Mapping the Normalized Difference Vegetation Index for the Contiguous U.S. Since 1850 Using 391 Tree-Ring Plots by Hang Li, Ichchha Thapa, Shuang Xu, Peisi Yang

“…Among three machine learning approaches for regressions—Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)—we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. …”
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Forecasting Container Throughput of Singapore Port Considering Various Exogenous Variables Based on SARIMAX Models by Geun-Cheol Lee, June-Young Bang

“…The predictive performance of the SARIMAX model was evaluated against a diverse set of benchmark methods, including the Holt–Winters method, linear regression, LASSO regression, Ridge regression, ECM (Error Correction Mechanism), Support Vector Regressor (SVR), Random Forest, XGBoost, LightGBM, Long Short-Term Memory (LSTM) networks, and Prophet. …”
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Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study by Md. Wira Putra Dananjaya, Putu Gita Pujayanti

“…The models were evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. …”
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Deep learning models to predict CO2 solubility in imidazolium-based ionic liquids by Amir Hossein Sheikhshoaei, Ali Sanati, Ali Khoshsima

“…Graphical and statistical analyses revealed that the GrowNet model, with a root mean square error of 0.0073 and a coefficient of determination of 0.9962, exhibited the lowest error compared to other models. …”
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Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation by Weidong Gan, Dianguang Ma, Yu Duan

“…Among these, the LightGBM model demonstrated the highest prediction accuracy, achieving a Root Mean Squared Error (RMSE) of 0.188, Mean Absolute Error (MAE) of 0.149, and a coefficient of determination (R²) of 0.978. …”
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Theoretical analysis of MOFs for pharmaceutical applications by using machine learning models to predict loading capacity and cell viability by Bader Huwaimel, Saad Alqarni

“…Evaluation metrics, including R2, Root Mean Squared Error (RMSE), and maximum error, indicated that the QR-MLP model outperformed the other models, achieving test R2 scores of 0.99917 for Drug Loading Capacity and 0.99111 for Cell Viability. …”
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Parametric optimization of the slot waveguide characteristics using a machine-learning approach by Yadvendra Singh, Suraj Jena, Harish Subbaraman

“…The RF method outperformed the other two with mean absolute error (MAE), root mean square error (RMSE), coefficient of determination ( $$\hbox {R}^{2}$$ ), and Nash–Sutcliffe efficiency (NSE) values corresponding $$\hbox {n}_{eff}$$ and $$\hbox {P}_{conf}$$ as 0.007, 0.054, 0.961, and 0.960, and 0.129, 0.185, 0.998, and 0.998, respectively. …”
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Assessment of Photogrammetric Performance Test on Large Areas by Using a Rolling Shutter Camera Equipped in a Multi-Rotor UAV by Alba Nely Arévalo-Verjel, José Luis Lerma, Juan Pedro Carbonell-Rivera, Juan F. Prieto, José Fernández

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Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations by Boya Zhang, Daniel Gann, Shimon Wdowinski, Chaohao Lin, Erin Hestir, Lukas Lamb-Wotton, Khandker S. Ishtiaq, Kaleb Smith, Yuepeng Li

“…Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. …”
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Estimation of the vertical distribution of the fine canopy fuel in Pinus sylvestris stands using low density LiDAR data by L. A. Fidalgo-González, S. Arellano-Pérez, J. G. Álvarez-González, F. Castedo-Dorado, A. D. Ruiz-González, E. González-Ferreiro

“…Models were fitted simultaneously to compensate the effects of the inherent cross-model correlation between errors. Heteroscedasticity was also analyzed, but correction in the fitting process was not necessary. …”
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Simplifying drone-based aboveground carbon density measurements to support community forestry. by Ben Newport, Tristram C Hales, Joanna House, Benoit Goossens, Amaziasizamoria Jumail

“…Community-based forest restoration has the potential to sequester large amounts of atmospheric carbon, avoid forest degradation, and support sustainable development. …”
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A Comparative Analysis of Buckling Pressure Prediction in Composite Cylindrical Shells Under External Loads Using Machine Learning by Hyung Gi Lee, Jung Min Sohn

“…</b> The results demonstrated that the random forest model and XGBoost regression achieved superior accuracy with minimal prediction errors. …”
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Rainfall Prediction Using Integrated Machine Learning Models With K-Means Clustering: A Representative Case Study of Harirud Murghab Basin-Afghanistan by Ziaul Haq Haq Doost, Ali Alsuwaiyan, Abdulazeez Abdulraheem, Nabil M. Al-Areeq, Zaher Mundher Yaseen

“…The models were evaluated at three stations (Nazdik-i Herat, Shinya, and Torghundi) using coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), Mean absolute error (MAE), and median absolute error (MedAE) as evaluation metrics. …”
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Outliers and anomalies in training and testing datasets for AI-powered morphometry—evidence from CT scans of the spleen by Yuriy Vasilev, Yuriy Vasilev, Anastasia Pamova, Tatiana Bobrovskaya, Anton Vladzimirskyy, Anton Vladzimirskyy, Olga Omelyanskaya, Elena Astapenko, Artem Kruchinkin, Novik Vladimir, Kirill Arzamasov, Kirill Arzamasov

“…Using visual methods (1.5 interquartile range; heat map; boxplot; histogram; scatter plot), machine learning algorithms (Isolation forest; Density-Based Spatial Clustering of Applications with Noise; K-nearest neighbors algorithm; Local outlier factor; One-class support vector machines; EllipticEnvelope; Autoencoders), and mathematical statistics (z-score, Grubb’s test; Rosner’s test).ResultsWe identified measurement errors, input errors, abnormal size values and non-standard shapes of the organ (sickle-shaped, round, triangular, additional lobules). …”
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Development and Evaluation of Solar Radiation Sensor Using Cost-Effective Light Sensors and Machine Learning Techniques by Jesús Antonio Nava-Pintor, Uriel E. Alcalá-Rodríguez, Héctor A. Guerrero-Osuna, Marcela E. Mata-Romero, Emmanuel Lopez-Neri, Fabián García-Vázquez, Luis Octavio Solís-Sánchez, Rocío Carrasco-Navarro, Luis F. Luque-Vega

“…Experimental validation demonstrated a strong correlation between sensor-measured illuminance and solar irradiance using the random forest model, achieving a coefficient of determination (R²) of 0.9922, a root mean squared error (RMSE) of 44.46 W/m², and a mean absolute error (MAE) of 27.12 W/m². …”
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Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing by Waqar Shehbaz, Qingjin Peng

“…Among the models, Random Forest yields the highest predictive accuracy and lowest mean squared error across all target sustainability indicators: energy consumption, part weight, scrap weight, and production time. …”
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Artificial intelligence aided microwave coagulation therapy: Analysis of heat transfer to tumor tissue via hybrid modeling by Zheng Yang, KeWei Dai, Wujun Zhang, Rui Zhou, QingBin Wu, Liang Liu, HuaiRong Qu

“…Obtaining a score of 0.9991 by R2 criterion (Coefficient of Determination), an MSE (Mean Squared Error) of 0.1526, and an MAE (Mean Absolute Error) of 0.2545, the results show that the LGBM is the best-fit model. …”
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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…The evaluation study proved that Random Forest is the most accurate developed intelligent model as it was characterized with acceptable R-squared (0.959), mean square error (1.65), average absolute relative error (6.85%) of unseen test datapoints as well as with correct trend prediction of IFT with regard to all input parameters of pressure, temperature and crude oil API. …”
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Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques by Vanessa Steindorf, Hamna Mariyam K. B., Nico Stollenwerk, Aitor Cevidanes, Jesús F. Barandika, Patricia Vazquez, Ana L. García-Pérez, Maíra Aguiar

“…Forecasting models, including random forest (RF) and seasonal autoregressive integrated moving average (SARIMAX), were evaluated using root mean squared error (RMSE) and mean absolute error (MAE) metrics. …”
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VIS/NIR Spectroscopy as a Non-Destructive Method for Evaluation of Quality Parameters of Three Bell Pepper Varieties Based on Soft Computing Methods by Meysam Latifi Amoghin, Yousef Abbaspour-Gilandeh, Mohammad Tahmasebi, Mohammad Kaveh, Hany S. El-Mesery, Mariusz Szymanek, Maciej Sprawka

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