Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…The XGBoost Regressor (XGBR) is optimized using genetic-algorithm-based hyperparameter tuning, reducing mean squared error (RMSE) from 20.9531 to 19.6387, mean absolute error (MAE) from 13.6821 to 12.6753, and improving coefficient of determination (R2) from 0.2791 to 0.2949. …”
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Ensemble machine learning model for forecasting wind farm generation by A. G. Rusina, Osgonbaatar Tuvshin, P. V. Matrenin, N. N. Sergeev

“…Thus, to ensure the normal operating modes of the energy system, it is necessary to predict the generation of renewable sources with an acceptable error.   PURPOSE. To forecast the generation of wind farms.   …”
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Implementation of Machine Learning in Flat Die Extrusion of Polymers by Nickolas D. Polychronopoulos, Ioannis Sarris, John Vlachopoulos

“…This ML-based methodology has the potential to reduce or even eliminate the use of trial and error procedures.…”
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Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…Among these, XGBoost demonstrated the highest overall accuracy, achieving an R² value of 0.9926, with low root mean square error (RMSE) and mean absolute error (MAE) of 0.0655 and 0.0191, respectively. …”
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Machine learning approach for optimizing usability of healthcare websites by Amandeep Kaur, Jaswinder Singh, Satinder Kaur

“…Key metrics such as R-square, Mean Square Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) confirmed the model’s predictive accuracy. …”
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Applying Machine Learning on Big Data With Apache Spark by Elias Dritsas, Maria Trigka

“…The study employed key performance metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) for regression and Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC) for classification. …”
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Machine learning analysis of breast cancer treatment protocols and cycle counts: A case study at Mohammed vi hospital, Morocco by Houda AIT BRAHIM, Salah EL-HADAJ, Abdelmoutalib METRANE

“…The second model, based on Random Forest Regressor algorithm, which integrates the results of the first model during the training, predicted the treatment cycle of patients with a Root Mean Square Error (RMSE) score of 0.050 and a Mean Absolute Percentage Error (MAPE) score of 0.020. …”
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MODELLING FLUCTUATIONS OF GROUNDWATER LEVEL USING MACHINE LEARNING ALGORITHMS IN THE SOKOTO BASIN by Samson Alfa, Haruna Garba, Augustine Odeh

“…Hyperparameters for the XGBoost model were fine-tuned using grid search techniques, resulting in optimal settings that significantly enhanced predictive accuracy with Mean Absolute Error (MAE) ranging from 0.016 – 0.757m and Root Mean Square Error (RMSE) ranging from 0.051 - 2.859m. …”
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On Predictive Modeling for the Al2O3 Data Using a New Statistical Model and Machine Learning Approach by Mahmoud El-Morshedy, Zahra Almaspoor, Gadde Srinivasa Rao, Muhammad Ilyas, Afrah Al-Bossly

“…Using the same data set, we implement various machine learning approaches including the support vector machine (SVR), group method of data handling (GMDH), and random forest (RF). To evaluate their forecasting performances, three statistical measures of accuracy, namely, root-mean-square error (RMSE), mean absolute error (MAE), and Akaike information criterion (AIC) are computed.…”
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Red Fox Optimization-Based Estimation Algorithms for Splitting Tensile Strength of Basalt Fiber Reinforced Concrete by Xiao Wu, Daoyong Zhu, Qin Yan

“…Also, the models’ results indicate that RFORF outperforms another model, with −34% lower values in symmetric mean absolute percentage error (SMAPE) and a significant −80% reduction in mean squared logarithmic error (MSLE). …”
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Construction of a NOx Emission Prediction Model for Hybrid Electric Buses Based on Two-Layer Stacking Ensemble Learning by Jiangyan Qi, Xionghui Zou, Ren He

“…The evaluation metrics of the proposed model—mean absolute error, root mean square error, and coefficient of determination—are 0.0068, 0.0283, and 0.9559, respectively, demonstrating a significant advantage compared to other benchmark models.…”
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Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars by Atchadeou Yranawa Katatchambo, Şinasi Bingöl

“…The model performance of the methods was compared using root mean square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE) performance statistics. …”
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Machine learning approaches for forecasting inflation: empirical evidence from Sri Lanka by W.M.S Bandara, W.A.R. De Mel

“…These techniques were used to estimate the parameters and hyper-parameters for each machine learning model with the aid of root mean square error. The mean absolute percentage error (MAPE) was used to compare the performance of the different SMLMs. …”
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Predictive modeling of ultimate tensile strength in dissimilar friction stir welded aluminum alloys via machine learning approach by Meghavath Mothilal, Atul Kumar

“…The models were evaluated for their prediction accuracy and reliability using mean absolute error (MAE), mean square error (MSE) and R2 score metrics. …”
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Large-scale groundwater pollution risk assessment research based on artificial intelligence technology: A case study of Shenyang City in Northeast China by Lingjun Meng, Yuru Yan, Haihua Jing, Muhammad Yousuf Jat Baloch, Shouying Du, Shanghai Du

“…Compared with the Artificial Neural Network (ANN) and Random Forest (RF) models, the performance evaluation parameters mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE) are closer to 0, and the coefficient of determination (R2) is closer to 1. …”
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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms by Ao Yang, Shirui Sun, Lu Qi, Zong Yang Kong, Jaka Sunarso, Weifeng Shen

“…., feed-forward neural networks (FNN), extreme gradient boosting (XGBoost), and random forest (RF). Using a dataset of 14,610 solvents (14,580 after data cleaning) and 21 molecular descriptors derived from SMILES representations, we rigorously evaluate these models based on R2, mean absolute error (MAE), root mean squared error (RMSE), and mean relative error (MRE). …”
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Machine learning and population pharmacokinetics: a hybrid approach for optimizing vancomycin therapy in sepsis patients by Keyu Chen, Chuhui Wang, Yu Wei, Sinan Ma, Weijia Huang, Yalin Dong, Yan Wang

“…In the testing set, AUC24 was predicted using all models, and performance was evaluated using mean absolute error, mean squared error, root mean squared error, mean absolute percentage error (MAPE), and R². …”
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Iron Ore Information Extraction Based on CNN-LSTM Composite Deep Learning Model by Haili Chen, Mengxiang Xia, Yaping Zhang, Ruonan Zhao, Bingran Song, Yang Bai

“…According to the model’s results, the particle size classification accuracy is 91.67%, the F1 score is 0.92, the coefficient of determination (R2) for the water content regression is 0.89023, the mean squared error (MSE) is 0.00082, the root mean square error (RMSE) is 0.02872, and the mean absolute error (MAE) is 0.01558. …”
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Live Weight Prediction in Norduz Sheep Using Machine Learning Algorithms by Cihan Çakmakçı

“…The results showed that the prediction performance validated using the test dataset indicated that RF had the lowest values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percent Error (MAPE). …”
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Bayesian optimization with Optuna for enhanced soil nutrient prediction: a comparative study with genetic algorithm and particle swarm optimization by Bamidele A. Dada, Nnamdi I. Nwulu, Seun O. Olukanmi

“…The concordance correlation coefficient (CCC), R-squared (R²), and mean absolute percentage error (MAPE) increased, while the root mean squared error (RMSE) and mean absolute error (MAE) decreased. …”
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