A machine learning system for detecting unusual market behaviour in daily equity price and volume data. The system identifies anomalous stock-days and market-wide stress periods using unsupervised ...

Abstract: Anomaly detection is a critical problem with a variety of applications since anomalies (which are unexpected observations that deviate significantly from other observations) pervasively ...

Unsupervised learning is a branch of machine learning that focuses on analyzing unlabeled data to uncover hidden patterns, structures, and relationships. Unlike supervised learning, which requires pre ...

Hairfall is a primary concern for many individuals worldwide today. Hair strands may fall due to various conditions such as hereditary factors, scalp health issues, nutritional deficiencies, hormonal ...

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...

Abstract: Efficient railway track maintenance is critical for safety, operational reliability, and cost-effective asset management. While traditional inspection methods are well-established and comply ...

Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions. Broadly speaking, a true anomaly is one that deviates from the ...