The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth's
land surface within the scope of Earth observation (EO). Technological advances in capture …

Hyperspectral image processing techniques involve time-consuming calculations due to the
large volume and complexity of the data. Indeed, hyperspectral scenes contain a wealth of …

Withthe rapiddevelopment of deep learning, the size of data sets anddeepneuralnetworks
(DNNs) models are also booming. As a result, the intolerable long time for models' training …

Despite the increasing amount of spaceborne synthetic aperture radar (SAR) images and
optical images, only a few annotated data can be used directly for scene classification tasks …

Coping with the growing demand for data and parameters in complex neural network (NN)
models of contemporary times typically involves distributing them across multiple devices …

Scaling up the capacity of deep neural networks (DNN) is one of the effective approaches to
improve the model quality for several different DNN-based applications, making the DNN …

With the ever increasing data deluge and the success of deep neural networks, the research
of distributed deep learning has become pronounced. Two common approaches to achieve …

Long Short-Term Memory (LSTM) Neural Network has great potential to predict sequential
data. Time series prediction is one of the most popular experimental subjects of LSTM. To …

The proliferation of large‐scale distributed Internet of Things (IoT) applications has resulted
in a surge in demand for network models such as deep neural networks (DNNs) to be …