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Detecting emerging technologies and their evolution using deep learning and weak signal analysis

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  • Ebadi, Ashkan
  • Auger, Alain
  • Gauthier, Yvan

Abstract

Emerging technologies can have major economic impacts and affect strategic stability. Yet, early identification of emerging technologies remains challenging. In order to identify emerging technologies in a timely and reliable manner, a comprehensive examination of relevant scientific and technological (S&T) trends and their related references is required. This examination is generally done by domain experts and requires significant amounts of time and effort to gain insights. The use of domain experts to identify emerging technologies from S&T trends may limit the capacity to analyse large volumes of information and introduce subjectivity in the assessments. Decision support systems are required to provide accurate and reliable evidence-based indicators through constant and continuous monitoring of the environment and help identify signals of emerging technologies that could alter security and economic prosperity. For example, the research field of hypersonics has recently witnessed several advancements having profound technological, commercial, and national security implications. In this work, we present a multi-layer quantitative approach able to identify future signs from scientific publications on hypersonics by leveraging deep learning and weak signal analysis. The proposed framework can help strategic planners and domain experts better identify and monitor emerging technology trends.

Suggested Citation

  • Ebadi, Ashkan & Auger, Alain & Gauthier, Yvan, 2022. "Detecting emerging technologies and their evolution using deep learning and weak signal analysis," Journal of Informetrics, Elsevier, vol. 16(4).
    • Handle: RePEc:eee:infome:v:16:y:2022:i:4:s1751157722000967
    DOI: 10.1016/j.joi.2022.101344
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    References listed on IDEAS

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    Cited by:

    1. Mauricio Olivares Faúndez & Hanns de la Fuente-Mella, 2022. "Data Analysis and Domain Knowledge for Strategic Competencies Using Business Intelligence and Analytics," Mathematics, MDPI, vol. 11(1), pages 1-33, December.

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