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Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood.
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS)ORCID iD: 0000-0002-0579-7181
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS)ORCID iD: 0000-0002-3122-693X
2011 (English)Conference paper, Poster (Refereed)
Abstract [en]

Electronic nose (e-nose) data represents multivariate time-series from an array of chemical gas sensors exposed to a gas. This data is a new data set for usewith deep learning methods, and is highly suitable since e-nose data is complexand difficult to interpret for human experts. Furthermore, this data set presentsa number of interesting challenges for deep learning architectures per se. In this work we present a first study of e-nose data classification using deep learningwhen testing for the presence of bacteria in blood and agar solutions. We showin this study that deep learning outperforms hand-selected strategy based methods which has been previously tried with the same data set.

Place, publisher, year, edition, pages
National Category
Engineering and Technology
Research subject
Computer and Systems Science
URN: urn:nbn:se:oru:diva-24197OAI: diva2:542614
NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning
Available from: 2012-08-06 Created: 2012-08-02 Last updated: 2016-08-10Bibliographically approved
In thesis
1. Modeling time-series with deep networks
Open this publication in new window or tab >>Modeling time-series with deep networks
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Örebro: Örebro university, 2014. 56 p.
Örebro Studies in Technology, ISSN 1650-8580 ; 63
multivariate time-series, deep learning, representation learning, unsupervised
National Category
Computer and Information Science
Research subject
Information technology
urn:nbn:se:oru:diva-39415 (URN)978-91-7529-054-6 (ISBN)
Public defence
2015-02-02, Hörsalen, Musikhögskolan, Örebro universitet, Fakultetsgatan 1, Örebro, 13:15 (English)
Available from: 2014-12-08 Created: 2014-12-08 Last updated: 2015-12-28Bibliographically approved

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Längkvist, MartinLoutfi, Amy
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ReferencesLink to record
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