Enhancing shipboard oil pollution prevention: Machine learning innovations in oil discharge monitoring equipment
G Camliyurt, EP Tapiquén, S Park, W Kang, D Kim… - Marine Pollution …, 2024 - Elsevier
Maritime operations face significant challenges in environmental stewardship, particularly in
managing oil discharges from tankers as mandated by the International Convention for the …
managing oil discharges from tankers as mandated by the International Convention for the …
Part-Level Fault Classification of Mass Flow Controller Drift in Plasma Deposition Equipment
MH Kim, HE Sim, SJ Hong - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Semiconductor manufacturing processing can be jeopardized due to process fluctuations,
and the degradation of equipment parts can significantly influence process variation. Timely …
and the degradation of equipment parts can significantly influence process variation. Timely …
[HTML][HTML] Fostering Sustainable Mining Practices in Rock Blasting: Assessment of Blast Toe Volume Prediction using Comparative Analysis of Hybrid Ensemble …
Blast toe volume, pivotal in explosive engineering, underpins explosive energy efficient
utilization, blast safety and mine production sustainability. While current research explores …
utilization, blast safety and mine production sustainability. While current research explores …
Implementing the XGBOOST Classifier for Bankruptcy Detection and Smote Analysis for Balancing Its Data
Equipped with a comprehensive range of established financial indicators and ratios, the
XGBoost model is capable of detecting patterns that may discern between solvable and …
XGBoost model is capable of detecting patterns that may discern between solvable and …
[PDF][PDF] Factors Influencing Voluntary Turnover Among Young College Graduates Using the XGBoost with Bagging Aggregation Algorithm: Findings from Nationwide …
H Byeon - researchgate.net
This study aims to analyze the factors influencing turnover among young professionals aged
20 to 30 and compare the predictive performance of various machine learning models using …
20 to 30 and compare the predictive performance of various machine learning models using …