A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index by Jawaria Nasir, Hasnain Iftikhar, Muhammad Aamir, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman

“…This study employs various statistical metrics to evaluate the predictive ability across both short-term noise and long-term trends, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Statistic (DS). …”
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Interpretable machine learning approach for TBM tunnel crown convergence prediction with Bayesian optimization by Wanrui Hu, Kai Wu, Kai Wu, Heng Liu, Weibang Luo, Xingxing Li, Peng Guan

“…The results indicate that the LightGBM model achieved the best prediction performance on the test set, with root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient values of 0.9122 mm, 0.6027 mm, 0.0644, and 0.9636, respectively; the average SHAP values for the six input features of the LightGBM model were ranked as follows: Time (0.1366) > Rock grade (0.0871) > Depth ratio (0.0528) > Still arch (0.0200) > Saturated compressive strength (0.0093) > Rock quality designation (0.0047). …”
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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River by Shuguang Li, Sultan Noman Qasem, Shahab S. Band, Rasoul Ameri, Hao-Ting Pai, Saeid Mehdizadeh

“…Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. …”
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A Robust Regression-Based Modeling to Predict Antiplasmodial Activity of Thiazolyl–Pyrimidine Hybrid Derivatives against <i>Plasmodium falciparum</i> by Kevin S. Umoette, Charles O. Nnadi, Wilfred O. Obonga

“…The models were evaluated using R², mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), <i>p</i>-values, <i>F</i>-statistic, and variance inflation factor (VIF). …”
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Marginal land identification and grain production capacity prediction of the coverage area of western route of China’s South-to-North Water Diversion Project by Heng Zhou, Jun Zhou, Jun Zhou, Kunming Lu, Minghui Niu, Chenyi Wang, Gaofeng Zhang, Jiawei Kou

“…For maize, the model yielded a root mean square error (RMSE) of 48.94, a mean absolute error (MAE) of 34.01, and a mean absolute percentage error (MAPE) of 7.65%. …”
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Regression models for predicting the effect of trash rack on flow properties at power intakes by Shuguang Li, Sultan Noman Qasem, Hojat Karami, Ely Salwana, Alireza Rezaei, Danyal Shahmirzadi, Shahab S. Band

“…Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared ([Formula: see text]) (0.9899) and Willmott’s index (WI) values (0.9508) in the testing stage metrics for [Formula: see text] estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), [Formula: see text] (0.9971), WI (0.9727) for [Formula: see text] estimation. …”
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Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy by Saswati Rakshit, Anal Ranjan Sengupta

“…The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. …”
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Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…Comparative tests with a random forest regression, the K-nearest neighbor, a back propagation (BP) neural network, and a long short-term memory (LSTM) neural network have demonstrated that the PSO-SVM model outperforms these methods in terms of mean absolute error, root mean square error, and correlation coefficient, underscoring its effectiveness. …”
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A novel deep learning approach for investigating liquid fuel injection in combustion system by Syed Azeem Inam, Abdullah Ayub Khan, Noor Ahmed, Tehseen Mazhar, Tariq Shahzad, Sunawar Khan, Mamoon M. Saeed, Habib Hamam

“…The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. …”
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Impact of atmospheric corrections on satellite imagery for corn yield prediction using machine learning by Octávio Pereira da Costa, Franklin Daniel Inácio, Jéssica Elaine da Silva, Thiago Orlando Costa Barboza, Wender Henrique Batista da Silva, Lorena Nunes Lacerda, Adão Felipe dos Santos

“…RF remained the best-performing model, with R² values exceeding 0.80 and errors below 0.20 t ha−1.…”
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Construction and Evaluation of a Cross-Regional and Cross-Year Monitoring Model for Millet Canopy Phenotype Based on UAV Multispectral Remote Sensing by Peng Zhao, Yuqiao Yan, Shujie Jia, Jie Zhao, Wuping Zhang

“…Various modeling approaches, including Random Forest, Gradient Boosting, and regularized regressions (e.g., Ridge and Lasso), were evaluated for cross-regional and cross-year extrapolation. …”
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Machine learning-based prediction of LDL cholesterol: performance evaluation and validation by Jing-Bi Meng, Zai-Jian An, Chun-Shan Jiang

“…Predictive performance was evaluated using R-squared (R2), mean squared error (MSE), and Pearson correlation coefficient (PCC) against measured LDL-C values. …”
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Machine learning-based predictive analysis of energy efficiency factors necessary for the HIFU treatment of adenomyosis by Ziyan Liu, Ziyi Liu, Yuan Wang, Xiyao Wan, Xiaohua Huang

“…Predictive features were selected using minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) methods, and two joint—based on decision tree and random forest algorithms—models were developed for EEF prediction.ResultsThe decision tree model achieved a mean absolute error (MAE) of 8.095 on the test set, while the random forest model exhibited an MAE of 8.231. …”
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Integration of Aerial Mapping using UAV and Low-cost Backpack LiDAR for Biomass and Carbon Stock Estimation Calculation by Q. P. A. N. Ila, M. N. Cahyadi, H. H. Handayani, A. B. Raharjo, R. Mardiyanto, I. W. Farid, D. Saptarini, E. E. Saratoga

“…The total forest area in Indonesia reaches 62.97% of Indonesia's land area or approximately 125.76 hectares, requiring effective and accurate inventory methods. …”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…The models’ performance was assessed using R² and root mean square error (RMSE). Feature importance was analyzed using permutation importance and SHAP methods. …”
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A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition by David Hollinger, Ryan S. Pollard, Mark C. Schall, Howard Chen, Michael Zabala

“…Compared with a hierarchical-based approach, the action-generic model had lower prediction error for backward walking, kneeling down, and kneeling up. …”
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Integration of Drone and Satellite Imagery Improves Agricultural Management Agility by Michael Gbenga Ogungbuyi, Caroline Mohammed, Andrew M. Fischer, Darren Turner, Jason Whitehead, Matthew Tom Harrison

“…The standard error of the mean (SEM) for the field biomass, derived from UAS-measured sward height changes, was 1240 kg DM/ha. …”
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Advances in Surveying Topographically Complex Ecosystems with UAVs: Manta Ray Foraging Algorithms by Shijie Yang, Jiateng Yuan, Zhibo Chen, Hanchao Zhang, Xiaohui Cui

“…Comparative experimental results on real terrain data and MATLAB r2018b simulation show that the error between the corrected energy calculation equation and the actual value is controlled within 5%, and the accuracy is improved by 10% over the original equation. …”
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Integration of Genetic Algorithm with Machine Learning for Properties Prediction by Rathachai Chawuthai, Siripan Murathathunyaluk, Nalin Amornratthamrong, Run Arunchaipong, Amata Anantpinijwatna

“…Algorithms such as Linear Regression, Support Vector Machine, Random Forest, and Gaussian Process are selected through trial-and-error to identify the most suitable approach. …”
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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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