A Pipeline for Multivariate Time Series Forecasting of Gas Consumption in Pelletization Process by Thadeu Pezzin Melo, Jefferson Andrade, Karin Satie Komati

“…The methodology was tested on a dataset with 45 operational parameters collected over 90 days from an industrial plant, with predictions evaluated using Root Mean Squared Error (RMSE). In step (iii), twelve features were identified as the most relevant based on the Random Forest importance index. …”
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OBM-RFEcv: An adaptive ensemble model for monitoring key growth indicators of Gerbera using multi-spectral image fusion features. by Xinrui Wang, Yingming Shen, Peng Tian, Mengyao Wu, Zhaowen Li, Jiawei Zhao, Jihong Sun, Ye Qian

“…An adaptive ensemble model, OBM-RFEcv, was then developed by integrating six base models (Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost Regressor, and Support Vector Regressor) with Recursive Feature Elimination (RFE) to predict the key growth indicators. …”
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Enhancing Flow Direction in Geothermal Fields Using Sentinel-1 Data for Sustainability Water Management by Utama Widya, Anjasmara Ira Mutiara, Handayani Hepi Hapsari, Indriani Rista Fitri

“…This study develops a flow direction prediction model using Sentinel-1 satellite imagery during rainy and dry seasons through the Random Forest machine learning algorithm. The pre-processing stage includes radiometric calibration, terrain flattening, speckle filtering, and Doppler terrain correction. …”
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Machine learning-driven insights into self-healing silicon-based anodes for high-performance lithium-ion batteries by Mahta Moazzenzadeh, Mahmoud Samadpour

“…To tackle this issue, researchers are investigating the integration of self-healing polymers as binding agents in the anode structure through trial-and-error approaches, which is both time-consuming and expensive. …”
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Assessing the Importance of Intraspecific Variability in Dung Beetle Functional Traits. by Hannah M Griffiths, Julio Louzada, Richard D Bardgett, Jos Barlow

“…Here we investigated variability in two functionally relevant dung beetle traits, measured from individuals collected from three primary forest sites containing distinct beetle communities: body mass and back leg length. …”
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Machine Learning Techniques In Wdm-Fso Systems: Comparative Study by Ranim Younes, Mohammad Nassr

“…The obtained results show that the Random Forest (RF) algorithm gave the lowest RMSE value and the highest R2 value in comparison with the Decision Tree (DT) and K-Nearest Neighbors algorithm (KNN) algorithms. …”
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Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets by Amin Salemnia, Seyedehmaryam Hosseini Boldaji, Vida Atashi, Manoochehr Fathi-Moghadam

“…To address the problem’s non-linearity, six machine learning models were tested: linear regression, K-nearest neighbors, decision tree, support vector regression, random forest, and XGBoost. The XGBoost model outperformed others, achieving an R-squared of 0.953 and a Root Mean Squared Error (RMSE) of 0.191. …”
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PSOA-LSTM: a hybrid attention-based LSTM model optimized by particle swarm optimization for accurate lung cancer incidence forecasting in China (1990–2021) by Nannan Xu, Guang Yang, Linlin Ming, Jiefei Dai, Kun Zhu

“…The proposed model was compared against traditional models including ARIMA, standard LSTM, Support Vector Regression (SVR), and Random Forest (RF).ResultsThe PSOA-LSTM model achieved superior performance across five key evaluation metrics: mean squared error (MSE) = 0.023, coefficient of determination (R2) = 0.97, mean absolute error (MAE) = 0.152, normalized root mean squared error (NRMSE) = 0.025, and mean absolute percentage error (MAPE) = 0.38%. …”
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Using electro-inductive sensor to trace moving and non-moving objects tracked by S. S. Kolmogorova, S. V. Biryukov

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TSSA-NBR: A Burned Area Extraction Method Based on Time-Series Spectral Angle with Full Spectral Shape by Dongyi Liu, Yonghua Qu, Xuewen Yang, Qi Zhao

“…Across diverse land cover types, including forests, grasslands, and shrublands, TSSA-NBR exhibited high adaptability, with DC values ranging from 0.53 to 0.97, CE from 0.03 to 0.27, and OE from 0.02 to 0.61. …”
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Data-Driven Computational Methods in Fuel Combustion: A Review of Applications by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…The most frequently applied methods include artificial neural networks (ANNs), support vector machines (SVMs), and random forests (RFs) for predictive modeling, as well as genetic algorithms (GAs) for system optimization. …”
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Productivity and socioeconomic sustainability of Bubalus bubalis in the western lowlands of Venezuela by Carlos Alberto Calles Navas, Verena Torres Cardenas

“…In contrast, buffalo farming requires forests. However, to convince farmers to apply this type of livestock; it was necessary to demonstrate its greater profitability. …”
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WetCH₄: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 by Q. Ying, Q. Ying, B. Poulter, J. D. Watts, K. A. Arndt, A.-M. Virkkala, L. Bruhwiler, Y. Oh, Y. Oh, B. M. Rogers, S. M. Natali, H. Sullivan, A. Armstrong, A. Armstrong, E. J. Ward, E. J. Ward, L. D. Schiferl, C. D. Elder, C. D. Elder, O. Peltola, A. Bartsch, A. R. Desai, E. Euskirchen, M. Göckede, B. Lehner, M. B. Nilsson, M. Peichl, O. Sonnentag, E.-S. Tuittila, T. Sachs, T. Sachs, A. Kalhori, M. Ueyama, Z. Zhang, Z. Zhang

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Stacking classical-quantum hybrid learning approach for corrosion inhibition efficiency of N-heterocyclic compounds by Muhamad Akrom, Supriadi Rustad, Totok Sutojo, Wahyu Aji Eko Prabowo, Hermawan Kresno Dipojono, Ryo Maezono, Hideaki Kasai

“…SCQM integrates classical models such as Multi-Layer Perceptron Neural Network (MLPNN) and Random Forest (RF) as base learners, with Quantum Neural Network (QNN) as the meta-learner. …”
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Comparative analysis of machine learning algorithms for predicting tibial intramedullary nail length from patient characteristics by Yujian Hui, Hengda Hu, Jinghua Xiang, Xingye Du

“…Results The XGBoost model demonstrated superior clinical precision, achieving the lowest testing RMSE (9.15 mm) and MAE (7.56 mm), with an R2 of 0.871, explaining 87.1% of variance in nail length. While the random forest model had the highest R2 (0.874) and correlation coefficient (r = 0.935), XGBoost outperformed all models in error metrics, critical for minimizing surgical complications. …”
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Stacking modeling with genetic algorithm-based hyperparameter tuning for uniaxial compressive strength prediction by Tanveer Alam Munshi, Khanum Popi, Labiba Nusrat Jahan, M. Farhad Howladar, Mahamudul Hashan

“…Regression metrics including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), maximum error (MaxE), and minimum error (MinE) were used to assess the effectiveness of the models. …”
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Interval Prediction Method for Solar Radiation Based on Kernel Density Estimation and Machine Learning by Meiyan Zhao, Yuhu Zhang, Tao Hu, Peng Wang

“…First, the V-SVR model performs best with the lowest mean absolute error (MAE) of 0.016 and mean relative error (MRE) of 0.001. …”
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Frost Resistance Prediction of Concrete Based on Dynamic Multi-Stage Optimisation Algorithm by Xuwei Dong, Jiashuo Yuan, Jinpeng Dai

“…These models are trained using 7090 datasets, which use nine features as input variables; relative dynamic elastic modulus (RDEM) and mass loss rate (MLR) as prediction indices; and six indices of the coefficient of determination (R²), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and standard deviation ratio (SDR) are selected to evaluate the models. …”
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DeepSeek-AI-enhanced virtual reality training for mass casualty management: Leveraging machine learning for personalized instructional optimization. by Zhe Li, Lei Shi, Mingyu Pei, Wan Chen, Yutao Tang, Guozheng Qiu, Xibin Xu, Liwen Lyu

“…The DeepSeek AI framework was employed to analyze the data, utilizing clustering analysis, principal component analysis (PCA), and random forest models. Descriptive statistics, error rates, and correlation analyses were performed using R software (version 4.1.2). …”
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Suitability of Mechanics-Based and Optimized Machine Learning-Based Models in the Shear Strength Prediction of Slender Beams Without Stirrups by Abayomi B. David, Oladimeji B. Olalusi, Paul O. Awoyera, Lenganji Simwanda

“…Performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE) showed that XGB and GBR consistently outperformed other models, yielding lower error margins. …”
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