While anomaly detection and the related concept of intrusion detection are widely studied,
detecting anomalies in new operating behavior in environments such as the Internet of …

Anomaly detection (AD) is an enduring topic, and it has been used in various fields, such as
fraud detection, industrial fault diagnosis, and medical image diagnosis. With the continuous …

Skin cancer (SC) is one of the most common cancers in the world and is a leading cause of
death in humans. Melanoma (M) is the most aggressive form of skin cancer and has an …

The cancer disease prediction and detection processes are crucial tasks in this emerging
world and it is tough to manage the diseases. Generally, the disease prediction processes …

Mura is defined as visual unevenness on the display panel. It can cause unpleasant
feelings, so it is necessary to perform Mura inspection during the display quality test …

Outlier detection is often considered a challenge due to the inherent class imbalance in
datasets, with the small number of available outliers that are insufficient to describe their …

Congenital heart disease is considered as one the most common groups of congenital
malformations which affects $6-11$ per $1000 $ newborns. In this work, an automated …

A careless driver can endanger their safety and passengers. To address the issue, we
propose generative adversarial networks (GANs) to detect the distracted driver and …

Detecting novel anomalies in medical imaging is challenging due to the limited availability of
labeled data for rare abnormalities, which often display high variability and subtlety. This …

Outlier detection aims to identify samples that do not match the expected patterns or major
distribution of the dataset. It has played an important role in many domains such as credit …