Forecasting Container Throughput of Singapore Port Considering Various Exogenous Variables Based on SARIMAX Models by Geun-Cheol Lee, June-Young Bang

“…The predictive performance of the SARIMAX model was evaluated against a diverse set of benchmark methods, including the Holt–Winters method, linear regression, LASSO regression, Ridge regression, ECM (Error Correction Mechanism), Support Vector Regressor (SVR), Random Forest, XGBoost, LightGBM, Long Short-Term Memory (LSTM) networks, and Prophet. …”
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Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study by Md. Wira Putra Dananjaya, Putu Gita Pujayanti

“…The models were evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. …”
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Deep learning models to predict CO2 solubility in imidazolium-based ionic liquids by Amir Hossein Sheikhshoaei, Ali Sanati, Ali Khoshsima

“…Graphical and statistical analyses revealed that the GrowNet model, with a root mean square error of 0.0073 and a coefficient of determination of 0.9962, exhibited the lowest error compared to other models. …”
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Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation by Weidong Gan, Dianguang Ma, Yu Duan

“…Among these, the LightGBM model demonstrated the highest prediction accuracy, achieving a Root Mean Squared Error (RMSE) of 0.188, Mean Absolute Error (MAE) of 0.149, and a coefficient of determination (R²) of 0.978. …”
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Theoretical analysis of MOFs for pharmaceutical applications by using machine learning models to predict loading capacity and cell viability by Bader Huwaimel, Saad Alqarni

“…Evaluation metrics, including R2, Root Mean Squared Error (RMSE), and maximum error, indicated that the QR-MLP model outperformed the other models, achieving test R2 scores of 0.99917 for Drug Loading Capacity and 0.99111 for Cell Viability. …”
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Parametric optimization of the slot waveguide characteristics using a machine-learning approach by Yadvendra Singh, Suraj Jena, Harish Subbaraman

“…Three different ML techniques, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF), were tested to compute performance parameters for the slot waveguide. …”
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A comprehensive model for concrete strength prediction using advanced learning techniques by Sagar Dhengare, Udaykumar Waghe, Ganesh Yenurkar, Anjana Shyamala

“…The main ingredient analysis (PCA) was used to reduce the dimension, while random forest regression (RFR), support vector regression (SVR), and the Convolutional Neural Network (CNN) were applied for the forecast. …”
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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

“…The proposed GBR model accurately predicts 1847 experimental datasets, showcasing mean squared error, mean absolute error, root mean squared error, relative absolute error percent, and regressing coefficient, of 0.06, 0.15, 0.24, 6.46%, and 0.9961 respectively. …”
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Advancing precision dentistry: the integration of multi-omics and cutting-edge imaging technologies—a systematic review by Neelam Das

“…CBCT reduced diagnostic error by 35% (CI: 30%–40%), while MRI improved soft-tissue evaluation by 25% (CI: 18%–32%). …”
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Design and prediction of high optical density photovoltaic polymers using machine learning-DFT studies by Aljaafreh Mamduh J., Hassan Abrar U.

“…The results show that extra gradient boosting regressor and random forest regressor are the best-performing models among all the tested ML models, whose good R-squared (R 2) values reveal their prediction accuracy. …”
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TECHNOLOGICAL ADVANCES IN ELECTROPLATING: ARTIFICIAL INTELLIGENCE TO PREDICT ZINC COATING THICKNESS ON SAE 1008 LOW CARBON STEELS by Luciano M. L. de Oliveira, Fabiana L. da Silva, Paulo R. Janissek, Juliano C. Toniolo

“…Statistical analysis and supervised machine learning algorithms, including multivariate regression, random forest, and extreme gradient boosting (XGBoost), were employed to develop prediction models. …”
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Integrating temporal convolutional networks with metaheuristic optimization for accurate software defect prediction. by Ahmed Abdelaziz, Alia Nabil Mahmoud, Vitor Santos, Mario M Freire

“…Additionally, the proposed model achieves a 13.6% higher area under the curve across all datasets compared to the Deep Forest method. These results confirm the effectiveness of the proposed hybrid model in accurately detecting defects across diverse software projects.…”
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Corrigendum: Bobinac M, Šušić N, Šijačić-Nikolić M, Kerkez Janković I, Veljović-Jovanović S., Photosynthetic insights into winter-green leaves in Quercus pubescens Willd. seedlings... by Editoral

“…Arch Biol Sci. 2024;76(2):223-32. have notified the Editorial Office of an error in the Funding section. The name “Ministry of Education, Science and Technological Development of the Republic of Serbia” was incorrectly stated and should instead read “Ministry of Science, Technological Development and Innovation of the Republic of Serbia.” …”
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Development of regional mixed-effects height–diameter models for natural black pine stands by Ramazan Ozçelik, Onur Alkan

“…Compared to the fixed-effects model, the mixed-effects model achieved a 32% reduction in the root mean square error (RMSE). The findings suggest that the proposed model is highly suitable for forest inventory studies to predict tree heights in black pine stands.…”
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A reversible database watermarking method non-redundancy shifting-based histogram gaps by Yan Li, Junwei Wang, Xiangyang Luo

“…First, an integer data histogram is constructed with the absolute value of the prediction error of the data as a variable. Second, the positional relationship between each column and the gap in the histogram is analyzed to find out all the columns adjacent to the gap. …”
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Augmented Reality (AR) for Precision Farming: Enhancing Farmer Decision-Making in Pest Control by Tandi Moti Ranjan, Kumar Yalakala Dinesh

“…Pest control in modern agriculture is a huge challenge where traditional ways are often labor intensive, error-prone, and require heavy use of pesticides that damage the environment. …”
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Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete by Omobolaji Opafola, Abisola Olayiwola, Ositola Osifeko, Adekunle David, Ajibola Oyedejı

“…The developed GB model achieved R-squared values of 91.60%, 91.43%, and 90.18% for the 10-fold, 5-fold, and 3-fold cross-validations, respectively, with mean absolute error, root mean squared error, and mean absolute percentage error values of 2.6776, 4.3523, and 9.19%, respectively. …”
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Machine learning vehicle fuel efficiency prediction by So-rin Yoo, Jae-woo Shin, Seoung-Ho Choi

“…To evaluate the machine learning model, MSE (Mean Square Error), RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R-squared ( $$R^2$$ Score) were used. …”
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Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization by Senyefia Bosson-Amedenu, Emmanuel Ayitey, Francis Ayiah-Mensah, Luyton Asare

“…Model performance was evaluated using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE), and Mean Squared Error (MSE) metrics across three sample sizes (25%, 50%, and 75%). …”
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A study on predicting the risk of coronary artery disease in OSAHS patients based on a four-variable screening tool potential predictive model and its correlation with the severity... by Yanli Yao, Yu Li, Yulan Chen, Xuan Qiu, Gulimire Aimaiti, Ayiguzaili Maimaitimin

“…A comprehensive analysis utilizing the random forest machine learning algorithm demonstrated that the AHI exhibits the highest predictive value. …”
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