activity
20242026
most citedAI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence

3 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks

Matteo De Sanctis, Riccardo De Sanctis, Stefano Faralli +2

Graph Neural Networks (GNNs) are increasingly adopted across domains such as molecular biology and social network analysis, yet their black-box nature hinders interpretability and…

cs.LG2026

A Multi-Agent Framework for Interpreting Multivariate Physiological Time Series

Davide Gabrielli, Paola Velardi, Stefano Faralli +1

Continuous physiological monitoring is central to emergency care, yet deploying trustworthy AI is challenging. While LLMs can translate complex physiological signals into clinical…

cs.LG20253 cited

AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence

Davide Gabrielli, Bardh Prenkaj, Paola Velardi +1

We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients using a fusion of wearable sensors, ambient intelligence, and advanced…

q-fin.TR20251 cited

TRADES: Generating Realistic Market Simulations with Diffusion Models

Leonardo Berti, Bardh Prenkaj, Paola Velardi

Financial markets are complex systems characterized by high statistical noise, nonlinearity, volatility, and constant evolution. Thus, modeling them is extremely hard. Here, we add…

cs.HC20241 cited

V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions

Luca Podo, Marco Angelini, Paola Velardi

NL2VIS (natural language to visualization) is a promising and recent research area that involves interpreting natural language queries and translating them into visualizations that…

cs.LG2024

Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences

Davide Gabrielli, Bardh Prenkaj, Paola Velardi

Monitoring the stress level in patients with neurodegenerative diseases can help manage symptoms, improve patient's quality of life, and provide insight into disease progression. I…