HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones

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...

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Bibliographic Details
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:
deep ensemble learning
smartphone-based human activity recognition
stacking ensemble
lightweight model
hierarchical deep features.
Online Access:https://ijournalse.org/index.php/ESJ/article/view/2571
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