Classification of Human Activity by Using a Stacked Autoencoder

被引：0

作者：

Badem, Hasan ^{[1
]}

Caliskan, Abdullah ^{[2
]}

Basturk, Alper ^{[1
]}

Yuksel, Mehmet Emin ^{[2
]}

机构：

[1] Erciyes Univ, Bilgisayar Muhendisligi Bolumu, Kayseri, Turkey

[2] Erciyes Univ, Biyomed Muhendisligi Bolumu, Kayseri, Turkey

来源：

2016 MEDICAL TECHNOLOGIES NATIONAL CONFERENCE (TIPTEKNO) | 2015年

关键词：

Deep Neural Network; Stacked Autoencoder; Softmax; Human Activity Recognition;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper investigates the application of a deep neural network architecture that consists of stackted autoencoder with two autoencoders and a softmax layer for the purpose of human activity classification. Th performance of the proposed architecture is tested on a commonly used data set known as Human Activity Recognition Using Smartphones. It is observed that the proposed method yields better classification results than the representative state-of-the-art methods provided that the parameters of the deep network are suitably optimized.

引用

页数：4

共 50 条

[1] Smartphone Based Human Activity and Postural Transition Classification with Deep Stacked Autoencoder Networks
Hicks, Luke
Hedley, Yih-Ling
Elshaw, Mark
Altahhan, Abdulrahman
Palade, Vasile
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT II, 2016, 9887 : 535 - 536
[2] Stacked Robust Autoencoder for Classification
Mehta, Janki
Gupta, Kavya
Gogna, Anupriya
Majumdar, Angshul
Anand, Saket
NEURAL INFORMATION PROCESSING, ICONIP 2016, PT III, 2016, 9949 : 600 - 607
[3] Segmentation and classification of intervertebral disc using capsule stacked autoencoder
Adibatti, Spurthi
Sudhindra, K. R.
Shivaram, Joshi Manisha
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 86
[4] Classification of Spirometry Using Stacked Autoencoder based Neural Network
Trivedy, Sudipto
Goyal, Manish
Mishra, Madhusudhan
Verma, Narsingh
Mukherjee, Anirban
2019 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC), 2019, : 394 - 398
[5] Plant classification based on Stacked Autoencoder
Yang, Meng-Meng
Nayeem, Arifur
Shen, Ling-Ling
PROCEEDINGS OF 2017 IEEE 2ND INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC), 2017, : 1082 - 1086
[6] Classification of data on stacked autoencoder using modified sigmoid activation function
Kumar, Arvind
Sodhi, Sartaj Singh
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 44 (01) : 1 - 18
[7] Classification of Alzheimer's Disease Using Stacked Sparse Convolutional Autoencoder
Baydargil, Husnu Baris
Park, Jang-Sik
Kang, Do-Young
2019 19TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2019), 2019, : 891 - 895
[8] Lung Sounds Classification Using Stacked Autoencoder and Support Vector Machine
Falah, Adnan Hassal
Jondri
2019 7TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT), 2019, : 460 - 464
[9] Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP
Liaw L.C.M.
Tan S.C.
Goh P.Y.
Lim C.P.
Neural Computing and Applications, 2024, 36 (22) : 13895 - 13913
[10] Classification of Imbalanced Bioassay Data with Features Learned Using Stacked Autoencoder
Shah, Jeni
Joshi, Manjunath
FIFTEENTH INTERNATIONAL CONFERENCE ON MACHINE VISION, ICMV 2022, 2023, 12701

← 1 2 3 4 5 →