Browsing by Author "Klouche, Badia"

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    A Modified NSGA-II with Silhouette Coefficient and K-means Clustering
    (University of Eloued جامعة الوادي, 2022-01-24) Mahammed, Nadir; Bekka, Abdelghani; Kazi Tani, Yassine; Bennabi, Souad; Fahci, Mahmoud; Klouche, Badia; Guelli, Zouaoui
    This article is a proposition for enhancing the genetic algorithm NSGA-II by some form of hybridization. The later explores the K-means clustering algorithm and the Silhouette coefficient features. It implies two specific phases. First, the right number of clusters generated automatically by K-means clustering is verified by Silhouette coefficient according to a number of iterations. Thereafter, NSGA-II is executed, in turn, for a defined number of iterations within the proposed algorithm. Obtained results of the algorithm for some benchmark test functions are used to illustrate the validity of the article proposition.
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    Sentiment Analysis of Algerian Dialect Using a Deep Learning Approach
    (University of Eloued جامعة الوادي, 2022-01-24) Klouche, Badia; Benslimane, Sidi Mohamed; Mahammed, Nadir
    Nowadays the Internet has become an essential tool for exchanging information, both on a personal and professional level. Today, the analysis of sentiment offers us a great interest for research, marketing and industry. With millions of comments and tweeting published every day, the information available on the Internet and in social media has become a gold mine for companies developing in their production, management and distribution. In this article, we propose a novel approach to analyze the sentiments of the Algerian dialect for the benefit of the Algerian Telephone Operator Ooredoo. The proposed approach is based on a deep learning model, which provides state-of-the-art results on a dataset written in Algerian dialect. In this study, the Facebook comments shared in Modern Standard Arabic (MSA) and Algerian dialect of the customers of the Algerian telephone operator Ooredoo are analyzed in order to allow the operator to retain and satisfy its customers to the maximum. Experimental results show that deep learning approaches outperformed traditional methods of sentiment.