Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study by Daniel Sumsion, Elijah Davis, Marta Fernandes, Ruoqi Wei, Rebecca Milde, Jet Malou Veltink, Wan-Yee Kong, Yiwen Xiong, Samvrit Rao, Tara Westover, Lydia Petersen, Niels Turley, Arjun Singh, Stephanie Buss, Shibani Mukerji, Sahar Zafar, Sudeshna Das, Valdery Moura Junior, Manohar Ghanta, Aditya Gupta, Jennifer Kim, Katie Stone, Emmanuel Mignot, Dennis Hwang, Lynn Marie Trotti, Gari D Clifford, Umakanth Katwa, Robert Thomas, M Brandon Westover, Haoqi Sun

“…These models were also externally validated by training the data on one hospital sample and testing on the other, and an overall estimated error was calculated using a completely random sample from both hospitals. …”
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Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns by Zhe Wang, Xiang Que, Meifang Li, Zhuoming Liu, Xun Shi, Xiaogang Ma, Chao Fan, Yan Lin

“…Compared with Geographically Weighted Regression (GWR), the STWR model proved more potent and reliable with higher R2 and lower estimated standard errors (SE). The STWR model is highly flexible in terms of spatiotemporal variations in data. …”
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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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Forecasting loan, deferred rate and customer segmentation in banking industry: A computational intelligence approach by Mahtab Vasheghani, Ebrahim Nazari Farokhi, Behrooz Dolatshah

“…Specifically, the GA-PSO-MLP model achieves a 15 % higher classification accuracy than Logistic Regression, a 12 % improvement over Decision Trees, and an 8 % gain over Random Forests. Additionally, false positive rates are reduced by 20 %, and mean squared error (MSE) is lowered by 18 %. …”
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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

“…In this regard, the results confirmed that the DLNN model attained the highest value of prediction performance with minimal root mean squared error (RMSE = 2.23). The study revealed that the highest prediction performance could be attained by increasing the number of variables in the prediction problem and using 90%-10% data division. …”
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Machine learning-based energy consumption models for rural housing envelope retrofits incorporating uncertainty: A case study in Jiaxian, China by Taoyuan Zhang, Zao Li, Zihuan Zhang, Yulu Chen, Xia Sun

“…Results indicate that a more uniform residual distribution within the 95 % interval balances data volume and prediction accuracy, reducing error variability and improving model performance. …”
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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

“…Using 10-fold cross-validation, we calculate metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and mean squared error. Our results reveal RF achieves superior accuracy (92.5%) in at-home CKD classification over ANNs (82.9%). …”
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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

“…Results show that georeferencing achieved satisfactory accuracy with a mean absolute error of 5.99 m. The classification approach that uniquely incorporated terrain variation analysis achieved 89.36% overall accuracy (OA) in categorizing different land use types. …”
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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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Classification of grassland community types and palatable pastures in semi-arid savannah grasslands of Kenya using multispectral Sentinel-2 imagery by James M. Muthoka, Pedram Rowhani, Edward E. Salakpi, Heiko Balzter, Heiko Balzter, Alexander S. Antonarakis

“…Sentinel-2 imagery was processed using MESMA to classify the fractional cover of four key grass species (Cynodon, Setaria, Themeda, and Kunthii) along with non-grass land cover types (bare ground, forests, shrubs, and water). An iterative endmember selection method optimized the classification, achieving a root mean square error (RMSE) of 23.5% and a 6% improvement in the overall accuracy compared to the unoptimized models. …”
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Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models by J. Gödeke, A. Richter, K. Lange, P. Maaß, H. Hong, H. Lee, J. Park

“…We demonstrate that using these time-contiguous inputs leads to reliable improvements regarding all considered performance measures, such as Pearson correlation or mean square error. For random forests, the average performance gains are between 4.5 % and 7.5 %, depending on the performance measure. …”
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Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index. by Ping Wang, Claus Holst, Malene R Andersen, Arne Astrup, Freek G Bouwman, Sanne van Otterdijk, Will K W H Wodzig, Marleen A van Baak, Thomas M Larsen, Susan A Jebb, Anthony Kafatos, Andreas F H Pfeiffer, J Alfredo Martinez, Teodora Handjieva-Darlenska, Marie Kunesova, Wim H M Saris, Edwin C M Mariman

“…A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.…”
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Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery by Xuemei Han, Huichun Ye, Yue Zhang, Chaojia Nie, Fu Wen

“…However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. …”
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Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids by Cristian Rojas, Doménica Muñoz, Ivanna Cordero, Belén Tenesaca, Davide Ballabio

“…The performance of the models was quantified by means of the non-error rate (<i>NER</i>) statistical parameter.…”
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AI-driven diagnosis and health management of autonomous electric vehicle powertrains: An empirical data-driven approach by Hicham El hadraoui, Adila El maghraoui, Oussama Laayati, Erroumayssae Sabani, Mourad Zegrari, Ahmed Chebak

“…Among the models, the optimized neural network combined with CA-selected features achieved the most consistent diagnostic performance, supported by low root mean square error and balanced evaluation metrics. The novelty of this work lies in the empirical benchmarking of reduced feature sets across diverse classifier families and the end-to-end validation of diagnostic robustness using real vibration signals under controlled EV-relevant fault scenarios. …”
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A Method for Predicting Coal-Mine Methane Outburst Volumes and Detecting Anomalies Based on a Fusion Model of Second-Order Decomposition and ETO-TSMixer by Qiangyu Zheng, Cunmiao Li, Bo Yang, Zhenguo Yan, Zhixin Qin

“…This approach enables multi-component joint modeling, thereby averting error accumulation. The experimental results demonstrate that the enhanced model attains RMSE, MAE, and R² values of 0.0151, 0.0117, and 0.9878 on the test set, respectively, thereby exhibiting a substantial improvement in performance when compared to the reference models. …”
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