Trust prediction using Z-numbers and Artificial Neural Networks

Azadeh, A; Kokabi, R; Saberi, M; Hussain, FK; Hussain, OK

Trust prediction using Z-numbers and Artificial Neural Networks

Azadeh, A Kokabi, R Saberi, M

Hussain, FK

Hussain, OK

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2014, pp. 522 - 528
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Azadeh, A	en_US
dc.contributor.author	Kokabi, R	en_US
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Hussain, OK	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2014, pp. 522 - 528	en_US
dc.identifier.isbn	9781479920723	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121598
dc.description.abstract	© 2014 IEEE. Trust modeling of both the interacting parties in a virtual world, is a critical element of business intelligence. A key aspect in trust modeling is to be able to accurately predict the future trust value of an interacting party. In this paper, we propose an intelligent method for predicting the future trust value of a trusted entity. We propose the use of Z-number to represent both the trust value and its corresponding reliability. Subsequently, we apply Artificial Neural Network (ANN) to predict future trust values. We generate a large number of synthetic time series, with a view to model real-world trust values of trusted entity. We validate the working of our methodology using the generated time series.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2014.6891602	en_US
dc.title	Trust prediction using Z-numbers and Artificial Neural Networks	en_US
dc.type	Conference Proceeding
utslib.for	080301 Bioinformatics Software	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Trust modeling of both the interacting parties in a virtual world, is a critical element of business intelligence. A key aspect in trust modeling is to be able to accurately predict the future trust value of an interacting party. In this paper, we propose an intelligent method for predicting the future trust value of a trusted entity. We propose the use of Z-number to represent both the trust value and its corresponding reliability. Subsequently, we apply Artificial Neural Network (ANN) to predict future trust values. We generate a large number of synthetic time series, with a view to model real-world trust values of trusted entity. We validate the working of our methodology using the generated time series.

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http://hdl.handle.net/10453/121598