Researchers in the discipline of Data Mining (DM) occasionally disregard the need of ensuring a dataset is evenly distributed. As such, it may have a major impact on how things are ultimately sorted.

A crucial part of the machine learning lifecycle is managing data drift to ensure the model remains effective and continues to provide business value. Data is an ever-changing landscape, after all.

Surveillance has come a long way from the watchful eyes of security guards to the all-seeing lenses of today's cameras.

Machine learning-powered security systems should be used as a tool, not as a replacement for security teams for web apps.

Machine learning (ML) has been widely used in trace link recovery (TLR) to reduce the manual maintenance cost of trace links by developers. However, the imbalanced distribution of valid links and ...

Check out these best practices that are designed to help your data preparation initiatives in machine learning.

While approaches and capabilities differ, all of these databases allow you to build machine learning models right where your data resides.

A data privacy expert explains how machine learning algorithms draw inferences and how that leads to privacy concerns.

Using wearable sensors and advanced machine learning algorithms, researchers offer a practical and cost-effective solution for capturing detailed movement data, essential for balance analysis.