Publications
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Gas Management in Smart Homes: A Multi-agent and Knowledge Graph Approach
To address key challenges in IoT systems, including efficient resource allocation, adaptive service composition, and Quality of Service (QoS) under dynamic conditions, we develop a framework called DRL-MAS integrating multi-agent systems (MAS) and deep reinforcement learning (DRL). DRL-MAS leverages MAS’s decentralized decision-making capabilities and DRL’s adaptive learning strengths to ensure scalability, energy efficiency, and responsiveness […]
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Multimodal Route Planning Integrating Soft Mobility: A Real-World Case Study for Student Mobility
Soft and active mobility (SAM) integration into multimodal route planning is a critical innovation for advanc ing sustainable transportation. This study explores the inclusion of shared (SSAM) and personal (PSAM) soft and active mobility modes within public transport systems. Leveraging a time-expanded model, the proposed approach optimizes route planning by introducing reliability as a novel […]
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Data generation and deep neural network predictions for aged mechanical properties
The aim of this work is the data generation of aged mechanical properties following the Arrhenius equation and large deformation theory for a transversely isotropic bio-based polyurethane foam, and the application of this dataset in the training process of different deep neural network architectures to evaluate their capacity to predict the full stress–strain behavior of […]
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Smart cities services and solutions: A systematic review
Throughout history, cities have represented enduring symbols of human civilization and progress. Today, we are witnessing a technological revolution fueled by the rapid advancement of Information and Communication Technologies (ICT). This transformation has dramatically improved data analysis capabilities through the integration of the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and other cutting-edge […]
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Ensuring the federation correctness: Formal verification of Federated Learning in industrial cyber-physical systems
In industry 4.0, Industrial Cyber–Physical Systems (ICPS) integrate industrial machines with computer control and data analysis. Federated Learning (FL) improves this by enabling collaborative machine learning and improvement while maintaining data privacy. This method improves the security, and intelligence of industrial processes. FL-based frameworks proposed in the literature do not perform rigorous validation of collaborators’ […]
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NETLOGOPY: UNLOCKING ADVANCED SIMULATION AND INTEGRATION FOR NETLOGO USING PYTHON
NetLogo is widely recognized as one of the most popular software tools for agent-based simulation. However, it has notable limitations, particularly the lack of advanced libraries in specialized areas such as optimization, artificial intelligence (AI), and mechanical or electrical modeling. On the other hand, Python is a feature-rich programming language that is increasingly used in […]
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A memetic method for solving portfolio optimization problem under cardinality, quantity, and pre-assignment constraints
In industrial finance, portfolio selection has emerged as a critical challenge that has received considerable attention over the past few decades. The standard approach to this problem is the Markowitz mean–variance model, which seeks to balance two inherently conflicting objectives: maximizing returns and minimizing risk. This study investigates portfolio optimization under realistic constraints, including cardinality, […]
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Deep Reinforcement Learning of Simulated Students Multimodal Mobility Behavior: Application to the City of Toulouse
This study presents a Deep Reinforcement Learning (DRL) approach to address the multimodal mobility behavior of daily commuters, focusing specifically on students’ home-university multimodal trips. The proposed mesoscopic model addresses key limitations of recent macro and microscopic models by balancing individual mobility preferences with significant group-level student factors. At its core, the model employs a […]
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A Secure Architecture for Digital Twins in Resource-Constrained Industrial Systems
Digital Twins (DT) are transforming the Industrial Internet of Things (IIoT) by providing virtual replicas of physical assets, enabling enhanced predictive analytics and data-driven decision-making. However, as Industry 5.0 integrates DT with generative AI technologies via APIs and tokens, security risks increase. Outdated modeling tools, coupled with vulnerabilities in resource-constrained environments, such as limited computational […]
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Integrating Centrality Measures in Federated Learning-Based Intrusion Detection Systems
Network Intrusion Detection Systems (NIDS) are mechanisms designed to improve security by monitoring networks for signs of potential intrusions. While data-driven deep learningbased NIDSs have been popular for their superior performance, they are limited by their reliance on large amounts of data, often processed in a centralized manner. Federated Learning (FL) has thus emerged as […]
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A Novel Federated Learning Client Selection With Anomaly Detection Approach for IoT Systems
Federated Learning (FL) is emerging as a crucial approach to enhance data privacy and security, particularly in smart buildings and Internet of Things (IoT) ecosystems. By distributing learning across multiple clients, FL minimizes the need for centralized data transfers. This decentralized approach allows clients to collaboratively improve machine learning models without sharing raw data, and […]
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Improving predictive maintenance: Evaluating the impact of preprocessing andmodel complexity on the effectiveness of eXplainable Artificial Intelligence methods
Due to their performance in this field, Long-Short-Term Memory Neural Network (LSTM) approaches are often used to predict the remaining useful life (RUL). However, their complexity limits the interpretability of their results. So, eXplainable Artificial Intelligence (XAI) methods are used to understand the relationship between the input data and the predicted RUL. Modeling involves making […]