• Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
View Item 
  •   DSpace Home
  • Meslek Yüksek Okulları / Vocational Schools
  • Kazan Meslek Yüksekokulu / Kazan Vocational School
  • View Item
  •   DSpace Home
  • Meslek Yüksek Okulları / Vocational Schools
  • Kazan Meslek Yüksekokulu / Kazan Vocational School
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A Deep LSTM Approach for Activity Recognition

Thumbnail
Date
2019
Author
Guney, Selda
Erdas, Cagatay Berke
Metadata
Show full item record
Abstract
Since 1990s, activity recognition effectual field in machine learning literature. Most of studies that relevant activity recognition, use feature extraction method to achieve higher classification performance. Moreover, these studies mostly use traditional machine learning algorithms for classification. In this paper, we focus on a deep (Long Short Term Memory) LSTM neural network for feature free classification of seven daily activities by using raw data that collected from three-dimensional accelerometer. Based on the results, the proposed deep LSTM approach can classify raw data with high performance. The results show that the proposed deep LSTM approach achieved 91.34, 96.91, 88.78, 87.58 as percent classification performance in terms of accuracy, sensitivity, specificity, F-measure respectively.
URI
http://hdl.handle.net/11727/4917
Collections
  • Kazan Meslek Yüksekokulu / Kazan Vocational School [12]
  • Wos İndeksli Yayınlar Koleksiyonu [2880]

DSpace software copyright © 2002-2016  DuraSpace
Contact Us | Send Feedback
Theme by 
Atmire NV
 

 


Politika
Kullanıcı Rehberi
Başkent Üniversitesi Kütüphanesi
Başkent Üniversitesi

sherpa/romeo

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsxmlui.ArtifactBrowser.Navigation.browse_typexmlui.ArtifactBrowser.Navigation.browse_languagexmlui.ArtifactBrowser.Navigation.browse_publicationcategoryThis CollectionBy Issue DateAuthorsTitlesSubjectsxmlui.ArtifactBrowser.Navigation.browse_typexmlui.ArtifactBrowser.Navigation.browse_languagexmlui.ArtifactBrowser.Navigation.browse_publicationcategory

My Account

LoginRegister

Statistics

View Usage Statistics

DSpace software copyright © 2002-2016  DuraSpace
Contact Us | Send Feedback
Theme by 
Atmire NV