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By Tsau Young Lin, Abraham Kandel, Yan-Qing Zhang

This overview quantity introduces the unconventional clever net thought known as computational net intelligence (CWI) in keeping with computational intelligence (CI) and internet know-how (WT). It takes an in-depth examine hybrid internet intelligence (HWI), that's in response to man made organic and computational intelligence with net know-how and is used to construct hybrid clever net platforms that serve stressed out and instant clients extra successfully. the fundamental ideas of CWI and numerous e-applications of CWI and HWI are mentioned. For completeness, six significant CWI options - fuzzy internet intelligence, neural net intelligence, evolutionary net intelligence, granular net intelligence, tough net Intelligence and probabilistic net intelligence - are defined. With the massive strength for clever e-business purposes of CWI and HWI, those strategies signify the way forward for clever internet purposes.

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Extra info for Computational Web Intelligence: Intelligent Technology for Web Applications

Example text

The word “magazine”, which highly relates to “book”, is associated by iterating propagation of activation values in CFS unit. The activation value of the word “information” is also at the peak in the third to fifth iteration. Although the word “information” is not connected directly to “personal computer” nor “book”, the iteration of the propagation of activation values enables the association of the word. e computer busslness -+-software 01 -. K- . I.. 0 1 I 3 7 9 II U 15 17 19 U. bookseller 29 Repeat number of propagation Fig.

1 Introduction The web environment is, for the most part, unstructured and imprecise. 19 20 Part I: Fuzzy, Rough, and Probabilistic Web Intelligence To deal with information in the web environment what is needed is a logic that supports modes of reasoning which are approximate rather than exact. While searches may retrieve thousands of hits, finding decisionrelevant and query-relevant information in an imprecise environment is a challenging problem, which has to be addressed. Another, and less obvious, is deduction in an unstructured and imprecise environment given the huge stream of complex information.

1997). Recommender systems, Communications of the ACM 40(3), pp. 56-58. Shardanand, U. and Maes, P. (1995). Social information filtering: algorithms for automating word of mouth, Proceedings of the Computer Human Interaction95 Conference, Denver, pp. 210-217. Sugeno, M. (1977). N. R. ), Amsterdam: North-Holland Pub, pp. 89-102. Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Transactions on Systems, Man and Cybernetics 18, pp. 183-190.

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