Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…The model performances were evaluated based on mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). …”
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Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring by Rinaldi Anwar Buyung, Alhadi Bustamam, Muhammad Remzy Syah Ramazhan

“…We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. …”
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A multi-level, multi-scale comparison of LiDAR- and LANDSAT-based habitat selection models of Mexican spotted owls in a post-fire landscape by Ho Yi Wan, Michael A. Lommler, Samuel A. Cushman, Jamie S. Sanderlin, Joseph L. Ganey, Andrew J. Sánchez Meador, Paul Beier

“…Optimizing predictors across spatial scales revealed that large trees, high canopy cover, and mixed-conifer forests were consistently critical for habitat selection, regardless of the data source. …”
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Digital mapping of soil erodibility factor in response to land use change using machine learning models by Wudu Abiye, Orhan Dengiz

“…These models were trained using the repeated tenfold cross-validation method and evaluated based on root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). …”
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Drug-Resistant Tuberculosis Hotspots in Oliver Reginald Tambo District Municipality, Eastern Cape, South Africa by Lindiwe Modest Faye, Mojisola Clara Hosu, Teke Apalata

“…The Linear Regression model predicts a continued decline to zero cases by 2026, with an R² = 0.865, a mean squared error (MSE) of 507.175, and a mean absolute error (MAE) of 18.65. …”
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Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods by Minghui Ma, Oguzhan Pektezel, Vincenzo Ballerini, Paolo Valdiserri, Eugenia Rossi di Schio

“…By comparing the predictive accuracy and modeling time of the three models built, the results demonstrate that the random forest model achieves the best prediction performance, with a mean absolute error (MAE) of 2.42% and a root mean squared error (RMSE) of 4.01% on the train set. …”
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Data-driven models for significant wave height forecasting: Comparative analysis of machine learning techniques by Ahmet Durap

“…Three ML models—Linear Regression, Decision Tree, and Random Forest—were trained and evaluated using performance metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). …”
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Development of data driven machine learning models for the prediction and design of pyrimidine corrosion inhibitors by Aeshah H. Alamri, N. Alhazmi

“…Rigorous internal and external validation were performed using the PLS and RF to further verify the robustness and predictive ability of the models. The random forest yielded the best results with the mean standard error (MSE) of 32.602 compared to the PLS with MSE of 64.641. …”
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From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh by Md. Abu Saleh, H.M. Rasel, Briti Ray

“…Results demonstrate that the random forest regression technique exhibited superior accuracy compared to SVM and GBM in training and testing stages. …”
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Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR by Sackdavong Mangkhaseum, Yogesh Bhattarai, Sunil Duwal, Akitoshi Hanazawa

“…The results highlight Random Forest’s superior performance, achieving the highest train and test Area Under the Curve of Receiver Operating Characteristic (AUROC) (1.00 and 0.993), accuracy (0.957), F1-score (0.962), and kappa value (0.914), with the lowest mean squared error (0.207) and Root Mean Squared Error (0.043). …”
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Forecasting Wind Farm Production in the Short, Medium, and Long Terms Using Various Machine Learning Algorithms by Gökhan Ekinci, Harun Kemal Ozturk

“…Prediction performance was evaluated using Mean Absolute Error (MAE), Coefficient of Determination (R²), and Root Mean Square Error (RMSE) metrics. …”
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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

“…Results Machine learning models outperformed traditional methods, with Random Forest and XGB achieving the highest accuracy (R2 = 0.94, MSE = 89.25) on the internal dataset. …”
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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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Scalable AI-driven air quality forecasting and classification for public health applications by Mohammad Wasil Jalali, Bahir Saidi, Habibullah Farahmand, Mohammad Aref Rezvan Panah, Eda Nur Saruhan

“…Results The TSMixer model stood out in regression tasks, achieving a high R² score of 0.9861 and a low mean squared error (MSE) of 0.0278. In classification tasks, the Random Forest model performed best with an accuracy of 99.96%, slightly outperforming XGBoost at 99.48%. …”
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Assessing Heterogeneity of Surface Water Temperature Following Stream Restoration and a High-Intensity Fire from Thermal Imagery by Matthew I. Barker, Jonathan D. Burnett, Ivan Arismendi, Michael G. Wing

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Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features by Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam

“…In datasets abundant in spatial correlations, the CNN-LSTM hybrid demonstrated its utility by providing novel insights into spatial–temporal patterns; nonetheless, it lagged considerably in accuracy, with a mean squared error (MSE) of 0.020 and a R2 of 0.87. Conversely, traditional models demonstrated a reliable albeit less dynamic ability to elucidate the complexities of renewable energy data; for instance, Random Forest exhibited a mean squared error (MSE) of 0.025, while Support Vector Regression (SVR) recorded an MSE of 0.030. …”
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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis by Fernando Pedro Silva Almeida, Mauro Castelli, Nadine Côrte-Real

“…This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. …”
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A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application by Md. Ibne Joha, Md Minhazur Rahman, Md Shahriar Nazim, Yeong Min Jang

“…The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). …”
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A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles by Hani Alnami, Imad Mahgoub, Hamzah Al-Najada, Easa Alalwany

“…The proposed model is evaluated and compared to other base-line models, Linear Regression (LR), Logistic Regression (LogR), and K Nearest Neighbor (KNN) regression in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Adjusted R-Squared (AR²). …”
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Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm by El Assri Nasima, Ennejjar Mohammed, Jallal Mohammed Ali, Chabaa Samira, Zeroual Abdelouhab

“…Furthermore, the Random Forest model exhibits enhanced predictive accuracy, as reflected by a lower RMSE of 1.392. …”
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