Forecasting energy demand is essential for efficient energy management and planning, allowing utilities and policymakers to make data-driven decisions regarding energy generation, distribution, and consumption. This abstract examines the latest developments in forecasting methods, such as...

Suitable facility location selection for customer-required capacity localization is an emerging topic in semiconductor supply chain management. However, this topic has not been thoroughly investigated. For this reason, an explainable artificial intelligence (XAI)-interpreted fuzzy group...

In the evolving landscape of cyber threats, phishing attacks pose significant challenges, particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy. Conventional and machine learning (ML)-based detection systems struggle to detect phishing...

For the IT industry, employee attrition is a major problem that reduces operational effectiveness and raises training and recruitment expenses. Accurately predicting staff turnover can enable businesses to put proactive retention tactics into place. This study forecasts employee attrition based...

This study develops a machine learning framework to improve the prediction of automobile loan defaults by integrating explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression, Random Forest, eXtreme Gradient Boosting...

Research indicates the effectiveness of machine learning for condition monitoring in sheet metal shearing. However, existing studies primarily focused on model accuracy while neglecting model explainability. In consequence, potential biases and novel insights captured by the models remained...