Zobrazeno 1 - 10
of 233
pro vyhledávání: '"Velardi, Paola"'
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. In the literature, ECG, actigraphy, speech, voice, and facial analysis
Externí odkaz:
http://arxiv.org/abs/2407.03821
NL2VIS (natural language to visualization) is a promising and recent research area that involves interpreting natural language queries and translating them into visualizations that accurately represent the underlying data. As we navigate the era of b
Externí odkaz:
http://arxiv.org/abs/2406.15259
Autor:
Prata, Matteo, Masi, Giuseppe, Berti, Leonardo, Arrigoni, Viviana, Coletta, Andrea, Cannistraci, Irene, Vyetrenko, Svitlana, Velardi, Paola, Bartolini, Novella
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 models focusing on Stock Price Trend Prediction (SPTP) based on Limit Ord
Externí odkaz:
http://arxiv.org/abs/2308.01915
Programming Skills are Not Enough: a Greedy Strategy to Attract More Girls to Study Computer Science
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 the prejudices of girls with respect to these disciplines is very diffic
Externí odkaz:
http://arxiv.org/abs/2302.06304
Autor:
Prenkaj, Bardh, Velardi, Paola
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering 2023
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, anomaly detection algorithms have been proposed for the early detection
Externí odkaz:
http://arxiv.org/abs/2302.06228
Publikováno v:
TVCG (2024)
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 information discovery. Among the many contributions proposed in this ar
Externí odkaz:
http://arxiv.org/abs/2302.00569
Autor:
Maranghi, Marianna, Anagnostopoulos, Aris, Cannistraci, Irene, Chatzigiannakis, Ioannis, Croce, Federico, Di Teodoro, Giulia, Gentile, Michele, Grani, Giorgio, Lenzerini, Maurizio, Leonardi, Stefano, Mastropietro, Andrea, Palagi, Laura, Pappa, Massimiliano, Rosati, Riccardo, Valentini, Riccardo, Velardi, Paola
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 paper presents the initial results of an ongoing project whose focus i
Externí odkaz:
http://arxiv.org/abs/2206.06182
Objective: Human-curated disease ontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. The classification principles underlying these ontologies are guided by the analysis of ob
Externí odkaz:
http://arxiv.org/abs/2104.00386
We predict disease-genes relations on the Human Interactome network using a methodology that jointly learns functional and connectivity patterns surrounding proteins. Contrary to other data structures, the Interactome is characterized by high incompl
Externí odkaz:
http://arxiv.org/abs/1902.10117
Motivations: People are generating an enormous amount of social data to describe their health care experiences, and continuously search information about diseases, symptoms, diagnoses, doctors, treatment options and medicines. The increasing availabi
Externí odkaz:
http://arxiv.org/abs/1902.06548