prof. dr. ir. Sofie Van Hoecke
- Work address
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Technologiepark Zwijnaarde 122
9052 Zwijnaarde - Sofie.VanHoecke@UGent.be
- ORCID iD
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0000-0002-7865-6793
- Bio (via ORCID)
- I graduated from the Engineering Department from the Ghent University in 2003. Following up on my studies in computer science, I achieved a PhD in computer science engineering at the Department of Information Technology at the same university. After being a postdoctoral research engineer at the Department of Information Technology, I started as lecturer ICT and ICT research coordinator at the University College West-Flanders. Since 2013, I joined Ghent University again. Currently, I am associate professor at IDLab, Ghent University-imec and my research focuses on machine learning and hybrid machine learning for predictive maintenance and predictive healthcare.
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- Journal Article
- open access
Leak localization in water distribution networks using GIS-Enhanced autoencoders
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- Journal Article
- A1
- open access
Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients
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- Journal Article
- A1
- open access
Pathogen-based target attainment of optimized continuous infusion dosing regimens of piperacillin-tazobactam and meropenem in surgical ICU patients : a prospective single center observational study
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- Journal Article
- A1
- open access
A microservice architecture for leak localization in water distribution networks using hybrid AI
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- Journal Article
- open access
TALK : tracking activities by linking knowledge
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- Journal Article
- A1
- open access
Real-time estimation and monitoring of COVID-19 aerosol transmission risk in office buildings
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Do not sleep on traditional machine learning Simple and interpretable techniques are competitive to deep learning for sleep scoring
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- Journal Article
- A1
- open access
Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
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- Conference Paper
- C1
- open access
Powershap : a power-full shapley feature selection method
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- Conference Paper
- C1
- open access
Leak localization in water distribution networks by directly fitting the learning parameters of a Gaussian naive Bayes classifier