IEEE

D 3 Former: Dual-Decoder Dual-Transformer Reconstruction Network for Hyperspectral Anomaly Detection

Abstract: Hyperspectral anomaly detection (HAD) aims to identify and locate anomalous targets in hyperspectral image (HSI) data that are significantly different from the surrounding background or ...
Scientific Research Publishing

Sonani, R. and Govindarajan, V. (2022) A Hybrid Cloud-Integrated Autoencoder-GNN Architecture for Adaptive, High-Dimensional Anomaly Detection in US Financial Services ...

ABSTRACT: 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 ...
Frontiers

Leveraging real-world data for safety signal detection and risk management in pre- and post-market settings

The evolving regulatory landscape has increasingly recognized the value of real-world data (RWD) in enhancing drug safety surveillance across the clinical development lifecycle. Enabled by frameworks ...
IEEE

Contrastive Learning-Based Dual Autoencoder for Anomaly Detection in Loader Gearboxes

Abstract: Anomaly detection (AD) of gearboxes is essential for ensuring the operational safety and reliability of the loader. However, identifying anomalies in non-stationary signals remains ...
Morningstar

NetApp First to Embed Data Breach Detection into Enterprise Data Storage

New capabilities reinforce NetApp as the most secure storage on the planet NetApp® (NASDAQ: NTAP), the Intelligent Data Infrastructure company, today announced new industry-leading cyber resilience ...
Frontiers

HairSentinel: a time-aware anomaly detection framework for forecasting hairfall trends using temporal fusion transformers

1 Analytics Department, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India 2 Department of Data Science, School of Computer Science and Engineering ...