Harnessing machine learning for transmembrane pressure prediction in MBR systems during textile wastewater treatment by Onaira Zahoor, Sher Jamal Khan, Muhammad Usama, Henry J. Tanudjaja, Noreddine Ghaffour, Muhammad Saqib Nawaz

“…A total of 60 datasets were compiled for training and testing of the models. The random forest model attained an R2 of 0.95 and 0.86 and root mean square error values of 1.75 kPa and 3.3 kPa for the training and test datasets, respectively, demonstrating the best predictive accuracy. …”
Get full text

Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study by Sultan Almuaythir, Muhammad Syamsul Imran Zaini, Rida Hameed Lodhi

“…The analysis of absolute error highlighted the accuracy and consistency of XG-Boost and Random Forest in predicting the maximum dry density.…”
Get full text

Machine Learning Applications for Predicting High-Cost Claims Using Insurance Data by Esmeralda Brati, Alma Braimllari, Ardit Gjeçi

“…In order to evaluate and compare the performance of the models, we employed evaluation criteria, including classification accuracy (CA), area under the curve (AUC), confusion matrix, and error rates. We found that Random Forest performs better, achieving the highest classification accuracy (CA = 0.8867, AUC = 0.9437) with the lowest error rates, followed by the XGBoost model. …”
Get full text

Calibration of Low-cost Gas Sensors for Air Quality Monitoring by Dimitris Margaritis, Christos Keramydas, Ioannis Papachristos, Dimitra Lambropoulou

“…The three alternative methodologies had similar calibration performance overall. The random forest algorithm appeared to have an advantage in several cases, mostly in terms of following the pattern in the O3 and SO2 time series, but also in terms of the average error and bias for all pollutants.…”
Get full text

Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning by Muhammad Umair, Jawad Ahmad, Nada Alasbali, Oumaima Saidani, Muhammad Hanif, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan

“…State of the art machine learning models i.e., Random Forest, Support Vector Machine, and Gradient Boosting are utilized, while deep learning models such as Transformers and Autoencoders are selected for their robust feature-extraction capabilities. …”
Get full text

Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing by Waqar Shehbaz, Qingjin Peng

“…Among the models, Random Forest yields the highest predictive accuracy and lowest mean squared error across all target sustainability indicators: energy consumption, part weight, scrap weight, and production time. …”
Get full text

Advanced Machine Learning Approaches for Predicting Machining Performance in Orthogonal Cutting Process by Sabrina Al Bukhari, Salman Pervaiz

“…It also outperforms the Random Forest Regression model, achieving a 19.8% decrease in the mean squared error and a 7.1% decrease in the mean absolute error.…”
Get full text

USING OF ELECTRONIC COMPASS IN NAVIGATION OF MOBILE ROBOT by XUAN LONG TRINH

“…In the past, compasses were used to determine the coordinate of ships on the sea and position of people in the deep forests. Nowaday with the development of technology, electronic compasses contribute the excellent solutions in navigation, especially in navigation of mobile robot. …”
Get full text

Development and External Validation of [18F]FDG PET-CT-Derived Radiomic Models for Prediction of Abdominal Aortic Aneurysm Growth Rate by Simran Singh Dhesi, Pratik Adusumilli, Nishant Ravikumar, Mohammed A. Waduud, Russell Frood, Alejandro F. Frangi, Garry McDermott, James H. F. Rudd, Yuan Huang, Jonathan R. Boyle, Maysoon Elkhawad, David E. Newby, Nikhil Joshi, Jing Yi Kwan, Patrick Coughlin, Marc A. Bailey, Andrew F. Scarsbrook

“…A multi-layer perceptron (MLP) was developed for AAA growth prediction and compared against Random Forest (RF), XGBoost, and Support Vector Machine (SVM). …”
Get full text

A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors by Seren Smith, Theodore Trefonides, Anusha Srirenganathan Malarvizhi, Shyra LaGarde, Jiakang Liu, Xiaoguo Jia, Zifu Wang, Jacob Cain, Thomas Huang, Mohammad Pourhomayoun, Grace Llewellyn, Wai Phyo, Sina Hasheminassab, Joe Roberts, Kevin Marlis, Daniel Q. Duffy, Chaowei Yang

“…Tree-boosted models including XGBoost (0.7612, 5.377 µg/m³) in RStudio and Random Forest (RF) (0.7632, 5.366 µg/m³) in TensorFlow offered good performance with shorter training times (<1 min) and may be suitable for such applications. …”
Get full text

Prediction of room temperature in Trombe solar wall systems using machine learning algorithms by Seyed Hossein Hashemi, Zahra Besharati, Seyed Abdolrasoul Hashemi, Seyed Ali Hashemi, Aziz Babapoor

“…The accuracy of the algorithms was assessed using R² and root mean squared error (RMSE) values. The results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance, with R² and RMSE values of 1 and 0. …”
Get full text

Using traffic data to identify land-use characteristics based on ensemble learning approaches by Jiahui Zhao, Zhibin Li, Pan Liu

“…The Random Forest model performs better in labels with high regularity, such as education, residence, and work activities. …”
Get full text

Prediction of ultimate load capacity of demountable shear stud connectors using machine learning techniques by Ahmed I. Saleh, Nabil S. Mahmoud, Fikry A. Salem, Mohamed Ghannam

“…Eight supervised ML algorithms were evaluated: Linear Regression, Ridge, Lasso, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree, Random Forest, and XGBoost. Model performance was assessed using R², Mean Absolute Error (MAE), and Mean Squared Error (MSE). …”
Get full text

Metabolites Link Intake of a Healthy Diet to Better Insulin and Glucose Homeostasis in the Microbiome and Insulin Longitudinal Evaluation Study (MILES) by Alexis C Wood, Danielle J Lee, Patricia A Sheridan, Elizabeth T Jensen, Gautam Ramesh, Alain G Bertoni, Stephen S Rich, Yii-Der I Chen, David M Herrington, Jerome I Rotter, Mark O Goodarzi

Get full text

Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

“…The results demonstrate the superiority of the LSTM model over ARIMA and Random Forest in terms of predictive accuracy, as confirmed by visual comparison of forecasts with test data and by the mean squared error metric. …”
Get full text

Machine learning model optimization for compressional sonic log prediction using well logs in Shahd SE field, Western Desert, Egypt by Khaled Saleh, Walid M. Mabrouk, Ahmed Metwally

“…Model performance is optimized through hyperparameter tuning and evaluated using correlation coefficients and root mean square error (RMSE) metrics. Results indicate that ensemble models (Random Forest, CatBoost, XGBoost) achieve the highest accuracy, with correlation coefficients ranging from 89 to 89.6% and RMSE between 5.85 and 6.03. …”
Get full text

An improved multiclass classification of acute lymphocytic leukemia using enhanced glowworm swarm optimization by Saranya N, Kalamani M

“…Most of the diagnostic techniques like bone marrow aspiration, imaging techniques, etc. are time consuming, error-prone, costly and depend on the skill set of experts. …”
Get full text

Leveraging Artificial Intelligence for Smart Healthcare Management: Predicting and Reducing Patient Waiting Times with Machine Learning by Kristijan CINCAR, Todor IVAŞCU

“…Preliminary experiments contrasted different machine-learning strategies, showing that the ensemble methods Random Forest and XGBoost far surpassed the traditional approaches with a mean absolute error for waiting time prediction of fewer than ten minutes. …”
Get full text

Intelligent Feature Selection Ensemble Model for Price Prediction in Real Estate Markets by Daniel Cristóbal Andrade-Girón, William Joel Marin-Rodriguez, Marcelo Gumercindo Zuñiga-Rojas

“…This study examines the effects of dimensionality reduction through Recursive Feature Elimination (RFE), Random Forest (RF), and Boruta on real estate price prediction, assessing ensemble models like Bagging, Random Forest, Gradient Boosting, AdaBoost, Stacking, Voting, and Extra Trees. …”
Get full text

The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines by Christos Emexidis, Panagiotis Gkonis

“…The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). …”
Get full text