Ryszard Kowalczyk

Abstract Explainable artificial intelligence (XAI) is increasingly required for anomaly detection in high-dimensional sensor systems operating in safety-critical and resource-constrained environments. While existing post-hoc explanation methods provide useful insights, they often suffer from high computational cost, unstable attributions, and limited applicability in unlabeled or unsupervised settings.

Abstract With the continued appeal and adoption of cloud computing, an assessment of cloud run costs and migration affordability prior to adoption would assist enterprises that have several legacy applications targeted for cloud migration. However, as cloud migrations have become more prevalent, many have been characterised by unsuccessful migration or application modernisation attempts.