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Onderzoeker
Maxime Van Haeverbeke
- Trefwoorden:Machinaal Leren
- Disciplines:Machine learning en besluitvorming
Affiliaties
- Vakgroep Data-analyse en wiskundige modellering (Departement)
Lid
Vanaf30 jul 2019 → Heden
Projecten
1 - 1 of 1
- Geavanceerde impedimetrische systeemkarakterisatie voor ingenieurs- en levenswetenschappenVanaf1 feb 2024 → HedenFinanciering: BOF - postdoctorale mandaten
Publicaties
1 - 7 van 7
- Impedimetric biofilm characterization with microelectrode arrays using equivalent electrical circuit features and ensemble classifiers(2024)
Auteurs: Maxime Van Haeverbeke, Charlotte Cums, Thijs Vackier, Dries Braeken, Michiel Stock, Hans Steenackers, Bernard De Baets
- Plant impedance spectroscopy : a review of modeling approaches and applications(2023)
Auteurs: Maxime Van Haeverbeke, Bernard De Baets, Michiel Stock
- Evaluating the potential of Distribution of Relaxation Times analysis for plant agriculture(2023)
Auteurs: Maxime Van Haeverbeke, Bernard De Baets, Michiel Stock
- On the pivotal role of water potential to model plant physiological processes(2022)
Auteurs: Tom De Swaef, Olivier Pieters, Simon Appeltans, Irene Borra-Serrano, Willem Coudron, Valentin Couvreur, Sarah Garré, Peter Lootens, Bart Nicolaï, Leroi Pols, et al.
- Altered intravenous drug disposition in people living with cystic fibrosis : a meta‐analysis integrating top‐down and bottom‐up data(2022)
Auteurs: Pieter-Jan De Sutter, Maxime Van Haeverbeke, Eva Van Braeckel, An Vermeulen, Elke Gasthuys
Pagina's: 951 - 966 - Equivalent electrical circuits and their use across electrochemical impedance spectroscopy application domains(2022)
Auteurs: Maxime Van Haeverbeke, Michiel Stock, Bernard De Baets
Pagina's: 51363 - 51379 - Practical equivalent electrical circuit identification for electrochemical impedance spectroscopy analysis with gene expression programming(2021)
Auteurs: Maxime Van Haeverbeke, Michiel Stock, Bernard De Baets