Development and validation of a prognostic model for postoperative hypotension in patients receiving epidural analgesia by Carlos E. Guerra-Londono, Erika Taco Vasquez, Efrain Riveros, Ehsan Noori, David Greiver, Srikanth Pillai, Theodore Schiff, James Soetedjo, Maylyn Wu, Jaime Garzon Serrano

“…Exposures identified as statistically significant were used for logistic regression, linear discriminant analysis, and decision-tree model of random forest. Classification error was used to compare models, and variable importance was used for random forest analysis. …”
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Federated learning based reference evapotranspiration estimation for distributed crop fields. by Muhammad Tausif, Muhammad Waseem Iqbal, Rab Nawaz Bashir, Bayan AlGhofaily, Alex Elyassih, Amjad Rehman Khan

“…The evaluation reveals that Random Forest Regressor (RFR) based federated learning outperformed other models with coefficient of determination (R2) = 0.97%, Root Mean Squared Error (RMSE) = 0.44, Mean Absolute Error (MAE) = 0.33 mm day-1, and Mean Absolute Percentage Error (MAPE) = 8.18%. …”
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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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Investigating the effect of land use type on surface water quality in the Talar watershed in Mazandaran by Fatemeh Shokrian, Karim solaimani, Aref Saberi

“…Residential land use was the main factor changing the quality of water resources in the Talar catchment, while forests and pastures had the least negative impact on water quality. …”
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Enhancing Pear Tree Yield Estimation Accuracy by Assimilating LAI and SM into the WOFOST Model Based on Satellite Remote Sensing Data by Zehua Fan, Yasen Qin, Jianan Chi, Ning Yan

“…Three advanced algorithms, Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) and Random Forest (RF), were used to construct the regression models of LAI and vegetation index in four key periods using Sentinel-2 satellite remote sensing data. …”
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Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Frimpong Twum, Gaddafi Abdul-Salaam

“…Logistic regression appears to have the highest negative-predicted value score of 75%, with the smallest error margin of 25% and the highest accuracy score of 0.90, and the random forest had the lowest negative predicted value score of 22.22%, registering the highest error margin of 77.78%. …”
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Machine learning models for performance estimation of solar still in a humid sub-tropical region by Farooque Azam, Naiem Akhtar, Shahid Husain

“…The results showed that random forest was the most reliable model, achieving the highest correlation coefficient (0.9812) and the lowest mean absolute percentage error (8.8221 MJ/m2 day). …”
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Inside and outside bark volume models for jack pine (Pinus banksiana) and black spruce (Picea mariana) plantations in Ontario, Canada by Mahadev Sharma

“…Accurate estimates of tree stemwood volume are a fundamental requirement for sustainable forest management. As jack pine (Pinus banksiana Lamb.) and black spruce (Picea mariana Mill. …”
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Improving network security using keyboard dynamics: A comparative study by Ugwunna, C.O., Chukwuogo, O.E., Alabi, O.A., Kareem, M.K., Belonwu, T.S., Oloyede, S.O.

“…Based on the comparing results, Random Forest outperforms the other models, suggesting that Random Forest can be used as the system model for Keystroke Dynamic authentication.…”
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Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand by Oraya Sahat, Supot Kamsa-ard, Apiradee Lim, Siriporn Kamsa-ard, Matias Garcia-Constantino, Idongesit Ekerete

“…Model performance was evaluated using Root Mean Square Error (RMSE) and R2 with 70:30 train-test validation. …”
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Correlation Analysis and Prediction of the Physical and Mechanical Properties of Coastal Soft Soil in the Jiangdong New District, Haikou, China by Yongchang Yang, Xinying Song, Shuai Zhang, Jun Hu, Ming Ruan, Dongling Zeng, Han Luo, Jiangsi Wang, Zhixin Wang

“…By conducting a correlation analysis of various physical properties of soil and utilizing the random forest algorithm, we developed a predictive model for the compressibility and shear strength of coastal soft soil. …”
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Machine Learning‐Based Failure Prediction in Concrete Slabs and Cubes Under Impact Loading by Mohammad Hematibahar, Ahmed Deifalla, Adham E. Ragab, Gebre Tesfaldet

“…The research uses gradient boosting, random forest, lasso, linear regression, and support vector regression to create predictive models for the behavior of these two concrete models. …”
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TECs (v1): A Terrestrial Ecosystem Carbon Cycle Simulator Integrated With Spectral Reflection and Emission by Haoran Liu, Min Chen

“…We calibrated and tested TECs simulations at the Harvard Forest (HARV) National Ecological Observation Network site using a range of ecological and satellite data. …”
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LIMITING THE VERTICAL VARIANCE OF ANNUAL RING AREA OF POPULUS NIGRA PLANTATION IN NINEVAH by Muzahem Younis

“…More over it could be explaining the translocation along the bole estimated of annual ring area at any location . it is very important for the forest management to table discern manger related with siliviculture activities, which apply in the forest, for this reason, we chose (12) tree of populous nigra grown normally in the forest plantation of Nineveh. …”
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Analyzing the Accuracy of Satellite-Derived DEMs Using High-Resolution Terrestrial LiDAR by Aya Hamed Mohamed, Mohamed Islam Keskes, Mihai Daniel Nita

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A method for calculating the variance and confidence intervals for tree biomass estimates obtained from allometric equations by Alecia Nickless, Robert Scholes, Sally Archibald

“…Plant allometric equations allow managers and scientists to quantify the biomass contained in a tree without cutting it down, and therefore can play a pivotal role in measuring carbon sequestration in forests and savannahs. These equations have been available since the beginning of the 20th century, but their usefulness depends on the ability to estimate the error associated with the equations - something which has received scant attention in the past. …”
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Integrating machine learning-based classification and regression models for solvent regeneration prediction in post-combustion carbon capture: An absorption-based case by Farzin Hosseinifard, Mostafa Setak, Majid Amidpour

“…The Random Forest model, optimized via Grid Search Cross-Validation, delivered the most accurate results, achieving an R² of 0.942, a Mean Absolute Error (MAE) of 0.028, and a Mean Squared Error (MSE) of 0.004. …”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…Different machine learning algorithms, including random forest, support vector regression, and extreme gradient boosting, were evaluated using Sentinel-2 spectral bands as predictors. …”
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Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV by Mercadal-Orfila G, Serrano López de las Hazas J, Riera-Jaume M, Herrera-Perez S

“…Additionally, the model demonstrated strong reliability in maintaining standardized prediction errors below 0.2 for most instruments, with probabilities of achieving this threshold being 96.43% for WHOQoL HIV Bref and 88.44% for ESTAR, while lower probabilities were observed for TSQM (44%) and WRFQ (51%).Conclusion: The results from our machine learning algorithms are promising for predicting PROMs and PREMs in AIDS settings. …”
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Assessing SWOT interferometric SAR altimetry for inland water monitoring: insights from Lake Léman by Henri Bazzi, Nicolas Baghdadi, Yen-Nhi Ngo, Cassandra Normandin, Frédéric Frappart, Cecile Cazals

“…To explore the sources of errors in SWOT WSE, a random forest analysis showed that atmospheric perturbations had the most significant impact on the SWOT WSE estimation accuracy. …”
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