Results 261 to 270 of about 24,713,244 (334)

Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network. [PDF]

Sensors (Basel)
Ou H, Yao Y, He Y.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation

IEEE Transactions on Big Data, 2023
The traffic data corrupted by noise and missing entries often lead to the poor performance of Intelligent Transportation Systems (ITS), such as the bad congestion prediction and route guidance.
Ye Yuan, Yong Zhang, Boyue Wang, Yuan Peng, Yongli Hu, Baocai Yin   +5 more
semanticscholar   +1 more source

HRST-LR: A Hessian Regularization Spatio-Temporal Low Rank Algorithm for Traffic Data Imputation

IEEE transactions on intelligent transportation systems (Print), 2023
Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and improving traffic efficiency. Due to the delay of network transmission and failure of detectors, massive missing traffic data are often produced in ITSs, which ...
Xiuqin Xu, Mingwei Lin, Xin Luo, Zeshui Xu   +3 more
semanticscholar   +1 more source

Missing Data Imputation

International Journal of Decision Support System Technology, 2022
Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is
openaire   +1 more source

Robust data imputation

Computational Biology and Chemistry, 2009
Single imputation methods have been wide-discussed topics among researchers in the field of bioinformatics. One major shortcoming of methods proposed until now is the lack of robustness considerations. Like all data, gene expression data can possess outlying values.
vanden Branden, Karlien, Verboven, Sabine   +1 more
openaire   +3 more sources

FIGAN: A Missing Industrial Data Imputation Method Customized for Soft Sensor Application

IEEE Transactions on Automation Science and Engineering, 2022
Missing data is quite common in the industrial field, resulting in problems in downstream applications, as most data driven methods used in these applications rely on complete and high-quality dataset to build a high-quality model.
Zoujing Yao, Chunhui Zhao
semanticscholar   +1 more source

Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

Structural Health Monitoring, 2021
Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type.
Huachen Jiang, C. Wan, Kangzhen Yang, You-liang Ding, Songtao Xue   +4 more
semanticscholar   +1 more source

A customized deep learning approach to integrate network-scale online traffic data imputation and prediction

Transportation Research Part C: Emerging Technologies, 2021
Online data imputation and traffic prediction based on real-time data streams are essential for the intelligent transportation systems, particularly online navigation applications based on the real-time traffic information.
Zhengchao Zhang, Xi Lin, Meng Li, Yinhai Wang   +3 more
semanticscholar   +1 more source