22 citations · 29 across the 11 of their papers we have counts for
4 papers · 1 filter
Leveraging Duration Pseudo-Embeddings in Multilevel LSTM and GCN Hypermodels for Outcome-Oriented PPM
Fang Wang, Paolo Ceravolo, Ernesto Damiani
Existing deep learning models for Predictive Process Monitoring (PPM) struggle with temporal irregularities, particularly stochastic event durations and overlapping timestamps, lim…
HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data
Fang Wang, Paolo Ceravolo, Ernesto Damiani
We propose HGCN(O), a self-tuning toolkit using Graph Convolutional Network (GCN) models for event sequence prediction. Featuring four GCN architectures (O-GCN, T-GCN, TP-GCN, TE-G…
Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring
Fang Wang, Paolo Ceravolo, Ernesto Damiani
Predictive Business Process Monitoring (PBPM) aims to forecast future outcomes of ongoing business processes. However, existing methods often lack flexibility to handle real-world…
HH4AI: A methodological Framework for AI Human Rights impact assessment under the EUAI ACT
Paolo Ceravolo, Ernesto Damiani, Maria Elisa D'Amico +10
This paper introduces the HH4AI Methodology, a structured approach to assessing the impact of AI systems on human rights, focusing on compliance with the EU AI Act and addressing t…