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1. Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model.

2. Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM 2.5.

3. Student achievement prediction using deep neural network from multi-source campus data.

4. Air-pollution prediction in smart city, deep learning approach.

5. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting.

6. A fundamental diagram based hybrid framework for traffic flow estimation and prediction by combining a Markovian model with deep learning.

7. Application of a Parallel Particle Swarm Optimization-Long Short Term Memory Model to Improve Water Quality Data.

8. Recognition of Traffic Information with the Help of Social Media Tweets.

9. A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network.

10. A long short-term memory approach to predicting air quality based on social media data.