In order to improve the performance of any machine learning model, it is important to focus
more on the data itself instead of continuously develo** new algorithms. This is exactly the …

Financial institutions increasingly rely upon data-driven methods for develo** fraud
detection systems, which are able to automatically detect and block fraudulent transactions …

Anomaly detection methods identify examples that do not follow the expected behaviour,
typically in an unsupervised fashion, by assigning real-valued anomaly scores to the …

Detecting intrusion in network traffic has remained a problematic task for years. Progress in
the field of machine learning is paving the way for enhancing intrusion detection systems …

Mediation analysis is central to theory building and testing in organizational sciences.
Scholars often use linear regression analysis based on normal-theory maximum likelihood …

Poverty data are usually collected through on-the-ground household-based socioeconomic
surveys. Unfortunately, data collection with such conventional methods is expensive …

The innovation and evolution of hacking methodologies have led to a sharp rise in cyber
attacks, highlighting the need for enhanced network security approaches. Network intrusion …

Air pollution refers to the presence of hazardous substances in the air that has adverse
effects on health, causing millions premature deaths annually. Ground-based stations can …

Breakout is one of the major accidents that often arise in the continuous casting shops of
steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss …

It is well-known that real data often contain outliers. The term outlier usually refers to a case,
usually denoted by a row of the n× d data matrix. In recent times a different type has come …