activity
20152023
most citedUnsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis

10 citations · 28 across the 9 of their papers we have counts for

collaborators

9 papers

q-fin.TR2023★ 1 cited

LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study

Matteo Prata, Giuseppe Masi, Leonardo Berti +6

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL mod…

cs.CY2023

Programming Skills are Not Enough: a Greedy Strategy to Attract More Girls to Study Computer Science

Tiziana Catarci, Luca Podo, Daniel Raffini +1

It has been observed in many studies that female students in general are unwilling to undertake a course of study in ICT. Recent literature has also pointed out that undermining th…

cs.LG2023★ 10 cited

Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis

Bardh Prenkaj, Paola Velardi

Real-time monitoring of human behaviours, especially in e-Health applications, has been an active area of research in the past decades. On top of IoT-based sensing environments, an…

cs.LG2023★ 8 cited

Agnostic Visual Recommendation Systems: Open Challenges and Future Directions

Luca Podo, Bardh Prenkaj, Paola Velardi

Visualization Recommendation Systems (VRSs) are a novel and challenging field of study aiming to help generate insightful visualizations from data and support non-expert users in i…

cs.LG2022★ 4 cited

AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes

Marianna Maranghi, Aris Anagnostopoulos, Irene Cannistraci +13

The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database. This…

q-bio.MN2021

A network-based analysis of disease modules from a taxonomic perspective

Giorgio Grani, Lorenzo Madeddu, Paola Velardi

Objective: Human-curated disease ontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. The classification p…

q-bio.MN2019★ 5 cited

Network-based methods for disease-gene prediction

Lorenzo Madeddu, Giovanni Stilo, Paola Velardi

We predict disease-genes relations on the Human Interactome network using a methodology that jointly learns functional and connectivity patterns surrounding proteins. Contrary to o…

cs.CY2019

Quality of Life Assessment of Diabetic patients from health-related blogs

Andrea Lenzi, Marianna Maranghi, Giovanni Stilo +1

Motivations: People are generating an enormous amount of social data to describe their health care experiences, and continuously search information about diseases, symptoms, diagno…

cs.SI2015

Analysis of women leadership in enterprise social networks

Giorgia Di Tommaso, Giovanni Stilo, Paola Velardi

This paper describes a Social Network Analysis toolkit to monitor an Enterprise Social Network and help analyzing informal leadership as a function of social ties and topic discuss…