Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep ...

The existing deep learning based reversible data hiding (RDH) predictors typically adopt standard convolutions for extracting features, which inherently fails to capture contextual information across ...

This paper proposes DRL-ED-TSPP, a deep reinforcement learning (DRL) model with an Encoder-Decoder architecture, to solve the Traveling Salesman Problem with Profits (TSPP) for sustainable cultural ...

This article extends deep learning frameworks for trajectory prediction tasks by exploring how recurrent encoder–decoder neural networks can be tasked not only to predict but also to yield a ...

In this article, we pioneer a deep quasi-recurrent self-attention structure that works with a dual encoder-decoder. The proposed novel deep quasi-recurrent self-attention architecture evokes parameter ...

Deep learning (DL) has been garnering increasing attention in remote sensing (RS) due to its powerful data representation ability. In particular, deep models have been proven to be effective for RS ...

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve ...

In order to further improve the prediction accuracy for the spatiotemporal sequence forecasting problem, we propose an encoder–decoder deep residual attention prediction network, which adaptively ...

Abstract P3089: Predicting Reductions In Hemoglobin A1c From Large Dietary Datasets Using Artificial Intelligence Pipeline With Interpretable Encoder-Decoder Deep Neural Networks Martha Tamez, ScD, ...