An Augmented Reality Question Answering System Based on Ensemble Neural Networks

Chen, CH; Wu, CL; Lo, CC; Hwang, FJ

An Augmented Reality Question Answering System Based on Ensemble Neural Networks

Chen, CH Wu, CL Lo, CC Hwang, FJ

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Publication Type:: Journal Article
Citation:: IEEE Access, 2017, 5 pp. 17425 - 17435
Issue Date:: 2017-08-26

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Field	Value	Language
dc.contributor.author	Chen, CH	en_US
dc.contributor.author	Wu, CL	en_US
dc.contributor.author	Lo, CC	en_US
dc.contributor.author	Hwang, FJ https://orcid.org/0000-0002-1741-5590	en_US
dc.date.issued	2017-08-26	en_US
dc.identifier.citation	IEEE Access, 2017, 5 pp. 17425 - 17435	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117113
dc.description.abstract	© 2013 IEEE. This paper proposes a classification algorithm based on ensemble neural networks. In the training phase, the proposed algorithm uses a random number of training data to develop multiple random artificial neural network (ANN) models until those ANN models converge. Those models with lower accuracy than the threshold are filtered out. The remaining highly accurate models will be used to predict the output in the testing phase. Meanwhile, the accuracy of ANN models is presented as a weighting value in the testing phase. In the testing phase, the testing data are loaded into the selected ANN models to predict the output class. The output values are multiplied by the corresponding weighting values of ANN models. Then the weighted average of the outputs can be obtained. Finally, the predicted output is converted into the predicted class. We design an augmented reality question answering system (AR-QAS) applying and implementing the proposed algorithm on mobile devices. AR-QAS offers an interactive user interface and automatically replies according to user's queries. By comparing with the logistic regression method and the ANN method, the experiment results demonstrate that the proposed algorithm offers the highest accuracy.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2017.2743746	en_US
dc.title	An Augmented Reality Question Answering System Based on Ensemble Neural Networks	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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 Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

© 2013 IEEE. This paper proposes a classification algorithm based on ensemble neural networks. In the training phase, the proposed algorithm uses a random number of training data to develop multiple random artificial neural network (ANN) models until those ANN models converge. Those models with lower accuracy than the threshold are filtered out. The remaining highly accurate models will be used to predict the output in the testing phase. Meanwhile, the accuracy of ANN models is presented as a weighting value in the testing phase. In the testing phase, the testing data are loaded into the selected ANN models to predict the output class. The output values are multiplied by the corresponding weighting values of ANN models. Then the weighted average of the outputs can be obtained. Finally, the predicted output is converted into the predicted class. We design an augmented reality question answering system (AR-QAS) applying and implementing the proposed algorithm on mobile devices. AR-QAS offers an interactive user interface and automatically replies according to user's queries. By comparing with the logistic regression method and the ANN method, the experiment results demonstrate that the proposed algorithm offers the highest accuracy.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/116237