{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T21:01:00Z","timestamp":1791406860503,"version":"4.3.3"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2019,1,12]],"date-time":"2019-01-12T00:00:00Z","timestamp":1547251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,1,12]],"date-time":"2019-01-12T00:00:00Z","timestamp":1547251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2020,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The risk of tampering exists for conventional user recognition methods\n                based on biometrics such as face and fingerprint. Recently, research on user\n                recognition using biometric signals such as electrocardiogram (ECG),\n                electroencephalogram (EEG), and electromyogram (EMG) has been actively performed to\n                overcome this issue. We herein propose a user recognition method applying a deep\n                learning technique based on ensemble networks after transforming ECG signals into\n                two-dimensional (2D) images. A preprocessing process for one-dimensional ECG signals\n                is performed to remove noise or distortion; subsequently, they are projected onto a\n                2D image space and transformed into image data. For the proposed algorithm, we\n                designed deep learning-based ensemble networks to improve the degraded performance\n                arising from overfitting in a single network. Our experimental results demonstrate\n                that the proposed ensemble networks exhibit an accuracy that is 1.7% higher than\n                that of the single network. In particular, the performance of the ensemble networks\n                is up to 13% higher compared to the single network that degrades the recognition\n                rate by displaying similar features between classes.<\/jats:p>","DOI":"10.1007\/s12652-019-01195-4","type":"journal-article","created":{"date-parts":[[2019,1,12]],"date-time":"2019-01-12T11:26:32Z","timestamp":1547292392000},"page":"1859-1867","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["RETRACTED ARTICLE: A study on user\n            recognition using 2D ECG based on ensemble of deep convolutional neural\n            networks"],"prefix":"10.1007","volume":"11","author":[{"given":"Min-Gu","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoon","family":"Ko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Bum","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,1,12]]},"reference":[{"key":"1195_CR1","volume-title":"Fundamentals of deep learning: designing next-generation machine intelligence algorithms","author":"N Buduma","year":"2017","unstructured":"Buduma N, Locascio N (2017) Fundamentals of deep learning:\n                    designing next-generation machine intelligence algorithms. 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Please see the Retraction Notice for more detail:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s12652-024-04934-4","URL":"https:\/\/doi.org\/10.1007\/s12652-024-04934-4","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}