5 papers · 1 filter
When does distribution shift break graph neural networks calibration?
Abderaouf Bahi
Graph neural networks (GNNs) are increasingly deployed in real-world applications where distribution shift is un-avoidable. However, how such shifts affect model calibration, defin…
Graph Neural Networks Applications Across Domains: All Insights You Need
Abderaouf Bahi
Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. The interesting question is no…
FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting
Abderaouf Bahi, Amel Ourici, Ibtissem Gasmi +3
Accurate forecasting of renewable energy generation is essential for efficient grid management and sustainable power planning. However, traditional supervised models often require…
GNN-ASE: Graph-Based Anomaly Detection and Severity Estimation in Three-Phase Induction Machines
Moutaz Bellah Bentrad, Adel Ghoggal, Tahar Bahi +1
The diagnosis of induction machines has traditionally relied on model-based methods that require the development of complex dynamic models, making them difficult to implement and c…
Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
Abderaouf Bahi, Amel Ourici, Hasan Dincer +2
The exponential growth of digital services has positioned data centers among the most energy-intensive infrastructures in the modern economy, raising critical concerns regarding op…