The rapidly developed Health 2.0 technology has provided people with more opportunities
to conduct online medical consultation than ever before. Understanding contexts within …

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction
(FE) method is presented for hyperspectral image (HSI) classification using a convolutional …

In this paper, we propose a spectral–spatial unified network (SSUN) with an end-to-end
architecture for the hyperspectral image (HSI) classification. Different from traditional …

Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing
to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as …

Residential load forecasting is a challenging problem due to complex relations among the
hourly electrical load values along the time and also nonlinear relationships among the …

Hyperspectral image (HSI) classification aims to identify categories of hyperspectral pixels.
Recently, many convolutional neural networks (CNNs) have been designed to explore the …

Fault diagnostics and prognostics are important topics both in practice and research. There
is an intense pressure on industrial plants to continue reducing unscheduled downtime …

The goal of object navigation is to reach the expected objects according to visual information
in the unseen environments. Previous works usually implement deep models to train an …

Convolutional neural networks (CNNs) have made remarkable progress on scene
recognition, partially due to these recent large-scale scene datasets, such as the Places and …

Most algorithms for land surface temperature (LST) retrieval depend on acquiring prior
knowledge. To overcome this drawback, we propose a novel LST retrieval method based on …