Investigating classification performance of hybrid deep learning and machine learning architectures on activity recognition-Reference-Cited by-同舟云学术

Investigating classification performance of hybrid deep learning and machine learning architectures on activity recognition

Published:2022-04-17 Issue:4 Volume:38 Page:1402-1449
ISSN:0824-7935
Container-title:Computational Intelligence
language:en
Short-container-title:Computational Intelligence

Author:

Uzunhisarcıklı Esma¹^ORCID,Kavuncuoğlu Erhan²^ORCID,Özdemir Ahmet Turan³^ORCID

Affiliation:

1. Department of Electronics Technology, Kayseri Vocational School Kayseri University Kayseri Turkey

2. Department of Computer Technology, Gemerek Vocational School Cumhuriyet University Sivas Turkey

3. Department of Electrical and Electronics Engineering, Engineering Faculty Erciyes University Kayseri Turkey

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12517

Reference52 articles.

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3. Recognition of Human Activities Using Continuous Autoencoders with Wearable Sensors

4. YangJ NguyenMN SanPP LiXL KrishnaswamyS.Deep convolutional neural networks on multichannel time series for human activity recognition. Twenty‐Fourth International Joint Conference on Artificial Intelligence; 2015.

5. AlsheikhMA SelimA NiyatoD DoyleL LinS TanHP.Deep activity recognition models with triaxial accelerometers. Proceedings of the AAAI Workshop—Technical Report. AI Access Foundation; 2016:8‐13.

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