A machine learning-based Bayesian model for predicting the duration of ship detention in PSC inspection
Year of publication: |
2023
|
---|---|
Authors: | Yang, Zhisen ; Wan, Chengpeng ; Yu, Qing ; Yin, Jingbo ; Yang, Zaili |
Published in: |
Transportation research / E : an international journal. - Amsterdam : Elsevier, ISSN 1366-5545, ZDB-ID 1380969-6. - Vol. 180.2023, p. 1-25
|
Subject: | Bayesian network | Duration of detention | Duration prediction | Inspection efficiency | ITAN learning | Maritime safety | PSC | Bayes-Statistik | Bayesian inference | Prognoseverfahren | Forecasting model | Dauer | Duration | Seeverkehrssicherheit | Künstliche Intelligenz | Artificial intelligence | Statistische Bestandsanalyse | Duration analysis |
-
A data-driven Bayesian model for evaluating the duration of detention of ships in PSC inspections
Yang, Zhisen, (2024)
-
Machine learning algorithms for predicting unemployment duration in Russia
Maigur, Anna A., (2024)
-
Duration analysis for recurrent ship accidents
Luo, Meifeng, (2017)
- More ...
-
A data-driven Bayesian model for evaluating the duration of detention of ships in PSC inspections
Yang, Zhisen, (2024)
-
A risk-based game model for rational inspections in port state control
Yang, Zhisen, (2018)
-
Application of Bayesian networks in analysing tanker shipping bankruptcy risks
Wang, Grace W. Y., (2017)
- More ...