This study introduces a novel machine learning-based methodology for detecting and forecasting the strength of weak signals in the labor market, using Greece as a case study and utilizing Eurostat time series data (2000-2023). Weak signals, conceptualized as subtle anomalies within otherwise...

Anomaly detection is an important research area in a diverse range of real-world applications. Although many algorithms have been proposed to address anomaly detection for numerical datasets, categorical and mixed datasets remain a significant challenge, primarily because a natural distance...

The digital advertising industry, where clients advertise to existing and potential customers through digital channels, is going through a rapid transformation. This is necessitated by evolving privacy laws such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act...

The shift to a bioeconomy necessitates radical innovations to move from a fossil- to a bio-based economy, with bioclusters playing a crucial role by fostering collaboration among a wide range of co-located actors to find solutions. Through a novel methodological approach in regional studies...

Purpose: Automatic grouping of data according to certain characteristics is made possible by clustering algorithms, which makes them an essential tool when working with large datasets. However, although they are unsupervised tools, they generally require the specification of the number of...

The K-means algorithm and its variants are well-known clustering techniques. In actuarial applications, these partitioning methods can identify clusters of policies with similar attributes. The resulting partitions provide an actuarial framework for creating maps of dominant risks and...