AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain
<p dir="ltr">This article proposes a new deep learning framework for time series classification in the discrete cosine transform (DCT) domain with spectral enhancement and self-attention mechanisms. The time series signal is first partitioned into discrete segments. Each segment is r...
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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| _version_ | 1864513537623195648 |
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| author | Amine Haboub (22392097) |
| author2 | Hamza Baali (14603380) Abdesselam Bouzerdoum (17900021) |
| author2_role | author author |
| author_facet | Amine Haboub (22392097) Hamza Baali (14603380) Abdesselam Bouzerdoum (17900021) |
| author_role | author |
| dc.creator.none.fl_str_mv | Amine Haboub (22392097) Hamza Baali (14603380) Abdesselam Bouzerdoum (17900021) |
| dc.date.none.fl_str_mv | 2025-06-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/tai.2025.3534141 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/AttDCT_Attention-Based_Deep_Learning_Approach_for_Time_Series_Classification_in_the_DCT_Domain/30306052 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Mathematical sciences Applied mathematics Attention convolutional neural networks (CNN) deep learning discrete cosine transform (DCT) time series classification (TSC) |
| dc.title.none.fl_str_mv | AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This article proposes a new deep learning framework for time series classification in the discrete cosine transform (DCT) domain with spectral enhancement and self-attention mechanisms. The time series signal is first partitioned into discrete segments. Each segment is rearranged into a matrix using a sliding window. The signal matrix is then transformed to spectral coefficients using a two-dimensional (2-D) DCT. This is followed by logarithmic contrast enhancement and spectral normalization to enhance the DCT coefficients. The resulting enhanced coefficient matrix serves as input to a deep neural network architecture comprising a self-attention layer, a multilayer convolutional neural network (CNN), and a fully connected multilayer perceptron (MLP) for classification. The AttDCT CNN model is evaluated and benchmarked on 13 different time series classification problems. The experimental results show that the proposed model outperforms state-of-the-art deep learning methods by an average of 2.1% in classification accuracy. It achieves higher classification accuracy on ten of the problems and similar results on the remaining three.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Artificial Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tai.2025.3534141" target="_blank">https://dx.doi.org/10.1109/tai.2025.3534141</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_715c8399e1a9aba0b6549c812329f46b |
| identifier_str_mv | 10.1109/tai.2025.3534141 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30306052 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT DomainAmine Haboub (22392097)Hamza Baali (14603380)Abdesselam Bouzerdoum (17900021)Information and computing sciencesArtificial intelligenceMachine learningMathematical sciencesApplied mathematicsAttentionconvolutional neural networks (CNN)deep learningdiscrete cosine transform (DCT)time series classification (TSC)<p dir="ltr">This article proposes a new deep learning framework for time series classification in the discrete cosine transform (DCT) domain with spectral enhancement and self-attention mechanisms. The time series signal is first partitioned into discrete segments. Each segment is rearranged into a matrix using a sliding window. The signal matrix is then transformed to spectral coefficients using a two-dimensional (2-D) DCT. This is followed by logarithmic contrast enhancement and spectral normalization to enhance the DCT coefficients. The resulting enhanced coefficient matrix serves as input to a deep neural network architecture comprising a self-attention layer, a multilayer convolutional neural network (CNN), and a fully connected multilayer perceptron (MLP) for classification. The AttDCT CNN model is evaluated and benchmarked on 13 different time series classification problems. The experimental results show that the proposed model outperforms state-of-the-art deep learning methods by an average of 2.1% in classification accuracy. It achieves higher classification accuracy on ten of the problems and similar results on the remaining three.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Artificial Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tai.2025.3534141" target="_blank">https://dx.doi.org/10.1109/tai.2025.3534141</a></p>2025-06-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/tai.2025.3534141https://figshare.com/articles/journal_contribution/AttDCT_Attention-Based_Deep_Learning_Approach_for_Time_Series_Classification_in_the_DCT_Domain/30306052CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303060522025-06-01T00:00:00Z |
| spellingShingle | AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain Amine Haboub (22392097) Information and computing sciences Artificial intelligence Machine learning Mathematical sciences Applied mathematics Attention convolutional neural networks (CNN) deep learning discrete cosine transform (DCT) time series classification (TSC) |
| status_str | publishedVersion |
| title | AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain |
| title_full | AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain |
| title_fullStr | AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain |
| title_full_unstemmed | AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain |
| title_short | AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain |
| title_sort | AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain |
| topic | Information and computing sciences Artificial intelligence Machine learning Mathematical sciences Applied mathematics Attention convolutional neural networks (CNN) deep learning discrete cosine transform (DCT) time series classification (TSC) |