Tit wit: environmental and genetic drivers of cognitive variation along an urbanization gradient by Megan J. Thompson, Laura Gervais, Dhanya Bharath, Samuel P. Caro, Alexis S. Chaine, Charles Perrier, Denis Réale, Anne Charmantier

“…We find that wild urban and forest tits do not clearly differ in inhibitory control performance (number of errors or the latency to escape) during a motor detour task; a result that was consistent in birds from urban and forest origins reared in a common garden (N = 73) despite average performance differing between wild and captive birds. …”
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Validation of Cross-Calibration for the Time-Series of HJ-2A/B CCD Sensor Based on Baotou Sandy Site by Yidan Chen, Jie Han, Yutong Li, Yong Xie, Wen Shao, Aonan Hao

“…When compared with the measured reflectance from the Baotou sandy site, the results of this study show better consistency with the 1:1 line and have average relative errors of less than 8.07%, while the OCCs have average relative errors of less than 34.65%. …”
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Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network by Min Seop So, Mohammad Mahruf Mahdi, Duck Bong Kim, Jong-Ho Shin

“…The model achieved mean absolute percentage error (MAPE) values of 0.014% for the width and 0.012% for the height, and root mean squared error (RMSE) values of 0.122 for the width and 0.153 for the height. …”
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Prediction and evaluation of environmental quality for nursing sow buildings via multisource sensor information fusion by Chong Chen, Xingqiao Liu, Chaoji Liu, Chengyang Yu

“…The validation test results indicate that this model outperformed four other models, achieving a coefficient of determination (R2) of 0.9086, a Mean Absolute Error (MAE) of 0.0639, a Root Mean Squared Error (RMSE) of 0.1787, and a computational time of 7.5862 s. …”
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Climatic characteristics of snow water equivalent in the Perm Krai area by N. A. Kalinin, A. D. Kryuchkov, I. A. Sidorov, R. K. Abdullin, A. N. Shikhov

“…In general, the ERA5-Land reanalysis reproduces SWE in the Perm region satisfactorily. Mean relative error for SWE in March does not exceed 15 %. The average correlation coefficient between the reanalysis data and the same from the observations is 0.72 for non-forest locations and 0.83 for locations in forest. …”
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Predicting Residential Energy Consumption in South Africa Using Ensemble Models by David Attipoe, Donatien Koulla Moulla, Ernest Mnkandla, Alain Abran

“…The accuracy of each ensemble model was evaluated by assessing various performance indicators, including the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination R2. …”
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Machine learning for predicting earthquake magnitudes in the Central Himalaya by Ram Krishna Tiwari, Rudra Prasad Poudel, Harihar Paudyal

“…We also checked the performance of these models by three parameters Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) and noticed the better performance of RFR model. …”
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Effect of Hyperparameter Tuning on Performance on Classification model by Muhammad Sholeh, Uning Lestari, Dina Andayati

“…This research aims to analyze the effect of hyperparameter tuning on the performance of Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest, Random Forest Classifier, Naive Bayes algorithms.  …”
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Boreal tree species classification using airborne laser scanning data annotated with harvester production reports, and convolutional neural networks by Raul de Paula Pires, Christoffer Axelsson, Eva Lindberg, Henrik Jan Persson, Kenneth Olofsson, Johan Holmgren

“…The ALS data were acquired in managed Norway spruce-dominated forests in southern Sweden using a dual-wavelength system composed by two monochromatic sensors. …”
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Evaluating Spatio-Temporal Kriging with Machine Learning Considering the Sources of Spatio-Temporal Variation by Min Jeong, Hyeongmo Koo

“…Global trend estimates differ by the models, with polynomial regression producing smoother patterns but larger errors, while random forest and boosting yield more abrupt patterns with smaller errors. …”
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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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Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy by Leonardo Pace, Simone Priori, Monica Zanini, Valerio Cristofori

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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

“…Notably, the coefficient of determination reached 97 % during the testing phase, emphasizing the robustness of this model. While Mean Absolute Error is lower (1085.071 cubic meter per second), in training, the model captures relative changes (Mean Absolute Percentage Error = 0.154) better during prediction. …”
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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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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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Determining water status of walnut orchards using the crop water stress index and canopy temperature measurements by Lian Mao, Sen Lu, Linqi Liu, Zhipeng Li, Baoqing Wang, Dong Pei, Yongchao Bai

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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

“…The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. …”
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