Evaluation of GraphRAG Strategies for Efficient Information Retrieval
Traditional RAG systems struggle to capture relationships and cross-references between different sources unless explicitly mentioned. This challenge is common in real-world scenarios, where information is often distributed and interlinked, making graphs a more effective representation. Our work provides a technical contribution through a comparative evaluation of retrieval strategies within GraphRAG.
Flight Load Factor Predictions based on Analysis of Ticket Prices and other Factors
The ability to forecast traffic and to size the operation accordingly is a determining factor, for airports. However, to realise its full potential, it needs to be considered as part of a holistic approach, closely linked to airport planning and operations. To ensure airport resources are used efficiently, accurate information about passenger numbers and their effects on the operation is essential. Therefore, this study explores machine learning capabilities enabling predictions of aircraft load factors.
Beyond the cloud, advanced AI computer vision
From Cloud to Edge: how to transform your business model with Edge AI? Dive into embedded computer vision and edge AI algorithm development with STMicroelectronics STM32 and Euranova.
Investigating a Feature Unlearning Bias Mitigation Technique for Cancer-type Bias in AutoPet Dataset
We proposed a feature unlearning technique to reduce cancer-type bias, which improved segmentation accuracy while promoting fairness across sub-groups, even with limited data.
Muppet: A Modular and Constructive Decomposition for Perturbation-based Explanation Methods
The topic of explainable AI has recently received attention driven by a growing awareness of the need for transparent and accountable AI. In this paper, we propose a novel methodology to decompose any state-of-the-art perturbation-based explainability approach into four blocks. In addition, we provide Muppet: an open-source Python library for explainable AI.
Tech insights from GTC Paris 2025
Among the NVIDIA GTC Paris crowd was our CTO Sabri Skhiri, and from quantum computing breakthroughs to the full-stack AI advancements powering industrial digital twins and robotics, there is a lot to share!
Development & Evaluation of Automated Tumour Monitoring by Image Registration Based on 3D (PET/CT) Images
Tumor tracking in PET/CT is essential for monitoring cancer progression and guiding treatment strategies. Traditionally, nuclear physicians manually track tumors, focusing on the five largest ones (PERCIST criteria), which is both time-consuming and imprecise. Automated tumor tracking can allow matching of the numerous metastatic lesions across scans, enhancing tumor change monitoring.
Unlocking the power of unsupervised learning with interpretable graph embeddings
Unsupervised learning offers immense potential, but deciphering the results is often a challenge. Discover INGENIOUS, a groundbreaking framework from Euranova that generates interpretable graph embeddings that reveal the 'why' behind complex data patterns, empowering organizations to make informed decisions with confidence.
Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings
In this paper, we study graph representation learning and show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis.
Euranova et l’Institut Carnot CALYM s’associent pour améliorer la découverte de biomarqueurs grâce à l'IA
Le centre privé de recherche et développement (R&D) Euranova, spécialisé dans la science des données et l’IA, et l’Institut Carnot CALYM annoncent une collaboration avec le soutien institutionnel de Roche pour faire avancer la recherche clinique en imagerie médicale, qui se concentrera sur le lymphome folliculaire.
Une société wallonne reconnue dans le monde comme leader en stream processing
Euranova est bien plus qu'un consultant IT. L'entreprise se positionne aussi comme centre de recherche privé. Elle est une des rares entreprises en Belgique à publier des articles scientifiques pour des conférences internationales. Par exemple, les experts d'Euranova organisent chaque année un workshop sur l'analyse...
AI for bias detection
Discover how Euranova is helping Ellpha to increase diversity in the business world.
Smart Law Hub's work accepted in the Conference On AI And Law 2021
The latest research paper by the HEC Paris SMART Law Hub team in collaboration with Eura Nova research team was accepted by the International Conference on AI and Law (ICAIL) 2021.
Thirty-Fourth AAAI Conference On Artificial Intelligence
In 2020, research engineers Hounaida Zemzem and Rania Saidi attended the 34th AAAI Conference on Artificial Intelligence in New York. They engaged in technical sessions at this forum designed to promote AI research and scientific exchange among global experts.
Schloss Dagstuhl: where computer science meets
Which direction stream and complex event processing is going to take? Last week, the world’s best-known international researchers met in Schloss Dagstuhl.
An analytics-aware conceptual model for evolving graphs
Graphs are ubiquitous data structures commonly used to represent highly connected data. Many real-world applications, such as social and biological networks, are modeled as graphs. To answer the surge
Towards trust inference from bipartite social networks
The emergence of trust as a key link between users in social networks has provided an effective means of enhancing the personalization of online user content.