Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Anomaly analysis is of great interest to diverse fields, including data mining and machine
learning, and plays a critical role in a wide range of applications, such as medical health …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

Anomaly detection problems arise in multiple applications, as discussed in the preceding
chapter. such as financial fraud, cyber intrusion, video surveillance, and medical image …

Identifying anomalies from data has attracted increasing attention in recent years due to its
broad range of potential applications. Although many efforts have been made for anomaly …

How to tackle high dimensionality of data effectively and efficiently is still a challenging issue
in machine learning. Identifying anomalous objects from given data has a broad range of …

Accurate short-term forecasting of the individual residential load is a challenging task due to
the nonlinear behavior of the residential customer. Moreover, there are a large number of …

Outlier detection is a hot topic in machine learning. With the newly emerging technologies
and diverse applications, the interest of outlier detection is increasing greatly. Recently, a …

The advancement of visual intelligence is intrinsically tethered to the availability of data. In
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …

An outlier is an event or observation that is defined as an unusual activity, intrusion, or a
suspicious data point that lies at an irregular distance from a population. The definition of an …