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

“…LightGBM achieved the highest predictive accuracy at 87.9 %, a classification error rate of 12.1 %, and the top area under the receiver operating characteristic curve (0.951) and the precision‐recall curve (0.930). …”
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A reconstruction of the ice thickness of the Antarctic Peninsula Ice Sheet north of 70° S by K. Shahateet, K. Shahateet, K. Shahateet, J. J. Fürst, F. Navarro, T. Seehaus, D. Farinotti, D. Farinotti, M. Braun

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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

“…The estimated HH total backscattering coefficients show a high agreement with the UAVSAR-measured HH backscattering with a root-mean-square error (RMSE) of approximately 3 dB across the entire image in nonforested regions. …”
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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

“…The optimal model for predicting FRS at 12 months was category boosting (R2=0.712; root mean square error, 7.32). The 5 variables with highest Shapley values for category boosting were baseline FRS (Shapley additive explanation [SHAP], 8.42 of total of 12.12), age (SHAP, 1.58), systolic blood pressure (SHAP, 0.23), male sex (SHAP, 1.05), and London Handicap (SHAP, 0.18). …”
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On using dynamical seasonal forecasts to develop management-driven wildland fire outlooks in Alaska by Cecilia Borries-Strigle, Uma S. Bhatt, Peter A. Bieniek, Mitchell Burgard, Eric Stevens, Heidi Strader, Richard L. Thoman, Alison York, Robert H. Ziel

“…Variables from these forecasts are used to calculate Buildup Index (BUI), an operationally used fire weather index from the Canadian Forest Fire Danger Rating System. The BUI outlooks are evaluated based on Alaska wildfire subseason, BUI tercile, and predictive service area subregion with the area under the ROC curve (AUROC), Heidke, and mean squared error (MSE) skill scores. …”
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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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Long-Term Retrospective Predicted Concentration of PM_2.5 in Upper Northern Thailand Using Machine Learning Models by Sawaeng Kawichai, Patumrat Sripan, Amaraporn Rerkasem, Kittipan Rerkasem, Worawut Srisukkham

“…The best ML prediction model was selected considering root mean square error (RMSE), mean prediction error (MPE), relative prediction error (RPE) (the lower, the better), and coefficient of determination (R²) (the bigger, the better). …”
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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

“…It effectively establishes a relatively accurate complex nonlinear relationship between the injected water volume and the production of natural gas and oil, providing valuable guidance for layered allocation in injection wells. The relative error of the calculation results of the optimized neural network prediction model is approximately ±2.3 %. …”
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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

“…The EPRV model yielded satisfactory accuracies in capturing the hotspot signatures with Root-Mean-Square-Error (RMSE) of 0.0034 and 0.0056, and Bias of −0.0019 and −0.0028, for the red and near-infrared (NIR) bands, respectively. …”
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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

“…The accuracy of depth inversion based on the MLP model surpasses traditional empirical formula methods, achieving a mean absolute error of 0.42 m. The trained MLP model is then used to estimate the depth over the entire lake areas. …”
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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

“…Our findings showed that XGB and RF could predict alfalfa crop height with an R² of 0.79 and a mean absolute error (MAE) of around 4 cm Our findings indicated that SVR exhibited the lowest accuracy among the three algorithms tested, with R² of 0.69 and an MAE of 4.63 cm. …”
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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

“…Models were trained and tested on a South Korean landslide dataset, with the EGB predictions yielding the highest coefficient of determination (<span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">=</span> 0.8841) and the lowest mean absolute error (MAE <span class="inline-formula">=</span> 146.6120 m<span class="inline-formula">³</span>), followed by RF predictions (<span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">=</span> 0.8435, MAE <span class="inline-formula">=</span> 330.4876 m<span class="inline-formula">³</span>), on the holdout set. …”
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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

“…Results The combination of vegetation indices and optical traits greatly improved predictive power in both the biomass (R2 = 0.60, Root mean square error (RMSE) = 88.55 g/m2) and relative nitrogen models (R2 = 0.34, RMSE = 0.28%). …”
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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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USING OF ELECTRONIC COMPASS IN NAVIGATION OF MOBILE ROBOT by XUAN LONG TRINH

“…In this paper we suggest using of electronic compass in mobile robot navigation to reduce the errors. This method has higher effect and the process of navigation become simplier.…”
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Development and External Validation of [18F]FDG PET-CT-Derived Radiomic Models for Prediction of Abdominal Aortic Aneurysm Growth Rate by Simran Singh Dhesi, Pratik Adusumilli, Nishant Ravikumar, Mohammed A. Waduud, Russell Frood, Alejandro F. Frangi, Garry McDermott, James H. F. Rudd, Yuan Huang, Jonathan R. Boyle, Maysoon Elkhawad, David E. Newby, Nikhil Joshi, Jing Yi Kwan, Patrick Coughlin, Marc A. Bailey, Andrew F. Scarsbrook

“…RF, XGBoost, and SVM models produced comparable accuracies internally (1.4–1.5 mm/year) but showed higher errors during external validation (1.9–1.97 mm/year). …”
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A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors by Seren Smith, Theodore Trefonides, Anusha Srirenganathan Malarvizhi, Shyra LaGarde, Jiakang Liu, Xiaoguo Jia, Zifu Wang, Jacob Cain, Thomas Huang, Mohammad Pourhomayoun, Grace Llewellyn, Wai Phyo, Sina Hasheminassab, Joe Roberts, Kevin Marlis, Daniel Q. Duffy, Chaowei Yang

“…Long Short-Term Memory (LSTM) models trained in RStudio and TensorFlow excelled, with high R² scores of 0.856 and 0.857 and low Root Mean Squared Errors (RMSEs) of 4.25 µg/m³ and 4.26 µg/m³, respectively. …”
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