Alzheimer's disease (AD) is a pressing global issue, demanding effective diagnostic
approaches. This systematic review surveys the recent literature (2018 onwards) to …

Remote sensing object detection (RSOD), one of the most fundamental and challenging
tasks in the remote sensing field, has received long-standing attention. In recent years, deep …

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …

Due to the broad usage and widespread popularity of drones, the demand for a more
accurate object detection algorithm for images captured by drone platforms has become …

Supervised deep learning models depend on massive labeled data. Unfortunately, it is time-
consuming and labor-intensive to collect and annotate bitemporal samples containing …

With the advancements in space technology and the development of lightweight synthetic
aperture radar (SAR) satellites by commercial companies, such as ICEYE, Capella Space …

In recent years, deep learning has significantly reshaped numerous fields and applications,
fundamentally altering how we tackle a variety of challenges. Areas such as natural …

Deep learning has revolutionized computer vision with phenomenal success and
widespread applications. Despite impressive results in complex problems, neural networks …

In satellite communication downlinks, physical layer security is challenging to achieve due to
their broadcasting nature and Line-of-Sight channel characteristics. This paper provides a …

The domain of object detection was revolutionized with the introduction of Convolutional
Neural Networks (CNNs) in the field of computer vision. This article aims to explore the …