Predicting Nonlinear Behavior of Cellular Cross-Laminated Timber Under Bending and Rolling Shear by Suman Pradhan, Mostafa Mohammadabadi

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Improving the quantification of peak concentrations for air quality sensors via data weighting by C. Frischmon, J. Silberstein, A. Guth, E. Mattson, J. Porter, M. Hannigan

“…When compared to unweighted colocation data, we demonstrate significant reductions in both error (root mean square error, RMSE) and bias (mean bias error, MBE) for pollutant peaks across all three datasets when data weighting is employed. …”
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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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Reliability analysis in curriculum development for social science education driven by machine learning by Rui Mao

“…Performance evaluation was conducted on the linear regression, random forest and artificial neural networks (ANN) through statistical metrics such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). …”
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Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon by Moumita Saha, Arun Chakraborty, Pabitra Mitra

“…Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. …”
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Advanced Machine Learning Approaches for Predicting Machining Performance in Orthogonal Cutting Process by Sabrina Al Bukhari, Salman Pervaiz

“…It also outperforms the Random Forest Regression model, achieving a 19.8% decrease in the mean squared error and a 7.1% decrease in the mean absolute error.…”
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Application and optimization of BP prediction model driven by internet of things in tourism education by Qi Lv

“…Compared to support vector machine (SVM) and random forest (RF) models, the optimized BP model exhibits marked improvements in accuracy, precision, mean squared error, and prediction time. …”
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A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python by Li Taobei

“…With the highest R2 value and the lowest error metrics, Random Forest Regression fared better than the other models, suggesting higher predictive accuracy, according to the data. …”
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Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset by Menghay Phoeuk, Minho Kwon

“…Results demonstrate that the proposed models are highly accurate and generalizable, with high coefficients of determination and low error predictions. The CatBoost model performed the best, exhibiting an R2 of 0.938 and low mean absolute error and root mean squared error values of 2.639 and 3.885, respectively, in the blind evaluation process. …”
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Field scale wheat yield prediction using ensemble machine learning techniques by Sandeep Gawdiya, Dinesh Kumar, Bulbul Ahmed, Ramandeep Kumar Sharma, Pankaj Das, Manoj Choudhary, Mohamed A. Mattar

“…The ensemble model, which combines random forest (RF) and artificial neural networks (ANN), demonstrated better performance by achieving a mean absolute percentage error of 4.65 %, and R2 value of 98.48 % and 98.18 % accuracy on test data.Our results demonstrate that ensemble models combining RF with support vector regression (SVR) outperformed individual models such as RF, SVR, ANN, multivariate adaptive regression splines (MARS). …”
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LiDAR point cloud denoising for individual tree extraction based on the Noise4Denoise by Xiangfei Lu, Zongyu Ye, Liyong Fu, Huaiyi Wang, Kaiyu Wang, Yaquan Dou, Dongbo Xie, Xiaodi Zhao

“…The processing of LiDAR point cloud data is of critical importance in the context of forest resource surveys, as well as representing a pivotal element in the realm of forest physiological and ecological studies.Nonetheless, conventional denoising algorithms frequently exhibit deficiencies with regard to adaptability and denoising efficacy, particularly when employed in relation to disparate datasets.To address these issues, this study introduces DEN4, an unsupervised, deep learning-based point cloud denoising algorithm designed to improve the accuracy of single tree segmentation in LiDAR point clouds.DEN4 introduces a multilevel noise separation module that effectively distinguishes between signal and noise, thereby improving the signal-to-noise ratio (SNR) and reducing the error.The experimental results demonstrate that DEN4 significantly outperforms traditional denoising methods in several key metrics, including mean square error (MSE), SNR, Hausdorff distance, and structural similarity index (SSIM).In the 60 sample dataset, DEN4 achieved the best mean and standard deviation on all metrics: Specifically, the MSE mean was found to be 0.0094, with a standard deviation of 0.0008, the SNR mean was 149.1570, with a standard deviation of 0.5628, the Hausdorff mean was 0.8503, with a standard deviation of 0.0947, and the SSIM mean was 0.8399, with a standard deviation of 0.0054. …”
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A Method for the 3D Reconstruction of Landscape Trees in the Leafless Stage by Jiaqi Li, Qingqing Huang, Xin Wang, Benye Xi, Jie Duan, Hang Yin, Lingya Li

“…Three-dimensional models of trees can help simulate forest resource management, field surveys, and urban landscape design. …”
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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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An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USA by Tongrui Chen, Lingmei Jiang, Jianwei Yang, Dan Kong, Yong Pang

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Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study by Sultan Almuaythir, Muhammad Syamsul Imran Zaini, Rida Hameed Lodhi

“…The analysis of absolute error highlighted the accuracy and consistency of XG-Boost and Random Forest in predicting the maximum dry density.…”
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Quality assured spatial dataset of wildfire containment firelines and engagement outcomes 2017 to 2024 by Alexander Arkowitz, Scott M. Ritter, Matthew P. Thompson, Jesse D. Young, Brad Pietruszka, David E. Calkin

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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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Machine Learning to Retrieve Gap-Free Land Surface Temperature from Infrared Atmospheric Sounding Interferometer Observations by Fabio Della Rocca, Pamela Pasquariello, Guido Masiello, Carmine Serio, Italia De Feis

“…Overall, the methods significantly enhanced spatial sampling, keeping errors in terms of Root Mean Square Error (RMSE) and bias (Mean Absolute Error, MAE) very low. …”
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An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications by Shuchismita Biswas, Tianzhixi Yin, Syed Ahsan Raza Naqvi, Jim Follum, Antos Cheeramban Varghese, Tawsif Ahmad, Pavel Etingov

“…In the first use case, a random forest (RF) classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. …”
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