| DOI | Resolve DOI: https://doi.org/10.1109/ICMLA61862.2024.00269 |
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| Author | Search for: Ebadi, Ashkan1ORCID identifier: https://orcid.org/0000-0002-4542-9105; Search for: Auger, Alain; Search for: Gauthier, Yvan1 |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2024 International Conference on Machine Learning and Applications, ICMLA, December 18 - 20, 2024, Miami, Florida, United States |
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| Subject | emerging technologies; future signals; burst detection; machine learning; underwater sensing |
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| Abstract | Detecting emerging technologies is crucial as it enables industries and policymakers to anticipate future trends, allocate resources efficiently, and foster innovation. Research plays a pivotal role in developing and advancing emerging technologies, driving innovation and shaping the trajectory of future technological landscapes. In this work, inspired by stock market analysis and leveraging machine learning, we analyze three decades of scientific publications in the field of underwater sensing, as the case technology, to identify bursty terms. We also employ historical data to develop a classifier predicting their future popularity trends. |
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| Publication date | 2024-12-18 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 9de089de-5e5e-4e94-bf4e-22f4f634d74f |
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| Record created | 2025-03-12 |
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| Record modified | 2025-03-18 |
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