Doctorate Mohamed El-Moussaoui: Ph.D graduate at Chouaib Doukkali University, Morocco

Mohamed El-Moussaoui is a distinguished expert with over 15 years of experience in the field of monetic engineering, specializing in the intersection of financial technology, artificial intelligence, and data science. With a Doctorate in Computer Science and AI from Chouaib Doukkali University, he has contributed significantly to both academic research and the development of innovative payment solutions. His professional journey spans leadership roles in major firms such as Hightech Payment Systems, Al Barid Bank, and HPS Group, where he successfully directed projects across Europe, the Americas, and Asia. Mohamed’s research bridges theory and application, focusing on community detection in social networks, using cutting-edge AI methodologies to enhance the security, analysis, and optimization of payment systems.

Online Profiles

Google Scholar Profile

Research Metrics

  • Total Citations: 84

  • Citations Since 2020: 82

  • h-index: 2

  • i10-index: 1

These metrics reflect Mohamed’s growing influence in the field of artificial intelligence and network science. With an h-index of 2, his publications are showing a consistent level of academic impact, and the high number of citations since 2020 indicates increasing recognition of his research on community detection and AI applications.

Education

Mohamed earned his Ph.D. in Computer Science and Artificial Intelligence from the prestigious Chouaib Doukkali University, graduating with the highest honors and commendations from his committee. His thesis was focused on the application of advanced algorithms for community detection in social networks, specifically leveraging association rule learning and fuzzy logic. He also holds a Master’s degree in Networks and Systems from Cadi Ayyad University, where he graduated with honors, and a Professional Bachelor’s degree in Computer Science. In addition to his academic journey, Mohamed completed military training at the École Royale de l’Air (ERA) in Marrakech, which shaped his discipline and leadership skills. His educational foundation has provided him with the analytical skills and technical expertise necessary to excel in both industry and academia.

Research Focus

Mohamed’s research primarily revolves around the development of sophisticated community detection techniques in social networks, utilizing association rule learning, fuzzy logic, and deep learning algorithms. He is especially interested in exploring how these methods can be applied to the field of monetic engineering to enhance payment security, fraud detection, and customer experience. His recent works focus on semantic-driven community models, multi-criteria decision-making techniques, and how AI-driven solutions can optimize both large-scale financial transactions and online social network analysis. His research also delves into systematizing deep learning approaches for network community detection, with a goal to bring practical applications to the field of digital payment systems.

Experience

Mohamed has a wealth of practical experience in both the public and private sectors, with a specific focus on the design and implementation of complex payment systems. Currently, he is serving as the Project Director at Hightech Payment Systems, where he is responsible for overseeing the company’s international projects related to digital payment infrastructures across Europe, the Americas, and Asia. Previously, he worked as the Head of the Monetic Engineering Division at Al Barid Bank, managing the integration and innovation of payment systems, including developing and launching new payment services for both retail and corporate clients. He has also held pivotal roles at HPS Group and S2M, where he contributed to product development, systems integration, and managed teams across various regions, ensuring that their solutions met high security standards and customer requirements. Mohamed’s leadership and technical expertise have consistently resulted in the successful deployment of state-of-the-art monetic technologies.

Research Timeline

  • 2025 (Q1): Published “ARLClustering: An R Package for Community Detection” in Applied Network Science (IF 1.4), marking a significant contribution to the development of user interaction-based community detection algorithms.

  • 2024 (Q2): Published “A Multi-Agent-Based Approach for Community Detection” in The International Journal of Data Science and Analytics (IF 3.4), presenting a novel approach to analyzing user behaviors in online platforms.

  • 2025 (Q2): Currently working on a paper titled “A Fuzzy Logic-Based Approach for Community Detection,” under review at International Journal of Fuzzy Systems.

  • 2025 (Q2): A comprehensive systematic review titled “A Review of Deep Learning Methods for Community Detection” is being published in Frontiers in Artificial Intelligence (IF 3.0), providing an in-depth look at the latest advancements in AI for network analysis.

Awards & Honors

  • 2025: Honored with the “Very Honorable” distinction for his Ph.D. dissertation, awarded by Chouaib Doukkali University. This recognition acknowledges his substantial contributions to the field of AI and monetic engineering.

  • 2024: Best Paper Award at the ICEET’24 International Conference for his development of the ARLClustering R package, praised for its innovation and potential applications in social network analysis.

  • 2022: Selected as the Technical Program Chair for the International Workshop on Modeling and Analysis of Complex Networks with Applications (MACNA’22) held in Ontario, Canada, a significant recognition of his expertise and leadership in the field of network science.

  • 2020: Awarded for Best Communication at the International Conference on Smart City Applications (SCA’20) for his innovative work on community detection using association rule learning.

Top-Noted Publication

  • “A Comprehensive Literature Review on Community Detection: Approaches and Applications”
    M. El-Moussaoui, T. Agouti, A. Tikniouine, M. El Adnani. Procedia Computer Science, Vol. 151, 76 (2019)

    • This paper provides an in-depth review of various community detection methods, summarizing state-of-the-art approaches and their practical applications in diverse domains.

  • “A Novel Approach of Community Detection Using Association Rules Learning: Application to User’s Friendships of Online Social Networks”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. Innovations in Smart Cities Applications, Volume 4 (2021)

    • A groundbreaking study that introduces the application of association rule learning (ARL) for detecting communities based on user friendships in online social networks.

  • “A Multi-Agent-Based Approach for Community Detection Using Association Rules”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. International Journal of Data Science and Analytics, Vol. 18 (4), 379-392 (2024)

    • This paper explores a multi-agent-based framework for community detection, applying association rules to analyze complex social networks and user behavior patterns.

  • “A k-Mean Classification Study of Eight Community Detection Algorithms: Application to Synthetic Social Network Datasets”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. AI and IoT for Sustainable Development in Emerging Countries, 557-572 (2022)

    • A comparative study evaluating the effectiveness of various community detection algorithms using synthetic datasets, with a focus on scalability and performance.

  • “ARLClustering: An R Package for Community Detection in Social Networks Based on User Interaction and Association Rule Learning”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. Applied Network Science, Vol. 10 (25), (2025)

    • Introduces ARLClustering, an R package designed to enhance community detection in social networks by utilizing user interaction data and association rule learning.

  • “ARLClustering: R Package for Community Detection-Based Association Rules Learning”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. International Conference on Engineering and Emerging Technologies (ICEET), 1-5 (2025)

    • Presentation of ARLClustering at ICEET’25, demonstrating its applications and potential for improving community detection processes in dynamic social networks.

  • “Community Detection in Social Networks: A State of the Art”
    M. El-Moussaoui, M. Hanine, A. Kartit, T. Agouti. International Conference on Mathematics & Data Science (ICMDS’20), 102 (2020)

    • A detailed state-of-the-art review on community detection methods, examining algorithms, challenges, and the impact of machine learning in social network analysis.

Mohamed El-Moussaoui, Computer Science, Best Researcher Award