We proposed a convolutional autoencoder with sequential and channel attention (CAE-SCA) to address this issue. Sequential attention (SA) is based on long short-term memory (LSTM), which captures ...

Accurate prediction of protein subcellular localization is critical for understanding cellular functions and guiding drug design. However, current computational methods have limited and insufficient ...

Recently Convolution Neural Network (CNN) has become very popular in AI applications. CNN requires a lot of computational resources that has significant impact on chip size. In this paper we proposed ...

Addressing the issue of low accuracy in detecting suitable grasping regions for unknown objects through visual information, this paper proposes an Inception Transformer-CNN architecture for robot ...

This paper presents a trade-off study concerning the impact of convolutional neural network (CNN) architectures design on the performance of deep learning (DL) based channel estimation under ...

Breast cancer detection through mammograms is very crucial for early diagnosis, and tumor segmentation is an important step in treatment planning. This paper proposes a novel approach by combining ...

In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on training combined with ...

AUTOVC is a voice-conversion method that performs self-reconstruction using an autoencoder structure for zero-shot voice conversion. AUTOVC has the advantage of being easy and simple to learn because ...

Therefore, in this work, we propose a hybrid-feature-based autoencoder (HFAE) model to predict traffic flow and accelerate ambulance medical response time in Internet of Vehicles. Specifically, the ...

In this study, we propose a fault detection algorithm based on an autoencoder-based classification model. Unlike traditional neural networks, autoencoders have equal numbers of neurons in the input ...

Therefore, this article proposes a deeply integrated autoencoder anomaly detection method for anomaly detection and critical parameter identification of UAV actuators. First, to reduce the influence ...