Projects

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Qualitative Evaluation of Machine Learning Models. University of Antwerp

Bart Goethals
ADReM Data Lab (ADReM)
A common and recently widely accepted problem in the field of machine learning is the black box nature of many algorithms. In practice, machine learning algorithms are typically being viewed in terms of their inputs and outputs, but without any knowledge of their internal workings. Perhaps the most notorious examples in this context are artificial neural networks and deep learning techniques, but they are certainly not the only techniques that ...

Enhancing data analytics for IoT by enabling semantic enrichment of machine learning tasks Ghent University

Filip De Turck
Department of Information technology

The recent spread of sensors, actuators and mobile devices, comprising the Internet of Things (IoT), provides ample opportunity to improve our quality of life through data analytics. However, as IoT data is bound by the four Vs of Big DataU+2014volume, variety, velocity, and veracityU+2014deriving meaningful insights becomes challenging. Today, two approaches have been employed side by side. Relying on knowledge graphs (KGs) and logical ...

Enabling personalized medicine by optimizing disease treatments with hybrid machine learning Ghent University

Sofie Van Hoecke
Department of Information technology, Department of Electronics and information systems

Dynamic Treatment Regimen (DTR) are adaptive treatment strategies using a sequence of expert decision rules, ideally one per stage of intervention, to individualize treatments for patients. They are an important tool towards enabling personalized medicine. Attempts are already being made to tackle treatment individualization with machine learning using deep reinforcement learning for better accuracy, however, these solutions require immense ...

Physics-enhanced machine learning for domain-aware predictive maintenance. Ghent University

Sofie Van Hoecke
Department of Electromechanical, Systems and Metal Engineering, Department of Electronics and information systems

Unexpected breakdowns in manufacturing occur frequently, resulting in periods of downtime and potential revenue losses. Recent research advancements apply machine learning to predict the occurrence of these breakdowns. Based on the collection of historical data describing the circumstances before these malfunctions appear, models are trained to find patterns in the data that predict the remaining life time of a machine. The main drawback of ...

Machine learning platform for better cardiovascular health assessment and risk stratification KU Leuven

Tatiana Kouznetsova
Public Health and Primary Care, Kulak Kortrijk Campus, Hypertension and Cardiovascular Epidemiology

Precise description of cardiovascular health and risk assessment are essential for optimal prevention and treatment strategies, but require a complex integration of many factors. The specific objectives of this project are: (1) Applying machine-learning (ML) algorithms to the available longitudinal, highly standardized, general population data to assess the complexity of clinical and behavioral variables, cardiac imaging sequences and ...

Forging connections between machine learning techniques and strongly correlated physical systems Ghent University

Jan Ryckebusch
Department of Electronics and information systems, Department of Physics and astronomy

In this research project, we aim at developing novel ways for understanding physical systems
consisting of many interacting constituents using techniques developed in the field of machine
learning. A common use of machine learning models is to classify pictures of different objects into
categories. Likewise, machine learning methods can be exploited to find hidden connections and
correlations between the different ...

Machine learning for fraud analytics KU Leuven

Wouter Verbeke
Research Centre for Information Systems Engineering (LIRIS), Leuven

Fraud remains a major challenge for businesses. The Association of Certified Fraud Examiners (ACFE) estimates that a typical organization loses 5% of its revenues due to fraud. Furthermore, fraudsters continuously adapt their techniques in response to fraud detection efforts, creating a need for adaptive fraud detection systems. Given the abundant availability of data, machine learning techniques seem well suited to tackle this problem. ...

Using machine learning to model the prognosis of multiple sclerosis patients KU Leuven

Celine Vens
Public Health and Primary Care, Kulak Kortrijk Campus, Laboratory for Neuroimmunology

Multiple sclerosis is a complex disease with a highly heterogeneous disease course. In this project, we will develop machine learning methods to study this disease course using a combination of demographical, clinical, genomic, and radiomic data. This study is done on the patient level (individualized prognosis models), on the group level (patient stratification models), and on the population level (identification of prognostic biomarkers). ...

Detection and attribution of changes in global wildfire activity to anthropogenic drivers using machine learning Vrije Universiteit Brussel

Wim Thiery
Faculty of Engineering, Hydrology and Hydraulic Engineering
Exceptional wildfire activity in recent years highlighted the
catastrophic impact these extreme events have on communities,
ecosystems, and economies. Despite the growing concerns over
wildfire activity under continued climate warming, there is little
scientific evidence causally linking observed wildfire changes to
anthropogenic forcings, climatic or non-climatic. In addition, future
projections of burned ...

Hybrid machine learning with BIM for energy optimization and dweller well-being in smart buildings Ghent University

Sofie Van Hoecke
Department of Electronics and information systems

Residential and commercial buildings consume 40% of the total energy, 70% of the total electricity and are responsible for 41% of the greenhouse gas emissions. Therefore, it is important to eliminate any energy wastage in buildings. The dwellers of these buildings, i.e. the knowledge workers, spend 86% of their time indoors, so this building environment has a large impact on their well-being. The management of these buildings is today done ...