Dr. Myriam Bounhas
Dr. Myriam Bounhas is an Assistant Professor of Computer Science and Information Technology at the Emirates College of Technology in Abu Dhabi (UAE) since January 2013. She obtained her PhD in Informatics Applied to Management, in January 2013, from the Higher Institute of Management of Tunis in Tunisia. Her research focuses on Machine Learning, Possibilistic Classification, Bayesian Classification, Certain/Uncertain Numerical data classification, Analogical Proportions and Preferences, Analogical Classification and Analogical NLP and IR. She has been, since 2005, a member of the LARODEC research laboratory at the Higher Institute of Management of Tunis in Tunisia, and she has been an invited researcher at the Informatics Research Institute of Toulouse (IRIT), since 2009 to date. Dr. Bounhas has supervised a number of Masters and Ph.D. theses in Artificial Intelligence, Machine Learning, Analogical Learning and Proportions. Dr. Bounhas has published, since 2006 to date, about 25 refereed scientific papers in many reputed international journals and conferences. Dr. Bounhas is a reviewer for many international peer-reviewed journals and conferences.
- Bilel Elayeb, Amina Chouigui, Myriam Bounhas and Oussama Ben Khiroun, “Automatic Arabic Text Summarization using Analogical Proportions”. Cognitive Computation, Springer, Vol. 12, No. 6, pp. 1-37, 2020. DOI: 10.1007/s12559-020-09748-y
- Myriam Bounhas, Henri Prade and Gilles Richard, “Oddness‐based classification: A new way of exploiting neighbours”. International Journal of Intelligent Systems (IJIS), Wiley, Vol. 33, No. 12, pp. 2379-2401, 2018.
- Myriam Bounhas, Henri Prade and Gilles Richard, “Analogy-based classifiers for nominal or numerical data”. In International Journal of Approximate Reasoning (IJAR), Elsevier, Vol. 91, pp. 36-55, 2017.
- Myriam Bounhas, Henri Prade and Gilles Richard, “Oddness/evenness-based classifiers for Boolean or numerical data”. International Journal of Approximate Reasoning (IJAR), Elsevier, Vol. 82, No. 1, pp. 81-100,
- Myriam Bounhas, Mohamed Hamed Ghasemi, Henri Prade, Mathieu Serrurier, and Khaled Mellouli. “Naïve possibilistic classifiers for imprecise or uncertain numerical data”. In Fuzzy Set and System, Elsevier, Vol. 239, pp. 137-156, 2014.
- Myriam Bounhas, Khaled Mellouli, Henri Prade and Mathieu Serrurier, “Possibilistic Classifiers for numerical data”, In Soft Computing Journal, Springer-Verlag, Vol. 17, No. 5, pp. 733-751, 2013.