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Comparative Analysis of Pre-trained based CNN-RNN Deep Learning Models on Anomaly-5 Dataset for Action Recognition

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Bibliographic Details
Published in:Sukkur IBA Journal of Computing and Mathematical Sciences
Format: Online Article RSS Article
Published: 2024
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container_title Sukkur IBA Journal of Computing and Mathematical Sciences
description
discipline_display Computer Engiineering
discipline_facet Computer Engiineering
format Online Article
RSS Article
genre Journal Article
id rss_article:78955
institution FRELIP
journal_source_facet Sukkur IBA Journal of Computing and Mathematical Sciences
publishDate 2024
publishDateSort 2024
record_format rss_article
spellingShingle Comparative Analysis of Pre-trained based CNN-RNN Deep Learning Models on Anomaly-5 Dataset for Action Recognition
Computer Engiineering
General
Computer Engiineering
sub_discipline_display General
sub_discipline_facet General
subject_display Computer Engiineering
General
Computer Engiineering
Computer Engiineering
General
Computer Engiineering
subject_facet Computer Engiineering
General
Computer Engiineering
title Comparative Analysis of Pre-trained based CNN-RNN Deep Learning Models on Anomaly-5 Dataset for Action Recognition
title_auth Comparative Analysis of Pre-trained based CNN-RNN Deep Learning Models on Anomaly-5 Dataset for Action Recognition
title_full Comparative Analysis of Pre-trained based CNN-RNN Deep Learning Models on Anomaly-5 Dataset for Action Recognition
title_fullStr Comparative Analysis of Pre-trained based CNN-RNN Deep Learning Models on Anomaly-5 Dataset for Action Recognition
title_full_unstemmed Comparative Analysis of Pre-trained based CNN-RNN Deep Learning Models on Anomaly-5 Dataset for Action Recognition
title_short Comparative Analysis of Pre-trained based CNN-RNN Deep Learning Models on Anomaly-5 Dataset for Action Recognition
title_sort comparative analysis of pre-trained based cnn-rnn deep learning models on anomaly-5 dataset for action recognition
topic Computer Engiineering
General
Computer Engiineering
url https://journal.iba-suk.edu.pk:8089/index.php/sjcms/article/view/1444