Deep Learning for Human Activity Recognition in Mobile Computing.
By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field’s core a...
| Publicado en: | Computer (00189162) Vol. 51; no. 5; pp. 50 - 60 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
IEEE
May2018
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=129840991&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 129840991 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: May2018 vid: 51 iid: 5 pid: 13605 pub: IEEE artinfo: ui: 129840991 10.1109/MC.2018.2381112 ppf: 50 ppct: 10 formats: tig: atl: Deep Learning for Human Activity Recognition in Mobile Computing. aug: au: Plotz, Thomas Guan, Yu affil: Georgia Tech Newcastle University, UK su: Deep learning Human activity recognition Mobile computing Computer vision Problem solving sug: subj: Deep learning Human activity recognition Mobile computing Computer vision Problem solving keyword: artificial intelligence complexity deep learning embedded systems HAR human activity recognition intelligent systems machine learning mobile mobile and embedded deep learning modeling pattern recognition ab: By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field’s core application domains, including the ongoing transformation of human activity recognition technology through machine learning. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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