Modeling time-series with deep networks
Year: 2014
Type: dissertation
Abstract: 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.
Author: Martin Längkvist
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W438355678