Clustering-Based Online News Topic Detection and Tracking Through Hierarchical Bayesian Nonparametric Models

2021 ◽  
Author(s):  
Wentao Fan ◽  
Zhiyan Guo ◽  
Nizar Bouguila ◽  
Wenjuan Hou
Keyword(s):  
Online News ◽  
Topic Detection ◽  
Hierarchical Bayesian ◽  
Bayesian Nonparametric ◽  
Nonparametric Models ◽  
Topic Detection And Tracking ◽  
Detection And Tracking ◽  
Bayesian Nonparametric Models

scholarly journals Research on Topic Detection and Tracking for Online News Texts

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 58407-58418 ◽  
Author(s):  
Guixian Xu ◽  
Yueting Meng ◽  
Zhan Chen ◽  
Xiaoyu Qiu ◽  
Changzhi Wang ◽  
...  
Keyword(s):  
Online News ◽  
Topic Detection ◽  
Topic Detection And Tracking ◽  
Detection And Tracking

scholarly journals Topic Detection and Tracking Based on Windowed DBSCAN and Parallel KNN

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 3858-3870
Author(s):  
Chuanzhen Li ◽  
Minqiao Liu ◽  
Juanjuan Cai ◽  
Yang Yu ◽  
Hui Wang
Keyword(s):  
Topic Detection ◽  
Topic Detection And Tracking ◽  
Detection And Tracking

Bayesian Nonparametric Approach to Average Annual Daily Traffic Estimation for Bridges

2021 ◽  
pp. 036119812199459
Author(s):  
Grace Ashley ◽  
Nii Attoh-Okine
Keyword(s):  
Performance Indicators ◽  
Federal Funding ◽  
Percentage Error ◽  
Bayesian Nonparametric ◽  
Transportation Services ◽  
Nonparametric Approach ◽  
Nonparametric Models ◽  
Bayesian Nonparametric Models ◽  
Average Annual Daily Traffic ◽  
The Mean

Every year, the U.S. government provides several billions of dollars in the form of federal funding for transportation services in the U.S.A. Decision making with regard to the use of these funds largely depends on performance indicators like average annual daily traffic (AADT). In this paper, Bayesian nonparametric models are developed through machine learning for the estimation of AADT on bridges. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian nonparametric models is also assessed. The predictions produced using the Bayesian nonparametric approach are then compared with predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the mean absolute percentage error are subsequently employed in model evaluation. Based on the results, the best methods for AADT forecasting for the selected bridges are recommended.

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