Prevention of medical errors

Residual-INR: Communication Efficient On-Device Learning Using Implicit Neural Representation

Edge computing is a distributed computing paradigm that collects and processes data at or near the...

Multimodal generative semantic communication based on latent diffusion model

In emergencies, the ability to quickly and accurately gather environmental data and command inform...

scHyper: reconstructing cell-cell communication through hypergraph neural networks.

Cell-cell communications is crucial for the regulation of cellular life and the establishment of cel...

Development of an Intelligent Health Education System Based on Large Language Model for Elderly Pulmonary Aspiration Prevention.

As the aging process accelerates, the incidence of chronic diseases in the elderly is rising. As a r...

EidetiCom: A Cross-modal Brain-Computer Semantic Communication Paradigm for Decoding Visual Perception

Brain-computer interface (BCI) facilitates direct communication between the human brain and extern...

PPCRKB: a risk factor knowledge base of postoperative pulmonary complications.

Postoperative pulmonary complications (PPCs) are highly heterogeneous disorders with diverse risk fa...

FedsLLM: Federated Split Learning for Large Language Models over Communication Networks

Addressing the challenges of deploying large language models in wireless communication networks, t...

FedLog: Personalized Federated Classification with Less Communication and More Flexibility

Federated representation learning (FRL) aims to learn personalized federated models with effective...

An automata-based approach for synchronizable mailbox communication

We revisit finite-state communicating systems with round-based communication under mailbox semanti...

See-Through Face Display: Enabling Gaze Communication for Any Face$\unicode{x2013}$Human or AI

We present See-Through Face Display, an eye-contact display system designed to enhance gaze awaren...

D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions

Large vision language models (VLMs) have progressed incredibly from research to applicability for ...

Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis

Medical image classification plays a crucial role in computer-aided clinical diagnosis. While deep...

Development and validation of a machine learning-based model to predict isolated post-challenge hyperglycemia in middle-aged and elder adults: Analysis from a multicentric study.

INTRODUCTION: Due to the high cost and complexity, the oral glucose tolerance test is not adopted as...

A Transformer-Based Multi-Stream Approach for Isolated Iranian Sign Language Recognition

Sign language is an essential means of communication for millions of people around the world and s...

Advancing dermatology: artificial intelligence-based solutions to reducing the risk of misdiagnosis.

[An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptom...

KI-Bilder und die Widerständigkeit der Medienkonvergenz: Von primärer zu sekundärer Intermedialität?

The article presents some current observations (as of April 10, 2024) on the integration of AI-gen...

Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification

Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication effici...

Transformative Influence of LLM and AI Tools in Student Social Media Engagement: Analyzing Personalization, Communication Efficiency, and Collaborative Learning

The advent of Large Language Models (LLMs) and Artificial Intelligence (AI) tools has revolutioniz...

Object-Attribute-Relation Representation Based Video Semantic Communication

With the rapid growth of multimedia data volume, there is an increasing need for efficient video t...

Practical offloading for fine-tuning LLM on commodity GPU via learned sparse projectors

Fine-tuning large language models (LLMs) requires significant memory, often exceeding the capacity...