Enhancing Human Activity Recognition through Transformer-based GANs – Ihab AHMED
Ihab AHMED
Master's thesis
Enhancing Human Activity Recognition through Transformer-based GANs
Abstract:
A custom Transformer-based GAN tailored for time-series data was developed to improve Human Activity Recognition systems. The effectiveness of the synthetic data generated was assessed, showing dependence on the diversity and quality of the training datasets. The synthetic data's impact on enhancing system performance and its potential for privacy-sensitive applications were also explored.Abstract:
A custom Transformer-based GAN tailored for time-series data was developed to improve Human Activity Recognition systems. The effectiveness of the synthetic data generated was assessed, showing dependence on the diversity and quality of the training datasets. The synthetic data's impact on enhancing system performance and its potential for privacy-sensitive applications were also explored.
Jazyk práce: English
Datum vytvoření / odevzdání či podání práce: 20. 8. 2024
Obhajoba závěrečné práce
- Vedúci: prof. Dr. Patrick Glauner
Citační záznam
Jak správně citovat práci
AHMED, Ihab. Enhancing Human Activity Recognition through Transformer-based GANs. České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Plný text práce
Obsah online archivu závěrečné práce
Zveřejněno v Theses:- Soubory jsou nedostupné do 20. 8. 2027
- Po tomto datu bude práce dostupná: světu
Jak jinak získat přístup k textu
Instituce archivující a zpřístupňující práci: JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH, Přírodovědecká fakulta
UNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE
Faculty of ScienceMaster programme / odbor:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science
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