GitHub

cloudtrail_ml_analyzer.jsx

{ name:"ConsoleLogin", source:"signin.amazonaws.com", risk:1 }, { name:"GetObject", source:"s3.amazonaws.com", risk:1 }, { name:"ListBuckets", source:"s3.amazonaws ...
Computer Weekly

Glasgow researchers use machine learning to build network digital twin

Researchers at the University of Glasgow have developed a new way to test networks, which they claim is 25,000 times faster than traditional approaches. Shenjia Ding, a research student at the ...
IEEE

A Comprehensive Survey on Cognitive Cyber Security Analysis Using Machine Learning Approaches

Abstract: Cybersecurity threats have evolved in sophistication, necessitating the development of intelligent and adaptive defense mechanisms. Cognitive cybersecurity systems present the opportunity to ...
techtimes

AI Cyberattacks Rising: How Hackers Use Machine Learning to Launch Smarter Attacks

AI cyberattacks are rapidly transforming the cybersecurity landscape, enabling attackers to automate and scale operations with unprecedented speed. Through machine learning hacking, adversaries can ...
IEEE

Wastewater Attributes Analysis using Machine Learning

This research addresses the critical challenge of inefficient wastewater management by developing machine learning-driven predictive models for real-time analysis. Traditional wastewater analysis, ...
BMJ

Predicting cardiovascular events from routine mammograms using machine learning

10 The George Institute for Global Health, School of Public Health, Imperial College London, London, UK Background Cardiovascular risk is underassessed in women. Many women undergo screening ...
The Lancet

When to and when not to use machine learning in risk prediction models

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...
Journal of Medical Internet Research

Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic ...

Pooled effect sizes (r) were calculated using random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman correction, and subgroup and meta-regression analyses were conducted to explore potential ...
Frontiers

Traditional machine learning in biomedical image analysis: before you go too deep

Traditional machine learning (TML) algorithms remain indispensable tools for the analysis of biomedical images, offering significant advantages in multimodal data integration, interpretability, ...
acm.org

Integrating Machine Learning into Physical Security Architecture

Recent advancements in the field of artificial intelligence (AI) have exposed many modern organizations to the benefits of intelligent automation. According to research published by McKinsey, almost ...
Phys.org

Machine learning automates material analysis and design using X-ray spectroscopy data

Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy (XAS) is an important technique for this, as it reveals detailed insights ...