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A comprehensive survey of deep learning-based lightweight object detection models for edge devices
P Mittal - Artificial Intelligence Review, 2024 - Springer
This study concentrates on deep learning-based lightweight object detection models on
edge devices. Designing such lightweight object recognition models is more difficult than …
edge devices. Designing such lightweight object recognition models is more difficult than …
A systematic review of generative adversarial networks for traffic state prediction: overview, taxonomy, and future prospects
In recent years, advances in deep learning have had a significant impact in the
transportation domain, notably through the use of generative adversarial networks (GAN). As …
transportation domain, notably through the use of generative adversarial networks (GAN). As …
ADCT-Net: Adaptive traffic forecasting neural network via dual-graphic cross-fused transformer
The rapid development of road traffic networks has provided a wealth of research data for
intelligent transportation systems. We are faced with vast high-dimensional traffic flow data …
intelligent transportation systems. We are faced with vast high-dimensional traffic flow data …
STGAFormer: Spatial–temporal gated attention transformer based graph neural network for traffic flow forecasting
Z Geng, J Xu, R Wu, C Zhao, J Wang, Y Li, C Zhang - Information Fusion, 2024 - Elsevier
Traffic flow prediction is a critical component of Intelligent Transportation Systems (ITS).
However, the dynamic temporal variations in traffic flow, especially in potential occurrence of …
However, the dynamic temporal variations in traffic flow, especially in potential occurrence of …
STFGCN: Spatial–temporal fusion graph convolutional network for traffic prediction
H Li, J Liu, S Han, J Zhou, T Zhang… - Expert Systems with …, 2024 - Elsevier
Accurate traffic prediction plays a crucial role in improving traffic conditions and optimizing
road utilization. Effectively capturing the multi-scale temporal dependencies and dynamic …
road utilization. Effectively capturing the multi-scale temporal dependencies and dynamic …
Information fusion for multi-scale data: Survey and challenges
Q Zhang, Y Yang, Y Cheng, G Wang, W Ding, W Wu… - Information …, 2023 - Elsevier
Abstract Information fusion is a useful technique of combining and merging different
information to form a more complete and accurate result. Traditional information fusion …
information to form a more complete and accurate result. Traditional information fusion …
Federated deep learning for smart city edge-based applications
The growing quantities of data allow for advanced analysis. A prime example of it are smart
city applications with forecasting urban traffic flow as a key application. However, data …
city applications with forecasting urban traffic flow as a key application. However, data …
Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach
Recent technological advancements have opened doors for real-time adjustments and
controls during public transport operations. In particular, the introduction of modular vehicles …
controls during public transport operations. In particular, the introduction of modular vehicles …
SFGCN: synergetic fusion-based graph convolutional networks approach for link prediction in social networks
Abstract Accurate Link Prediction (LP) in Social Networks (SNs) is crucial for various
practical applications, such as recommendation systems and network security. However …
practical applications, such as recommendation systems and network security. However …
Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network
Traffic flow prediction plays a crucial role in the management and operation of urban
transportation systems. While extensive research has been conducted on predictions for …
transportation systems. While extensive research has been conducted on predictions for …