Selecting samples with non-landslide attributes significantly impacts the deep-learning
modeling of landslide susceptibility mapping. This study presents a method of information …

The problem of estimating earthquake risk is one of the primary themes for researchers and
investigators in the field of geosciences. The combined assessment of spatial probability …

Accurate prediction results can provide an excellent reference value for the prevention of
large-scale flight delays. Most of the currently available regression prediction algorithms use …

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have
gained a lot of attention for speech enhancement. A number of DL models have achieved …

Phase filtering is a key issue in interferometric synthetic aperture radar (InSAR) applications,
such as deformation monitoring and topographic mapping. The accuracy of the deformation …

The prediction of stock price movement is a popular area of research in academic and
industrial fields due to the dynamic, highly sensitive, nonlinear and chaotic nature of stock …

The objective of knowledge graph question answering is to assist users in answering
questions by utilizing the information stored within the graph. Users are not required to …

The carbon emission of fuel vehicles is a major consideration that affects the dual carbon
goal in urban traffic. The problem of “difficult parking and disorderly parking” in static traffic …

The Array for Real-time Geostrophic Oceanography (Argo) program provides valuable data
for maritime research and rescue operations. This paper is based on Argo historical and …

With the development of deep learning, several graph neural network (GNN)-based
approaches have been utilized for text classification. However, GNNs encounter challenges …