Graph neural networks in IoT: A survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
Transformer for skeleton-based action recognition: A review of recent advances
Skeleton-based action recognition has rapidly become one of the most popular and
essential research topics in computer vision. The task is to analyze the characteristics of …
essential research topics in computer vision. The task is to analyze the characteristics of …
Parallel and distributed graph neural networks: An in-depth concurrency analysis
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …
routinely solve complex problems on unstructured networks, such as node classification …
Skeleton graph-neural-network-based human action recognition: A survey
M Feng, J Meunier - Sensors, 2022 - mdpi.com
Human action recognition has been applied in many fields, such as video surveillance and
human computer interaction, where it helps to improve performance. Numerous reviews of …
human computer interaction, where it helps to improve performance. Numerous reviews of …
Detecting human actions in drone images using YOLOv5 and stochastic gradient boosting
Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones,
has emerged as a popular technical challenge in recent years, since it is related to many …
has emerged as a popular technical challenge in recent years, since it is related to many …
D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation
Y Mourchid, R Slama - Computers in Biology and Medicine, 2023 - Elsevier
This paper tackles the challenge of automatically assessing physical rehabilitation exercises
for patients who perform the exercises without clinician supervision. The objective is to …
for patients who perform the exercises without clinician supervision. The objective is to …
SSTFormer: bridging spiking neural network and memory support transformer for frame-event based recognition
Event camera-based pattern recognition is a newly arising research topic in recent years.
Current researchers usually transform the event streams into images, graphs, or voxels, and …
Current researchers usually transform the event streams into images, graphs, or voxels, and …
Identification of drug-side effect association via multi-view semi-supervised sparse model
The association between drugs and side effects encompasses information about approved
medications and their documented adverse drug reactions. Traditional experimental …
medications and their documented adverse drug reactions. Traditional experimental …
Hardvs: Revisiting human activity recognition with dynamic vision sensors
The main streams of human activity recognition (HAR) algorithms are developed based on
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …
Multivariate Time-Series Representation Learning via Hierarchical Correlation Pooling Boosted Graph Neural Network
Representation learning is vital for the performance of multivariate time series (MTS)-related
tasks. Given high-dimensional MTS data, researchers generally rely on deep learning …
tasks. Given high-dimensional MTS data, researchers generally rely on deep learning …