Water quality prediction for smart aquaculture using hybrid deep learning models

KPRA Haq, VP Harigovindan - Ieee Access, 2022 - ieeexplore.ieee.org
Water quality prediction (WQP) plays an essential role in water quality management for
aquaculture to make aquaculture production profitable and sustainable. In this work, we …

Variational graph neural networks for road traffic prediction in intelligent transportation systems

F Zhou, Q Yang, T Zhong, D Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
As one of the most important applications of industrial Internet of Things, intelligent
transportation system aims to improve the efficiency and safety of transportation networks. In …

A new crude oil price forecasting model based on variational mode decomposition

Y Huang, Y Deng - Knowledge-Based Systems, 2021 - Elsevier
Crude oil price prediction helps to get a better understanding of the global economic
situation. Recently, variational mode decomposition (VMD) is introduced into the field of …

A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism

Y Yang, Q Xiong, C Wu, Q Zou, Y Yu, H Yi… - … Science and Pollution …, 2021 - Springer
The water environment plays an essential role in the mangrove wetland ecosystem.
Predicting water quality will help us better protect water resources from pollution, allowing …

Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system

S Hao, DH Lee, D Zhao - Transportation Research Part C: Emerging …, 2019 - Elsevier
The accurate short-term passenger flow prediction is of great significance for real-time public
transit management, timely emergency response as well as systematical medium and long …

A spatial–temporal attention approach for traffic prediction

X Shi, H Qi, Y Shen, G Wu, B Yin - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Accurate traffic forecasting is important to enable intelligent transportation systems in a smart
city. This problem is challenging due to the complicated spatial, short-term temporal and …

Directed graph contrastive learning

Z Tong, Y Liang, H Ding, Y Dai… - Advances in neural …, 2021 - proceedings.neurips.cc
Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …

Unist: a prompt-empowered universal model for urban spatio-temporal prediction

Y Yuan, J Ding, J Feng, D Jin, Y Li - Proceedings of the 30th ACM …, 2024 - dl.acm.org
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …

Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels

Z Zhang, HN Dai, J Zhou, SK Mondal… - Expert Systems with …, 2021 - Elsevier
After the invention of Bitcoin as well as other blockchain-based peer-to-peer payment
systems, the cryptocurrency market has rapidly gained popularity. Consequently, the …

Spatial-temporal hypergraph self-supervised learning for crime prediction

Z Li, C Huang, L Xia, Y Xu, J Pei - 2022 IEEE 38th international …, 2022 - ieeexplore.ieee.org
Crime has become a major concern in many cities, which calls for the rising demand for
timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the …