Driss Mammass

Work place: IRF-SIC Laboratory, Faculty of Science, Ibn Zohr University, Agadir, Morocco

E-mail: mammass@uiz.ac.ma


Research Interests: Pattern Recognition, Image Compression, Image Manipulation, Image Processing, Database Management System, Data Structures and Algorithms


Driss MAMMASS is professor of Higher Education at the Faculty of Sciences, University Ibn Zohr, Agadir Morocco. He received a Doctorat in Mathematics in 1988 from Paul Sabatier University (Toulouse - France) and a doctorat d'Etat-es-Sciences degrees in Mathematics and Image Processing from Faculty of Sciences, University Ibn Zohr Agadir Morocco, in 1999. He supervises several Ph.D theses in the various research themes of mathematics and computer science such as remote sensing and GIS, digital image processing and pattern recognition, the geographic databases, knowledge management, semantic web, etc. He is currently Vice-Dean of the Faculty of Sciences Agadir and the head of IRF-SIC Laboratory (Image Reconnaissances des Formes, Systèmes Intelligents et Communicants) and an unit of formation and research in doctorat on mathematics and informatics.

Author Articles
An Evolutionary Model for Selecting Relevant Textual Features

By Taher Zaki Mohamed Salim EL Bazzi Driss Mammass

DOI: https://doi.org/10.5815/ijmecs.2018.11.06, Pub. Date: 8 Nov. 2018

From a philosophical point of view, the words of a text or a speech are not held just for informational purposes, but they act and react; they have the power to react on their counterparts. Each word, evokes similar or different senses that can influence and interact with the following words, it has a vibratory property. It's not the words themselves that have the impact, but the semantic reaction behind the words. In this context, we propose a new textual data classification approach while trying to imitate human altruistic behavior in order to show the semantic altruistic stakes of natural language words through statistical, semantic and distributional analysis. We present the results of a word extraction method, which combines a distributional proximity index, a selection coefficient and a co-occurrence index with respect to the neighborhood.

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