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dc.contributor.authorSido, Jakub
dc.contributor.authorKonopík, Miloslav
dc.contributor.editorPinker, Jiří
dc.date.accessioned2019-10-18T12:51:07Z
dc.date.available2019-10-18T12:51:07Z
dc.date.issued2019
dc.identifier.citation2019 International Conference on Applied Electronics: Pilsen, 10th – 11th September 2019, Czech Republic, p. 141-146.en
dc.identifier.isbn978–80–261–0812–2 (Online)
dc.identifier.isbn978–80–261–0813–9 (Print)
dc.identifier.issn1803–7232 (Print)
dc.identifier.issn1805-9597 (Online)
dc.identifier.urihttp://hdl.handle.net/11025/35532
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherZápadočeská univerzita v Plznics
dc.rights© Západočeská univerzita v Plznics
dc.subjecthluboké učenícs
dc.subjectneuronové sítěcs
dc.subjectmobilní výpočetní technikacs
dc.subjectCNNcs
dc.subjectLSTMcs
dc.titleDeep Learning for Text Data on Mobile Devicesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedWith the rise of Artificial Intelligence (AI), it is becoming a significant phenomenon in our lives. As with many other powerful tools, AI brings many advantages but many risks as well. Predictions and automation can significantly help in our everyday lives. However, sending our data to servers for processing can severely hurt our privacy. In this paper, we describe experiments designed to find out whether we can enjoy the benefits of AI in the privacy of our mobile devices. We focus on text data since such data are easy to store in large quantities for mining by third parties. We measure the performance of deep learning methods in terms of accuracy (when compared to fully-fledged server models) and speed (number of text documents processed in a second). We conclude our paper with findings that with few relatively small modifications, mobile devices can process hundreds to thousands of documents while leveraging deep learning models.en
dc.subject.translateddeep learningen
dc.subject.translatedneural networksen
dc.subject.translatedmobile computingen
dc.subject.translatedCNNen
dc.subject.translatedLSTMen
dc.type.statusPeer-revieweden
Appears in Collections:Články / Articles (KIV)
Applied Electronics 2019
Applied Electronics 2019

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