Original title: Enhancing Human Activity Recognition through Transformer-based GANs
Authors: AHMED, Ihab
Document type: Master’s theses
Year: 2024
Language: eng
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.
Keywords: Artificial Neural Networks; Deep Learning; Generative Adverserial Networks; Generative AI; Human Activity Recognition; time-series data analysis; Transformers
Citation: AHMED, Ihab. Enhancing Human Activity Recognition through Transformer-based GANs. České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta

Institution: University of South Bohemia in České Budějovice (web)
Document availability information: Fulltext is available in the Digital Repository of University of South Bohemia.
Original record: http://www.jcu.cz/vskp/76761

Permalink: http://www.nusl.cz/ntk/nusl-695372


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Universities and colleges > Public universities > University of South Bohemia in České Budějovice
Academic theses (ETDs) > Master’s theses