Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
Purpose: Nearly all amyotrophic lateral sclerosis (ALS) patients develop dysarthria, with many progr...
Privacy-preserving model co-training in medical research is often hindered by server-dependent archi...
Snakebite is a neglected public health problem that results in significant morbidity and mortality, ...
BackgroundThe accuracy and safety of generating medication orders by large language models (LLMs) mu...
UNLABELLED: This study aims to develop an exploratory classification model for Juvenile Myoclonic Ep...
Learning robot manipulation policies from raw, real-world image data requires a large number of ro...
Federated Learning (FL) faces inherent challenges in balancing model performance, privacy preserva...
Federated Learning (FL), as a distributed learning paradigm, trains models over distributed client...
Federated learning (FL) provides a promising paradigm for collaboratively training machine learnin...
In recent years, hip arthroscopy has made great progress and has been extended to the treatment of i...
6G networks promise revolutionary immersive communication experiences including augmented reality ...
This paper provides an in-depth characterization of GPU-accelerated systems, to understand the int...
This study investigates the application of a simultaneous transmitting and reflecting reconfigurab...
With the booming development of generative artificial intelligence (GAI), semantic communication (...
Traditional simulator-based training for maritime professionals is critical for ensuring safety at...
Linear attention mechanisms deliver significant advantages for Large Language Models (LLMs) by pro...
Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessm...
The marriage of deep neural network (DNN) and secure 2-party computation (2PC) enables private infer...
Federated learning collaborates with multiple clients to train a global model, enhancing the model g...
Distributed machine learning in mobile adhoc networks faces significant challenges due to the limite...
Recently, transformer-based methods have shown remarkable success in object re-identification. Howev...