Proposing Machine Learning Models Suitable for Predicting Open Data Utilization

Опубликована: Июнь 19, 2024

As the digital transformation accelerates in our society, open data is being increasingly recognized as a key resource for innovation public sector. This study explores following two research questions: 1) Can machine learning approach be appropriately used measuring and evaluating utilization? 2) Should different models applied utilization depending on attributes (field usage type)? single-model (Random Forest, XGBoost, LightGBM, CatBoost) multi-model (Stacking Ensemble) methods. A finding that best-performing differed type of use). The applicability advance was also confirmed. contributes to application its intrinsic value society.

Язык: Английский

Proposing Machine Learning Models Suitable for Predicting Open Data Utilization

Sustainability, Год журнала: 2024, Номер 16(14), С. 5880 - 5880

Опубликована: Июль 10, 2024

As the digital transformation accelerates in our society, open data are being increasingly recognized as a key resource for innovation public sector. This study explores following two research questions: (1) Can machine learning approach be appropriately used measuring and evaluating utilization? (2) Should different models applied utilization depending on attributes (field usage type)? single-model (random forest, XGBoost, LightGBM, CatBoost) multi-model (stacking ensemble) methods. A finding is that best-performing differed type of use). The applicability advance was also confirmed. contributes to application its intrinsic value society.

Язык: Английский

Predicting tool life and sound pressure levels in dry turning using machine learning models

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер 135(7-8), С. 3777 - 3793

Опубликована: Окт. 31, 2024

Язык: Английский