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Deep learning for air pollutant concentration prediction: A review
Air pollution has become one of the critical environmental problem in the 21st century and
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
A hybrid dipper throated optimization algorithm and particle swarm optimization (DTPSO) model for hepatocellular carcinoma (HCC) prediction
Hepatocellular carcinoma (HCC) is a form of liver cancer that is widespread in Europe,
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
A hybrid-convolution spatial–temporal recurrent network for traffic flow prediction
X Zhang, S Wen, L Yan, J Feng, Y **a - The Computer Journal, 2024 - academic.oup.com
Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐
temporal dependencies at different scales. Recently, several hybrid deep learning models …
temporal dependencies at different scales. Recently, several hybrid deep learning models …
Self-attention convlstm for spatiotemporal prediction
Spatiotemporal prediction is challenging due to the complex dynamic motion and
appearance changes. Existing work concentrates on embedding additional cells into the …
appearance changes. Existing work concentrates on embedding additional cells into the …
Adaptive graph convolutional recurrent network for traffic forecasting
Modeling complex spatial and temporal correlations in the correlated time series data is
indispensable for understanding the traffic dynamics and predicting the future status of an …
indispensable for understanding the traffic dynamics and predicting the future status of an …