Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale by Monica Casella, Nicola Milano, Pasquale Dolce, Davide Marocco

“…Various factors contribute to this issue, including participant non-response, dropout, or technical errors during data collection. Traditional methods like mean imputation or regression, commonly used to handle missing data, rely upon assumptions that may not hold on psychological data and can lead to distorted results.MethodsThis study aims to evaluate the effectiveness of transformer-based deep learning for missing data imputation, comparing ReMasker, a masking autoencoding transformer model, with conventional imputation techniques (mean and median imputation, Expectation–Maximization algorithm) and machine learning approaches (K-nearest neighbors, MissForest, and an Artificial Neural Network). …”
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Forecasting Urban Rail Transit Vehicle Interior Noise and Its Applications in Railway Alignment Design by Yifeng Wang, Ping Wang, Zihan Li, Zhengxing Chen, Qing He

“…In this study, a data-driven interior noise prediction model is developed for vehicles on an urban rail transit system based on random forest (RF) and a vehicle/track coupling dynamic model (VTCDM). …”
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Improving Streamflow Prediction Using Multiple Hydrological Models and Machine Learning Methods by Hiren Solanki, Urmin Vegad, Anuj Kushwaha, Vimal Mishra

“…We used Multiple Linear Regression, Random Forest (RF), Extreme Gradient Boosting (XGB), and Long Short‐Term Memory (LSTM) for the post‐processing of simulated streamflow from HMs. …”
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Analysing and Forecasting the Energy Consumption of Healthcare Facilities in the Short and Medium Term. A Case Study by Ali Koç, Serap Ulusam Seçkiner

“…Furthermore, all regression algorithms have undergone hyper-parameter optimisation using random search, grid search and Bayesian optimisation to achieve the minimum prediction errors represented by different metrics. The results displayed that the two ensemble models, Extreme Gradient Boosting and Random Forest, outperformed single models in hourly, daily, and monthly energy load prediction. …”
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Robust asphaltene onset pressure prediction using ensemble learning by Jafar Khalighi, Alexey Cheremisin

“…This paper adopts a robust approach to training three machine learning models—Multi-Layer Perceptron (MLP), CatBoost, and Random Forest (RF)—to predict AOP as a function of oil composition, SARA fractions, saturation pressure, and temperature. …”
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Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks by Yanhong Peng, Yuxin Wang, Fangchao Hu, Miao He, Zebing Mao, Xia Huang, Jun Ding

“…KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.012 for pressure and flow rate predictions, respectively. …”
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Short-term forecasting of solar irradiance using decision tree-based models and non-parametric quantile regression. by Amon Masache, Precious Mdlongwa, Daniel Maposa, Caston Sigauke

“…Introducing the random forests (RFs) model and its hybridisation with quantile regression modelling, the quantile regression random forest (QRRF), can help improve the forecasts' accuracy. …”
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ML modeling of ultimate and relative bond strength for corroded rebars based on concrete and steel properties by Alireza Hosseinzadeh Kashani, Mansour Ghalehnovi, Hossein Etemadfard

“…A comprehensive dataset was compiled from experimental studies, and six ML algorithms, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GBoost), and Extreme Gradient Boosting (XGBoost), were trained to forecast UBS and RBS simultaneously. …”
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Automatic Extraction Method of Phenotypic Parameters for <i>Phoebe zhennan</i> Seedlings Based on 3D Point Cloud by Yang Zhou, Yikai Qi, Longbin Xiang

“…The results of this study provide a valuable reference for forest breeding and the cultivation of high-quality tree seedlings.…”
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Comparison of Machine-Learning Algorithms for Near-Surface Air-Temperature Estimation from FY-4A AGRI Data by Ke Zhou, Hailei Liu, Xiaobo Deng, Hao Wang, Shenglan Zhang

“…The performance of each model and the temporal and spatial distribution of the estimated Tair errors were analyzed. The results showed that the XGB model had better overall performance, with R2 of 0.902, bias of −0.087°C, and root-mean-square error of 1.946°C. …”
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Spaceborne remote sensing effectively maps species richness across taxonomic groups in a mountain landscape by Cornelius Senf, Lisa Geres, Tobias Richter, Kristin Braziunas, Felix Glasmann, Rupert Seidl, Sebastian Seibold

“…However, validating models by habitat type revealed higher errors within habitat types (i.e., forest or open habitat), especially for immobile species (fungi and plants) that likely vary at smaller spatial scales than the resolution of the spaceborne systems used in this study. …”
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Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…However, due to the low and sparse vegetation in alpine meadows, it is challenging to obtain pure vegetation pixels from Sentinel-2 imagery, resulting in errors in the FVC estimation using traditional pixel dichotomy models. …”
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Multi-Output Regression for the Prediction of World-Class Performances in Women&#x2019;s Handball by Rayane Elimam, Nicolas NICOLAS, Jacques Prioux, Jacky Montmain, Stephane Perrey

“…We compared 4 single-output models (kNN, regression tree, random forest and(NN) Predictive models inspired by the human brain, used in this study for multi-output prediction in sports performance analysis (neural networks)), their multi-output counterparts and aA baseline model predicting future performance as the average of each player&#x2019;s past performance, serving as a simple reference for comparison with more complex models (dummy baseline) (predicting the average performance of each player over the last month) in terms of average(Root Mean Squared Error) A measure of the quadratic difference between predicted and actual values in regression models (RMSE) (aRMSE) during aAn evaluation method where past training and game data are used sequentially to predict performance of the next game (chronological evaluation) where previous trainings and games data are used to train models to predict the next game performances. …”
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Multi-Source DEM Vertical Accuracy Evaluation of Taklimakan Desert Hinterland Based on ICESat-2 ATL08 and UAV Data by Mingyu Wang, Huoqing Li, Yongqiang Liu, Haojuan Li

“…While slope aspect has a relatively minor impact on errors, certain DEMs exhibit error variations in the SE and NW directions. …”
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From data to decision: Alleviating poverty and promoting development through measuring the unmeasurable economic numbers by Emmanuel A. Onsay, Jomar F. Rabajante

“…Using community-based monitoring system (CBMS) data, we achieved a prediction accuracy of 92.60–98.00 % using Random Forest classification and reduced traditional survey and data processing costs by up to 70 %. …”
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Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. by Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari

“…The RBF algorithm with highest Coefficient of Determination (R2) value of 0.81 and the lowest Mean Absolute Errors (MAE) and Root Mean Square Error (RMSE) values of 148.61 kg.ha-1, and 185.31 kg.ha-1, respectively, was the most accurate algorithm and, therefore, selected as the metaClassifier for the E-B algorithm. …”
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Present-day vegetation helps quantifying past land cover in selected regions of the Czech Republic. by Vojtěch Abraham, Veronika Oušková, Petr Kuneš

“…Vegetation proportions of 17 taxa were obtained by combining the CORINE Land Cover map with forest inventories, agricultural statistics and habitat mapping data. …”
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Spatially-informed interpolation for reconstructing lake area time series using semantic neighborhood correlation by Chen Liu

“…Compared with polynomial fitting, Random Forest, and Long Short-Term Memory, SNCI consistently achieves lower interpolation errors. …”
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Rapid prediction algorithm for flow field in fully mechanized excavation face based on POD and machine learning by JIN Bing, ZHANG Lang, LI Wei, ZHENG Yi, LIU Yanqing, ZHANG Yibin

“…The support vector machine (SVM) model outperformed the Random Forest and Neural Network models in predicting mode coefficients. …”
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A hybrid model for improving customer lifetime value prediction using stacking ensemble learning algorithm by Amir Mohammad Haddadi, Hodjat Hamidi

“…., Elastic Net, Random Forest, XGBoost, and SVM. The results demonstrate that integrating those features and using the Stacking Ensemble model substantially increases the prediction accuracy and decreases the errors. …”
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