smart.lab.tunisia@gmail.com +216 71 588 443
Connexion

Computational Intelligence

Description

Aucune description disponible pour cet axe de recherche.

Membres

Ahmad Ben Taleb Ahmad Ben Taleb
Imen Oueslati Imen Oueslati
Ameni Azzouz Ameni Azzouz
Boutheina JLIFI Boutheina JLIFI
Syrine Ferjani Syrine Ferjani
Sabeur Elkosantini Sabeur Elkosantini
Saoussen Bel Haj Kacem Saoussen Bel Haj Kacem

Publications

  • 2025
    Boutheina JLIFI, Syrine Ferjani, Claude Duvallet

    A Genetic Algorithm based Three HyperParameter optimization of Deep Long Short Term Memory (GA3P-DLSTM) for Predicting Electric Vehicles energy consumption

    Computers and Electrical Engineering, 123, 110185., 2025

    Résumé

    To overcome Climate Change, countries are turning to greener transportation systems. Therefore, the use of Electric Vehicles (EVs) is leveraging substantially since they present multiple advantages, like reducing hazardous emissions. Recently, the demand for EVs has increased, which means that more charging stations need to be available. By the year 2030, 15 million EVs will be accessible, and since the number of charging stations is limited, the charging needs should be defined for better management of the charging infrastructure. In this research, we aim to tackle this problem by efficiently predicting the energy consumption of EVs. We proposed a Genetic Algorithm (GA) based Three HyperParameter optimization of Deep Long Short Term Memory (GA3P-DLSTM), which is an optimized LSTM model that incorporates a GA for Hyperparameter Tuning. After experimenting our methodology and performing a comparative analysis with previous studies from the literature, the obtained results showed the efficiency of our novel model, with Mean Squared Error (MSE) equals to 0.000112 and a Determination Coefficient (R) equals to 0.96470. It outperformed other models of the literature for predicting energy use based on real-world data collected from the campus of Georgia Tech in Atlanta, USA.

  • 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.

    Ameni Hedhli, Haithem Mezni, Lamjed Ben Said

    BPaaS placement over optimum cloud availability zones

    Cluster Computing, 27(5), 5845-5865., 2024

    Résumé

    Business Process as a Service (BPaaS) has recently emerged from the synergy between business process management and cloud computing, allowing companies to outsource and migrate their businesses to the cloud. BPaaS management refers to the set of operations (decomposition, customization, placement, etc.) that maintain a high-quality of the deployed cloud-based businesses. Like its ancestor SaaS, BPaaS placement consists on the dispersion of its composing fragments over multiple cloud availability zones (CAZ). These latter are characterized by their huge, diverse and dynamic data, which are exploited to select the high-performance servers holding BPaaS fragments, while preserving their constraints. These fragments’ relations and their placement schemes constitute a dynamic BPaaS information network. However, the few existing BPaaS solutions adopt a static placement strategy, while it is important to take the CAZ dynamic and uncertain nature into account. Also, current solutions do not properly model the BPaaS environment. To offer an efficient BPaaS placement scheme, we combine prediction and learning capabilities, which will help identify the migrating fragments and their new hosting servers. We first model the BPaaS context as a heterogeneous information network. Then, we apply an incremental representation learning approach to facilitate its processing. Using the principles of proximity-aware representation learning, we infer useful knowledge regarding BPaaS fragments and the available servers at different CAZ. Finally based on the degree of closeness between the BPaaS environment’s entities (e.g., fragments, servers), we select the optimum cloud availability zone on which the resource-consuming BPaaS fragments are migrated based on a proposed placement scheme. Obtained results were very promising compared to traditional BPaaS placement solutions.

  • 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.

  • Ameni Hedhli, Haithem Mezni

    A DFA‐based approach for the deployment of BPaaS fragments in the cloud‏

    Concurrency and computation: Practice and experience, 32(14), e5075.‏, 2020

    Résumé

    Cloud computing is an emerging technology that is largely adopted by the current computing industry. With the growing number of Cloud services, Cloud providers' main focus is how to best offer efficient services (eg, SaaS, BPaaS, mobile services, etc) in order to hook the eventual customers. To meet this goal, services arrangement and placement in the cloud is becoming a serious problem because an optimal placement of these applications and their related data in accordance with the available resources can increase companies' benefits. Since there is a widespread deployment of business processes in the cloud, the hereinafter conducted research works aim to enhance the business processes' outsourcing by providing an optimized placement scheme that would attract cloud customers. In the light of these facts, the purpose of this paper is to deal with the BPaaS placement problem while optimizing both the total execution time and cloud resources' usage. To do so, we first determine the redundant BPaaS fragments using a DNA Fragment Assembly technique. We apply a variant of the Genetic Algorithm to resolve it. Then, we propose a placement algorithm, which produces an optimized placement scheme on the basis of the determined fragments relations. We follow that by an implementation of the whole placement process and a set of experimental results that have shown the feasibility and efficiency of the proposed approach.