TMCnet

Machine Learning Advances in Predictive Cyber Threat Detection

Cyber threats are increasing in speed and complexity, driving the need for advanced detection techniques. Machine learning is ...

SardineAI Corp Announces Fraud Operations Framework Reframing Machine Learning vs Generative AI in Risk Decisioning

SardineAI Corp announces the release of a fraud risk operations guide focused on the distinction between machine learning vs generative AI as an operational consideration within financial crime ...

Sysdig 2026 Cloud-Native Security Report Signals That Human-Driven Security Is Coming to an End

Sysdig, the leader in real-time AI-powered cloud defense, today released the "Sysdig 2026 Cloud-Native Security and Usage ...
The Silicon Review

How Artificial Intelligence Is Strengthening Fraud Detection Across Online Platforms

Discover how artificial intelligence is enhancing fraud detection across online platforms, using real-time analytics and ...
Que.com on MSN

Artemis raises $70M for AI-driven defense against cyber attacks

Introduction In today’s digital age, organizations face an escalating wave of cyber threats that can compromise sensitive data, disrupt operations, ...
EurekAlert!

Attention detection using EEG signals and machine learning: A review

An overview of attention detection using EEG signals, which includes six steps: an experimental paradigm design, in which the task and the stimuli are defined and presented to the subjects; EEG data ...
The American Journal of Managed Care

Novel Machine Learning Model Improves MASLD Detection in Type 2 Diabetes

MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...