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Emerging technologies can have major economic impacts and affect strategicstability. Yet, early identification of emerging technologies remainschallenging. In order to identify emerging technologies in a timely andreliable manner, a comprehensive examination of relevant scientific andtechnological (S&T) trends and their related references is required. Thisexamination is generally done by domain experts and requires significantamounts of time and effort to gain insights. The use of domain experts toidentify emerging technologies from S&T trends may limit the capacity toanalyse large volumes of information and introduce subjectivity in theassessments. Decision support systems are required to provide accurate andreliable evidence-based indicators through constant and continuous monitoringof the environment and help identify signals of emerging technologies thatcould alter security and economic prosperity. For example, the research fieldof hypersonics has recently witnessed several advancements having profoundtechnological, commercial, and national security implications. In this work, wepresent a multi-layer quantitative approach able to identify future signs fromscientific publications on hypersonics by leveraging deep learning and weaksignal analysis. The proposed framework can help strategic planners and domainexperts better identify and monitor emerging technology trends.

Detecting Emerging Technologies and their Evolution using Deep Learning and Weak Signal Analysis