Publications
-
A Review on Financial Fraud Detection: Techniques, Challenges, Solutions, and Perspectives
Financial fraud is one of the most serious criminal activities, resulting in losses exceeding hundreds of billions of dollars each year. Most existing fraud detection frameworks still use static rule sets or traditional Machine Learning (ML) models, which fail with decentralized systems characterized by anonymity and dynamic behavior. Despite recent developments, the deployment of advanced […]
-
Multi-Agent System for Solving the Vehicle Routing Problem: A Hybrid Metaheuristic Approach
This paper introduces a novel Multi-Agent System (MAS) designed to solve the Vehicle Routing Problem (VRP), a well-known optimization challenge in logistics. The proposed MAS integrates seven established metaheuristics—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Golden Ball Algorithm (GBA), Hill Climbing (HC), Tabu Search (TS), and Simulated Annealing (SA)—using a Bi-directional […]
-
Des Jumeaux Numériques d’Enseignement : entre instrument et finalité de l’acte éducatif
Dans la continuité des Jumeaux Numériques (JN) industriels, la formation est devenue un champ d’application visé par le développement de Jumeaux Numériques d’Enseignement (JNE). À partir d’une étude sur l’introduction d’un JNE dans une formation d’ingénieurs, cet article se propose de comprendre dans quelle mesure le JNE peut être instrumenté comme objet pédagogique dans une […]
-
Systematic review and future directions in dynamic flexible job shop scheduling: a decade of research
This paper presents a comprehensive overview of the current state-of-the-art on Dynamic Flexible Job Shop Scheduling problems (DFJSSP). It reviews a wide range of studies that address different scheduling strategies, various types of disruptive events, and multiple objectives. The numerous solution methods developed in the literature and tested on significant benchmarks are also analyzed. This […]
-
Impact Detection in Fall Events: Leveraging Spatio-temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeleton Data
Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern. A fall is defined as any event where a person loses balance and moves to an off-position, which may or may not result in an impact where the person hits the ground. While fall detection systems have […]
-
Effects of Thermal Activation on Mechanical Performance and Sustainability of Slag-Based Geopolymers
Ground granulated blast furnace slag (GBFS)-based geopolymers represent a viable binder system that combines mechanical efficiency with a significantly lower carbon footprint when compared to conventional Portland cement. This work examines how thermal curing between 20 ◦C and 80 ◦C affects setting time, mechanical performance, shrinkage, and porosity of GBFS-based geopolymers. Curing at 40 ◦C […]
-
Dynamic mechanical analysis of shape memory polymers: thermomechanical behavior and influence of thermal stimuli
This study explores the thermomechanical properties of polymethacrylate-based shape memory polymers (SMPs), focusing on hot water as a thermal stimulus for shape recovery. Using differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA), the research evaluates thermal transitions, viscoelastic behavior, and energy dissipation. DSC identified a glass transition temperature (Tg) of 67 °C, critical for […]
-
AGCD-Net: Attention Guided Context Debiasing Network for Emotion Recognition
Context-aware emotion recognition (CAER) enhances affective computing in real-world scenarios, but traditional methods often suffer from context bias-spurious correlation between background context and emotion labels (e.g. associating “garden” with “happy”). In this paper, we propose AGCD-Net, an Attention Guided Context Debiasing model that introduces Hybrid ConvNeXt, a novel convolutional encoder that extends the ConvNeXt backbone […]
-
MANAGING READINESS FOR CHANGE TO UNLOCK PROJECT PERFORMANCE -A quantitative study at the cross-section of change- and project management practices-
This study explores the influence of perceptions on individual readiness for change as an explanatory factor in the field of organizational behavior and renewed human resource practices. Hereby, this study offers a fresh perspective on the comparative significance of culture versus nationality as explanatory constructs to further explore the realm of diversity-, equity- and inclusion […]
-
Unmet Needs of Visually Impaired Pedestrians and Urban Cyclists in French Cities
Abstract. The current transition toward sustainable transportation encourages active mobility, such as walking and cycling. However, grouping cyclists and pedestrians under a single category of “vulnerable users” often overlooks their distinct safety needs. This study explores the specific mobility constraints and expectations of two vulnerable road user groups, visually impaired pedestrians and urban cyclists, to […]
-
Smart Fleet Management for Shared Micro-mobility: Balanced demand, Redistribution and Charging via Deep Reinforcement Learning
Shared electric micro-mobility, as an emerging mode of urban transportation, has been booming worldwide in recent years. Al though it provides sustainable, eco-friendly, and cost-effective mobility, it also faces several challenges, particularly due to existing inefficient fleet management strategies. These typically rely on fixed redistribution schedules that fail to adapt to highly dynamic user demand […]
-
Implementation of an AI-driven dynamic control system to optimize excess photovoltaic energy management in grid-connected sustainable BIPV
This study proposes an integrated approach for optimizing grid-connected photovoltaic (PV) systems through AI-based forecasting and a Dynamic Automatic Control System (DACS). Using Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and a hybrid CNN-LSTM model, we predict PV production and building energy demand. The CNN-LSTM model achieved the best performance for PV forecasting […]