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Sabeur Elkosantini

Informations générales

Sabeur Elkosantini
Grade

Maître de conférences

Biographie courte

Sabeur Elkosantini is a university lecturer and researcher in computer science, with expertise in artificial intelligence, distributed systems, and multimodal data fusion. He has supervised several PhD theses in areas such as intelligent transportation systems, logistics, and digital health. Actively involved in digital transformation initiatives, he founded and coordinates an innovation hub  (Innotech, FSEG Nabeul) that bridges academia and industry. His expertises cover data quality, MLOps, and interoperability of information systems. His work lies at the intersection of research, teaching, and applied innovation, supporting both public institutions and private companies.

Équipes

Axes de recherche

Artificial Intelligence
Computational Intelligence
Multimodal data fusion

Publications

  • 2024
    Rihab Chaouch, Jihene Tounsi, Issam Nouaouri, Sabeur Elkosantini

    Mixed Integer Programming For Patient Admission Scheduling in Hospital Network

    This work presents a mixed-integer programming model to optimize patient admission scheduling in hospital networks, with the aim of improving bed assignment and coordination of care., 2024

    Résumé

    The Patient Admission Scheduling (PAS) process involves efficiently managing the admission of patients to specific beds within relevant departments while addressing all their medical needs over a defined time horizon. This study delves into PAS within hospital network, emphasizing the collaborative nature of their interactions. Collaborative interactions are a common challenge in hospitals, where they need to collaborate and share resources to allocate patients to a limited number of beds within a specified timeframe, ensuring all necessary medical conditions are met. To address this challenge, a mixed-integer mathematical programming model for the PAS problem within hospital network is proposed with the aim of minimizing a weighted sum of unsatisfied constraints.

    Salah Ghodhbani, Sabeur Elkosantini

    A Spatial-Temporal DLApproach for Traffic Flow Prediction Using Attention Fusion Method

    The proposed model can extract comprehensive features from various transportation data and effectively capture the spatial-temporal dependencies. By merging these features, it aims to generate more accurate and robust traffic flow predictions. This method, 2024

    Résumé

    in recent years, traffic flow prediction has presented challenges in the management of transportation systems. It is a crucial part of Intelligent Transportation Systems (ITS). The complexities of various transportation data, spatial and temporal dependencies on road networks, and multimodalities, such as public transit, pedestrian flow, and bike sharing, make it a challenging task to forecast traffic flow accurately. Numerous works have been introduced to address these challenges, but few have simultaneously considered these factors, resulting in limited success. In this study, a model is proposed to integrate Graph Convolutional Networks (GCN) and Bidirectional Long Short-Term Memory (BiLSTM). This model utilizes the advantages of GCN in handling spatial data and capturing dependencies in road networks, combined with BiLSTM's capability in learning temporal dynamics. The proposed model can extract comprehensive features from various transportation data and effectively capture the spatial-temporal dependencies. By merging these features, it aims to generate more accurate and robust traffic flow predictions. This method addresses the limitations of existing methods that fail to consider spatial-temporal dependencies and multimodalities, leading to improved prediction accuracy and efficiency

  • Arwa Kochkach, Saoussen Bel Haj Kacem, Sabeur Elkosantini, Wonho Suh, Seongkwan M. Lee

    On the Different Concepts and Taxonomies of eXplainable Artificial Intelligence

    In : International Conference on Intelligent Systems and Pattern Recognition. Cham : Springer Nature, 2023, 75-85., 2023

    Résumé

    Presently, Artificial Intelligence (AI) has seen a significant shift in focus towards the design and development of interpretable or explainable intelligent systems. This shift was boosted by the fact that AI and especially the Machine Learning (ML) field models are, currently, more complex to understand due to the large amount of the treated data. However, the interchangeable misuse of XAI concepts mainly “interpretability” and “explainability” was a hindrance to the establishment of common grounds for them. Hence, given the importance of this domain, we present an overview on XAI, in this paper, in which we focus on clarifying its misused concepts. We also present the interpretability levels, some taxonomies of the literature on XAI techniques as well as some recent XAI applications.

    Salah Ghodhbani, Sabeur Elkosantini

    ADL based Framework For Multimodal Data Fusion in Traffic Jam prediction

    In this paper, we propose new Hybrid method based on Deep Learning combine two independent model such as CNN, LSTM models to fuse multimodal and spatial temporal data. The proposed model uses Extended Kalman Filter (EKF) to combine result of the proposed, 2023

    Résumé

    Recently, intelligent transportation system (ITS) is considered as one of the most important issues in smart city applications. Its supports urban and regional development and promotes economic growth, social development, and enhances human well-being. ITS integrates new technologies of information and communication including sensors, social media IoT devices which can generate a massive amount of heterogeneous and multimodal data known as big data term. In this context, Data Fusion techniques (DF) seem promising and have emerged from transportation applications and hold a promising opportunity to deal with imperfect raw data for capturing reliable, valuable and accurate information. In literature many DF techniques based on machine learning remarkably renovates fusion techniques by offering the strong ability of computing and predicting. In this paper, we propose new Hybrid method based on Deep Learning combine two independent model such as CNN, LSTM models to fuse multimodal and spatial temporal data. The proposed model uses Extended Kalman Filter (EKF) to combine result of the proposed DL classifiers. In the other side, the proposed approach uses CBOA algorithm for feature selection in order to provide effective exploration of significant features with faster convergence

  • Houyem Ben Hassen, Jihene Tounsi, Rym Ben Bachouch, Sabeur Elkosantini

    Case-based reasoning for home health care planning considering unexpected events

    IFAC-PapersOnLine, 55(10), 1171-1176, 2022

    Résumé

    In recent years, Home Health Care (HHC) has gained popularity in different countries around the world (e.g. France, US, Germany, etc.). The HHC consists in providing medical services to patients at home. During the HHC service, caregivers’ planning may be disrupted by some unexpected events (e.g. urgent request, caregiver absence, traffic congestion, etc.), which makes HHC activities infeasible. This paper addresses the daily HHC routing and scheduling problem by considering unpredicted events. To solve this problem, we propose a Case-Based Reasoning (CBR) methodology. Our purpose is to create the HHC case base which contains the knowledge about the perturbation.

    Salah Ghodhbani, Sabeur Elkosantini

    Transfer Learning Based Architecture for Urban Transportation Big Data Fusion

    the propose method use CBOA algorithm for feature selection in to order to provide effective exploration of significant features with faster convergence. The proposed model demonstrated its effective results on the applied dataset by offering good results, 2022

    Résumé

Projets