Publications
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Metaheuristics and Machine Learning Convergence: A Comprehensive Survey and Future Prospects
The integration of machine learning techniques with optimization algorithms has garnered increasing interest in recent years. Two primary purposes emerge from the literature: leveraging metaheuristics in machine learning applications such as regression, classification, and clustering, and enhancing metaheuristics using machine learning to improve convergence time, solution quality, and flexibility. Machine learning techniques offer real-time decision-making […]
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Supply Chain Optimization with the Digital Twin: Case study of a warehouse
In a complex logistics landscape, the need for efficient and optimized Supply Chain’s (SC’s) is becoming increasingly important. This is crucial to creating an efficient SC and ensuring consistent, optimized use of resources. Efficient logistics management is essential if companies are to maintain their competitiveness in the marketplace. Digital technologies, such as digital warehouses, are […]
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Industrial Process Optimization with Digital Twin Solutions: Modeling, Monitoring and Decision-Making. Application to a water filtration unit.
The digital transformation of industry is an essential pillar of the global economy, aimed at improving productivity, promoting sustainable development and optimizing industrial performance. It relies on the use of advanced technologies such as cyber-physical systems, artificial intelligence and the Internet of Things, which make industrial systems not only more complex, but also more communicative, […]
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Medical Compression Prototype in a Wireless Sensor network
Medical Compression Prototype in a Wireless Sensor network (Submitted).
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Effet de la complexité du réseau LSTM sur l’explicabilité en Maintenance Prédictive
La nature complexe des données en maintenance prédictive impose souvent l’utilisation de modèles d’apprentissage profonds. Malgré leur efficacité dans la prédiction du RUL (durée de vie résiduelle des machines), ces « boites noires » fournissent des résultats qui ne sont pas directement compréhensibles. Ainsi, des méthodes XAI post hoc sont gé néralement utilisées pour les […]
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Hyperparameter Impact on Computational Efficiency in Federated Edge Learning
The heterogeneity induced by the federated edge learning execution environment poses many performance challenges. Indeed, a balance between efficient resource usage and inference accuracy must be found. Our work therefore aims at characterizing the hyperparameter influence by creating a variety of simulated execution circumstances. We designed an experimentation platform to simulate the execution of a […]
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Optimizing Shapley Value for Client Valuation in Federated Learning through Enhanced GTG-Shapley
In the ever-evolving realm of federated learning (FL), the question of data worth resonates with newfound urgency across organizations and individuals. In the dynamic FL ecosystem, where data resides across distributed nodes, evaluating the value of each client’s data is paramount. The evaluation mechanism helps to understand individual contributions to the overall process and incentivizes […]
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Enhancing Explainability in Predictive Maintenance: Investigating the Impact of Data Preprocessing Techniques on XAI Effectiveness
In predictive maintenance, the complexity of the data often requires the use of Deep Learning models. These models, called ”black boxes”, have proved their worth in predicting the Remaining Useful Life (RUL) of industrial machines . However, the inherent opacity of these models requires the incorporation of post-hoc explanation methods to enhance transparency. The quality […]
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GDLC: A new Graph Deep Learning framework based on centrality measures for intrusion detection in IoT networks
The increasing growth of the Internet of Things (IoT) with the diverse and dynamic nature of devices made detecting and preventing network intrusions more important and challenging. As new and sophisticated cyber-attacks are being used, there is an increasing need for advanced intrusion detection systems that can adapt to emerging threats. The majority of existing […]
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An intelligent mechanism for energy consumption scheduling in smart buildings
In recent years, the incorporation of sensing technology into residential buildings has given rise to the concept of ”smart buildings”, aimed at enhancing resident comfort. These buildings are typically part of interconnected neighborhoods sharing common energy sources, which makes the energy consumption a critical consideration in decision-making processes. Consequently, optimizing energy usage in smart buildings […]
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Multi-Task Learning for PBFT Optimisation in permissioned Blockchains
Finance, supply chain, healthcare, and energy have an increasing demand for secure transactions and data exchange. Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participants agree on a common value. One of the most widely used protocols in private blockchains is the Practical Byzantine Fault Tolerance (PBFT) which tolerates up […]
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Analyse de sensibilité du modèle énergétique d’un bâtiment pédagogique constitué de conteneurs maritimes
Les modèles énergétiques, bien que très utiles pour prédire le comportement physique du bâtiment, comportent des incertitudes (paramètres inconnus, approximations…). Ces incertitudes ont un impact sur la fiabilité des prédictions, qu’il est possible de limiter via la calibration du modèle. Nous présentons dans cet article une modélisation énergétique d’un bâtiment pédagogique construit à partir de […]
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