Military Medicine

Vision-Language Models for Edge Networks: A Comprehensive Survey

Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual question answering, and video analysis. While VLMs show impressive capabilities across domains such as autonomous vehicles, smart surveillance, and healthcare, their deployment on resource-constrained edge devices remains challenging due to processing po...

A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications

Advancements in embedded systems and Artificial Intelligence (AI) have enhanced the capabilities of Unmanned Aircraft Vehicles (UAVs) in computer vision. However, the integration of AI techniques o-nboard drones is constrained by their processing capabilities. In this sense, this study evaluates the deployment of object detection models (YOLOv8n and YOLOv8s) on both resource-constrained edge dev...

Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions

Individual's general well-being is greatly impacted by mental health conditions including depression and Post-Traumatic Stress Disorder (PTSD), unde...

OPTIC: Optimizing Patient-Provider Triaging & Improving Communications in Clinical Operations using GPT-4 Data Labeling and Model Distillation

The COVID-19 pandemic has accelerated the adoption of telemedicine and patient messaging through electronic medical portals (patient medical advice ...

Deep learning model for ECG reconstruction reveals the information content of ECG leads

This study introduces a deep learning model based on the U-net architecture to reconstruct missing leads in electrocardiograms (ECGs). The model was...

FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities

Effective traffic signal control (TSC) is crucial in mitigating urban congestion and reducing emissions. Recently, reinforcement learning (RL) has b...

Contrastive Representation Learning Helps Cross-institutional Knowledge Transfer: A Study in Pediatric Ventilation Management

Clinical machine learning deployment across institutions faces significant challenges when patient populations and clinical practices differ substan...

A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition

The deployment of neural networks in vehicle platforms and wearable Artificial Intelligence-of-Things (AIOT) scenarios has become a research area th...

Sentiment-guided Commonsense-aware Response Generation for Mental Health Counseling

The crisis of mental health issues is escalating. Effective counseling serves as a critical lifeline for individuals suffering from conditions like ...

Dr. Tongue: Sign-Oriented Multi-label Detection for Remote Tongue Diagnosis

Tongue diagnosis is a vital tool in Western and Traditional Chinese Medicine, providing key insights into a patient's health by analyzing tongue att...

Region of Interest based Medical Image Compression

The vast volume of medical image data necessitates efficient compression techniques to support remote healthcare services. This paper explores Regio...

A Study about Distribution and Acceptance of Conversational Agents for Mental Health in Germany: Keep the Human in the Loop?

Good mental health enables individuals to cope with the normal stresses of life. In Germany, approximately one-quarter of the adult population is af...

Cancer Alpha: A Production-Ready AI System for Multi-Modal Cancer Genomics Classification

The integration of multi-modal genomic data for cancer classification remains challenging in precision oncology. While machine learning approaches hav...

Your Emotions, My Brain: Generalizable Neural Signatures of Emotional Memory Reactivation During Sleep

Reactivation in sleep alters the structure of memories and can potentially be used to restructure upsetting representations. Reactivation can be trigg...

Detection dog performance state estimation from pre-stimulus video and physiological signals using deep learning and Bayesian inference

Detection dogs play a critical role in operational settings ranging from explosives detection to medical diagnostics. Their unmatched olfactory capabi...

Grounded large language models for diagnostic prediction in real-world emergency department settings

Emergency departments face increasing pressures from staff shortages, patient surges, and administrative burdens. While large language models (LLMs) s...

Machine Learning in Psychiatric Health Records: A Gold Standard Approach to Trauma Annotation

Psychiatric electronic health records present unique challenges for machine learning due to their unstructured, complex, and variable nature. This stu...

Identifying Predictors of Benzodiazepine Discontinuation in Medical Cannabis Patients with Post-traumatic Stress Disorder Using a Machine Learning Approach

Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition commonly treated with medications like benzodiazepines (BZDs), despite...

Artificial Intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy

Chagas disease affects 6–7 million people worldwide and causes approximately 12,000 deaths annually. Diagnostic methods vary by disease stage, with se...

Revolutionizing COPD and Asthma Management with Artificial Intelligence

The integration of artificial intelligence (AI) into the management of chronic obstructive pulmonary disease (COPD) and asthma offers significant adva...