AI captures particle accelerator behavior to optimize machine performance

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...
Duke Pratt School of Engineering

Mapping Behavior with Machine Learning

By transforming movement into data, Timothy Dunn is reshaping how scientists can study behavior and the brain.
Frontiers

Smart Additive Manufacturing of Advanced Materials Using Artificial Intelligence and Machine Learning

The field of additive manufacturing is undergoing a profound transformation as artificial intelligence (AI) and machine learning (ML) become integral to the ...
The Lancet

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

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

2026 Data Science Course Created by FAANG+ Data Scientists Focused on Production-Ready Skills in AI and Machine Learning

SANTA CLARA, CA - February 12, 2026 - - Interview Kickstart has launched a new Data Science course designed for working ...
North Penn Now

Machine Learning Using Python: A Complete Learning Path With Practical Projects

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Sophelio Launches Data Fusion Labeler (dFL) to Accelerate Sensor Data Preparation for Machine Learning

Sophelio Introduces the Data Fusion Labeler (dFL) for Multimodal Time-Series Data - The only labeling and harmonization studio built for multimodal time-series with full provenance you can replay “dFL ...
The Manila Times

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...
Open Heart

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...
EDN

Designing edge AI for industrial applications

Edge AI addresses high-performance, low-latency requirements by embedding intelligence directly into industrial devices.
GitHub

A machine-learning derived heatmap for predicting hypoglycemia risk during exercise for people with type 1 diabetes

Preprocessing of EXTOD 101 for ML: Prepares the EXTOD 101 data, consisting of 35 individuals with 8 weeks of data, for machine learning analysis. Preprocessing EXTOD Education Dataset: Processes data ...
IEEE

Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning

Abstract: With the rapid advancements in deep learning, IoT intrusion detection systems have increasingly adopted deep learning models as the state-of-the-art solution due to their ability to handle ...