Maitre Assistant at Ecole Nationale d’Ingenieurs de Tunis, Tunisia
Dr. Rania Loukil is a Tunisian researcher and academic specializing in Artificial Intelligence, Embedded Systems, and Control Engineering. Currently serving as a Maître Assistant (Assistant Professor) at the Higher Institute of Technology and Computer Science (ISTIC), University of Carthage, she has over a decade of experience in teaching, research, and interdisciplinary collaboration. Her research merges deep learning with practical domains like IoT, smart grids, and fault diagnosis, reflecting a strong commitment to innovation and applied AI solutions.
🔹Professional Profile:
Scopus Profile
Orcid Profile
🎓Education Background
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Ph.D. in Electrical Engineering, National Engineering School of Sfax (ENIS), University of Sfax, Tunisia | 2010–2014
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Master Project, INRIA Paris / ENIS | 2008–2009
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Engineering Degree in Electrical Engineering, ENIS, Sfax | 2005–2008
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Preparatory Classes (MP), IPEIS, Sfax | 2003–2005
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Baccalaureate in Mathematics, Tunisia | 2002–2003 – Mention Bien
💼 Professional Development
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Maître Assistant in Artificial Intelligence, ISTIC, University of Carthage | Jan 2018–Present
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Coach Junior, BIAT Foundation | Nov 2018–Present
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Maître Assistant in AI, ISI Gabes | Sep 2015–Dec 2017
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Head of Electrical Engineering Department, Ecole Polytechnique Centrale Privée de Tunis | Feb 2015–Aug 2015
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Permanent Faculty, Ecole Polytechnique Centrale Privée de Tunis | Oct 2014–Jan 2015
🔬Research Focus
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Artificial Intelligence & Deep Learning (RNNs, Transformers, Bayesian Networks)
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Fault Diagnosis and Nonlinear Control (Sliding Mode, Observers)
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IoT and Embedded Systems
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Smart Grids and Microgrid Energy Management
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Nanocomposite Classification and Materials Informatics
📈Author Metrics:
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Published in leading journals including Expert Systems with Applications and Scientific Reports
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Recent works involve hybrid deep learning approaches for nanocomposite classification and smart energy systems
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Selected publications:
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Classification of Nanocomposites using RNN Transformer & Bayesian Network, ESWA, 2025
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Probabilistic and Deep Learning Approaches for Conductivity-Driven Nanocomposite Classification, Scientific Reports, 2025
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IoT Solution for Energy Management, IREC 2023
🏆Awards and Honors:
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Recognized contributor to interdisciplinary AI projects
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Regular presenter at international conferences on AI, control systems, and energy informatics
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Acknowledged for excellence in education and mentorship through BIAT Foundation coaching initiatives
📝Publication Top Notes
1. Classification of a Nanocomposite Using a Combination Between Recurrent Neural Network Based on Transformer and Bayesian Network for Testing the Conductivity Property
Journal: Expert Systems with Applications
Publication Date: April 2025
DOI: 10.1016/j.eswa.2025.126518
ISSN: 0957-4174
Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Abstract: This study presents a hybrid AI model combining Transformer-based RNN and Bayesian Networks to classify nanocomposites based on conductivity, demonstrating improved interpretability and predictive accuracy.
2. Probabilistic and Deep Learning Approaches for Conductivity-Driven Nanocomposite Classification
Journal: Scientific Reports
Publication Date: March 7, 2025
DOI: 10.1038/s41598-025-91057-1
ISSN: 2045-2322
Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Abstract: This paper explores probabilistic learning and deep learning methods for classifying nanocomposites with a focus on electrical conductivity, emphasizing model generalizability.
3. Enhanced Nanoparticle Classification Through Optimized Artificial Neural Networks
Conference: 2024 International Conference on Decision Aid Sciences and Applications (DASA)
Presentation Date: December 11, 2024
DOI: 10.1109/dasa63652.2024.10836425
Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Abstract: The paper demonstrates how optimized ANN architectures can significantly improve nanoparticle classification in terms of conductivity profiling, offering an efficient pipeline for smart material characterization.
4. Improving the Classification of a Nanocomposite Using Nanoparticles Based on a Meta-Analysis Study, Recurrent Neural Network and Recurrent Neural Network Monte-Carlo Algorithms
Journal: Nanocomposites
Publication Date: July 8, 2024
DOI: 10.1080/20550324.2024.2367181
ISSN: 2055-0324, 2055-0332
Authors: Rania Loukil, Wejden Gazehi, Mongi Besbes
Abstract: Through a comparative analysis using RNN and Monte-Carlo RNN algorithms, this work proposes a robust framework for classifying nanocomposites, supported by meta-analytical insights.
5. Design and Implementation of an IoT Solution for Energy Management\
Conference: 14th International Renewable Energy Congress (IREC 2023)
Presentation Date: December 16, 2023
Authors: Rania Loukil, Neila Bediou, Hatem Oueslati, Majdi Hazami
Abstract: This contribution introduces a practical IoT-based architecture for optimizing energy consumption and monitoring within renewable energy systems, aligning with smart grid principles.
.Conclusion:
Dr. Rania Loukil stands out as an exemplary scholar combining deep learning, embedded systems, and energy informatics. Her cross-disciplinary work addresses both academic challenges and societal needs, aligning well with the objectives of a Best Scholar Award. Given her solid track record, thematic relevance, and academic leadership, she is highly deserving of this recognition.
➡️ Recommendation: Strongly endorse her nomination for the Best Scholar Award, with suggestions to highlight international collaborations, quantitative metrics, and applied impacts during the award presentation or application.