Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing by Xiaofei Yang, Hao Zhou, Qiao Li, Xueliang Fu, Honghui Li

“…For the single-fertility and full-fertility phases, respectively, the optimal coefficients of determination (R²s) were 0.60, 0.66, and 0.87, the root-mean-square errors (RMSEs) were 2.63, 3.23, and 2.39, and the mean absolute errors (MAEs) were 2.00, 2.75, and 1.99.…”
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GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments by Peng Gao, Jinzhen Fang, Junlin He, Shuang Ma, Guanghua Wen, Zhen Li

“…Compared with the conventional ES-EKF, the proposed method achieves reductions in position root mean square error (PRMSE) of 48.74% (East), 41.94% (North), and 61.59% (Up), and reductions in velocity root mean square error (VRMSE) of 71.5% (East), 39.31% (North), and 56.48% (Up) in the East–North–Up (ENU) coordinate frame. …”
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Predicting hospital outpatient volume using XGBoost: a machine learning approach by Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang

“…Model performance was assessed using three metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) , Mean Absolute Percentage Error (MAPE), and R-squared (R2) metrics. …”
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Resource Optimization for Grid-Connected Smart Green Townhouses Using Deep Hybrid Machine Learning by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

“…In particular, the Mean Absolute Percentage Error (MAPE) is below 5%, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are within acceptable levels, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> is consistently above 0.85. …”
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Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking by Gang Yu, Shuang Zhang, Min Hu, Y. Ken Wang

“…The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. …”
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Application of Extra-Trees Regression and Tree-Structured Parzen Estimators Optimization Algorithm to Predict Blast-Induced Mean Fragmentation Size in Open-Pit Mines by Madalitso Mame, Shuai Huang, Chuanqi Li, Jian Zhou

“…Among the evaluated models, the TPE-ET model exhibits the best performance with a coefficient of determination (<i>R</i>²), root mean squared error (RMSE), mean absolute error (MAE), and max error of 0.93, 0.04, 0.03, and 0.25 during the testing phase. …”
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Spatial interpolation of cropland soil bulk density by increasing soil samples with filled missing values by Aiwen Li, Jinli Cheng, Dan Chen, Wendan Li, Yaruo Mao, Xinyi Chen, Bin Zhao, Wenjiao Shi, Tianxiang Yue, Qiquan Li

“…The RBFNN model, tailored for each sub-watershed, yielded the highest accuracy in filling missing BD, with an increase in coefficient of determination (R 2) by 19.54–37.36% and reductions in mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) by 8.91–14.81%, 9.02–16.22% and 7.71–13.61%, respectively. …”
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Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network by Yuxin Cong, Roxana Dev Omar Dev, Shamsulariffin Bin Samsudin, Kaihao Yu

“…The results show that the proposed LSTM + CNN model has achieved significant improvement on the test set. Its mean absolute error is only 0.072, the mean square error is 0.00596, and the root mean square error is 0.077, which is remarkably superior to traditional machine learning methods such as random forest and support vector regression. …”
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Analysis and prediction of land use/land cover change in the Llanganates-Sangay Connectivity Corridor by 2030 by Luis Jonathan Jaramillo Coronel, Andrea Cecilia Mancheno Herrera, Adriana Catalina Guzmán Guaraca, Juan Gabriel Mollocana Lara

“…MapBiomas LULC maps reveals annual change rates (2018–2022) of -0.37 %/year (-1147.33 ha) for Forest Formation, -1.17 %/year (-30.01 ha) for Non-Forest Natural Formation, 2.21 %/year (906.19 ha) for Agriculture and Livestock Areas, 8.50 %/year (250.84 ha) for Non-Vegetated Areas, and 0.17 %/year (30.31 ha) for Water Bodies. …”
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Deep learning meets tree phenology modelling: PhenoFormer versus process‐based models by Vivien Sainte Fare Garnot, Lynsay Spafford, Jelle Lever, Christian Sigg, Barbara Pietragalla, Yann Vitasse, Arthur Gessler, Jan Dirk Wegner

“…When predicting under climatic conditions similar to the training data, our model improved over the best process‐based models by 6% normalised root‐mean‐squared error (nRMSE) for spring phenology and 7% nRMSE for autumn phenology. …”
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A Method of the Vibration Information Detection for Rotating Machinery Based on the Rolling-Shutter CMOS and Digital Image Processing by Yonggong Yuan, Ji Zhang, Nanwangdi Li, Haifeng Wang, Yuxin Hu, Yilong Wang, Ning Mei, Han Yuan

“…Comparative analysis of the diagnostic results using the K-Nearest Neighbor, AdaBoost, CatBoost, and Random Forest algorithms revealed that the Random Forest algorithm achieved the highest diagnostic accuracy, exceeding 98%. …”
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Enhancing solar irradiance prediction precision: A stacked ensemble learning-based correction paradigm by Bo Tian, Ningbo Wang, Yuanxin Lin, Shuangquan Shao

“…Experimental results demonstrate significant improvements over the original NWP forecasts: the corrected model reduces the mean absolute error (MAE) and root mean square error (RMSE) by 47% and 41%, respectively, while increasing the R² determination coefficient by 11%. …”
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Canopy height mapping in French Guiana using multi-source satellite data and environmental information in a U-Net architecture by Kamel Lahssini, Nicolas Baghdadi, Guerric le Maire, Guerric le Maire, Ibrahim Fayad, Ibrahim Fayad, Ludovic Villard

“…Canopy height is a key indicator of tropical forest structure. In this study, we present a deep learning application to map canopy height in French Guiana using freely available multi-source satellite data (optical and radar) and complementary environmental information. …”
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ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized by Vinicius Lins Costa Ok Melo, Pedro Emmanuel Alvarenga Americano do Brasil, PhD

“…A logistic model with lasso or elastic net regularization, a random forest classification model, and a random forest regression model were developed and validated to estimate the risk of disease progression. …”
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Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants by Vipin Kumar, Ravi Prakash Tewari, Anubhav Rawat

“…With a performance evaluation of 0.06 mean absolute error (MAE), 0.17 Root Mean Square Error (RMSE), and 0.96 R2, the Random Forest Regression is found to be the best model. …”
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A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal Denial by Yangming Hu, Liyou Xu, Xianghai Yan, Ningjie Chang, Qigang Wan, Yiwei Wu

“…In farmland environments where GNSS signals are obstructed, such as forested areas or in adverse weather conditions, traditional GNSS/INS integrated navigation systems suffer from positioning errors and instability. …”
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Enhanced SOC estimation method for lithium-ion batteries using Bayesian-optimized TCN–LSTM neural networks by Taotao Hu, Xiting Zhu, Maokai Tian

“…Experimental results demonstrate the effectiveness of the proposed method, achieving a coefficient of determination (R2) of 0.996, a mean absolute error of 0.146%, and a root mean square error of 0.207%. …”
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Interactive online learning method for students based on artificial intelligence by Cizhang Li, Wenfen Yin

“…The model was evaluated through experimental analysis using key regression and classification metrics, including Mean Absolute Error, Root Mean Square Error, R2 Score, Accuracy, Precision, Recall, Sensitivity, Specificity, and F1-Score with training time. …”
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Machine Learning Impact on Modern Business Intelligence by Raziyeh Moghaddas, Farinaz Tanhaei, Maryam Al Moqbali, Solmaz Safari

“…Finally, we assessed the performance of each model using standard evaluation metrics, i.e., Mean Absolute Error (MAE), Mean Squared Error (MSE), and the R-squared (R²) score. …”
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Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors by Chibuike Chiedozie Ibebuchi

“…A custom rolling window cross-validation, with 24 h validation blocks sliding daily across 2372 folds, evaluates an Extreme Gradient Boosting (XGBoost) model’s performance under diverse market conditions, achieving a median mean absolute error of 6.26 USD/MWh and root mean squared error of 8.27 USD/MWh, with variability reflecting market volatility. …”
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