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Conference Paper/Proceeding/Abstract 204 views 66 downloads

Exploring Human Activity Recognition with Acoustic Data: A Comparative Study of CNN-LSTM, ViViT, and ResNet-Temporal Transformer Model

AALAA HUMAIDAN, Jeny Roy Orcid Logo, Sara Sharifzadeh Orcid Logo, Ruchita Mehta, Andrea Tales Orcid Logo, Joe MacInnes Orcid Logo

2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media Companion (CISM Companion), Pages: 1 - 5

Swansea University Authors: AALAA HUMAIDAN, Jeny Roy Orcid Logo, Sara Sharifzadeh Orcid Logo, Andrea Tales Orcid Logo, Joe MacInnes Orcid Logo

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DOI (Published version): 10.1109/cismcompanion65074.2025.11032695

Published in: 2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media Companion (CISM Companion)
ISBN: 979-8-3315-0852-4 979-8-3315-0851-7
Published: IEEE 2025
URI: https://cronfa.swan.ac.uk/Record/cronfa69135
Keywords: Accuracy, Computational modeling, Noise, Pipelines, Transformers, Feature extraction, Data models, Acoustics, Human activity recognition, Convolutional neural networks
College: Faculty of Science and Engineering
Funders: This project is partly supported by Swansea University IAA funding scheme and Coventry University
Start Page: 1
End Page: 5