tang, Bo-Hui Tang, Bo-Hui Tang, Bo-Hui Tang, Xinming Zhu, Xinming Zhu, Xinming Zhu, Dong Fan, Dong Fan, Dong Fan, Menghua Li, Menghua Li, Menghua Li, Junyi Chen, Junyi Chen, Junyi Chen

“…Based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), XGBoost demonstrated the best performance. …”
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Estimation of Silage Maize Plant Moisture Content Based on UAV Multispectral Data and Ensemble Learning Methods by Xuchun Li, Jixuan Yan, Caixia Huang, Weiwei Ma, Zichen Guo, Jie Li, Xiangdong Yao, Qihong Da, Kejing Cheng, Hongyan Yang

“…The standalone models demonstrated coefficient of determination (R²) values ranging from 0.43 to 0.69, with root mean square error (RMSE) spanning 0.61% to 1.43%. In contrast, the ensemble model exhibited superior accuracy, achieving R² values between 0.61 and 0.87 and RMSE values from 0.54% to 1.38%. …”
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Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models by Wenxuan Yu, Xizhe Li, Wei Guo, Hongming Zhan, Xuefeng Yang, Yongyang Liu, Xiangyang Pei, Weikang He, Longyi Wang, Yaoqiang Lin

“…It achieves R² values of 0.978 and 0.959, respectively, on the test set. The error metrics (MAE, MSE, RMSE) of the XGBoost model are significantly lower than those of SVM and Random Forest, demonstrating its precise capture of the nonlinear relationships between logging parameters and in situ stress. …”
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Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring by Yushuai Wang, Yuxin Chen, Zhou Shu, Shaolong Zhu, Weijun Zhang, Tao Liu, Chengming Sun

“…In the early stage, the BPNN model achieved a coefficient of determination (R²) of 0.92 and an RMSE of 0.054 on the training set, and it obtained an R² of 0.89 with a root mean square error (RMSE) of 0.074 on the validation set. The random forest regression (RFR) model performed best during the early stage of taro formation with multispectral vegetation indices as input variables. …”
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