Projects
Business-Oriented Data Analytics: Advances in Profit-Driven Model Building and Fraud Detection KU Leuven
In recent years, the ever-larger becoming avalanche of data poses a new set of challenges for businesses. Yet, where there is risk, there also lies opportunity! Consequently, businesses rely more than ever on “smart” algorithms to stay ahead of their competition.
As for the mature market of a mobile phone service provider, for example, it is vital to the provider’s business to prevent its customers from leaving the company (i.e., from ...
Deep Learning Models for the Detection and Segmentation of Tumoral Lesions Using PET and MRI KU Leuven
The advancement of high-performance computing together with the availability of large medical imaging datasets have enabled the use of deep learning models for medical image analysis, and in particular convolutional neural networks (CNNs) which are currently dominating the field. The main goal of this thesis was to broaden the clinical applicability of CNN models, including the application of these models for the automatic detection and ...
Conditional Generative Models for Transparent Discrimination Detection and Mitigation in Algorithmic Decision-Making Systems Vrije Universiteit Brussel
Validation and development of deep learning models for automated glaucoma detection and progression quantification using Tays Eye Centre data KU Leuven
Validation and development of deep learning models for automated glaucoma detection and progression quantification using Tays Eye Centre data
Detection and avoidance of low probability phenomena using probabilistic graphical models in electromechanical actuators Ghent University
Due to its interaction with the environment and internal changes due to wear, every machine is bounded to evolve over time. This evolution might entail several unwanted consequences such as production loss, unexpected outages, etc. Actuators constitute the driving force of every machine. They convert energy into a form that is required by the application. Electromechanical actuators, that convert electrical energy into motion, take up the ...
Detection and avoidance of low probability phenomena using probabilistic graphical models in electromechanical actuators. Ghent University
Current condition monitoring methods for electromechanical actuators are lacking robustness. This is a consequence of the large number of external (e.g. load) and internal (e.g. manufacturing tolerances) factors that influence the measurements on which the condition monitoring is based. This research project aims to improve the robustness by augmenting the models used for condition monitoring with a probabilistic model which avoids false ...