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Mohamed Fakir
University Sultan Moulay Slimane

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Ontology Matching using BabelNet Dictionary and Word Sense Disambiguation Algorithms Mohamed Biniz; Rachid el Ayachi; Mohamed Fakir
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 1: January 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i1.pp196-205

Abstract

Ontology matching is a discipline that means two things: first, the process of discovering correspondences between two different ontologies, and second is the result of this process, that is to say the expression of correspondences. This discipline is a crucial task to solve problems merging and evolving of heterogeneous ontologies in applications of the Semantic Web. This domain imposes several challenges, among them, the selection of appropriate similarity measures to discover the correspondences. In this article, we are interested to study algorithms that calculate the semantic similarity by using Adapted Lesk algorithm, Wu & Palmer Algorithm, Resnik Algorithm, Leacock and Chodorow Algorithm, and similarity flooding between two ontologies and BabelNet as reference ontology, we implement them, and compared experimentally. Overall, the most effective methods are Wu & Palmer and Adapted Lesk, which is widely used for Word Sense Disambiguation (WSD) in the field of Automatic Natural Language Processing (NLP).
A Hybrid the Nonsubsampled Contourlet Transform and Homomorphic Filtering for Enhancing Mammograms Khaddouj Taifi; Rachid Ahdid; Mohamed Fakir; Said Safi
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp539-545

Abstract

Mammogram is important for early breast cancer detection. But due to the low contrast of microcalcifications and noise, it is difficult to detect microcalcification. This paper presents a comparative study in digital mammography image enhancement based on three different algorithms: homomorphic filtering, unsharp masking and our proposed methods. This latter use a hybrid method Combining contourlet and homomorphic filtering. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to-background enhancement measure based on standard deviation (TBES) and target-to-background enhancement measure based on entropy (TBEE). The proposed methods were tested with the referents mammography data Base MiniMIAS. Experimental results show that the proposed method improves the visibility of microcalcification.
Co-Authors Khaddouj Taifi Mohamed Biniz Rachid Ahdid Rachid el Ayachi Said Safi