Projects
Enhancing data analytics for IoT by enabling semantic enrichment of machine learning tasks Ghent University
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 Data—volume, variety, velocity, and veracity—deriving meaningful insights becomes challenging. Today, two approaches have been employed side by side. Relying on knowledge graphs (KGs) and logical rules, ...
Scholarly Communications Data: Using Supervised and Unsupervised Machine learning Techniques towards fraud detection and enhancing quality KU Leuven
Machine learning techniques for estimating crop areas at the sub-pixel level. KU Leuven
Accurate forecasts of local and regional agricultural production are essential for agricultural market contractors and operators to assist prize agreements as early as possible in the crop growing season. These forecasts are also helpful for societies to anticipate to limited food availability. Satellite remote sensing is a fairly recent but already established technology for large-scale agricultural production forecasting thanks to its ...
Building personalised machine learning models in health informatics with limited datasets KU Leuven
Healthcare services are being transformed by technological advancements and the availability of health-related data, from wearable device monitoring to treatment personalisation. Machine learning (ML) has the potential to harness this data by identifying patterns and developing prediction models to assist stakeholders and, ultimately, improve healthcare. The applications of machine learning in healthcare have grown exponentially, from drug ...
Efficient and Versatile Methods for Relational Machine Learning KU Leuven
The field of machine learning concerns computer algorithms that automatically improve their performance on a task through experience from data. Most common machine learning approaches expect data in the form of a table. Therefore, they cannot directly learn from more expressive data formats, such as relational databases, knowledge bases or logic programs, which are able to represent relations between complex instances. To learn from these ...
DNS Abuse and Active Authentication: Applications of Machine Learning in Cyber Security KU Leuven
In today’s digital world, cyber security is essential for a fair and well-functioning society. People, as well as companies and governments must be able to trust their computers, mobile devices and the services they use from companies and governments.
An important element of cyber security is the fight against cyber crime. This fight has surpassed the deployment of ‘passive infrastructure’ such as firewalls and access control systems ...
Machine learning Modeling of Time-dependent Patient Trajectories KU Leuven
The increasing availability of large-scale medical datasets has fueled the hope of an acceleration toward precision medicine, driven by data. Indeed, the systematic collection of Electronic Health Records (EHR) across several healthcare institutions has resulted in large numbers of patient records even for low-prevalence diseases and allowed uncovering more specific patterns in the evolution of the disease of individual patients. Ultimately, ...
Enabling personalized medicine by optimizing disease treatments with hybrid machine learning Ghent University
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 ...
Automated Analysis of Histological Images Using Machine Learning and Image Processing Techniques KU Leuven
SUMMARY
Histology studies microscopic tissue appearance and properties.
Histology analysis is used to study disease at the cellular level. This analysis happens through microscopic examination of tissue sections,
thin slices of tissue that were obtained from biopsies, mounted on a microscope slide, and made visible using a specific dye. Digital imaging allows individual microscopic structures, ...