In a bid to improve quality control and reduce production errors, manufacturers are increasingly adopting automated anomaly ...

Industrial anomaly detection is crucial for maintaining quality control and reducing production errors, but traditional ...

Explainable AI (XAI) can improve safety, security, and the overall user experience in IoT applications.

Industrial processes and manufacturing systems depend on consistency and accuracy. Unusual data readings, or anomalies, can signal issues like equipment malfunction, faulty components or deteriorating ...

Applying anomaly detection to embedded systems is a challenge, but it's one that can provide benefits like identifying a device that's not working properly or one that might be under attack by ...

The Data Science Lab Autoencoder Anomaly Detection Using PyTorch Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a ...

“The team we sold to was a team of data scientists,” Cohen says of Microsoft. “They were tasked with [building the real-time anomaly detection system], but they had a lot of additional things they ...

As many people highlight, signature or rule-based security systems can be a hazard insofar as they struggle with the unknown-unknowns. Additionally, an anomaly detection system can identify a threat ...

5 Things to Pay Attention To When Building An AI-Powered Anomaly Detection Solution In order to truly unlock the potential of boosting cybersecurity with anomaly detection, it’s important to put ...

For an autoencoder anomaly detection system, model overfitting is characterized by a situation where all reconstructed inputs match the source inputs very closely, and therefore all reconstruction ...