ABSTRACT: This work contributes to the development of intelligent data-driven approaches to improve intrusion management in smart IoT environments. The proposed model combines a hybrid ...

Credit: Getty Images Researchers evaluated whether a deep learning model could distinguish between autoimmune neuroinflammatory disorders based on retinal thickness. A deep learning model, using optic ...

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...

Within the past few years, models that can predict the structure or function of proteins have been widely used for a variety of biological applications, such as identifying drug targets and designing ...

Machine learning and AI are now entering the field of textile technology and at this year’s ITMA Asia + CITME exhibition from October 28-31 in Singapore, UK-based Shelton Vision will demonstrate how ...

A group of scientists led by researchers from the University of New South Wales (UNSW) in Australia has developed a novel deep-learning method for denoising outdoor electroluminescence (EL) images of ...

Skimming articles and using quick AI-generated summaries is killing my ability, and yours, to understand complex topics. While the shortcut offers headline-level comprehension, it often miss vital ...

WWDC 2025 revealed some upgraded tools for game developers that'll make games render at higher resolutions, faster frame rates, and improved ray tracing with fewer resources. Apple didn't make that't ...

X-ray computed tomography (CT) is widely used in clinical practice for screening and diagnosing patients, as it enables the acquisition of high-resolution images of internal tissues and organs in a ...

In PhotoniX, researchers report a self-supervised deep learning method that denoises dynamic fluorescence images in vivo without requiring clean training data. The figure shows in vivo venule images ...