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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sangsung Park & Sunghae Jun, 2020. "Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis," Sustainability, MDPI, vol. 12(2), pages 1-11, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:505-:d:306756
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    References listed on IDEAS

    as
    1. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    2. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    3. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    4. Jong-Min Kim & Bainwen Sun & Sunghae Jun, 2019. "Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    5. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    6. Scutari, Marco, 2017. "Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i02).
    7. Sangsung Park & Sunghae Jun, 2017. "Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
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    Cited by:

    1. Sangsung Park & Seongyong Choi & Sunghae Jun, 2021. "Bayesian Structure Learning and Visualization for Technology Analysis," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    2. Yulia Turovets & Konstantin Vishnevskiy & Artem Altynov, 2020. "How To Measure Ai: Trends, Challenges And Implications," HSE Working papers WP BRP 116/STI/2020, National Research University Higher School of Economics.

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