Fatima Ardjani

Work place: EEDIS Laboratory, University of Djillali Liab├Ęs, Sidi BEL-ABBES, Algeria

E-mail: ardjanif@yahoo.fr


Research Interests: Applied computer science, Computer systems and computational processes, Theoretical Computer Science


Ardjani Fatima received his engineering degree in computer science from the University of Saida, Algeria, in 2007, and M. Sc. In Computer Science from the University of Oran, Algeria, in 2010, and in 2012, she joined the Department of Computer Science, University Center of Naama, Algeria. Currently, she is Assistant Professor at the University Center of El Bayadh. Her research interests include semantic Web, ontology alignment, and optimization methods.

Author Articles
An Approach for Discovering and Maintaining Links in RDF Linked Data

By Fatima Ardjani Djelloul Bouchiha Mimoun Malki

DOI: https://doi.org/10.5815/ijmecs.2017.03.07, Pub. Date: 8 Mar. 2017

Many datasets are published on the Web using semantic Web technologies. These datasets contain data that represent links to similar resources. If these datasets are linked together by properly constructed links, users can easily query the data through a uniform interface, as if they were querying a single dataset. In this paper we propose an approach to discover (semi) automatically links between RDF data based on the description models that appear around the resources. Our approach also includes a (semi) automatic process to maintain links when a data-change occurs.

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Optimization of SVM Multiclass by Particle Swarm (PSO-SVM)

By Fatima Ardjani Kaddour Sadouni

DOI: https://doi.org/10.5815/ijmecs.2010.02.05, Pub. Date: 8 Dec. 2010

In many problems of classification, the performances of a classifier are often evaluated by a factor (rate of error).the factor is not well adapted for the complex real problems, in particular the problems multiclass. Our contribution consists in adapting an evolutionary method for optimization of this factor. Among the methods of optimization used we chose the method PSO (Particle Swarm Optimization) which makes it possible to optimize the performance of classifier SVM (Separating with Vast Margin). The experiments are carried out on corpus TIMIT. The results obtained show that approach PSO-SVM gives a better classification in terms of accuracy even though the execution time is increased.

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Other Articles