Hybrid Dynamic Bayesian network method for performance analysis of safety barriers considering multi-maintenance strategies

As the most important component of the capital market, the stock market has always been regarded as the “barometer” of the macroeconomy. However, many researchers have found that the stock market and macroeconomy are operating separately. This paper uses the dynamic Bayesian network method to study the dynamic relationship between the Chinese macroeconomic system and the stock market. The study found that the correlation between the macroeconomic system and the stock market is not consistent in different time periods. For most of the time, the stock system and the macroeconomic system are relatively independent. However, several macroeconomic factors such as Purchase Management Index could affect the stock market through some industries. A conclusion is drawn that the “barometer” function of the stock market is weak and easy to be damaged by factors such as the irrational sentiment of investors.

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A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019953 ◽

2019 ◽

Vol 33 ◽

pp. 9953-9954

Author(s):

Kherroubi Zine el Abidine ◽

Aknine Samir ◽

Bacha Rebiha

Keyword(s):

Bayesian Network ◽

Road Safety ◽

Autonomous Vehicles ◽

Dynamic Bayesian Network ◽

Real Data ◽

Vehicle Trajectories ◽

Network Method ◽

Vehicle Motion ◽

Connected Cars ◽

And Behavior

This work explores the design of a central collaborative driving strategy between connected cars with the objective of improving road safety in case of highway on-ramp merging scenario. Based on a suitable method for predicting vehicle motion and behavior for a central collaborative strategy, a dynamic Bayesian network method that predicts the intention of drivers in highway on-ramp is proposed. The method was validated using real data of detailed vehicle trajectories on a segment of interstate 80 in Emeryville, California.

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Stability-based Dynamic Bayesian Network method for dynamic data mining

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2018.09.016 ◽

2019 ◽

Vol 77 ◽

pp. 283-310 ◽

Cited By ~ 1

Author(s):

Mohamed Naili ◽

Mustapha Bourahla ◽

Makhlouf Naili ◽

AbdelKamel Tari

Keyword(s):

Data Mining ◽

Bayesian Network ◽

Dynamic Bayesian Network ◽

Dynamic Data ◽

Network Method

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A Resamping Approach for Customer Gender Prediction Based on E-Commerce Data

Journal of Science and Technology Issue on Information and Communications Technology ◽

10.31130/jst.2017.40 ◽

2017 ◽

Vol 3 (1) ◽

pp. 76 ◽

Cited By ~ 1

Author(s):

Duong Tran Duc ◽

Pham Bao Son ◽

Tan Hanh ◽

Le Truong Thien

Keyword(s):

Data Mining ◽

Bayesian Network ◽

Web Applications ◽

Service Providers ◽

Main Idea ◽

Classification Methods ◽

Cost Sensitive Learning ◽

Network Method ◽

Demographic Attributes ◽

Privacy Issues

Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

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