41 citations · 166 across the 31 of their papers we have counts for
5 papers · 1 filter
Can recurrent neural networks learn process model structure?
Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
Various methods using machine and deep learning have been proposed to tackle different tasks in predictive process monitoring, forecasting for an ongoing case e.g. the most likely…
Predicting student performance using sequence classification with time-based windows
Galina Deeva, Johannes De Smedt, Cecilia Saint-Pierre +2
A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID…
Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors
Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn +1
Reliable remaining time prediction of ongoing business processes is a highly relevant topic. One example is order delivery, a key competitive factor in e.g. retailing as it is a ma…
Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring
Hans Weytjens, Jochen De Weerdt
The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use. We distinguish two types of learnable uncertain…
Can deep neural networks learn process model structure? An assessment framework and analysis
Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event…