The main objective of this systematic literature review is to unveil the prevailing trend of
employing cutting-edge models for bankruptcy prediction for a period spanning from 2012 to …

This study explores the complex relationship between information and communication
technologies (ICTs) and socioeconomic characteristics. We employ a cutting-edge …

The economic costs of white-collar crimes, such as corruption, bribery, embezzlement,
abuse of authority, and fraud, are substantial. How to eradicate them is a mounting task in …

Vaccine hesitancy (VH) might represent a serious threat to the next COVID‐19 mass
immunization campaign. We use machine learning algorithms to predict communities at a …

Abstract Geographical Indications (GIs), as Protected Designation of Origin (PDO) and
Protected Geographical Indication (PGI), offer a unique protection scheme to preserve high …

Machine learning models can assess the financial health of companies and predict the
likelihood of them going bankrupt. Early detection gives companies and stakeholders more …

We use a machine learning approach, namely AdaBoost, to rank the determinants of
banking crises over time and across countries. We cover a total of 100 countries, advanced …

We investigate the different roles played by nodes' network and non-network attributes in
explaining the formation of European university collaborations from 2011 to 2016, in three …

Predicting university dropout is crucial. Identifying at-risk students can inform dropout
prevention policies, safeguarding the nation's resources and mitigating the long-term …

The widespread and substantial effect of the global financial crisis in history underlines the
importance of forecasting financial crisis effectively. In this paper, we propose temporal …