A Comparative Study of Electric Vehicles Battery State of Charge Estimation Based on Machine Learning and Real Driving Data by Salma Ariche, Zakaria Boulghasoul, Abdelhafid El Ouardi, Abdelhadi Elbacha, Abdelouahed Tajer, Stéphane Espié

“…The neural networks consistently show high predictive precision across different scenarios within the datasets, outperforming other models by achieving the lowest mean squared error (MSE) and the highest R² values.…”
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Predicting student retention: A comparative study of machine learning approach utilizing sociodemographic and academic factors by Reymark D. Deleña, Norniña J. Dia, Redeemtor R. Sacayan, Joseph C. Sieras, Suhaina A. Khalid, Amer Hussien T. Macatotong, Sacaria B. Gulam

“…Results indicate that XGBoost outperformed all other models, achieving the highest cross-validated accuracy (90.66 %), F1 Score (90.72), and one of the lowest error values (Mean Square Error (MSE) = 9.34, Log Loss = 0.26). …”
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Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data by Donato Romano, Michele Magarelli, Pierfrancesco Novielli, Domenico Diacono, Pierpaolo Di Bitonto, Nicola Amoroso, Alfonso Monaco, Roberto Bellotti, Sabina Tangaro

“…This study investigated the predictive performance of three regression models—Gradient Boosting (GB), Random Forest (RF), and XGBoost—in forecasting mortality due to endocrine, nutritional, and metabolic diseases across Italian provinces. …”
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Accurate Time-to-Target Forecasting for Autonomous Mobile Robots by Stefan-Alexandru Precup, Arpad Gellert, Alexandru Matei, Bogdan-Constantin Pirvu, Constantin-Bala Zamfirescu

“…Among the evaluated models, BiLSTM and Transformers were the only methods able to outperform the Long Short-Term Memory implementation, achieving a Mean Average Error of 1.26 seconds and 1.27 seconds, respectively. …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…Among these, the Gradient Boosting model demonstrated superior predictive capability, overcoming both traditional regression methods and other machine learning algorithms as validated through the Taylor diagram and ranking system (i.e., r = 0.998, R² = 997, Root mean square error = 0.075, Variance Accounted For = 99.50%, Mean Absolute Percentage Error = 0.385%, Alpha 20 Index = 100, and performance index = 0.975). …”
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A student academic performance prediction model based on the interval belief rule base by Wenkai Zhou, Yunsong Li, Jiaxing Li, Tianhao Zhang, Xiping Duan, Ning Ma, Yuhe Wang

“…Finally, case studies on graduate applications and GPA of students demonstrate that the mean squared error (MSE) of the IBRB-C is 0.0024 and 0.1014, respectively. …”
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Lightweight CNC digital process twin framework: IIoT integration with open62541 OPC UA protocol by Arivazhagan Anbalagan, Waqir Yusuf Zanhar, Shone George, Marcos Kauffman, Tengfei Long

“…This data trained five ML models to predict sensor positions with high accuracies (Random-Forest: R²(0.9994), KNN: R²(0.9998). Predictions validated key digital twin functions, including error estimation, synthetic data fidelity, and system integrity. …”
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Modelling the Temperature of a Data Centre Cooling System Using Machine Learning Methods by Adam Kula, Daniel Dąbrowski, Marcin Blachnik, Maciej Sajkowski, Albert Smalcerz, Zygmunt Kamiński

“…The proposed solution compares two new neural network architectures, namely Time-Series Dense Encoder (TiDE) and Time-Series Mixer (TSMixer) with classical methods such as Random Forest and XGBoost and AutoARIMA. The obtained results indicate that the lowest prediction error was achieved by the TiDE model allowing to achieve 0.1270 of N-RMSE followed by the XGBoost model with 0.1275 of N-RMSE. …”
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Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models by Tarik Pinaffo Almeida, Shahin Alipour Bonab, Mohammad Yazdani-Asrami

“…With a Goodness of Fit ( R ^2 ) of 98.55%, root mean square error of 1.421 N, and mean absolute error of 0.453 N, XGBoost specifically, and AI in general, has demonstrated its superiority, by significantly improving on the accuracy of previously published empirical models for AF-MPDT thrust prediction. …”
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Uncertainty-aware approach for multiple imputation using conventional and machine learning models: a real-world data study by Romen Samuel Wabina, Panu Looareesuwan, Suphachoke Sonsilphong, Htun Teza, Wanchana Ponthongmak, Gareth McKay, John Attia, Anuchate Pattanateepapon, Anupol Panitchote, Ammarin Thakkinstian

“…In the CKD cohort, uncertainty-aware models significantly improved performance (evaluated by root mean squared error (RMSE) and mean absolute error (MAE)) over standard MICE, except for XGBoost. …”
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Genomic Prediction in a Self-Fertilized Progenies of <i>Eucalyptus</i> spp. by Guilherme Ferreira Melchert, Filipe Manoel Ferreira, Fabiana Rezende Muniz, Jose Wilacildo de Matos, Thiago Romanos Benatti, Itaraju Junior Baracuhy Brum, Leandro de Siqueira, Evandro Vagner Tambarussi

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Smart Agile Prioritization and Clustering: An AI-Driven Approach for Requirements Prioritization by Aya M. Radwan, Manal A. Abdel-Fattah, Wael Mohamed

“…Various machine learning algorithms are tested, with KNN and Random Forest demonstrating the highest accuracy and lowest Mean Squared Error (MSE), outperforming traditional prioritization techniques. …”
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Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study by Shenjuan Lv, Ning Sun, Chunhui Hao, Junqing Li, Yun Li

“…Performance metrics such as Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) were evaluated through internal and external validations. …”
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Machine Learning and Multilayer Perceptron-Based Customized Predictive Models for Individual Processes in Food Factories by Byunghyun Lim, Dongju Kim, Woojin Cho, Jae-Hoi Gu

“…The enhanced multi-layer perceptron model achieved a high performance, with a coefficient of determination (R²) of 0.9418, a coefficient of variation of root mean square error (CVRMSE) of 9.49%, and a relative accuracy of 93.28%. …”
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A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function by Tahsin Ali Mohammed Amin, Sabah Robitan Mahmood, Rebar Dara Mohammed, Pshtiwan Jabar Karim

“…Error metrics show that our model significantly outperforms the gold standard and other popular ML methods. …”
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Prediction Model of Household Carbon Emission in Old Residential Areas in Drought and Cold Regions Based on Gene Expression Programming by Shiao Chen, Yaohui Gao, Zhaonian Dai, Wen Ren

“…., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. …”
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Mapping soil organic carbon stocks of different land use types in the Southern Moscow region by applying machine learning to legacy data by Yury A. Dvornikov, Lukyan A. Mirniy, Ekaterina S. Mukvich, Kristina V. Ivashchenko

“…At the same time, the spectral reflectance in the near infrared band (B5) of Landsat‑5 TM made the greatest contribution in explaining the differences within individual types (among fallow lands and urbanized areas), and the spectral index NDVI has explained the spatial variability of soil organic carbon among forest ecosystems. The root mean square error of cross-validation (RMSEcv = 0.67 kg/m2) was chosen to describe the uncertainty of soil organic carbon stock prediction. …”
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Impact of lake expansion on the underwater topography: a case study of Lexiewudan and Yanhu Lakes on the Tibetan Plateau by Fangfei Zhu, Jianting Ju, Baojin Qiao, Liping Zhu

“…The average water depths of two lakes were 5.92 and 9.82 m, and the root mean square error of inversion values were 0.85 and 0.93 m, respectively. …”
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Numerical simulation and experimental validation of the oleogel formation from grape seed oil and beeswax by Zohreh Baratian Ghorghi, Ali Faezian, Samira Yeganehzad, Mohammad Ali Hesarinejad, Alireza Sheikhi Darani, Marcello Fidaleo, Hashem Ahmadi Tighchi

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Machine‐learning based spatiotemporal prediction of soil moisture in a grassland hillslope by Timo Houben, Pia Ebeling, Swamini Khurana, Julia Sabine Schmid, Johannes Boog

“…Performance metrics varied between the ML methods and the training‐test data split (R2 = 0.48–0.69, root‐mean‐square error [RMSE] = 0.06–0.10). Random forests and gradient‐boosted regression trees turned out to be promising and easy to parametrize as first choices to explore the potential of ML techniques. …”
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