Detecting emerging technologies and their evolution using deep learning and weak signal analysis |
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| Institution: | 1. Digital Technologies, National Research Council Canada, Montreal, Quebec, Canada;2. Science and Technology Foresight and Risk Assessment Unit, Defence Research and Development Canada, Ottawa, Ontario, Canada;3. Digital Technologies, National Research Council Canada, Ottawa, Ontario, Canada;1. School of Information Management, Nanjing University, Nanjing 210032, China;2. Department of Information Management, Peking University, Beijing 100871, China;1. School of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai 200433, China;2. School of International Relations and Public Affairs, Shanghai Center for Innovation and Governance, Fudan University, Shanghai 200433, China;1. Warsaw University of Technology, Faculty of Physics, ul. Koszykowa 75, Warsaw 00-662, Poland;2. Deakin University, School of IT, Geelong, VIC 3220, Australia;3. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, Warsaw 01-447, Poland;1. School of Statistics, Jilin University of Finance and Economics, Changchun, 130117, China;2. School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China;1. School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China;2. Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia;3. Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia;4. STEM, University of South Australia, Adelaide, SA 5001, Australia;5. College of Computer and Information Science, Southwest University, Chongqing 400715, China;6. Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3353, Australia;1. School of Information Management, Nanjing University, Nanjing, Jiangsu Province, 210093, China;2. Institute for Chinese Social Sciences Research and Assessment, Nanjing University, Nanjing, Jiangsu Province, 210093, China;3. School of Journalism and Communication, Hunan University, Changsha, Hunan Province, 410082, China |
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| 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. |
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