A Novel Framework for Medical Web Information Foraging Using Hybrid ACO and Tabu Search.

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ID: 51961
2016
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Abstract
We present in this paper a novel approach based on multi-agent technology for Web information foraging. We proposed for this purpose an architecture in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The system was implemented using a colony of artificial ants hybridized with tabu search in order to achieve more effectiveness and efficiency. To validate our proposal, experiments were conducted on MedlinePlus, a real website dedicated for research in the domain of Health in contrast to other previous works where experiments were performed on web logs datasets. The main results are promising either for those related to strong Web regularities and for the response time, which is very short and hence complies the real time constraint.
Reference Key
drias2016ajournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Drias, Yassine;Kechid, Samir;Pasi, Gabriella;
Journal Journal of medical systems
Year 2016
DOI
10.1007/s10916-015-0350-z
URL
Keywords
multi-agent systems information foraging medical data management medlineplus page ranking swarm intelligence web intelligence ant colony optimization (aco) tabu search

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