University of California, San Francisco

This Multiview AI Imaging Can Improve Cardiac Diagnostic Accuracy

Heart disease is the leading cause of adult death worldwide, making cardiovascular disease diagnosis and management a global health priority. An echocardiogram, or cardiac ultrasound, is one of the ...
Devdiscourse

Multi-modal artificial intelligence can improve smart city traffic analytics

Smart city initiatives are generating vast amounts of data from sensors, cameras, mobile devices, and digital service ...
Frontiers

DenseLes: slice-wise dense network for multiple sclerosis lesion segmentation and classification

Accurate and reliable segmentation of multiple sclerosis (MS) lesions from magnetic resonance imaging (MRI) is essential for diagnosis and monitoring disease progression. Therefore, a robust and ...
plataformamedia

MicroCloud Hologram Inc. Releases Next-Generation Quantum Convolutional Neural Network Multi-Class Classification Technology, Driving Quantum Machine Learning Towards ...

SHENZHEN, China, Nov. 15, 2025 /PRNewswire/ — MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, has launched a groundbreaking technological achievement ...
C&EN

From Mass to Class: Classification of Amphetamines MS/MS Spectra via Graph Neural Networks

Department of Environmental, Water and Agricultural Engineering, Faculty of Civil and Environmental Engineering Technion, Israel Institute of Technology, Haifa 3200003, Israel ...
C&EN

A Multi-Stage Graph Neural Network–Physics-Informed Neural Network (GNN–PINN) Framework for Thermodynamic Property Prediction

Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon 22212, Republic of Korea ...
IEEE

Stabilized classification control using multi-stage quantum convolutional neural networks for autonomous driving

Abstract: Real-time processing with high classification accuracy is a fundamental requirement in autonomous driving systems. However, existing neural network models for classification often face a ...
IEEE

PicoSleepNet: An Ultra Lightweight Sleep Stage Classification by Spike Neural Network using Single-channel EEG Signal

Abstract: This study introduces PicoSleepNet, an ultra-lightweight sleep stage classification method that utilizes a spiking neural network (SNN) with single-channel electroencephalogram (EEG) signals ...