Nous vivons dans un monde où les causes et les effets guident notre compréhension des phénomènes qui nous entourent. Une relation de cause à effet peut être considérée comme un système dont les entrées sont une cause et les sorties un effet. À l'ère de la profusion des données, les nombreux exemples entrées/sorties permettent de caractériser de nom...
Predicting the methylation status of the MGMT promoter in glioblastoma (GBM) is critical for optimizing temozolomide (TMZ) treatment. This study investigates an ensemble machine learning approach that integrates clinical and molecular data to achieve more accurate and reliable predictions of MGMT status. By combining multiple models to fully levera...
This article explores the integration of Machine Learning (ML) and Blockchain in personalised rehabilitation care for patients with chronic disease. By analysing vast sets of data, ML makes it possible to predict treatment results and personalise interventions. But these tools require large quantities of high-quality data. Blockchain technology cou...
Recent breakthroughs in deep learning have been driven by the growth of deep neural networks, improving their ability to memorize and generalize. However, this growth requires ever-increasing computational resources to train these networks.In this thesis, we propose to improve the standard deep learning training framework, which consists of paralle...
Algorithmic fairness is a critical challenge in building trustworthy Machine Learning (ML) models. ML classifiers strive to make predictions that closely match real-world observations (ground truth). However, if the ground truth data itself reflects biases against certain sub-populations, a dilemma arises: prioritize fairness and potentially reduce...
Electroencephalography (EEG) non-invasively measures the brain's electrical activity through electromagnetic fields generated by synchronized neuronal activity. This allows for the collection of multivariate time series data, capturing a trace of the brain electrical activity at the level of the scalp. At any given time instant, the measurements re...
Astronomical transients are among the most energetic phenomena in the Universe. In order to unveil their secrets, increasingly better telescopes have been built to perform large scale sky surveys. The upcoming Vera-C.-Rubin Observatory represents the state of the art of a new generation of such surveys. It is expected to detect around 10 million ca...
Graphs are a fundamental data structure used to represent complex patterns in various domains. Graph Neural Networks (GNNs), a deep learning paradigm specifically designed for graph-structured data, offer a powerful deep learning solution for extracting insights from these intricate relationships. This thesis explores the application of GNNs to add...
Cardiovascular diseases are the leading cause of death globally, represent-ing 31.4% of all deaths in the world in 2022. With more than half of all cardiovascu-lar disease-related deaths happening in settings outside of the hospital, there is a needfor constant at-home monitoring to prevent them. Such long-term monitoring solutionsnecessitate auton...
Collaboration between different cybersecurity actors is essential to fight against increasingly sophisticated and numerous attacks. However, stakeholders are often reluctant to share their data, fearing confidentiality and privacy issues and the loss of their competitive advantage, although it would improve their intrusion detection models. Federat...
