Tech Xplore

Easier parameter tuning for prediction using echo state networks

Neural networks, a fascinating technology inspired by the human brain, form the basis of artificial intelligence. These ...
Searchenginejournal.com

Machine-First Architecture: How To Build Websites Machines Can Identify, Read, Cite & Use

In the late 2000s, “mobile-first” emerged as a design discipline. The argument was a single sentence: don’t design for the big screen and squeeze it down. Start with the small screen, the harder ...
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 ...
MD&M East

AI-Powered Atrial Fibrillation Detection: How Machine Learning Is Transforming Cardiac Care Through ECG Analysis

At the April 2026 Heart Rhythm Society Annual Meeting in Chicago, panelists gathered to discuss a myriad of topics in the rapidly growing cardiovascular care space. Song Zuo, MD, a specialist at the ...
iaea.org

New IAEA Research Project Uses Machine Learning to Better Predict Polymer Changes under Radiation

Azillah Binti Othman, IAEA Department of Nuclear Sciences and Applications Ayhan Evrensel, IAEA Department of Nuclear Sciences and Applications The IAEA is inviting research organizations to join a ...
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 ...
Medical Xpress

AI model detects prediabetes using ECG data without need for blood tests

DiaCardia, a novel artificial intelligence model that can accurately identify individuals with prediabetes using either 12-lead or single-lead electrocardiogram (ECG) data, has been developed. This ...
IEEE

Unsupervised Feature Selection-Driven Active Learning for Semi-Supervised Automatic ECG Analysis

Abstract: Automatic analysis methods of electrocardiograms (ECGs) usually required large-scale annotated training data, but the annotation process is extremely time-consuming. While semi-supervised ...
Frontiers

Machine learning–guided feature selection and predictive model construction for attention-deficit/hyperactivity disorder

A total of 8,598 children were enrolled and classified into three groups: ADHD (n=3,678), subthreshold ADHD (s-ADHD) (n=1,495), and healthy controls (HC) (n=3,425). Data collection covered 40 ...
IEEE

An ECG Data Feature Selection Comparison: Machine Learning and Expert Doctors

Abstract: The ECG signals analysis is a frequently used approach for classification of cardiac patients. Traditionally, this analysis is made by expert doctors. However, recently, it is increasingly ...
ahajournals.org

Abstract 4368538: Fusion Machine Learning Architectures for Improving ECG classification of acute coronary events

Introduction: Acute coronary syndrome (ACS) is a life-threatening emergency, with occlusion myocardial infarction (OMI) requiring rapid diagnosis and treatment. The 12-lead ECG remains the primary ...