Mohammed Ramdani

Pr. Mohammed RAMDANI

Full Professor in the Computer Science Department
Faculty of Science and Techniques of Mohammedia
University Hassan II, Casablanca, Morocco

Short biography

received his PhD in Fuzzy Machine Learning in 1994, and his HDR in perceptual computation in 2001, at University Paris VI, France. Since 1996, he is a full Professor at the FSTM, University Hassan II of Casablanca, Morocco. In the same faculty, for the periods 1996 - 1998 and 2003 - 2005 he held the position of head of Computer Science department. Between 2008 and 2014, he was Pedagogical Director of the engineering department "Software Engineering and Systems Integration" (ILIS). Since 2006, he is Director of the Computer Science Lab. He is also President of the AMSI association since 2016. His research interests include Explanation in Machine Learning, perceptual computation with Fuzzy Logic and Big datamining. He is author of several articles in many indexed journals..

  • HDR in perceptual computation, 2001

    FSTM, University Paris VI

  • PhD in Fuzzy Machine Learning, in 1994

    FSTS, UH1S

  • Full Professor, since 1996

    FSTM, UH2C

Profile in indexing databases

ORCID, Google Scholar, Scopus, Publons

Scientific responsibilities

Funded research projects
  • Project 1: E-Orientation

    Thematic Center: SISA3

  • Project 2: Big Data and connected objects serving the Casablanca citizens(BDOC)

    Thematic center: 8

Scientific Participation
  • President of the AMSI association since 2016.

  • TPC chair or General chair of SITA conference series:

    SITA'04, SITA'06, SITA'08, SITA'10, SITA'12, SITA'14, SITA'16, SITA'18, SITA'20

Teaching & Outreach

• Propositional logic: Notions of model, satisfiability, validity of a formula; logical consequence relation, The SAT problem; propositional resolution, ….

• First-order logic: Predicates, functions, variables, quantifiers, Interpretations, valuations, models, Herbrand's theorem. Forms of Skolem, Clauses of Horn; SLD algorithm;

• Logic programming, Prolog

• Business Intelligence: Datawerhouse, OLAP, SSAS, KPIs,….

• Information System and Databases: Algebric, MCD, MLD, normal forms, OLTP, Entity-Relationship, SQL,….

• Artificial Intelligence: Problem solving, Tree search (informed and uninformed), Evolutionary algorithms, Two-player game trees, Symbolic AI, ….

• Artificial Intelligence (AI): expert systems, soft computing, reasoning under uncertainty (fuzzy logic, intuitionistic logic, neutrosophic logic),….

• Machine-learning: Supervised learning, Unsupervised learning and Rules of association.

Research Interests

• Explanation in Machine Learning.

• Perceptual computation with Fuzzy Logic.

• Big datamining.

• Parallel and distributed machine learning algorithms

• Machine learning.

• Meta-modeling

• Decision support systems.

• Reasoning under uncertainty and imprecision.


1. El Beggar, O., Letrache, K. & Ramdani, M. DAREF: MDA framework for modelling data warehouse requirements and deducing the multidimensional schema. Requirements Eng 26, 143–165 (2021).

2. Hamza, L., Mohammed, R. (2022). MaroBERTa: Multilabel Classification Language Model for Darija Newspaper. In: Hamlich, M., Bellatreche, L., Siadat, A., Ventura, S. (eds) Smart Applications and Data Analysis. SADASC 2022. Communications in Computer and Information Science, vol 1677. Springer, Cham.

3. Letrache, K., El Beggar, O., Ramdani, M. (2019). Comparative Analysis of Our Association Rules Based Approach and a Genetic Approach for OLAP Partitioning. In: Podelski, A., Taïani, F. (eds) Networked Systems. NETYS 2018. Lecture Notes in Computer Science(), vol 11028. Springer, Cham.

4. Letrache, K., El Beggar, O. & Ramdani, M. OLAP cube partitioning based on association rules method. Appl Intell 49, 420–434 (2019).

5. Khadija Letrache, Omar El Beggar, and Mohammed Ramdani. 2018. Green Data warehouse Design and Exploitation. In Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications (SITA'18). Association for Computing Machinery, New York, NY, USA, Article 29, 1–6.

6. Farsal, W., Ramdani, M., Anter, S. (2022). A Novel Graded Multi-label Approach to Music Emotion Recognition. In: Hamlich, M., Bellatreche, L., Siadat, A., Ventura, S. (eds) Smart Applications and Data Analysis. SADASC 2022. Communications in Computer and Information Science, vol 1677. Springer, Cham.

7. El Beggar, O., Letrache, K. and Ramdani, M. (2017), CIM for data warehouse requirements using an UML profile. IET Softw., 11: 181-194.

8. Letrache, K, El Beggar, O, Ramdani, M. The automatic creation of OLAP cube using an MDA approach. Softw Pract Exper. 2017; 47: 1887– 1903.

9. Letrache, Khadija, Omar El Beggar, and Mohamed Ramdani. "Modeling and creating KPIs in MDA approach." 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt). IEEE, 2016.

10. El Beggar, Omar, Khadija Letrache, and Mohammed Ramdani. "Towards an MDA-oriented UML profiles for data warehouses design and development." 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE, 2016.

11. Mawane, J., Naji, A., & Ramdani, M. (2022). A cluster validity for optimal configuration of Kohonen maps in e-learning recommendation. Indones. J. Electr. Eng. Comput. Sci, 26, 482-492.

12. Khalid Bahani, Mohammed Moujabbir, Mohammed Ramdani, An accurate fuzzy rule-based classification systems for heart disease diagnosis, Scientific African, Volume 14, 2021, e01019,

13. Farsal, Wissal; Ramdani, Mohammed; Anter, Samir. International Journal of Advanced Computer Science and Applications; West Yorkshire Vol. 12, N° 12, (2021). DOI:10.14569/IJACSA.2021.0121233

14. Essebaa, I., Chantit, S., & Ramdani, M. (2021). Integration of Agile Methodologies and Model-Driven Development: Case Study-Based Comparison. In Advancements in Model-Driven Architecture in Software Engineering (pp. 108-117). IGI Global.

15. Amazal, H., Ramdani, M., & Kissi, M. (2021). Ensemble feature selection approach for imbalanced textual data using MapReduce. International Journal of Business Intelligence and Data Mining, 19(4), 395-417.


He can be contacted at email: