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    Adaptive Fault Tolerant Checkpointing Algorithm for Cluster Based Mobile Ad Hoc Networks
    (Elsevier, 2015) Mansouri, Houssem; Badache, Nadjib; Aliouat, Makhlouf; Pathan, Al-Sakib Khan
    Mobile Ad hoc NETwork (MANET) is a type of wireless network consisting of a set of self-configured mobile hosts that can communicate with each other using wireless links without the assistance of any fixed infrastructure. This has made possible to create a distributed mobile computing application and has also brought several new challenges in distributed algorithm design. Checkpointing is a well explored fault tolerance technique for the wired and cellular mobile networks. However, it is not directly applicable to MANET due to its dynamic topology, limited availability of stable storage, partitioning and the absence of fixed infrastructure. In this paper, we propose an adaptive, coordinated and non-blocking checkpointing algorithm to provide fault tolerance in cluster based MANET, where only minimum number of mobile hosts in the cluster should take checkpoints. The performance analysis and simulation results show that the proposed scheme performs well compared to works related.
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    Using Clustering and Modified Classification algorithm without a learning corpus for automatic text summarization
    (2013-02-05) Aries, Abdelkrime; Oufaida, Houda; Nouali, Omar
    In this paper we describe a modified classification method destined for extractive summarization purpose. The classification in this method doesn’t need a learning corpus; it uses the input text to do that. First, we cluster the document sentences to exploit the diversity of topics, then we use a learning algorithm (here we used Naive Bayes) on each cluster considering it as a class. After obtaining the classification model, we calculate the score of a sentence in each class, using a scoring model derived from classification algorithm. These scores are used, then, to reorder the sentences and extract the first ones as the output summary. We conducted some experiments using a corpus of scientific papers, and comparing our system to another system which is UNIS system. Also, we experiment the impact of clustering threshold tuning, on the resulted summary, as well as the impact of adding more features to the classifier. We found that this method is interesting, and gives good performance, and the addition of new features (which is simple using this method) can improve summary’s accuracy.
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    A Clustering Application for the Web Usage Mining
    (CERIST, 2012-12) Kouici, Salima
    The Web Usage Mining constitutes a new branch of the Web Mining. It allows the study of the behavior of both users and potential customers via their site navigation. The mainly used source for the Web Usage Mining is the servers Log Files. A Log File contains an important mass of data, including user’s information (username, used software, etc.) and all the queries he has made on the website (requested files, the number of bytes transferred, time spent on each page, the page of entry to the site .... etc.). In this work we shall outline an application, made on this type of data, which is based on a clustering method, namely KMEANS. This application allows the definition of homogeneous groups constituting users profiles so that to anticipate the needs and with a view of communication adapted to each segment of users. In this application we have recorded some technical problems. These problems concerns the data cleaning (removing queries of images and multimedia files associated with web pages, removing queries from search bots... etc.) and the setting up of visitor sessions, knowing that a session is a sequence of pages viewed by the same user.