Spam emails have become more prevalent, necessitating the development of more effective
and reliable anti-spam filters. Internet users face security threats, and youngsters are …

Data has become much widely available in recent years. Since the past years, Learning
classifiers from unbalanced data is a crucial issue that comes up frequently in classification …

Novelty detection and concept drift detection are essential for the plethora of machine
learning applications. The statistical properties of application generated data change over …

Networks are strained by spam, which also overloads email servers and blocks mailboxes
with unwanted messages and files. Setting the protective level for spam filtering might …

The plethora of existing methods in the streaming environment is sensitive to extensive and
high-dimensional data. The distribution of these streaming data may change concerning …

Abstract Concept drift is one of the most prominent issues in streaming data that machine
learning models need to address. Most of the research in the field of concept drift targets …

Real-time applications generate an enormous amount of data that can potentially change
data distribution. The underline change in data distribution concerning time causes concept …

Exponential population growth and urbanisation pose potential challenges to mobility,
governance, well-being, the environment, and the safety of modern cities. This demands …

Because the insufficient new distribution training samples after concept drift occurs in
streaming data, the performance of online learning model degrades and cannot quickly …

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a
number of unsolicited messages are circulated over the internet. While plenty of machine …