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 ...

Unsupervised anomaly detection (AD) remains a notable challenge in computer vision research, due to the inherent absence of annotated anomalous data and the unpredictable nature of anomaly ...

What you might not expect, though, is that the Transformer-based DLSS model actually uses less video memory than the older, CNN-based model. Screenshot from the latest NVIDIA DLSS Programming Guide.

This study aims to address these gaps by developing an optimized ML-based framework for real-time anomaly detection in IoT/IIoT environments. The methodology integrates supervised (Random Forest), ...

An unsupervised autoencoder approach achieves moderate success for anomaly detection (accuracy = 0.881) but struggles with recall (0.070). These findings highlight the trade-off between detection ...

By leveraging agentic AI for container-based threat detection, organizations can move from reactive security to proactive defense, especially within the realm of identity and access management (IAM).

Performance evaluation of a reflex blood-based methylated ctDNA multi-cancer early detection test in individuals with obesity. A phase 1 study of intracerebroventricular (ICV) delivery of bivalent ...

After Daytona Beach implemented AI gun-detection software from ZeroEyes, Inc., Volusia County Schools followed suit, linking the program with cameras across nearly 70 schools.

The FDA has cleared the Lumipulse G pTau217/ß-Amyloid 1-42 Plasma Ratio blood test for early detection of amyloid plaques associated with Alzheimer disease.