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49 results on '"Chen, Fuzan"'

17. An Evolutionary RBFNN Learning Algorithm for Complex Classzification Problems

Author

Tian, Jin, Li, Minqiang, Chen, Fuzan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Liu, Derong, editor, Fei, Shumin, editor, Hou, Zengguang, editor, Zhang, Huaguang, editor, and Sun, Changyin, editor

Published
2007
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21. An Efficiently Algorithm Based on Itemsets-Lattice and Bitmap Index for Finding Frequent Itemsets

Author

Chen, Fuzan, Li, Minqiang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Lipo, editor, and Jin, Yaochu, editor

Published
2005
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36. Web service discovery among large service pools utilising semantic similarity and clustering.

Author

Chen, Fuzan, Li, Minqiang, Wu, Harris, and Xie, Lingli

Subjects
WEB services, SEMANTIC Web, VALUE added (Marketing), ELECTRONIC commerce, TELECOMMUNICATION in business
Abstract

With the rapid development of electronic business, Web services have attracted much attention in recent years. Enterprises can combine individual Web services to provide new value-added services. An emerging challenge is the timely discovery of close matches to service requests among large service pools. In this study, we first define a new semantic similarity measure combining functional similarity and process similarity. We then present a service discovery mechanism that utilises the new semantic similarity measure for service matching. All the published Web services are pre-grouped into functional clusters prior to the matching process. For a user’s service request, the discovery mechanism first identifies matching services clusters and then identifies the best matching Web services within these matching clusters. Experimental results show that the proposed semantic discovery mechanism performs better than a conventional lexical similarity-based mechanism. [ABSTRACT FROM PUBLISHER]

Published
2017
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41. Managing security risks for inter-organisational information systems: a multiagent collaborative model.

Author

Feng, Nan, Wu, Harris, Li, Minqiang, Wu, Desheng, Chen, Fuzan, and Tian, Jin

Subjects
INFORMATION storage & retrieval systems, FINANCIAL risk, INFORMATION sharing, BAYESIAN analysis, COMMUNICATION
Abstract

Information sharing across organisations is critical to effectively managing the security risks of inter-organisational information systems. Nevertheless, few previous studies on information systems security have focused on inter-organisational information sharing, and none have studied the sharing of inferred beliefs versus factual observations. In this article, a multiagent collaborative model (MACM) is proposed as a practical solution to assess the risk level of each allied organisation’s information system and support proactive security treatment by sharing beliefs on event probabilities as well as factual observations. In MACM, for each allied organisation’s information system, we design four types of agents: inspection agent, analysis agent, control agent, and communication agent. By sharing soft findings (beliefs) in addition to hard findings (factual observations) among the organisations, each organisation’s analysis agent is capable of dynamically predicting its security risk level using a Bayesian network. A real-world implementation illustrates how our model can be used to manage security risks in distributed information systems and that sharing soft findings leads to lower expected loss from security risks. [ABSTRACT FROM AUTHOR]

Published
2016
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