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

Informations générales

Boutheina JLIFI
Grade

Maître Assistant

Biographie courte

Boutheina Jlifi started her career as an Assistant Professor of AI and Model Engineering at Ecole Supérieure de Commerce de Tunis (ESCT) since 2005. She earned a PhD in Computer Science/Artificial Intelligence in 2007. In 2022, she became a Director of an MSc in Data Science in the same institution. In 2023, she becomes an active member of SMART Lab, ISG.
In 2024, she was appointed as an Associate Research Member at the LITIS Laboratory (Laboratoire d’Informatique et Traitement de l’Information et des Systèmes ), IUT LeHavre, Normandie, France, in the field of Sustainable Transport with AI. At the same year, she became an Affiliate Researcher at the Center of Research for Energy and Climate Change (CRECC ) at Paris School of Business, France, in the field of Sustainable Finance, especially, in ESG analytics and ESG Data and Accountability with AI.
With a strong knowledge in Big Data, Model Engineering, and Computational Intellgence, she is holding many projects as a senior AI consultant since 2005.

Axes de recherche

Computational Intelligence
Machine Learning
Combinatorial Optimization
Deep Learning

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.

  • Chayma Sakrani, Boutheina JLIFI, Claude Duvallet

    Towards a soft three-level voting model (Soft T-LVM) for fake news detection

    Journal of Intelligent Information Systems, 61(1), 249-269., 2023

    Résumé

    Fake news has a worldwide impact and the potential to change political scenarios and human behavior, especially in a critical time like the COVID-19 pandemic. This work suggests a Soft Three-Level Voting Model (Soft T-LVM) for automatically classifying COVID-19 fake news. We train different individual machine learning algorithms and different ensemble methods in order to overcome the weakness of individual models. This novel model is based on the soft-voting technique to calculate the class with the majority of votes and to choose the classifiers to merge and apply at every level. We use the Grid search method to tune the hyper-parameters during the process of classification and voting. The experimental evaluation confirms that our proposed model approach has superior performance compared to the other classifiers.

  • Ameni Azzouz, Meriem Ennigrou, Boutheina JLIFI

    Diversifying TS using GA in multi-agent system for solving flexible job shop problem

    12th International Conference on Informatics in Control, Automation and Robotics (ICINCO). Vol. 1. IEEE, 2015., 2015

    Résumé

    No doubt, the flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. For this reason, FJSP continues to attract the interests of researchers both in academia and industry. In this paper, we propose a new multi-agent model for FJSP. Our model is based on cooperation between genetic algorithm (GA) and tabu search (TS). We used GA operators as a diversification technique in order to enhance the searching ability of TS. The computational results confirm that our model MAS-GATS provides better solutions than other models.

  • Ameni Azzouz, Meriem Ennigrou, Boutheina JLIFI, Khaled Ghedira

    Combining tabu search and genetic algorithm in a multi-agent system for solving flexible job shop problem

    n International Conference on Enterprise Information Systems (Vol. 3, pp. 47-53), 2012

    Résumé

    The Flexible Job Shop problem (FJSP) is an important extension of the classical job shop scheduling problem, in that each operation can be processed by a set of resources and has a processing time depending on the resource used. The objective is to minimize the make span, i.e., the time needed to complete all the jobs. This works aims to propose a new promising approach using multi-agent systems in order to solve the FJSP. Our model combines a local optimization approach based on Tabu Search (TS) meta-heuristic and a global optimization approach based on genetic algorithm (GA).