In the last two decades, improvements in materials, sensors and machine learning
technologies have led to a rapid extension of electronic nose (EN) related research topics …

Anomaly detection is a popular and vital task in various research contexts, which has been
studied for several decades. To ensure the safety of people's lives and assets, video …

Since edge device failures (ie, anomalies) seriously affect the production of industrial
products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection …

Artificial Intelligence (AI) technology is a huge opportunity for the Industrial Internet of Things
(IIoT) in the fourth industrial revolution (Industry 4.0). However, most AI-driven applications …

The data produced by millions of connected devices and smart sensors in the Industrial
Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped …

Artificial Intelligence of Things (AIoT) is an emerging area of future Internet of Things (IoT) to
support intelligent IoT applications. In AIoT, intelligent edge computing technologies …

The need for robust unsupervised anomaly detection in streaming data is increasing rapidly
in the current era of smart devices, where enormous data are gathered from numerous …

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques …