Automatic maxillary sinus segmentation and age estimation model for the northwestern Chinese Han population by Yu-Xin Guo, Jun-Long Lan, Wen-Qing Bu, Yu Tang, Di Wu, Hui Yang, Jia-Chen Ren, Yu-Xuan Song, Hong-Ying Yue, Yu-Cheng Guo, Hao-Tian Meng

“…Age estimation models using multiple linear regression and random forest algorithms were built based on these variables. …”
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Endoscopic management of hemorrhagic pancreatic fluid collections: A propensity‐matched analysis by Rishi Pawa, Robert Dorrell, Greg Russell, Madison Nguyen, Clancy Clark, Girish Mishra, Swati Pawa

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A Study on the performance of Four Regression Models in Predicting Weather Temperature Based on Python by Li Taobei

“…With the highest R2 value and the lowest error metrics, Random Forest Regression fared better than the other models, suggesting higher predictive accuracy, according to the data. …”
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A Daily Snow Cover Dataset for Central Eurasia During Autumn From 2004 to 2021 by Junshan Wang, Baofu Li, Yupeng Li, Lishu Lian, Fangshu Dong, Yanbing Zhu, Mengqiu Ma

“…Overestimation and underestimation errors were 9.65% and 0.87%, with 69.75% of stations reporting overestimation errors below 10% and 85.03% reporting underestimation errors below 5%. …”
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Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects by Rashid Naseem, Bilal Khan, Arshad Ahmad, Ahmad Almogren, Saima Jabeen, Bashir Hayat, Muhammad Arif Shah

“…These techniques include Credal Decision Tree (CDT), Cost-Sensitive Decision Forest (CS-Forest), Decision Stump (DS), Forest by Penalizing Attributes (Forest-PA), Hoeffding Tree (HT), Decision Tree (J48), Logistic Model Tree (LMT), Random Forest (RF), Random Tree (RT), and REP-Tree (REP-T). …”
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Bias in Discontinuous Elevational Transects for Tracking Species Range Shifts by Shixuan Li, Jiannan Yao, Yang Lin, Siyu Wu, Zhongjie Yang, Chao Jin, Yuhan Zhang, Zhen Wang, Jinliang Liu, Guochun Shen, Mingjian Yu

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SNUH methylation classifier for CNS tumors by Kwanghoon Lee, Jaemin Jeon, Jin Woo Park, Suwan Yu, Jae-Kyung Won, Kwangsoo Kim, Chul-Kee Park, Sung-Hye Park

“…Results Seoul National University Hospital Methylation Classifier (SNUH-MC) addressed data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm and incorporated OpenMax within a Multi-Layer Perceptron to prevent labeling errors in low-confidence diagnoses. Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. …”
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Assessment of Photogrammetric Performance Test on Large Areas by Using a Rolling Shutter Camera Equipped in a Multi-Rotor UAV by Alba Nely Arévalo-Verjel, José Luis Lerma, Juan Pedro Carbonell-Rivera, Juan F. Prieto, José Fernández

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Triple-E Principle: Leveraging Occam’s Razor for Dance Energy Expenditure Estimation by Kuan Tao, Kun Meng, Bingcan Gao, Junchao Yang, Junqiang Qiu

“…A bidirectional stepwise regression model incorporating heart rate or triaxial motion sequences from accelerometers achieved an average goodness-of-fit of 0.73, identifying optimal accelerometer sites based on Efficiency principle. A random forest regression model minimized errors to 5% (MAPE) and 0.33 (RMSE) with data from all sites. …”
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Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment by Mohammed Achite, Manish Kumar, Nehal Elshaboury, Aman Srivastava, Ahmed Elbeltagi, Ali Salem

“…Model performances were compared using mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe efficiency (NSE) coefficient, and percentage bias (PBIAS). …”
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Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations by Boya Zhang, Daniel Gann, Shimon Wdowinski, Chaohao Lin, Erin Hestir, Lukas Lamb-Wotton, Khandker S. Ishtiaq, Kaleb Smith, Yuepeng Li

“…Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. …”
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Integration of UAV-sensed features using machine learning methods to assess species richness in wet grassland ecosystems by Clara Oliva Gonçalves Bazzo, Bahareh Kamali, Murilo dos Santos Vianna, Dominik Behrend, Hubert Hueging, Inga Schleip, Paul Mosebach, Almut Haub, Axel Behrendt, Thomas Gaiser

“…Models combining VI and GLCM features demonstrated the highest predictive accuracy, particularly in frequently cut grasslands, as indicated by higher R2 values (up to 0.52) and lower root mean square errors (rRMSE as low as 34.9 %). RF models generally outperformed PLS models across different feature sets, with the CH + VI + GLCM combination yielding the best results. …”
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Large variability of nitrate load estimated from sparse measurements by typical methods in Atlantic Canada by Kang Liang, Kang Liang, Yefang Jiang, Keith Fuller, Marcos Cordeiro, Xuesong Zhang, Junyu Qi, Xiaoyuan Geng, Tao Liu, Qian Zhang, Mohammad Amir Azimi, Fan-Rui Meng

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Do empirically-derived personality subtypes relate to cognitive inflexibility in anorexia nervosa and bulimia nervosa? by Lauren M. Schaefer, Glen Forester, Elizabeth N. Dougherty, Angeline R. Bottera, Erika E. Forbes, Jennifer E. Wildes

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Evaluation of Topographic Effect Parameterizations in Weather Research and Forecasting Model over Complex Mountainous Terrain in Wildfire-Prone Regions by Yong Han Jo, Seung Hee Kim, Yun Gon Lee, Chang Ki Kim, Jinkyu Hong, Junhong Lee, Keunchang Jang

“…The model performance was evaluated over the mountainous region in Gangwon-do, South Korea’s most significant forest area. The simulation results of the wildfire case in 2019 show that subgrid-scale orographic parameterization considerably improves model performance regarding wind speed, with a lower root mean square error (RMSE) and bias by 53% and 57%, respectively. …”
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SOC estimation for a lithium-ion pouch cell using machine learning under different load profiles by J. Harinarayanan, P. Balamurugan

“…The random forest approach showed outstanding accuracy while minimizing error metrics (RMSE: 0.0229, MSE: 0.0005, MAE: 0.0139) and effectively handled typical issues such as SOC drift and ageing effects. …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…However, random forest outperforms XGBoost on the testing dataset. …”
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Analysis and prediction of land use/land cover change in the Llanganates-Sangay Connectivity Corridor by 2030 by Luis Jonathan Jaramillo Coronel, Andrea Cecilia Mancheno Herrera, Adriana Catalina Guzmán Guaraca, Juan Gabriel Mollocana Lara

“…MapBiomas LULC maps reveals annual change rates (2018–2022) of -0.37 %/year (-1147.33 ha) for Forest Formation, -1.17 %/year (-30.01 ha) for Non-Forest Natural Formation, 2.21 %/year (906.19 ha) for Agriculture and Livestock Areas, 8.50 %/year (250.84 ha) for Non-Vegetated Areas, and 0.17 %/year (30.31 ha) for Water Bodies. …”
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Deep learning meets tree phenology modelling: PhenoFormer versus process‐based models by Vivien Sainte Fare Garnot, Lynsay Spafford, Jelle Lever, Christian Sigg, Barbara Pietragalla, Yann Vitasse, Arthur Gessler, Jan Dirk Wegner

“…Abstract Predicting phenology, that is the timing of seasonal events of plant life such as leaf emergence and colouration in relation to climate fluctuations, is essential for anticipating future changes in carbon sequestration and tree vitality in temperate forest ecosystems. Existing approaches typically rely on either hypothesis‐driven process models or data‐driven statistical methods. …”
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A rapid approach to profiling diverse fungal communities using the MinION™ nanopore sequencer by Korena K Mafune, Bruce J Godfrey, Daniel J Vogt, Kristiina A Vogt

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