HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones
Smartphone-based human activity recognition (HAR) is an important research area due to its wide-ranging applications in health, security, gaming, etc. Existing HAR models face challenges such as tedious manual feature extraction/selection techniques, limited model generalisation, high computational...
Saved in:
| Main Authors: | Sarmela Raja Sekaran, Ying Han Pang, Ooi Shih Yin, Lim Zheng You |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ital Publication
2025-02-01
|
| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2571 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing breast cancer prediction through stacking ensemble and deep learning integration
by: Fatih Gurcan
Published: (2025-02-01) -
Employing the concept of stacking ensemble learning to generate deep dream images using multiple CNN variants
by: Lafta Alkhazraji, et al.
Published: (2025-03-01) -
Deep learning-based ensemble stacking for enhanced intrusion detection in IoT-edge platforms
by: P. R. Chithra Rani, et al.
Published: (2025-08-01) -
DSEM-NIDS: Enhanced Network Intrusion Detection System Using Deep Stacking Ensemble Model
by: Loreen Mahmoud, et al.
Published: (2025-01-01) -
Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms
by: Olusogo Julius Adetunji
Published: (2025-06-01)