A review of recurrent neural networks: LSTM cells and network architectures
Y Yu, X Si, C Hu, J Zhang - Neural computation, 2019 - direct.mit.edu
Recurrent neural networks (RNNs) have been widely adopted in research areas concerned
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …
MFRFNN: Multi-functional recurrent fuzzy neural network for chaotic time series prediction
Chaotic time series prediction, a challenging research topic in dynamic system modeling,
has drawn great attention from researchers around the world. In recent years extensive …
has drawn great attention from researchers around the world. In recent years extensive …
Multivariate air quality forecasting with nested long short term memory neural network
Artificial intelligence-based air quality index (AQI) forecasting is a hot research topic in the
fields of sustainable and smart industrial environment design. There are mainly two …
fields of sustainable and smart industrial environment design. There are mainly two …
A hybrid deep learning technology for PM2.5 air quality forecasting
Z Zhang, Y Zeng, K Yan - Environmental Science and Pollution Research, 2021 - Springer
The concentration of PM 2.5 is one of the main factors in evaluating the air quality in
environmental science. The severe level of PM 2.5 directly affects the public health …
environmental science. The severe level of PM 2.5 directly affects the public health …
Spatial–temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism
Z Chen, Z Lu, Q Chen, H Zhong, Y Zhang, J Xue… - Information Sciences, 2022 - Elsevier
Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays
an important role in traffic management. The graph convolution network (GCN) is widely …
an important role in traffic management. The graph convolution network (GCN) is widely …
RETRACTED ARTICLE: Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM
K Sangeetha, D Prabha - Journal of Ambient Intelligence and Humanized …, 2021 - Springer
Classroom teaching becomes viable and efficient based on increase in participation of the
student. This can be made possible by taking needed measure by finding the emotions of …
student. This can be made possible by taking needed measure by finding the emotions of …
Forecasting transportation network speed using deep capsule networks with nested LSTM models
Accurate and reliable traffic forecasting for complicated transportation networks is of vital
importance to modern transportation management. The complicated spatial dependencies …
importance to modern transportation management. The complicated spatial dependencies …
Lexicon-enhanced LSTM with attention for general sentiment analysis
Long short-term memory networks (LSTMs) have gained good performance in sentiment
analysis tasks. The general method is to use LSTMs to combine word embeddings for text …
analysis tasks. The general method is to use LSTMs to combine word embeddings for text …
Deep learning to design nuclear-targeting abiotic miniproteins
There are more amino acid permutations within a 40-residue sequence than atoms on Earth.
This vast chemical search space hinders the use of human learning to design functional …
This vast chemical search space hinders the use of human learning to design functional …
Air quality forecasting with hybrid LSTM and extended stationary wavelet transform
Y Zeng, J Chen, N **, X **, Y Du - Building and Environment, 2022 - Elsevier
Air quality measurements and forecasting is one of the most popular research topics in the
field of sustainable intelligent environmental design, urban area development and pollution …
field of sustainable intelligent environmental design, urban area development and pollution …