Probing Nanoscale Structural Perturbation in a WS2 Monolayer via Explainable Artificial Intelligence

Background Introduction Two-dimensional (2D) materials exhibit significant potential in fields such as nanoelectronics and optoelectronics due to their unique physical and chemical properties. However, structural perturbations at the nanoscale have a profound impact on their performance. Traditional characterization methods like Raman spectroscopy,...

An Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach

Improved Electricity Price Forecasting Model Based on SHAP and Its Explainability Analysis Background and Research Motivation Electricity price forecasting (EPF) models have become a hot research topic in recent years, particularly due to the financial impact of market volatility on stakeholders. Especially in European energy markets, recent years ...

Empowering Glioma Prognosis with Transparent Machine Learning and Interpretative Insights Using Explainable AI

Enabling Explainable Artificial Intelligence for Glioma Prognosis: Translational Insights from Transparent Machine Learning Academic Background This study is dedicated to developing a reliable technique to detect whether patients have a specific type of brain tumor—glioma—using various machine learning methods and deep learning methods, combined wi...