Neuroscientists have been trying to understand how the brain processes visual information for over a century. The development of computational models inspired by the brain's layered organization, also ...
Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning ...
Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, and if it is accurately predicted ...
Bridging communication gaps between hearing and hearing-impaired individuals is an important challenge in assistive ...
As AI Music Tools Proliferate, Detection Technologies and Industry Responses EvolveThe music industry faces an unprecedented ...
Background Although chest X-rays (CXRs) are widely used, diagnosing mitral stenosis (MS) based solely on CXR findings remains ...
A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules
School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K. Kuano, Hauxton House, Mill Scitech Park, Mill Lane, Cambridge, England CB22 5HX, U.K. Department ...
Deep learning-based image steganalysis has progressed in recent times, with efforts more concerted toward prioritizing detection accuracy over lightweight frameworks. In the context of AI-driven ...
The First Hospital of Hunan University of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China Background: Breast cancer remains the most prevalent malignancy in women globally, ...
A Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks
Abstract: Recent advances in deep learning have driven significant success in synthetic aperture radar (SAR) automatic target recognition, particularly through convolutional neural network (CNN) based ...
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