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A Pre-trained Data Deduplication Model based on Active Learning
In the era of big data, the issue of data quality has become increasingly prominent.
A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder
We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features.
DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting
Time-series forecasting plays an important role in many real-world scenarios, such as equipment life cycle forecasting, weather forecasting, and traffic flow forecasting.
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction
Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix.
Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting
Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and urban computing.
A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting
Time series forecasting is widely used in the fields of equipment life cycle forecasting, weather forecasting, traffic flow forecasting, and other fields.
Fairness and Accuracy in Federated Learning
An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed.
Urban flows prediction from spatial-temporal data using machine learning: A survey
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc.
Deep Air Quality Forecasting Using Hybrid Deep Learning Framework
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management.
A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning
Traffic flow forecasting has been regarded as a key problem of intelligent transport systems.
