With the rise of technology and the continued economic growth evident in modern society,
acts of fraud have become much more prevalent in the financial industry, costing institutions …

Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Novelty detection is the identification of new or unknown data or signal that a machine
learning system is not aware of during training. Novelty detection is one of the fundamental …

Outliers once upon a time regarded as noisy data in statistics, has turned out to be an
important problem which is being researched in diverse fields of research and application …

Anomaly detection in crowded scenes plays a crucial role in automatic video surveillance to
avert any casualty in the areas witnessing the high amount of footfalls. The key challenge for …

Outlier detection has been a very important concept in the realm of data analysis. Recently,
several application domains have realized the direct mapping between outliers in data and …

Face spoofing detection is commonly formulated as a two-class recognition problem where
relevant features of both positive (real access) and negative samples (spoofing attempts) are …

Fraud can be seen in all insurance types including health insurance. Fraud in health
insurance is done by intentional deception or misrepresentation for gaining some shabby …