TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach
  for Signal Classification
Nelly Elsayed Constantinos L. Zekios Navid Asadizanjani Zag ElSayed
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.