Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition
While splitting datasets, researchers assume that training set is exchangeable with test set and expect good classification performance. This assumption is invalid due to subject variability due to age differences. Classification models trained on activity data from one particular age group such as...
Saved in:
Main Author: | Jimale, Ali Olow |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | http://eprints.usm.my/60094/1/ALI%20OLOW%20JIMALE%20-%20TESIS%20cut.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhanced Statistical Modelling For Variable Bit Rate Video Traffic Generated From Scalable Video Codec
by: Ahmadpour, Sima
Published: (2016) -
Enhanced and automated approaches for fish recognition and classification system
by: Samma, Ali Salem Ali
Published: (2011) -
Human Action Recognition With Temporal Dense Sampling Deep Neural Networks
by: Tan, Kok Seang
Published: (2019) -
Analysis Of Feature Reduction Algorithms To Estimate
Human Stress Conditions
by: Arasu, Darshan Babu
Published: (2022) -
Sensorml-Nt: Innovative Cloud Service Sensor Description For Mobile Devices Handling Environmental Issues.
by: Yazdi, Nazi Tabatabaei
Published: (2011)