Sensor-based and vision-based human activity recognition: A comprehensive survey
Human activity recognition (HAR) technology that analyzes data acquired from various types
of sensing devices, including vision sensors and embedded sensors, has motivated the …
of sensing devices, including vision sensors and embedded sensors, has motivated the …
A review on the long short-term memory model
Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …
neurocomputing fields. According to several online sources, this model has improved …
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Streamflow (Q flow) prediction is one of the essential steps for the reliable and robust water
resources planning and management. It is highly vital for hydropower operation, agricultural …
resources planning and management. It is highly vital for hydropower operation, agricultural …
Bidirectional LSTM with attention mechanism and convolutional layer for text classification
G Liu, J Guo - Neurocomputing, 2019 - Elsevier
Neural network models have been widely used in the field of natural language processing
(NLP). Recurrent neural networks (RNNs), which have the ability to process sequences of …
(NLP). Recurrent neural networks (RNNs), which have the ability to process sequences of …
Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects
Abstract Human Activity Recognition (HAR) plays a significant role in the everyday life of
people because of its ability to learn extensive high-level information about human activity …
people because of its ability to learn extensive high-level information about human activity …
Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms
This paper designs a hybridized deep learning framework that integrates the Convolutional
Neural Network for pattern recognition with the Long Short-Term Memory Network for half …
Neural Network for pattern recognition with the Long Short-Term Memory Network for half …
Human action recognition: A taxonomy-based survey, updates, and opportunities
Human action recognition systems use data collected from a wide range of sensors to
accurately identify and interpret human actions. One of the most challenging issues for …
accurately identify and interpret human actions. One of the most challenging issues for …
Deep learning-based approach for sign language gesture recognition with efficient hand gesture representation
Hand gesture recognition is an attractive research field with a wide range of applications,
including video games and telesurgery techniques. Another important application of hand …
including video games and telesurgery techniques. Another important application of hand …
Dynamic hand gesture recognition using multi-branch attention based graph and general deep learning model
The dynamic hand skeleton data have become increasingly attractive to widely studied for
the recognition of hand gestures that contain 3D coordinates of hand joints. Many …
the recognition of hand gestures that contain 3D coordinates of hand joints. Many …
Temporal decoupling graph convolutional network for skeleton-based gesture recognition
Skeleton-based gesture recognition methods have achieved high success using Graph
Convolutional Network (GCN), which commonly uses an adjacency matrix to model the …
Convolutional Network (GCN), which commonly uses an adjacency matrix to model the …