Current status and influencing factors of pelvic floor muscle training adherence in rectal cancer patients with prophylactic ostomy by LIU Na, PI Hongying, GAO Na

“…Results‍　‍The overall PFMT adherence score was 14.52±4.18 among the 247 patients. The random forest algorithm identified 7 key predictors when the minimum error was achieved at a λ value of 2.293. …”
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Characteristics and prediction methods of coal spontaneous combustion for deep coal mining in the Ximeng mining area by Li MA, Wenbo GAO, Longlong TUO, Pengyu ZHANG, Zhou ZHENG, Ruizhi GUO

“…Then, the hyperparameters of the random forest (RF) model were optimized using the crested porcupine optimizer (CPO) algorithm. …”
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Development of algorithms and software for classification of nucleotide sequences by V. R. Zakirava, D. A. Syrakvash, S. V. Hileuski, P. V. Nazarov, M. M. Yatskou

“…An error of the coding and non-coding sequences classification using the random forests method on a set of the 23 most informative features is 2,93 %.…”
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Crop choice advisory for the West African Sudan Savanna based on soil type and presowing rainfall forecasts: A machine learning residual model approach by Toshichika Iizumi, Kohtaro Iseki, Kenta Ikazaki, Toru Sakai, Shintaro Kobayashi, Benoit Joseph Batieno

“…Here, we present a modification of a process model simulation performed using a machine learning residual model trained to predict the error in the process model-simulated yields, relative to field experimental data, from growing conditions. …”
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A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism by Bixiong Luo, Peng Zuo, Lijun Zhu, Wei Hua

“…Notably, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) are substantially minimized compared to alternative models. …”
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An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields by Yi-Fan Wang, Max Yue-Feng Wang, Li-Ying Tu

“…Using historical data from the Federal Reserve Economic Data (FRED), this study finds that the RF model offers the most accurate short-term predictions, achieving the lowest mean squared error (MSE) and mean absolute error (MAE), with an R² value of 0.5760. …”
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Predicting New York Heart Association (NYHA) heart failure classification from medical student notes following simulated patient encounters by Ishan R. Perera, Taylor Daniels, Janella Looney, Kimberly Gittings, Frederic A. Rawlins

“…Abstract Random forest models have demonstrated utility in the determination of New York Heart Association (NYHA) Heart Failure Classifications. …”
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Smart prediction of rock crack opening displacement from noisy data recorded by distributed fiber optic sensing by Shuai Zhao, Shao-Qun Lin, Dao-Yuan Tan, Hong-Hu Zhu, Zhen-Yu Yin, Jian-Hua Yin

“…The proposed models are compared each other in terms of goodness of fit and mean squared error. The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.…”
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Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…Among the tested models, RF outperformed others by achieving the highest coefficient of determination (R2 = 0.9804), alongside the lowest Mean Absolute Deviation (MAD) and Root Mean Square Error (RMSE) for both training and testing phases. …”
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Estimating Spatiotemporal Dynamics of Carbon Storage in <i>Roinia pseudoacacia</i> Plantations in the Caijiachuan Watershed Using Sample Plots and Uncrewed Aerial Vehicle-Borne Las... by Yawei Hu, Ruoxiu Sun, Miaomiao He, Jiongchang Zhao, Yang Li, Shengze Huang, Jianjun Zhang

“…Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. …”
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Real-time prediction of the rate of penetration via computational intelligence: a comparative study on complex lithology in Southwest Iran by Mohammad Najafi, Yousef Shiri

“…Similarly, the ANN had root mean square errors (RMSEs) of 0.69, mean absolute percentage errors (MAPEs) of 5.01%, and correlation coefficients of 0.93. …”
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Short-Term Telephone-Traffic Prediction of Power Grid Customer Service Based on Adaboost-CNN by QIN Hao, SU Liwei, WU Guangbin, JIANG Chongying, XU Zhipeng, KANG Feng, TAN Huochao, ZHANG Yongjun

“…The case analysis shows that the proposed forecasting model reduces prediction error by an average of 11.05 percentage points compared to a single forecasting model and by 5.32 percentage points compared to a combined forecasting model, demonstrating better forecasting accuracy.…”
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Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights by V. S. Shaisundaram, P. V. Elumalai, S. Padmanabhan, U. Nalini Ramachandran, Abhishek Kumar Tripathi, Cui Yaping, B. Nagaraj Goud, S. Prabhakar

“…Among these, RF demonstrated the highest predictive accuracy, achieving the best R² values of 0.86 for Brake Thermal Efficiency (BTE) and 0.62 for Carbon Monoxide (CO) prediction, with the lowest Mean Absolute Error (MAE) of 1.30 and 2.88, respectively. These results highlight the potential of ML models in optimizing engine performance for sustainable energy systems across various engine types and fuel sources.…”
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Quantifying synthetic bacterial community composition with flow cytometry: efficacy in mock communities and challenges in co-cultures by Fabian Mermans, Ioanna Chatzigiannidou, Wim Teughels, Nico Boon

“…Flow cytometry was shown to have a lower average root mean squared error and outperformed the PCR-based methods in even mock communities (flow cytometry: 0.11 ± 0.04; qPCR: 0.26 ± 0.09; amplicon sequencing: 0.15 ± 0.01). …”
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Improving phenological event identification in trees using manually measured dendrometer data: conventional approaches vs. the novel two-stage threshold approach by Przemysław A. Jankowski, Przemysław A. Jankowski, Rafael Calama, Jorge Aldea, Matías García, Guillermo Madrigal, Marta Pardos

“…Accurate detection of phenological events, such as growth onset, cessation, and seasonal transitions, is essential for understanding tree growth dynamics, particularly in Mediterranean forests where bimodal growth patterns are common. …”
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Artificial Neural Network and Ensemble Models for Flood Prediction in North-Central Region of Nigeria by Sikiru Abdulganiyu Siyanbola, Aisha Olabisi Sowemimo, Zaid Habibu, Timothy Ebuka Eberechukwu

“…The metrics used in evaluating the performance of the models were accuracy score, mean absolute error (MAE), and root mean squared error (RMSE). …”
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Comparative Study of Machine Learning Techniques for Predicting UCS Values Using Basic Soil Index Parameters in Pavement Construction by Mudhaffer Alqudah, Haitham Saleh, Hakan Yasarer, Ahmed Al-Ostaz, Yacoub Najjar

“…The results indicate that the ANN-based model provided the most accurate predictions for UCS, achieving an R² of 0.83, a root-mean-squared error (RMSE) of 1.11, and a mean absolute relative error (MARE) of 0.42. …”
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Assessment of Future Flood Loss in the Daqing River Basin Based on Flood Loss Rate Function by SHI Rongqing, HUANG Lingmei, LI Jia, SHEN Ao

“…To identify flood-prone areas in the Daqing River Basin and classify flood risk levels, the Spearman's rank correlation coefficient and the random forest method were employed to analyze the correlation and importance between flood loss rates and influencing factors. …”
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Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach by Ghazi Al-Khateeb, Ali Alnaqbi, Waleed Zeiada

“…By employing the random forest algorithm, key predictors like age, climate zone, and total thickness are identified. …”
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Precise Apple Yield Prediction Utilizing Differential Fusion of UAV and Satellite Multispectral Images by Meixuan Li, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…Among the models, the RF model based on fused variables achieved the highest accuracy, with a validation <italic>R</italic>² of 0.84, normalized root-mean-square error of 0.14, and residual predictive deviation (RPD) of 2.01. …”
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