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Ines Ben Jaafar

Informations générales

Ines Ben Jaafar
Grade

Maître de conférences

Biographie courte

Research Profile – Ines Ben Jaafar, PhD

Dr. Ines Ben Jaafar is an Associate Professor In  Business Computing at the Ecole Supérieure de Commerce de Tunis (ESCT), University of Manouba, Tunisia, and a researcher at the SMART Laboratory (Strategies for Modeling and Artificial Intelligence), Institut Supérieur de Gestion de Tunis.

Her research focuses on Artificial Intelligence, Multi-Agent Systems, Combinatorial and Multi-Criteria Optimization, Vehicular Networks, Health care optimization and Smart Logistics. She has supervised numerous master’s and doctoral theses in these areas and collaborated internationally, notably with universities in France.

Dr. Ben Jaafar has published in leading international journals including Expert Systems with Applications, Engineering Applications of Artificial Intelligence, and International Journal on Artificial Intelligence Tools. She has also presented over 20 papers at international conferences (IEEE, Springer, ACM). Her work has received more than 260 citations (h-index: 10) on Google Scholar.

She serves as a reviewer for international journals such as Computers & Industrial Engineering (Elsevier), Telecommunication Systems (Springer), and Journal of Parallel and Distributed Computing, and is a program committee member in major AI and optimization conferences.

EDUCATION & DIPLOMES
Habilitation Universitaire, ISG, University of Tunis, 2019
PhD, Business Computing, ISG, University of Tunis, 2000
Postgraduate degree., Business Computing and Modeling, ISG, University of Tunis, 2000
Master’s degree, Business Computing , ISG, University of Tunis, 1998

Équipes

Axes de recherche

Artificial Intelligence
Optimization
Machine Learning
Cyber Security
Deep Learning
Health care
Multi-Objective Optimization
Combinatorial Optimization
Cloud computing
intelligent transportation
Smart Logistics.
Vehicular Networks

Publications

  • 2025
    Amel ZIDI, Issam Nouaouri, Ines Ben Jaafar

    Improving Emergency Triage in Crisis Situations: A Hybrid GAN-Boosting Approach with Machine Learning

    Second Edition IEEE Afro-Mediterranean Conference on Artificial Intelligence 2025 IEEE AMCAI IEEE AMCAI 2025, 2025

    Résumé

    Emergency departments (EDs) must quickly assess
    and prioritize patients, especially during crises when demand

    exceeds capacity. Traditional triage methods, such as the Jump-
    START protocol for pediatric cases and the START (Simple

    Triage and Rapid Treatment) method for adults, are commonly
    used but may lack precision under high-pressure situations.
    This paper proposes a hybrid approach combining ensemble
    models—XGBoost, AdaBoost, and CatBoost—with synthetic data
    augmentation using Generative Adversarial Networks (GANs) to
    enhance triage accuracy for critically ill patients.
    Models were trained on real-world ED data, including vital
    signs, symptoms, medical history, and demographics. GANs
    generated synthetic critical cases to address class imbalance,
    improving model sensitivity to high-risk profiles.

    Results show that GAN-augmented models outperform base-
    line models, with CatBoost offering the best balance between

    accuracy and computational efficiency. This approach improves
    patient prioritization, reduces delays, and supports better clinical
    decision-making in resource-limited environments.
    Index Terms—Emergency Department (ED), Patient Triage,

    Machine Learning (ML), AdaBoost, XGBoost, CatBoost, Genera-
    tive Adversarial Networks (GANs), Urgency Classification, Crisis

    Situations.

    Amel ZIDI, Rayen Jemili, Issam Nouaouri, Ines Ben Jaafar

    Optimizing Emergency Department Patient Flow Forecasting: A Hybrid VAE-GRU Model

    11th International Conference on Control, Decision and Information Technologies, 2025

    Résumé

    Emergency departments (EDs) face increasing
    patient demand, leading to overcrowding and resource strain.
    Accurate forecasting of ED visits is critical for optimizing
    hospital operations and ensuring efficient resource allocation.
    This paper proposes a hybrid model combining Variational
    Autoencoder (VAE) and Gated Recurrent Unit (GRU) to enhance
    patient flow predictions. The VAE extracts meaningful
    latent features while handling missing data, whereas the GRU
    captures complex temporal dependencies, improving forecasting
    accuracy. Compared to traditional models such as LSTM,
    GRU, and 1D CNN, our hybrid VAE-GRU model demonstrates
    superior predictive performance. Experimental results, based
    on real-world hospital data, highlight the model’s effectiveness
    in reducing prediction errors and improving decision-making
    in dynamic ED environments. Additionally, we compare the
    proposed model with ARIMA-ML, emphasizing the tradeoffs
    between computational efficiency and prediction accuracy.
    The findings suggest that hybrid deep learning approaches
    can significantly enhance healthcare resource management,
    reducing patient waiting times and improving overall hospital
    efficiency.

    Ahmed Yosreddin Samti, Ines Ben Jaafar, Issam Nouaouri, Patrick Hirsh

    A Novel NSGA-III-GKM++ Framework for Multi-Objective Cloud Resource Brokerage Optimization

    June 2025 Mathematics 13(13):2042, 2025

    Résumé

    Cloud resource brokerage is a fundamental challenge in cloud computing, requiring the efficient selection and allocation of services from multiple providers to optimize performance, sustainability, and cost-effectiveness. Traditional approaches often struggle with balancing conflicting objectives, such as minimizing the response time, reducing energy consumption, and maximizing broker profits. This paper presents NSGA-III-GKM++, an advanced multi-objective optimization model that integrates the NSGA-III evolutionary algorithm with an enhanced K-means++ clustering technique to improve the convergence speed, solution diversity, and computational efficiency. The proposed framework is extensively evaluated using Deb–Thiele–Laumanns–Zitzler (DTLZ) and Unconstrained Function (UF) benchmark problems and real-world cloud brokerage scenarios. Comparative analysis against NSGA-II, MOPSO, and NSGA-III-GKM demonstrates the superiority of NSGA-III-GKM++ in achieving high-quality tradeoffs between performance and cost. The results indicate a 20% reduction in the response time, 15% lower energy consumption, and a 25% increase in the broker’s profit, validating its effectiveness in real-world deployments. Statistical significance tests further confirm the robustness of the proposed model, particularly in terms of hypervolume and Inverted Generational Distance (IGD) metrics. By leveraging intelligent clustering and evolutionary computation, NSGA-III-GKM++ serves as a powerful decision support tool for cloud brokerage, facilitating optimal service selection while ensuring sustainability and economic feasibility.

    Boutheina Drira, Haykel Hamdi, Ines Ben Jaafar

    Hybrid Deep Learning Ensemble Models for Enhanced Financial Volatility Forecasting

    Second Edition IEEE Afro-Mediterranean Conference on Artificial Intelligence 2025 IEEE AMCAI IEEE AMCAI 2025, 2025

    Résumé

    this paper presents a novel ensemble
    methodology that integrates deep learning models to enhance
    the accuracy and robustness of financial volatility forecasts. By
    combining Convolutional Neural Networks (CNNs) and GRU
    networks, the proposed approach captures both spatial and
    temporal patterns in financial time series data. Empirical results
    demonstrate the superiority of this ensemble model over
    traditional forecasting methods in various financial markets.
    Keywords: Volatility Forecasting, Deep Learning, Ensemble
    Modeling, CNN, GRU, Financial Time Series

  • Samira Harrabi, Ines Ben Jaafar, Oumaima Omrani

    A vehicle-to-infrastructure communication privacy protocolused Blockchain

    LicenseCC BY 4.0, 2024

    Résumé

    Since several decade, the Internet of Things IoT hasattracted enormous interest in the research communityand industry. However, IoT technologies has completelytransformed vehicular ad hoc networks (VANETs) intothe "Internet of Vehicles" IoV. In IoV networks, we needto integrate many different technologies, services andstandards. However, the heterogeneity and large numberof vehicles will increase the need of data security.The IoV security issues are critical because of the vulnerabilitiesthat exist during the transmission of informationthat expose the IoV to attacks. Each attack hasa security procedure. Many protocols and mechanismsexist to combat or avoid this communication securityproblem. One of these protocols is VIPER (a Vehicleto-Infrastructure communication Privacy EnforcementpRotocol). In our work, we try to improve this protocolby using Blockchain technology and certificationauthority.

  • Samira Harrabi, Ines Ben Jaafar, Khaled Ghedira

    Survey on IoV Routing Protocols

    Wireless Personal Communications 128(1), 2023

    Résumé

    Internet of vehicles (IoV) can be considered as a superset of vehicular ad-hoc networks (VANETs). It extends VANET’s structure, applications and scale. Unlike, the traditional intelligent transportation system (ITS), IoV focus more on information interactions between vehicles, roadside units (RSU) and humans. The principal aim is to make people obtain road traffic information easily and in real-time, to ensure the travel convenience, and to increase the travel comfort. The goal behind the Internet of vehicles is essentially to be used in urban traffic environment to ensure network access for passengers and drivers. The environment of the IoV is the combination of different wireless network environment as well as road conditions. Despite its continuing expansion, the IOV contains different radio access technologies that lead to a heterogeneous network, and make it more crucial than the VANET. These drawbacks pose numerous challenges, especially the routing one. In IoV environment, the routing protocol must cope with events such as link failure and to find the best route to propagate the data toward the desired destination. In this paper, we mainly focus on surveying the IoV routing protocols, hence we present and compare unicast, multicast and broadcast protocols.

  • Khaled Ghedira, Ines Ben Jaafar, Samira Harrabi

    DARSV: a dynamic agent routing simulator for VANETs

    International Journal of Simulation and Process Modelling, 2021

    Résumé

    In this paper, a novel dynamic agent routing simulator for vehicular ad-hoc networks (DARSV) is presented. The main purpose of DARSV simulator is to realise a successful large-scale simulation of agent based routing approach in vehicular networks. To conduct this goal, the proposed simulator combines the Java Agent DEvelopment (JADE) which is a powerful multi-agent system (MAS) framework with the dynamic ad hoc routing simulator (DARS) that takes into account the dynamic nature of environment networks. The simulation results are discussed to evaluate the efficiency and the performance of the proposed simulator.

    Ines Ben Jaafar, Samira Harrabi, Khaled Ghedira

    Performance Analysis of Vanets Routing Protocols

    LicenseCC BY 4.0, 2021

    Résumé

    Vehicular Ad Hoc Networks (VANETs) are a particular class of Mobile Ad Hoc Networks (MANETs). The VANETs provide wireless communication among vehicles and vehicle-to-road-side units. Even though the VANETs are a specific type of MANETs, a highly dynamic topology is a main feature that differentiates them from other kinds of ad hoc networks. As a result, designing an efficient routing protocol is considered a challenge.  The performance of vehicle-to-vehicle communication depends on how better the routing protocol takes in consideration the particularities of the VANETs. Swarm Intelligence (SI) is considered as a promising solution to optimize vehicular communication costs. In this paper, we explore the SI approach to deal with the routing problems in the VANETs. We also evaluate and compare two swarming agent-based protocols using numerous QoS parameters, namely the average end-to-end delay and the ratio packet loss which influence the performance of network communication.

  • Ines Ben Jaafar

    Modèles et Architectures d’agents pour les Problèmes d’Optimisation

    Habilitation Universitaire, 2019

    Résumé

    Ce manuscrit présente une synthèse des travaux de recherche que j’ai effectués de 2007 à 2018 en tant que Maître Assistante, et chercheur depuis 1999 au sein de l’unité de recherche URIASIS qui était ensuite promue Laboratoire SOIE (et actuellement SMART) à l’ISG de Tunis.

  • Ines Seghir, Ines Ben Jaafar, khaled Ghedira

    A Multi-Agent Based Optimization Method for Combinatorial Optimization Problems

    International Journal of Artificial Intelligence Tools, 2018

    Résumé

    This paper introduces a Multi-Agent based Optimization Method for Combinatorial Optimization Problems named MAOM-COP. In this method, a set of agents are cooperatively interacting to select the appropriate operators of metaheuristics using learning techniques. MAOM-COP is a flexible architecture, whose objective is to produce more generally applicable search methodologies. In this paper, the MAOM-COP explores genetic algorithm and local search metaheuristics. Using these metaheuristics, the decision-maker agent, the intensification agents and the diversification agents are seeking to improve the search. The diversification agents can be divided into the perturbation agent and the crossover agents. The decision-maker agent decides dynamically which agent to activate between intensification agents and crossover agents within reinforcement learning. If the intensification agents are activated, they apply local search algorithms. During their searches, they can exchange information, as they can trigger the perturbation agent. If the crossover agents are activated, they perform recombination operations. We applied the MAOM-COP to the following problems: Quadratic assignment, graph coloring, winner determination and multidimensional knapsack. MAOMCOP shows competitive performances compared with the approaches of the literature.

    Samira Harrabi, Ines Ben Jaafar, Khaled ghedira

    A Swarm Intelligence-based Routing Protocol for Vehicular Networks

    International Journal of Vehicle Information and Communication Systems (IJVICS), 2018

    Résumé

    Vehicular Ad hoc Networks (VANETs) are a particular case of Mobile Ad hoc Networks (MANETs). They are applied to exchange information among vehicles and between vehicles and a nearby fixed infrastructure. Unlike the MANETs, the VANETs have highly mobile nodes that cause a dynamic topology, a disconnected network, etc. Consequently, these features pose numerous challenges. One of them is routing. In a vehicular environment, the routing protocol needs to cope with events like link failure and to find an effective path to propagate the information toward the desired destination. In this context, we assume in this paper that the vehicles are intelligent and have a knowledge base about their communication environment. Our aim is to carry out the routing of the data based on swarm intelligence. The optimum route is explored using the Particle Swarm Optimisation (PSO). The proposed approach is called the Optimised Agent-based AODV Protocol for VANET (OptA2PV).

  • Ines Sghir, Ines Ben Jaafar, Khaled Ghedira

    A Multi-Agent based Hyper-Heuristic Algorithm for the Winner Determination Problem

    Procedia Computer Science 112:117-126, 2017

    Résumé

    In this paper we propose a Multi-Agent based Hyper-Heuristic algorithm for theWinner Determination Problem named MAH2- WDP. This algorithm explores a set of cooperating agents to select the appropriate operation using learning techniques. MAH2- WDP is specialized for local search methods and evolutionary methods where the following agents are seeking to improve the search within reinforcement learning: the mediator agent, two local search agents, the perturbation agent and two recombination agents. Our computational study shows that the proposed algorithm performs well on the tested benchmark instances in terms of solution quality. Keywords: Multi-agent; Winner Determination Problem; hyper-heuristic; intensification; diversification; metaheuristics.

    Samira Harrabi, Ines Ben Jaafar, khaled ghedira

    Message Dissemination in Vehicular Networks on the Basis of Agent Technology

    An International Journal of Wireless Personal Communications, 2017

    Résumé

    Vehicular Ad hoc Network (VANET) is a sub-family of Mobile Ad hoc Network (MANET). The principal goal of VANET is to provide communications between nearby nodes or between nodes and fixed infrastructure. Despite that VANET is considered as a subclass of MANET, it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road and varying node density of vehicles existing in this road. That‘s why, the most proposed clustering algorithms for MANET are unsuitable for VANET. Various searches have been recently published deal with clustering for VANETs, but most of them are focused on minimizing network overhead value, number of created clusters and had not considered the vehicles interests which defined as any related data used to differentiate vehicle from another. In this paper, we propose a novel clustering algorithm based on agent technology to improve routing in VANET.

    Samira Harrabi, Ines Ben Jaafar, Khaled Ghedira

    Reliability and Quality of Service of an Optimized Protocol for Routing in VANETs

    In CTRQ 2017: The tenth international conference on communication theory, reliability, and quality of service., 2017

    Résumé

    Vehicular Ad hoc NETworks (VANETs) are a special kind of Mobile Ad hoc NETworks (MANETs), which can provide scalable solutions for applications such as traffic safety, internet access, etc. To properly achieve this goal, these applications need an efficient routing protocol. Yet, contrary to the routing protocols designed for the MANETs, the routing protocols for the VANETs must take into account the highly dynamic topology caused by the fast mobility of the vehicles. Hence, improving the MANET routing protocol or designing a new one specific for the VANETs are the usual approaches to efficiently perform the routing protocol in a vehicular environment. In this context, we previously enhanced the Destination-Sequenced Distance-Vector Routing protocol (DSDV) based on the Particle Swarm Optimization (PSO) and the Multi-Agent System (MAS). This motivation for the PSO and MAS comes from the behaviors seen in very complicated problems, in particular routing. The main goal of this paper is to carry out a performance evaluation of the enhanced version in comparison to a well-known routing protocol which is the Intelligent Based Clustering Algorithm in VANET (IBCAV). The simulation results show that integrating both the MAS and the PSO is able to guarantee a certain level of quality of service in terms of loss packet, throughput and overhead.

  • Samira Harrabi, Ines Ben Jaafar, Khaled Ghedira

    Novel Optimized Routing Scheme for VANETs

    Procedia Computer Science 98:32-39, 2016

    Résumé

    The Vehicular ad -hoc networks (VANETs) are a specific type of Mobile ad-hoc networks (MANETs). However, the main problem related to it is the potential high speed of moving vehicles. This special property causes frequent changing in network topology and instability of communication routes. Consequently, some of the challenges that researchers focus on are routing protocols for VANETs. They have proved that the existing MANET proactive routing protocols are the most used for vehicular communication. Yet, they are not as adequate as they are for VANETs. The main problem with these protocols in dynamic environment is their route instability. This paper combines multi-agent system approach and PSO algorithm to solve the above mentioned problems. We carried out a set of simulations tests to evaluate the performance of our scheme. The simulation part shows promising results regarding the adoption of the proposed scheme.

    Samira Harrabi, Ines Ben Jaafar, Khaled Ghedira

    Routing Challenges and Solutions in Vehicular Ad hoc Networks

    Sensors and Transducers 206(11):31-42, 2016

    Résumé

    Vehicular Ad-hoc Networks (VANETs) are known as a special type of Mobile Ad-hoc Networks (MANETs) specialized in vehicular communications. These networks are based on smart vehicles and basestations, which share data by means of wireless communications. To route these information, a routing protocol is required. Since the VANETs have a particular network features as rapidly changeable topology, designing an efficient routing scheme is a very hard task. In this paper, we mainly focus on surveying new routing protocols dedicated to VANETs. We present unicast, multicast and broadcast protocols. The experimental results are discussed to evaluate the performance of the presented methods.

    Samira Harrabi, Samira Harrabi, Ines Ben Jaafar, Khaled Ghedira

    A Novel Clustering Algorithm Based on Agent Technology for VANET

    Network Protocols and Algorithms 7(4), 2016

    Résumé

    Vehicular Ad-hoc Network (VANET) is a sub-family of Mobile Ad-hoc Network (MANET).The means goal of VANET is to provide communications between nearby nodes or between nodes and fixed infrastructure. Despite that VANET is considered as a subclass of MANET, it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road, varying node density and locations of vehicles existing in this road. That‘s why, the most proposed clustering algorithms for MANET are unsuitable for VANET. Various searches have been recently published deal with clustering for VANETs. But most of them are focused on minimizing network overhead value, number of created clusters and had not considered the vehicles interests which defined as any related data used to differentiate vehicle from another (such as traffic congestion, looking for free parking space etc). In this paper, we propose a novel clustering algorithm based on agent technology to solve the problems mentioned above and improve routing in VANET. Experimental part show promising results regarding the adoption of the proposed approach.

  • Ines Seghir, Jin-Kao Hao, Ines Ben Jaafar, Khaled Ghedira

    A multi-agent based optimization method applied to the quadratic assignment problem

    Expert Systems with Applications 42(23):9252-9262, 2015

    Résumé

    Inspired by the idea of interacting intelligent agents of a multi-agent system, we introduce a multi-agent based optimization method applied to the quadratic assignment problem (MAOM-QAP). MAOM-QAP is composed of several agents (decision-maker agent, local search agents, crossover agents and perturbation agent) which are designed for the purpose of intensified and diversified search activities. With the help of a reinforcement learning mechanism, MAOM-QAP dynamically decides the most suitable agent to activate according to the state of search process. Under the coordination of the decision-maker agent, the other agents fulfill dedicated search tasks. The performance of the proposed approach is assessed on the set of well-known QAP benchmark instances, and compared with the most advanced QAP methods of the literature. The ideas proposed in this work are rather general and could be adapted to other optimization tasks. This work opens the way for designing new distributed intelligent systems for tackling other complex search problems.