Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation by Scott A Helgeson, Zachary S Quicksall, Patrick W Johnson, Kaiser G Lim, Rickey E Carter, Augustine S Lee

“…The classification models showed a robust performance overall, with relatively low root mean square error and mean absolute error values observed across all predicted lung volumes. …”
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A Comparative Analysis of Price Forecasting Methods for Maximizing Battery Storage Profits by Alessandro Fiori Maccioni, Simone Sbaraglia, Rahim Mahmoudvand, Stefano Zedda

“…Using real market data from the Italian day-ahead electricity market over 2020–2024, we compared univariate singular spectrum analysis (SSA), ARIMA, SARIMA, random forests, and a 30-day simple moving average under a unified trading framework. …”
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Application of Sentinel-2A Images for Land Cover Classification Using NDVI in Jember Regency by Rufiani Nadzirah, Mochammad Kevin Rizqon, Indarto Indarto

“…The classification in this study encompassed five classes: water bodies, settlements, dry fields, irrigated paddy fields, and forests. Error analysis was conducted using a confusion matrix with the Overall and Kappa algorithms. …”
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Machine Learning Algorithms in Predicting Prices in Volatile Cryptocurrency Markets by Miguel Jiménez-Carrión, Gustavo A. Flores-Fernandez

“…In comparison, alternative models such as Support Vector Machines (SVM), Extreme Gradient Boosting (XGBoost), and Random Forests exhibited significantly higher error rates; for instance, XGBoost recorded an RMSE of $17,849.66 and a MAPE of 27.74%. …”
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Creation of ICESat-2 Footprint Level Global Geodetic Control Points Using Crossover Analysis by Amy Neuenschwander, Eric Guenther, Lori Magruder, Jonathan Sipps

“…Preliminary results indicate ICESat-2 crossovers are possible even in forested regions and these data can be used to vertically constrain terrain heights of other data products such as DEMs.…”
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Habitat Characteristics and the Species Response of Astragalus curvirostris Boiss. to the Environmental Factors in Lorestan Rangelands by Reza Siahmansour, Nadia Kamali, Hamid Reza Mirdavoodi, Javad Motamedi

“…Unfortunately, in recent years, disturbances such as livestock grazing, changes in land use, and climate change have caused the destruction of A. curvirostris habitats. Many pastures and forests throughout Iran experience vegetation destruction, loss of biodiversity, and soil erosion due to these threats. …”
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Mixed effect gradient boosting for high-dimensional longitudinal data by Oyebayo Ridwan Olaniran, Saidat Fehintola Olaniran, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi

“…In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes $$(p=2000)$$ ( p = 2000 ) . …”
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Suitability of multifunction mobile devices for registration of accounting data of game animals by V. M. Glushkov, Yu. V. Krotov

“…Connection speed, positioning accuracy and stability of the connection with the satellites in any natural landscapes, including forests with high crown closure were determined by the method of comparing the performance of the navigator Garmin GpsMap 62s and GPS-module of smartphone LG. …”
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Urban tree species benchmark dataset for time series classificationEasyData - Data Terra by Clément Bressant, Romain Wenger, David Michéa, Anne Puissant

“…Classification of urban tree species is essential for understanding their ecological functions, managing urban forests (public and private), and informing nature-based solutions for climate resilience. …”
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Hybrid ANFIS systems: Evaluation of bearing capacity of driven piles by Yan Peng, Haiquan Gao

“…Also, other algorithms were developed for comparison purposes, such as single ANFIS, support vector regression (SVR) M5P, multi-adaptive regression spline (MARS), random forests (RF), and random trees (RT). It was concluded that both ANFIS systems optimized with ARO, GWO, and COA accomplish admirably among the categories of trains and tests, with a minimum R^2 of 0.9285 in the learning dataset and 0.9313 in the examining dataset, respectively, indicating a strong similarity between experimental and estimated Qt. …”
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Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method by Bu-Yo Kim, Joo Wan Cha

“…In particular, increased water stress in farmland, forests, and mountainous areas with limited water resources can result in detrimental impacts such as droughts and wildfires. …”
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Compressive Strength Prediction Using Coupled Deep Learning Model with Extreme Gradient Boosting Algorithm: Environmentally Friendly Concrete Incorporating Recycled Aggregate by Mayadah W. Falah, Sadaam Hadee Hussein, Mohammed Ayad Saad, Zainab Hasan Ali, Tan Huy Tran, Rania M. Ghoniem, Ahmed A. Ewees

“…The developed approach is compared to the well-known artificial intelligence (AI) approaches named multivariate adaptive regression spline (MARS), extreme learning machines (ELMs), and random forests (RFs). The dataset was divided into three scenarios 70%-30%, 80%-20%, and 90%-10% for training/testing to explore the impact of data division percentage on the capacity of the developed AI model. …”
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Conservation communautaire et changement de statuts du bonobo dans le Territoire de Bolobo by Victor Narat, Flora Pennec, Sabrina Krief, Jean Christophe Bokika Ngawolo, Richard Dumez

“…There is a diverse range of community-based conservation projects, from a top-down process with projects initiated by national and international institutions to a bottom-up process based on trial and error. In every conservation project, new actors appear, new messages are spread, and each person takes these messages in their own way. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…Among these, XGBoost achieved superior accuracy with an R² of 0.974 and a mean squared error (MSE) of 0.0343, followed by LightGBM (R²=0.964, MSE = 0.0484) and CatBoost (R²=0.984, MSE = 0.0212). …”
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Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques by Raouf Hassan, Alireza Baghban

“…Their superior performance is evidenced by high R2 values of 0.9235 (SVR) and 0.9327 (CatBoost), coupled with low mean squared error values of 0.2207 (SVR) and 0.1942 (CatBoost). …”
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Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements by Brady Metherall, Anna K. Berryman, Georgia S. Brennan

“…We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. …”
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A Deep-Learning Workflow for CORONA-Based Historical Land Use Classifications by Wei Liu, Shuai Li, Di Fan, Yixin Wen, Austin Madson, Jessica Mitchell, Yaqian He, Di Yang

“…The combined system showed particular strength in distinguishing between different vegetation types such as forests and grasslands that appear similar in satellite images but have distinct terrain characteristics. …”
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Spatial and temporal characteristics of water conservation services and rapid response framework for water yield in key ecological zones of the Yiluo River basin by Junqiang Xu, Fan Wang, Chao Ren, Jianmin Bian, Tao Li, Zikai Ping

“…The high value of water conservation goes mainly in the forested mountainous areas of the upper reaches of the Yi River and concentrated in July–October, seasonal differences in the amount of water conservation are mainly influenced by precipitation (correlation of 0.79), and potential evapotranspiration determines its lower limit value. …”
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Investigating the Capabilities of Ensemble Machine Learning Model in Identifying Near-Fault Pulse-Like Ground Motions by Jafar Al Thawabteh, Jamal Al Adwan, Yazan Alzubi, Ahmad Al-Elwan

“…This study applies various ensemble machine learning models, such as random forests, gradient boosting machines, and extreme gradient boosting, for the identification and characterization of pulse-like ground motions. …”
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Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods by Carmina Liana Musat, Claudiu Mereuta, Aurel Nechita, Dana Tutunaru, Andreea Elena Voipan, Daniel Voipan, Elena Mereuta, Tudor Vladimir Gurau, Gabriela Gurău, Luiza Camelia Nechita

“…By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI’s ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. …”
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