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

Sangsung Park and Sunghae Jun
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Sangsung Park: Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, Korea
Sunghae Jun: Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, Korea

Sustainability, 2020, vol. 12, issue 2, 1-11

Abstract: 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.

Keywords: Bayesian statistics; disaster artificial intelligence; technology analysis; factor analysis; patent keyword analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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