Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

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 …

Despite the success of convolutional neural network (CNN) in conventional closed-set
recognition (CSR), it still lacks robustness for dealing with unknowns (those out of known …

Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

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

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

Social media platforms are popular for expressing personal views, emotions and beliefs.
Social media platforms are influential for propagating extremist ideologies for group …

Managing delivery risks is a critical challenge in modern supply chain management due to
the increasing complexity and interdependencies of global supply networks. Existing …

Anomaly detection has numerous applications in diverse fields. For example, it has been
widely used for discovering network intrusions and malicious events. It has also been used …

A wireless sensor network (WSN) is defined as a set of spatially distributed and
interconnected sensor nodes. WSNs allow one to monitor and recognize environmental …