scholarly journals Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis

2020 ◽  
Vol 12 (2) ◽  
pp. 505
Author(s):  
Sangsung Park ◽  
Sunghae Jun
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Bayesian Network ◽  
Network Modeling ◽  
Real Problem ◽  
Patent Data ◽  
Disaster Area ◽  
Proposed Model ◽  
Bayesian Network Modeling

At present, artificial intelligence (AI) contributes to most technological fields. AI has also been introduced in the disaster area to replace humans and contribute to the prevention of disasters and the minimization of damages. So, it is necessary to analyze disaster AI in order to effectively make use of it. In this paper, we analyze the patent documents related to disaster AI technology. We propose Bayesian network modeling and factor analysis for the technology analysis of disaster AI. This is based on probability distribution and graph theory. It is also a statistical model that depends on multivariate data analysis. In order to show how the proposed model can be applied to a real problem, we carried out a case study to collect and analyze the patent data related to disaster AI.

The Realization of the Data Mining Technology and its Application

2014 ◽  
Vol 971-973 ◽  
pp. 1820-1823
Author(s):  
Xi Long Ding
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Bayesian Network ◽  
Network Modeling ◽  
Present Situation ◽  
Network Technology ◽  
Mining Method ◽  
Bayesian Network Modeling ◽  
Database Technology ◽  
The Relationship

data mining using the database, a variety of technologies such as artificial intelligence and mathematical statistics. This paper introduces the present situation of database technology, according to the mining method and its application in how to build a Bayesian network technology, through the scattered according to the mining to solve concrete problems encountered in the process of Bayesian network modeling, namely how to from scale effect according to the library to find the relationship between each variable and how to determine the conditional probability problem.

Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk

2016 ◽  
Vol 126 ◽  
pp. 128-142 ◽  
Author(s):  
P. Fuster-Parra ◽  
P. Tauler ◽  
M. Bennasar-Veny ◽  
A. Ligęza ◽  
A.A. López-González ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Bayesian Network ◽  
System Analysis ◽  
Network Modeling ◽  
Bayesian Network Modeling

scholarly journals Bayesian Network Modeling to Improve Water Pricing Practices in Northwest China

Water ◽  
2015 ◽  
Vol 7 (10) ◽  
pp. 5617-5637 ◽  
Author(s):  
Yusuyunjiang Mamitimin ◽  
Til Feike ◽  
Reiner Doluschitz
Keyword(s):  
Bayesian Network ◽  
Network Modeling ◽  
Northwest China ◽  
Water Pricing ◽  
Bayesian Network Modeling

Bayesian Network Modeling of Transcription Factor Binding Sites

2007 ◽  
pp. 300-318
Author(s):  
Vipin Narang ◽  
Rajesh Chowdhary ◽  
Ankush Mittal ◽  
Wing-Kin Sung
Keyword(s):  
Transcription Factor ◽  
Parameter Estimation ◽  
Bayesian Network ◽  
Network Modeling ◽  
Network Models ◽  
Transcription Factor Binding Sites ◽  
Physical Problem ◽  
Network Applications ◽  
Bayesian Network Modeling ◽  
Bayesian Network Models

A predicament that engineers who wish to employ Bayesian networks to solve practical problems often face is the depth of study required in order to obtain a workable understanding of this tool. This chapter is intended as a tutorial material to assist the reader in efficiently understanding the fundamental concepts involved in Bayesian network applications. It presents a complete step by step solution of a bioinformatics problem using Bayesian network models, with detailed illustration of modeling, parameter estimation, and inference mechanisms. Considerations in determining an appropriate Bayesian network model representation of a physical problem are also discussed.

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