An Integrated Fuzzy Trust Prediction Approach in Product Design and Engineering

Azadeh, A; Sadri, S; Saberi, M; Yoon, JH; Chang, E; Khadeer Hussain, O; Pourmohammad Zia, N

An Integrated Fuzzy Trust Prediction Approach in Product Design and Engineering

Azadeh, A Sadri, S Saberi, M

Yoon, JH Chang, E Khadeer Hussain, O Pourmohammad Zia, N

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: International Journal of Fuzzy Systems, 2017, 19, (4), pp. 1190-1199
Issue Date:: 2017-08-01

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Field	Value	Language
dc.contributor.author	Azadeh, A
dc.contributor.author	Sadri, S
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Yoon, JH
dc.contributor.author	Chang, E
dc.contributor.author	Khadeer Hussain, O
dc.contributor.author	Pourmohammad Zia, N
dc.date.accessioned	2022-08-26T04:19:27Z
dc.date.available	2022-08-26T04:19:27Z
dc.date.issued	2017-08-01
dc.identifier.citation	International Journal of Fuzzy Systems, 2017, 19, (4), pp. 1190-1199
dc.identifier.issn	1562-2479
dc.identifier.issn	2199-3211
dc.identifier.uri	http://hdl.handle.net/10453/160898
dc.description.abstract	Nowadays, the success of a company is dependent to the novelty of the company in developing new items. Product design and engineering are a basic phase in developing new commodities which examines the product economically and technologically. In the proposed study, “Trust” is identified as an effective factor on the life cycle of the new designed product. This study addresses a simulation structure to generate all the possible trust modes between two agents over time and implements four prediction methods to forecast the trust value of the new item. The time horizon is considered to be short term and middle term, and 27 and 108 scenarios are designed, respectively, based on three categories involving high, medium and short trust. Here, three prediction techniques: conventional time series, artificial neural networks and adaptive neuro-fuzzy inference system, are recommended and compared. By comparing MAPEs of all prediction methods, the best technique is identified.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	International Journal of Fuzzy Systems
dc.relation.isbasedon	10.1007/s40815-017-0314-1
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	An Integrated Fuzzy Trust Prediction Approach in Product Design and Engineering
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0802 Computation Theory and Mathematics
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-26T04:19:26Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	4

Abstract:

Nowadays, the success of a company is dependent to the novelty of the company in developing new items. Product design and engineering are a basic phase in developing new commodities which examines the product economically and technologically. In the proposed study, “Trust” is identified as an effective factor on the life cycle of the new designed product. This study addresses a simulation structure to generate all the possible trust modes between two agents over time and implements four prediction methods to forecast the trust value of the new item. The time horizon is considered to be short term and middle term, and 27 and 108 scenarios are designed, respectively, based on three categories involving high, medium and short trust. Here, three prediction techniques: conventional time series, artificial neural networks and adaptive neuro-fuzzy inference system, are recommended and compared. By comparing MAPEs of all prediction methods, the best technique is identified.

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http://hdl.handle.net/10453/160898