The spatial heterogeneity of landslide influencing factors is the main reason for the poor
generalizability of the susceptibility evaluation model. This study aimed to construct a …

Digitalization and technologization affect numerous domains, promising advantages but
also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance …

Deep learning models have achieved high performance across different domains, such as
medical decision-making, autonomous vehicles, decision support systems, among many …

In credit risk estimation, the most important element is obtaining a probability of default as
close as possible to the effective risk. This effort quickly prompted new, powerful algorithms …

The class imbalance problem is common in the credit scoring domain, as the number of
defaulters is usually much less than the number of non-defaulters. To date, research on …

A major requirement for credit scoring models is to provide a maximally accurate risk
prediction. Additionally, regulators demand these models to be transparent and auditable …

External stakeholders require accurate and explainable financial distress prediction (FDP)
models. Complex machine learning algorithms offer high accuracy, but most of them lack …

Most credit scoring algorithms are designed with the assumption to be executed in an
environment characterized by an automatic processing of credit applications, without …

It is important to manage operational disruptions to ensure the success of supply chain
operations. To achieve this aim, researchers have developed techniques that determine the …

Electrical load forecasting of buildings is crucial in designing an energy operation strategy
for smart city realization. Although artificial intelligence techniques have demonstrated …