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Second, we apply the same CNNs to Lyapunov exponent estimation in noisy and non-noisy chaotic data, in estimating rates of disease transmission from epidemic curves, and in detecting the similarity of drug dissolution profiles. Finally, we apply the method to real-life data to detect Parkinson\u2019s disease patients in a classification problem. We performed ablation analysis and compared the new method with other commonly used neural networks for FD and showed that it outperforms them in all applications. Although simple, the method shows high accuracy and is promising for future use in engineering and medical applications.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad2627","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T22:22:36Z","timestamp":1707171756000},"page":"015030","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Functional data learning using convolutional neural networks"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1994-5931","authenticated-orcid":true,"given":"J","family":"Galarza","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T","family":"Oraby","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,2,19]]},"reference":[{"key":"mlstad2627bib1","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1208\/s12248-023-00795-5","volume":"25","author":"Abend","year":"2023","journal-title":"AAPS J."},{"key":"mlstad2627bib2","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3389\/fninf.2014.00014","volume":"8","author":"Abraham","year":"2014","journal-title":"Front. 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