Aboveground Carbon Estimation in a Mangrove Ecosystem Using UAV-Based Remote Sensing and Machine Learning by Menglei Duan, Arturo Sanchez-Azofeifa, Muhammad Abdulmajeed, David Turner, Kathleen Buckingham, Agatha Odari, Josphat Mtwana, Solomon Kipkoech, Neda Kasraee

“…We developed an Ensemble regression model to estimate AGC, achieving a Mean Absolute Error (MAE) of 1.79 kg when validated against ground truth data. …”
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Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants by Xiangjun Qi, Shujing Wang, Caishan Fang, Jie Jia, Lizhu Lin, Tianhui Yuan

“…Models constructed within the mlr3 framework included recursive partitioning and regression trees, random forest, kernel k-nearest neighbors, naïve bayes, and light gradient boosting machine (LightGBM). …”
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Optimizing the Radiative Transfer Model Using Deep Neural Networks for NISAR Soil Moisture Retrieval by Xiaodong Huang, Lorenzo Giuliano Papale, Marco Lavalle, Fabio Del Frate, Heresh Fattahi, Steven K. Chan, Rowena B. Lohman, Xiaolan Xu, Yunjin Kim

“…After imposing a physical constraint on the RTNet outputs, they are then applied to the ensemble random forest machine learning regressor to retrieve the volumetric soil moisture. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…Traditional coffee bean grading methods are labor-intensive and prone to error, necessitating automated and accurate classification of bean diseases. …”
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Framingham Risk Score Prediction at 12 Months in the STANDFIRM Randomized Control Trial by Thanh G. Phan, Velandai K. Srikanth, Dominique A. Cadilhac, Mark Nelson, Joosup Kim, Muideen T. Olaiya, Sharyn M. Fitzgerald, Christopher Bladin, Richard Gerraty, Henry Ma, Amanda G. Thrift

“…We determine the optimal machine learning and associated tuning parameters from the following: random forest, extreme gradient boosting, category boosting, support vector regression, multilayer perceptron neural network, and K‐nearest neighbor. …”
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A Comprehensive Ensemble Model for Marine Atmospheric Boundary-Layer Prediction in Meteorologically Sparse and Complex Regions: A Case Study in the South China Sea by Yehui Chen, Tao Luo, Gang Sun, Wenyue Zhu, Qing Liu, Ying Liu, Xiaomei Jin, Ningquan Weng

“…The SOEM outperformed random forest, extreme gradient boosting, and histogram-based gradient boosting models, achieving a robustness coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.95 and the lowest mean absolute error of 32 m under the clear/slightly cloudy condition. …”
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Performance analysis of dual-fuel engines using acetylene and microalgae biodiesel: The role of fuel injection timing by M. Sonachalam, R. Jayaprakash, V. Manieniyan, P.S. Raghavendra Rao, G. Vinodhini, Manish Sharma, Teku Kalyani, Mahammadsalman Warimani, Hasan Sh Majdi, T.M. Yunus Khan, Abdul Saddique Shaik, Keerthi Shetty

“…To predict engine performance and emission characteristics, advanced machine learning models were employed and evaluated using four statistical criteria, including R-squared, mean absolute error (MAE), and mean squared error (MSE). Experimental results indicated that the optimal configuration involved a dual-fuel mode combining B20 MEOA with acetylene gas and an advanced FIT of 25° before top dead center (bTDC). …”
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Stress can be detected during emotion-evoking smartphone use: a pilot study using machine learning by Lydia Helene Rupp, Akash Kumar, Misha Sadeghi, Lena Schindler-Gmelch, Marie Keinert, Bjoern M. Eskofier, Bjoern M. Eskofier, Matthias Berking

“…XGBoost showed to be more reliable for prediction, with lower error for both training and test data.DiscussionThe findings provide further evidence that non-invasive video recordings can complement standard objective and subjective markers of stress.…”
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Accuracy fluctuations of ICESat-2 height measurements in time series by Xu Wang, Xinlian Liang, Weishu Gong, Pasi Häkli, Yunsheng Wang

“…To bridge the knowledge gap, this study analyzes 59 months of ATL08 version 006 data in Finland to assess terrain and surface height accuracy, with a focus on temporal fluctuations across six major land cover types. A random forest (RF) model is employed to quantify the relative importance of error factors affecting height accuracy. …”
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Time Series Modelling of the Caspian Kutum (Rutilus frisii) Catch Using SARIMA Model by Fateh Moezzi, Hadi Poorbagher, Soheil Eagderi, Jahangir Feghhi

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Predicting specific wear rate of laser powder bed fusion AlSi10Mg parts at elevated temperatures using machine learning regression algorithm: Unveiling of microstructural morpholog... by Vijaykumar S. Jatti, R. Murali Krishnan, A. Saiyathibrahim, V. Preethi, Suganya Priyadharshini G, Abhinav Kumar, Shubham Sharma, Saiful Islam, Dražan Kozak, Jasmina Lozanovic

“…Within this set of models, GPR model has a lower Mean Absolute Error of 0.3177, Root Mean Square Error of 0.6704 and higher R2 value of 0.9686, resulting a prediction accuracy of 96.86%. …”
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Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms by Pablo Augusto de Souza Fonseca, Aroa Suarez-Vega, Cristina Esteban-Blanco, Héctor Marina, Rocío Pelayo, Beatriz Gutiérrez-Gil, Juan-José Arranz

“…The average performance of each model was based on the root mean squared error (RMSE) and squared Spearman correlation (rho2). …”
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Stratified allocation method for water injection based on machine learning: A case study of the Bohai A oil and gas field by Changlong Liu, Pingli Liu, Qiang Wang, Lu Zhang, Zechao Huang, Yuande Xu, Shaojiu Jiang, Le Zhang, Changxiao Cao

“…Second, the training and prediction effects of three machine learning prediction models—support vector machine, BP neural network, and random forest—were compared, and the BP neural network was selected as the machine learning mathematical model for injection allocation optimization. …”
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An enhanced RPV model to better capture hotspot signatures in vegetation canopy reflectance observed by the geostationary meteorological satellite Himawari-8 by Wei Yang, Zhi Qiao, Wei Li, Xuanlong Ma, Kazuhito Ichii

“…Moreover, the estimated CI based on the ERPV model (0.66) was closer to the field measurement (0.65) for a mixed forest site than the RPV-based CI estimate (0.72) and the MODIS CI product (0.705). …”
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Volumetric evolution of supraglacial lakes in southwestern Greenland using ICESat-2 and Sentinel-2 by T. Feng, T. Feng, X. Ma, X. Ma, X. Liu, X. Liu, X. Liu

“…First, the area of SGLs is extracted using a random forest (RF) model based on spectral features from Sentinel-2 imagery, achieving an intersection over union (IoU) of 90.20 % compared to manually delineated lake extents. …”
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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…Three machine learning algorithms were employed to estimate plant height from satellite images: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). …”
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Prediction of the volume of shallow landslides due to rainfall using data-driven models by J. Tuganishuri, C.-Y. Yune, G. Kim, S. W. Lee, M. D. Adhikari, S.-G. Yum

“…., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. …”
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Remote sensing reveals the role of forage quality and quantity for summer habitat use in red deer by Thomas Rempfler, Christian Rossi, Jan Schweizer, Wibke Peters, Claudio Signer, Flurin Filli, Hannes Jenny, Klaus Hackländer, Sven Buchmann, Pia Anderwald

“…In particular, our approach employed random forest regression algorithms, integrating various remote sensing variables such as reflectance values, vegetation indices and optical traits derived from a radiative transfer model. …”
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Post-hoc Evaluation of Sample Size in a Regional Digital Soil Mapping Project by Daniel D. Saurette, Richard J. Heck, Adam W. Gillespie, Aaron A. Berg, Asim Biswas

“…We then trained random forest (RF) models for four soil properties: pH, CEC, clay content, and SOC at five different depths. …”
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Development of Machine Learning Prediction Models to Predict ICU Admission and the Length of Stay in ICU for COVID‑19 Patients Using a Clinical Dataset Including Chest Computed Tom... by Seyed Salman Zakariaee, Negar Naderi, Hadi Kazemi-Arpanahi

“…Results showed that with a correlation coefficient of 0.42, a mean absolute error of 2.01, and a root mean squared error of 4.11, the RF algorithm with a correlation coefficient of 0.42, mean absolute error of 2.01, and root mean squared error of 4.11demonstrated the best performance in predicting the ICU LOS of COVID-19 patients. …”
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