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Author: search.filters.author.Mellah, Hakima

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    A Formal Approach for a Self Organizing Protocol Inspired by Bacteria Colonies: Production System Application
    (Springer Berlin Heidelberg, 2010) Mellah, Hakima; Hassas, Salima; Drias, Habiba; Raiah, A.; Tiguemoumine, A.
    Any dysfunction in production system (PS) is likely to be very expensive; so modelling by Multi Agent Systems (MAS) makes the production system (PS) possible to have aspects of robustness, reactivity and flexibility, which allow the PS control to be powerful and to react to all the risks being able to occur. In order to have a fault-tolerant PS, we propose when and how to recourse to a self organizing protocol making the MAS capable of changing its communication structure or organization, and thus reorganizing itself without any external intervention.
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    Tags Weighting Based on User Profile
    (Springer Berlin Heidelberg, 2011) Kichou, Saida; Mellah, Hakima; Amghar, Youssef; Dahak, Fouad
    The ’Collaborative Tagging’ is gaining popularity on Web 2.0, this new generation of Web which makes user reader/writer. The ’Tagging’ is a mean for users to express themselves freely through additions of label called ’Tags’ to shared resources. One of the problems encountered in current tagging systems is to define the most appropriate tag for a resource. Tags are typically listed in order of popularity, as del-icio-us. But the popularity of the tag does not always reflect its importance and representativeness for the resource to which it is associated. Starting from the assumptions that the same tag for a resource can take different meanings for different users, and a tag from a knowledgeable user would be more important than a tag from a novice user, we propose an approach for weighting resource’s tags based on user profile. For this we define a user model for his integration in the tag weight calculation and a formula for this calculation, based on three factors namely the user, the degree of approximation between his interest centers and the resource field, expertise and personal assessment for tags associated to the resource. A resource descriptor containing the best tags is created.