A new framework discretizes time-series data and splits it between global rule-based and Transformer-based local detectors, ...

DA, a post-selection inference framework that provides valid p-values and false positive rate control for semi-supervised ...

For developers and infrastructure architects, the implications of containers' lightweight runtime environment and associated ecosystem of automated cluster and workload management, image registries ...

A dataset for anomaly detection and localization in the 5-GHz ISM band was developed. The normal signals consisted of real-world Wi-Fi signals, whereas the anomalous signals were artificially added to ...

Explore how Lokeshkumar Madabathula combines Databricks, real-time anomaly detection, lakehouse architecture, and governed Agentic AI for data reliability.

Researchers analyze state-of-the-art approaches, limitations, and applications of deep learning-based anomaly detection in multivariate time series Monitoring financial security, industrial safety, ...

Anomaly detection is the process of identifying data points, entities or events that fall outside the normal range. An anomaly is anything that deviates from what is standard or expected. Humans and ...

Unlike pattern-matching, which is about spotting connections and relationships, when we detect anomalies we are seeing disconnections—things that do not fit together. Anomalies get much less attention ...

The US Army Analytics Group (AAG) provides analytical services for various organizational operations and functions, including cybersecurity. AAG signed a Cooperative Research and Development Agreement ...

Acoustic AI that worked well indoors was affected by wind noise and filter dirt outdoors. The PoC approach of identifying challenges in real environments to connect to primary equipment.When ...