Ensemble residual network-based gender and activity recognition method with signals
Authors:
- Turker Tuncer,
- Fatih Ertam,
- Sengul Dogan,
- Emrah Aydemir,
- Paweł Pławiak
Abstract
Nowadays, deep learning is one of the popular research areas of the computer sciences, and many deep networks have been proposed to solve artificial intelligence and machine learning problems. Residual networks (ResNet) for instance ResNet18, ResNet50 and ResNet101 are widely used deep network in the literature. In this paper, a novel ResNet-based signal recognition method is presented. In this study, ResNet18, ResNet50 and ResNet101 are utilized as feature extractor and each network extracts 1000 features. The extracted features are concatenated, and 3000 features are obtained. In the feature selection phase, 1000 most discriminative features are selected using ReliefF, and these selected features are used as input for the third-degree polynomial (cubic) activation-based support vector machine. The proposed method achieved 99.96% and 99.61% classification accuracy rates for gender and activity recognitions, respectively. These results clearly demonstrate that the proposed pre-trained ensemble ResNet-based method achieved high success rate for sensors signals.
- Record ID
- CUT4d6d6dfda9e84c8ca8080f5e6466d211
- Publication categories
- ;
- Author
- Journal series
- Journal of Supercomputing, ISSN 0920-8542, e-ISSN 1573-0484
- Issue year
- 2020
- Vol
- 76
- No
- 3
- Pages
- 2119-2138
- Other elements of collation
- rys.; schem.; tab.; Bibliografia (na s.) - 2136-2138; Bibliografia (liczba pozycji) - 49; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 76, Iss. 3
- Substantive notes
- Topical Collection: Deep Learning, Parallel Computing in Biomed Sciences & Healthcare
- Keywords in English
- ensemble residual network, gender identification, daily sport activity recognition, sensor signals, machine learning
- DOI
- DOI:10.1007/s11227-020-03205-1 Opening in a new tab
- URL
- https://link.springer.com/article/10.1007/s11227-020-03205-1 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 70
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT4d6d6dfda9e84c8ca8080f5e6466d211/
- URN
urn:pkr-prod:CUT4d6d6dfda9e84c8ca8080f5e6466d211
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.