TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification

Autores

DOI:

https://doi.org/10.32473/flairs.37.1.135278

Palavras-chave:

Deep Learning, Ensemble Classifiers, Convolution Neural Network, LSTM, Artificial Neural Network, emotion detection, ECG, Radar Signal Classification, ECG Classification

Resumo

Ensemble modeling has been widely used to solve complex problems as it helps to improve overall performance and generalization. In this paper, we propose a novel TemporalAugmenter approach based on ensemble modeling for augmenting the temporal information capturing for long-term and short-term dependencies in data integration of two variations of recurrent neural networks in two learning streams to obtain the maximum possible temporal extraction. Thus, the proposed model augments the extraction of temporal dependencies. In addition, the proposed approach reduces the preprocessing and prior stages of feature extraction, which reduces the required energy to process the models built upon the proposed TemporalAugmenter approach, contributing towards green AI. Moreover, the proposed model can be simply integrated into various domains including industrial, medical, and human-computer interaction applications. Our proposed approach empirically evaluated the speech emotion recognition, electrocardiogram signal, and signal quality examination tasks as three different signals with varying complexity and different temporal dependency features.

Biografia do Autor

Zag ElSayed, University of Cincinnati

https://orcid.org/0000-0001-9094-1469

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Publicado

2024-05-13

Como Citar

Elsayed, N., Zekios, C. L., Asadizanjani, N., & ElSayed, Z. (2024). TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification. The International FLAIRS Conference Proceedings, 37(1). https://doi.org/10.32473/flairs.37.1.135278

Edição

Seção

Main Track Proceedings