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Kavosh: An intelligent neuro-fuzzy search engine

Milani Fard, A ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/ISDA.2007.4389673
  3. Publisher: 2007
  4. Abstract:
  5. In this paper we propose a neuro-fuzzy architecture for Web content taxonomy using hybrid of Adaptive Resonance Theory (ART) neural networks and fuzzy logic concept. The search engine called Kavosh1 is equipped with unsupervised neural networks for dynamic data clustering. This model was designed for retrieving images without metadata and in estimating resemblance of multimedia documents; however, in this work only text mining method is implemented. Results show noticeable average precision and recall over search results. © 2007 IEEE
  6. Keywords:
  7. Artificial intelligence ; Computer software ; Fuzzy inference ; Fuzzy neural networks ; Information retrieval ; Information services ; Intelligent control ; Intelligent systems ; Internet ; Network architecture ; Neural networks ; Search engines ; Systems analysis ; Telecommunication networks ; World Wide Web ; Adaptive resonance theory (ART1) ; International conferences ; Neuro fuzzy (NF) ; Neuro fuzzy architectures ; Systems design ; Web contents ; Fuzzy logic
  8. Source: 7th International Conference on Intelligent Systems Design and Applications, ISDA'07, Rio de Janeiro, 22 October 2007 through 24 October 2007 ; November , 2007 , Pages 597-602 ; 0769529763 (ISBN); 9780769529769 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4389673