In addition, real-time anomaly detection poses high demands on low computational cost and model robustness, presenting substantial obstacles for unsupervised time-series anomaly detection.

Start small — Pilot an anomaly detection model on historical billing data. Integrate gradually — Connect AI models with live meter feeds.

Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the applicability of supervised methods.

Article citations More>> Mishra, P., Varadharajan, V., Tupakula, U. and Pilli, E.S. (2021) Unsupervised Anomaly Detection in IoT Using Autoencoders. IEEE Internet of Things Journal, 8, 9065-9078. has ...

A research team at Texas A&M University is studying the use of Siri-like virtual assistant technology for use in space. The ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

Explore how AI transforms cardiac amyloidosis detection with remarkable accuracy—keep reading to see its clinical potential.

As with DeepSeek’s models, Kimi K2 is open-weight, meaning it can be downloaded and built upon by researchers for free. It ...

A research team led by Prof. Li Hai from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has ...

Assessing the progress of new AI language models can be as challenging as training them. Stanford researchers offer a new approach.

Recently, a research team at the Hefei Institutes of Physical Science of the Chinese Academy of Sciences (CAS) proposed a novel model optimization algorithm named External Calibration-Assisted ...