Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Sido, Jakub | |
| dc.contributor.author | Konopík, Miloslav | |
| dc.contributor.editor | Pinker, Jiří | |
| dc.date.accessioned | 2019-10-18T12:51:07Z | |
| dc.date.available | 2019-10-18T12:51:07Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | 2019 International Conference on Applied Electronics: Pilsen, 10th – 11th September 2019, Czech Republic, p. 141-146. | en |
| dc.identifier.isbn | 978–80–261–0812–2 (Online) | |
| dc.identifier.isbn | 978–80–261–0813–9 (Print) | |
| dc.identifier.issn | 1803–7232 (Print) | |
| dc.identifier.issn | 1805-9597 (Online) | |
| dc.identifier.uri | http://hdl.handle.net/11025/35532 | |
| dc.format | 4 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | Západočeská univerzita v Plzni | cs |
| dc.rights | © Západočeská univerzita v Plzni | cs |
| dc.subject | hluboké učení | cs |
| dc.subject | neuronové sítě | cs |
| dc.subject | mobilní výpočetní technika | cs |
| dc.subject | CNN | cs |
| dc.subject | LSTM | cs |
| dc.title | Deep Learning for Text Data on Mobile Devices | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.rights.access | openAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | With 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.translated | deep learning | en |
| dc.subject.translated | neural networks | en |
| dc.subject.translated | mobile computing | en |
| dc.subject.translated | CNN | en |
| dc.subject.translated | LSTM | en |
| dc.type.status | Peer-reviewed | en |
| Vyskytuje se v kolekcích: | Články / Articles (KIV) Applied Electronics 2019 Applied Electronics 2019 | |
Soubory připojené k záznamu:
| Soubor | Popis | Velikost | Formát | |
|---|---|---|---|---|
| Sido.pdf | Plný text | 94,68 kB | Adobe PDF | Zobrazit/otevřít |
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