Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

The deep learning methods based on convolutional neural network (CNN) have been
widely explored in dataset augmentation and recognition of plant leaf diseases. The recently …

Deep learning achieves unprecedented success involves many fields, whereas the high
requirement of memory and time efficiency tolerance have been the intractable challenges …

Abstract Artificial Intelligence of Things (AIoT) which integrates artificial intelligence (AI) and
the Internet of Things (IoT), has attracted increasing attention recently. With the remarkable …

We show how feature maps in convolutional networks are susceptible to spatial bias. Due to
a combination of architectural choices, the activation at certain locations is systematically …

Due to the lack of rotation invariance in traditional convolution operations, even acting a
slight rotation on the input can severely degrade the performance of Convolutional Neural …

Numerous studies on short‐term load forecasting (STLF) have used feature extraction
methods to increase the model's accuracy by incorporating multidimensional features …

Pansharpening aims to fuse a high spatial resolution panchromatic (PAN) image and a low
resolution multispectral (LR-MS) image to obtain a multispectral image with the same spatial …

With the advent of smart devices that support 4K and 8K resolution, Single Image Super
Resolution (SISR) has become an important computer vision problem. However, most super …

Efficient and accurate rice identification based on high spatial and temporal resolution
remote sensing imagery is essential for achieving precision agriculture and ensuring food …