Impact of lake expansion on the underwater topography: a case study of Lexiewudan and Yanhu Lakes on the Tibetan Plateau by Fangfei Zhu, Jianting Ju, Baojin Qiao, Liping Zhu

“…The average water depths of two lakes were 5.92 and 9.82 m, and the root mean square error of inversion values were 0.85 and 0.93 m, respectively. …”
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Numerical simulation and experimental validation of the oleogel formation from grape seed oil and beeswax by Zohreh Baratian Ghorghi, Ali Faezian, Samira Yeganehzad, Mohammad Ali Hesarinejad, Alireza Sheikhi Darani, Marcello Fidaleo, Hashem Ahmadi Tighchi

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Machine‐learning based spatiotemporal prediction of soil moisture in a grassland hillslope by Timo Houben, Pia Ebeling, Swamini Khurana, Julia Sabine Schmid, Johannes Boog

“…Performance metrics varied between the ML methods and the training‐test data split (R2 = 0.48–0.69, root‐mean‐square error [RMSE] = 0.06–0.10). Random forests and gradient‐boosted regression trees turned out to be promising and easy to parametrize as first choices to explore the potential of ML techniques. …”
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Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations by Yuhang Zhang, Ming Ou, Liang Chen, Yi Hao, Qinglin Zhu, Xiang Dong, Weimin Zhen

“…For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. …”
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Fisher Discriminant Analysis for Extracting Interpretable Phenological Information From Multivariate Time Series Data by Conor T. Doherty, Meagan S. Mauter

“…We find that using multivariate data inputs can reduce prediction root mean squared error (RMSE, in days) by 20% relative to models using only univariate inputs. …”
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Application research and effectiveness evaluation mechanism of hybrid intelligent algorithm integrating cognitive computing and deep learning for dynamically adjusting employee per... by Zhenlin Luo, Kebin Lu

“…In terms of fairness, the hybrid intelligent algorithm outperforms the random forest algorithm (Gini coefficient of 0.22), with a lower Gini coefficient of 0.18, effectively reducing assessment bias and ensuring a fairer performance evaluation. …”
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Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods by Ahmet Burak Tatar

“…Within this framework, prediction models including Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gaussian Process Regression (GPR), and Multi-Layer Perceptron (MLP) were developed, and their performances were assessed using metrics such as accuracy (R²), error rates (RMSE, MSE, MAE), and computational time. …”
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Improving the Skill of Subseasonal to Seasonal (S2S) Wind Speed Forecasts Over India Using Statistical and Machine Learning Methods by Aheli Das, Dondeti Pranay Reddy, Somnath Baidya Roy

“…The quality and skill of raw and calibrated forecasts are evaluated using root mean squared error (RMSE), ratio of standard deviation, and continuous ranked probability skill score (CRPSS). …”
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Reconstruction of Sea Surface Chlorophyll-a Concentration in the Bohai and Yellow Seas Using LSTM Neural Network by Qing Xu, Guiying Yang, Xiaobin Yin, Tong Sun

“…Compared with Gated Recurrent Unit, Random Forest, XGBoost, and Extra Trees models, the LSTM model exhibits the highest accuracy. …”
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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. …”
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Interpretable machine learning models for predicting Ebus battery consumption rates in cold climates with and without diesel auxiliary heating by Kareem Othman, Diego Da Silva, Amer Shalaby, Baher Abdulhai

“…The results indicate that the Random Forest method emerges as the superior choice for predicting the energy consumption rate, with a resulting mean absolute error of 0.09–0.1 ​kWh/km observed across the different models. …”
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Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media by Fatih Tarlak

“…., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). …”
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Rate of penetration prediction in drilling operations: a comparative study of AI models and meta-heuristic approaches by Fatemeh Mohammadinia, Ali Ranjbar, Fatemeh Ghazi, Seyyed Taha Hosseini

“…Among the tested models, the LSSVM-CSA framework achieved the best results, with a remarkable R-squared (R2) value of 92.55, a Root Mean Square Error (RMSE) of 2.98. These results underscore the superior accuracy, robustness, and adaptability of the proposed methodology. …”
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Structural time series modelling for weekly forecasting of enterovirus outpatient, inpatient, and emergency department visits. by Cathy W S Chen, Leon L Hsieh, Betty X Y Chu

“…Using an expanding window approach, the analysis applies Bayesian structural time series (BSTS) models, exponential smoothing, and random forest to forecast one-week-ahead cases over the 27 weeks in 2024. …”
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Evaluating the precision and reliability of real-time continuous glucose monitoring systems in ambulatory settings: a systematic review by Valentina Dávila-Ruales, Laura F. Gilón, Ana M. Gómez, Oscar M. Muñoz, María N. Serrano, Diana C. Henao

“…Heterogeneity was assessed by visual examination of forest plot and summary receiver operating characteristic curves. …”
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Monitoring Crop Condition at Field Scales and at a Daily Time Step Using Synthetic Aperture Radar (SAR): Surveiller l’état des cultures à l’échelle du champ et à une étape de temps... by Heather McNairn, Xianfeng Jiao

“…Data from fully polarimetric RADARSAT-2 and dual-polarization Sentinel-1B imagery were calibrated to Sentinel-2 NDVI. Random Forest Regression (RFR) and Least Squares Boosting (LSBoost) were tested to calibrate SAR to NDVI (SARcal-NDVI) for six crops (corn, canola, soybeans, wheat, oats and barley). …”
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Automated Analysis of Vertebral Body Surface Roughness for Adult Age Estimation: Ellipse Fitting and Machine-Learning Approach by Erhan Kartal, Yasin Etli

“…Performance was quantified with the standard error of the estimate (SEE). <b>Results:</b> DS values correlated moderately to strongly with age (peak r = 0.60 at L3–L5). …”
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Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm. by Muhammad Sajid, Kaleem Razzaq Malik, Ali Haider Khan, Sajid Iqbal, Abdullah A Alaulamie, Qazi Mudassar Ilyas

“…This research proposes an optimized approach by harnessing machine learning classifiers, including Random Forest, Support Vector Machine, and XGBoost, to develop a robust framework for accurate diabetes prediction. …”
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An Ensemble Learning Approach for Drought Analysis and Forecasting in Central Bangladesh by Md. Alomgir Hossain, Momotaz Begum, Md. Nasim Akhtar, Md. Alamin Talukder, Nomanur Rahman, Mahfuzur Rahman

“…Its error metrics included MAE (0.055–0.068), MSE (0.0032–0.0052), RMSE (0.056–0.072), and R2 (0.914–0.965) across an 80% training and 20% testing split. …”
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Increasing Neural-Based Pedestrian Detectors’ Robustness to Adversarial Patch Attacks Using Anomaly Localization by Olga Ilina, Maxim Tereshonok, Vadim Ziyadinov

“…The proposed method consists of a Deep Convolutional Neural Network to reconstruct a benign image from the adversarial one; a Calculating Maximum Error block to highlight the mismatches between input and reconstructed images; a Localizing Anomalous Fragments block to extract the anomalous regions using the Isolation Forest algorithm from histograms of images’ fragments; and a Clustering and Processing block to group and evaluate the extracted anomalous regions. …”
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