A survey on temporal knowledge graph completion: Taxonomy, progress, and prospects
Temporal characteristics are prominently evident in a substantial volume of knowledge,
which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia …
which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia …
Quality prediction modeling for industrial processes using multiscale attention-based convolutional neural network
Soft sensors have been increasingly applied for quality prediction in complex industrial
processes, which often have different scales of topology and highly coupled spatiotemporal …
processes, which often have different scales of topology and highly coupled spatiotemporal …
An intelligent deep learning framework for traffic flow imputation and short-term prediction based on dynamic features
The accurate prediction of traffic flow has emerged as a focal point in the cutting-edge
sphere of intelligent transportation. Extant methodologies rely on deep learning for short …
sphere of intelligent transportation. Extant methodologies rely on deep learning for short …
MLMSeg: a multi-view learning model for ultrasound thyroid nodule segmentation
Accurate segmentation of the thyroid gland in ultrasound images is an essential initial step
in distinguishing between benign and malignant nodules, thus facilitating early diagnosis …
in distinguishing between benign and malignant nodules, thus facilitating early diagnosis …
MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion
Temporal knowledge graphs (TKGs) are receiving increased attention due to their time-
dependent properties and the evolving nature of knowledge over time. TKGs typically …
dependent properties and the evolving nature of knowledge over time. TKGs typically …
Graph-based multi-ode neural networks for spatio-temporal traffic forecasting
There is a recent surge in the development of spatio-temporal forecasting models in the
transportation domain. Long-range traffic forecasting, however, remains a challenging task …
transportation domain. Long-range traffic forecasting, however, remains a challenging task …
A novel hybrid framework based on temporal convolution network and transformer for network traffic prediction
Z Zhang, S Gong, Z Liu, D Chen - Plos one, 2023 - journals.plos.org
Background Accurately predicting mobile network traffic can help mobile network operators
allocate resources more rationally and can facilitate stable and fast network services to …
allocate resources more rationally and can facilitate stable and fast network services to …
Spatial–Temporal Dynamic Graph Convolutional Network With Interactive Learning for Traffic Forecasting
A Liu, Y Zhang - IEEE Transactions on Intelligent Transportation …, 2024 - ieeexplore.ieee.org
Accurate traffic forecasting is essential in urban traffic management, route planning, and flow
detection. Recent advances in spatial-temporal models have markedly improved the …
detection. Recent advances in spatial-temporal models have markedly improved the …
PLU-MCN: Perturbation learning enhanced U-shaped multi-graph convolutional network for traffic flow prediction
Traffic flow prediction is a challenging task in intelligent transportation systems. To improve
the accuracy of traffic flow prediction, graph convolutional neural networks and traffic …
the accuracy of traffic flow prediction, graph convolutional neural networks and traffic …
Residual attention enhanced Time-varying Multi-Factor Graph Convolutional Network for traffic flow prediction
Precise and timely traffic flow prediction holds significant importance in alleviating traffic
congestion. Despite the success of graph convolution traffic flow prediction methods, there is …
congestion. Despite the success of graph convolution traffic flow prediction methods, there is …