Evapotranspiration Disaggregation Using an Integrated Indicating Factor Based on Slope Units by Linjiang Wang, Bingfang Wu, Weiwei Zhu, Abdelrazek Elnashar, Nana Yan, Zonghan Ma

“…This model offers a novel approach for estimating forest evapotranspiration in mountainous areas and significant potential for water resource management and sustainable land–water allocation.…”
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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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The Role of Landscape Metrics and Spatial Processes in Performance Evaluation of GEOMOD (Case Study: Neka River Basin) by Shrif Joorabian Shooshtari, Kamran Shayesteh, Mehdi Gholamalifard, Mahmood Azari, Juan Ignacio López-Moreno

“…According to the modeling results, a decrease 4225 ha was revealed in this class of land cover. The area under forest showed a decreasing trend from 2001 to 2010, and the model showed a good consistency between the forest areas of reference and simulated maps with a relative error value of zero. …”
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Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes by Wenjie Xuan, Xiaofo Li, Honglei Gao, Luyao Zhang, Jili Hu, Liping Sun, Hongxing Kan

“…This reduces manual effort and minimizes errors in large language model outputs, ensuring high-quality results.These innovations make AI-ZYMES a valuable tool for accelerating nanozyme research and application, including antimicrobial therapy, biosensing, and environmental remediation. …”
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Fatigue strength prediction of Cobalt alloys using material composition and monotonic properties: ML-based approach by Subraya Krishna Bhat, Amritanshu Ranjan, Y S Upadhyaya, Vishwanath Managuli

“…Eight different ML models were developed and tested, including Linear Regression, Lasso Regression, Ridge Regression, Random Forest Method, Support Vector Regression, Gradient Boosting, XGBoost, and Artificial Neural Networks (ANN). …”
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Improving the quality of payment fraud detection by using a combined approach of transaction analysis by Світлана Гавриленко, Олексій Абдуллін

“…Methods based on data classification technology are considered: XGBoost, SVC, Logistic Regression, Logistic Regression, AdaBoostClassifier, K-Nearest Neighbors, Isolation Forest and their software models are built. The dataset used is "creditcard.csv", which contains transactions made by European cardholders over two days and contains 492 fraud cases out of 284,807 transactions. …”
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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 finger on the pulse of cardiovascular health: estimating blood pressure with smartphone photoplethysmography-based pulse waveform analysis by Ivan Shih-Chun Liu, Fangyuan Liu, Qi Zhong, Shiguang Ni

“…Random forest models further improved these values to 7.34, 5.79, and 4.45 mmHg. …”
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Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…Our results demonstrate that PCA effectively detected critical variables for soil moisture estimation, with the ANN model outperforming other machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (XGBoost). …”
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Polyomaviruses and the risk of breast cancer: a systematic review and meta-analysis by Tahoora Mousavi, Fatemeh Shokoohy, Mahmood Moosazadeh

“…A random effects model was used to determine prevalence rates, and a forest plot diagram was used to present results with 95% confidence intervals. …”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…ML-driven solutions, including LSTM networks and random forest models, enable real-time anomaly detection (e.g., 85% accurate algal bloom prediction 7 days in advance) and operational optimization (15% cost reduction in wastewater treatment). …”
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NIRS identification of cashmere and wool fibers based on spare representation and improved AdaBoost algorithm by Zhu Yaolin, Li Zheng, Chen Xin, Chen Jinni, Zhang Hongsong

“…This has increased misclassification errors. This article uses AdaBoost to assign weights to samples of different species. …”
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Optimizing concrete strength: How nanomaterials and AI redefine mix design by Dan Huang, Guangshuai Han, Ziyang Tang

“…Model performances were evaluated using metrics including Root Mean Square Errors (RMSE), Mean Absolute Error (MAE), R-squared (R2), Normalized Mean Bias Error (NMBE), and Mean Absolute Percentage Error (MAPE). …”
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Recognition of field-grown tobacco plant type characteristics based on three-dimensional point cloud and ensemble learning by JIA Aobo, DONG Tianhao, ZHANG Yan, ZHU Binglin, SUN Yanguo, WU Yuanhua, SHI Yi, MA Yuntao, GUO Yan

“…The accuracy of plant type discrimination was 93.7% using Stacking ensemble learning method, which was significantly higher than those using random forest, support vector machine and naive Bayesian. …”
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Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters by Raed H. Allawi, Watheq J. Al-Mudhafar, Mohammed A. Abbas, David A. Wood

“…This study employs random forest (RF), gradient boosting modeling (GBM), extreme gradient boosting (XGBoost), and adaptive boosting (Adaboost) models to generate ROP predictions. …”
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PM2.5 concentration prediction using machine learning algorithms: an approach to virtual monitoring stations by Ahmad Makhdoomi, Maryam Sarkhosh, Somayyeh Ziaei

“…The ML methods include Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Regressor (XGBR), Random Forest (RF) and Gradient Boosting Regressor (GBR). …”
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Ensemble prediction modeling of flotation recovery based on machine learning by Guichun He, Mengfei Liu, Hongyu Zhao, Kaiqi Huang

“…The hit rates, within an error range of ±2% and ±4%, are 82.4% and 94.6%. …”
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Study on the evolution patterns and predictive modeling of ambient air quality in oasis cities of arid regions: a case study of Urumqi by Bian Jing, Ren Xiaofeng

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Automatic Detection of Equatorial Plasma Bubbles in Airglow Images Using Two-Dimensional Principal Component Analysis and Explainable Artificial Intelligence by Moheb Yacoub, Moataz Abdelwahab, Kazuo Shiokawa, Ayman Mahrous

“…These bubbles can cause signal scintillation, leading to signal loss and errors in position calculations. EPBs can be detected in images captured by All-Sky Imager (ASI) systems. …”
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The effects of rhythm control strategies versus rate control strategies for atrial fibrillation and atrial flutter: A systematic review with meta-analysis and Trial Sequential Anal... by Naqash J Sethi, Joshua Feinberg, Emil E Nielsen, Sanam Safi, Christian Gluud, Janus C Jakobsen

“…We used Trial Sequential Analysis (TSA) to control for random errors. Statistical heterogeneity was assessed by visual inspection of forest plots and by calculating inconsistency (I2) for traditional meta-analyses and diversity (D2) for TSA. …”
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