International Conference Papers

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    A genetic algorithm feature selection based approach for Arabic Sentiment Classification
    (IEEE Computer Society, 2016-11-29) Aliane, Hassina; Aliane, A.A; Ziane, M.; Bensaou, N.
    With the recently increasing interest for opinion mining from different research communities, there is an evolving body of work on Arabic Sentiment Analysis. There are few available polarity annotated datasets for this language, so most existing works use these datasets to test the best known supervised algorithms for their objectives. Naïve Bayes and SVM are the best reported algorithms in the Arabic sentiment analysis literature. The work described in this paper shows that using a genetic algorithm to select features and enhancing the quality of the training dataset improve significantly the accuracy of the learning algorithm. We use the LABR dataset of book reviews and compare our results with LABR’s authors’ results.
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    Automatic Construction of Ontology from Arabic Texts
    (Université Djillali LIABES Sidi-Bel-Abbès, 2012-04-29) Mazari, Ahmed Cherif; Aliane, Hassina; Alimazighi, Zaia
    The work which will be presented in this paper is related to the building of an ontology of domain for the Arabic linguistics. We propose an approach of automatic construction that is using statistical techniques to extract elements of ontology from Arabic texts. Among these techniques we use two; the first is the "repeated segment" to identify the relevant terms that denote the concepts associated with the domain and the second is the "co-occurrence" to link these new concepts extracted to the ontology by hierarchical or non- hierarchical relations. The processing is done on a corpus of Arabic texts formed and prepared in advance.
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    Side Channel Attack using Machine Learning
    (IEEE, 2022-12-15) Amrouche, Amina; Boubchir, Larbi; Yahiaoui, Said
    The overwhelming majority of significant security threats are hardware-based, where the attackers attempt to steal information straight from the hardware that our secure and encrypted software operates on. Unquestionably, side-channel attacks are one of the most severe risks to hardware security. Rather than depending on bugs in the program itself, a side-channel attack exploits information leaked from the program implementation in order to exfiltrate sensitive secret information such as cryptographic keys. A side channel assault could manifest in different ways including electromagnetic radiation, power consumption, timing data, or even acoustic emanation. Ever since the side-channel attacks were introduced in the 1990s, a number of significant attacks on cryptographic implementations utilizing side-channel analysis have emerged, such as template attacks, and attacks based on power analysis and electromagnetic analysis. However, Artificial Intelligence has become more prevalent. Attackers are now more interested in machine learning and deep learning technologies that enable them to interpret the extracted raw data. The aim of this paper is to highlight the main methods of machine learning and deep learning that are used in the most recent studies that deal with different types of side-channel attacks.
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    A Graph Approach for Enhancing Process Models Matchmaking
    (IEEE, 2015-06-27) Belhoul, Yacine; Yahiaoui, Saïd; Haddad, Mohammed; Gater, Ahmed; Kheddouci, Hamamache; Bouzeghoub, Mokrane
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    Graph Edit Distance Compacted Search Tree
    (Springer, Cham, 2022) Chegrane, Ibrahim; Hocine, Imane; Yahiaoui, Saïd; Bendjoudi, Ahcene; Nouali_Taboudjemat, Nadia
    We propose two methods to compact the used search tree during the graph edit distance (GED) computation. The first maps the node information and encodes the different edit operations by numbers and the needed remaining vertices and edges by BitSets. The second represents the tree succinctly by bit-vectors. The proposed methods require 24 to 250 times less memory than traditional versions without negatively influencing the running time.