{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T22:14:58Z","timestamp":1773526498151,"version":"3.50.1"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T00:00:00Z","timestamp":1645920000000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FEDER\/Ministerio de Ciencia, Innovaci\u00f3n y Universidades - Agencia Estatal de Investigaci\u00edn\/Proyecto","award":["TIN2017-88209-C2"],"award-info":[{"award-number":["TIN2017-88209-C2"]}]},{"name":"Andalusian Regional Government","award":["US-1263341"],"award-info":[{"award-number":["US-1263341"]}]},{"name":"Andalusian Regional Government","award":["P18-RT-2778"],"award-info":[{"award-number":["P18-RT-2778"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The design of the asynchronous dual-pipeline DL framework allows making predictions of incoming instances and updating the model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency, but are also the most sensitive to concept drifts.<\/jats:p>","DOI":"10.1093\/jigpal\/jzac033","type":"journal-article","created":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T20:13:11Z","timestamp":1643055191000},"page":"688-700","source":"Crossref","is-referenced-by-count":3,"title":["Data streams classification using deep learning under different speeds and drifts"],"prefix":"10.1093","volume":"31","author":[{"given":"Pedro","family":"Lara-Ben\u00edtez","sequence":"first","affiliation":[{"name":"Division of Computer Science, University of Sevilla , ES-41012 Seville, Spain"}]},{"given":"Manuel","family":"Carranza-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Division of Computer Science, University of Sevilla , ES-41012 Seville, Spain"}]},{"given":"David","family":"Guti\u00e9rrez-Avil\u00e9s","sequence":"additional","affiliation":[{"name":"Division of Computer Science, University of Sevilla , ES-41012 Seville, Spain"}]},{"given":"Jos\u00e9 C","family":"Riquelme","sequence":"additional","affiliation":[{"name":"Division of Computer Science, University of Sevilla , ES-41012 Seville, Spain"}]}],"member":"286","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"2023072813243226900_ref1","doi-asserted-by":"crossref","first-page":"112832","DOI":"10.1016\/j.eswa.2019.112832","article-title":"Recurring concept meta-learning for evolving data streams","volume":"138","author":"Anderson","year":"2019","journal-title":"Expert Systems with Applications"},{"key":"2023072813243226900_ref2","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1145\/2783258.2783372","article-title":"Efficient online evaluation of big data stream classifiers","volume-title":"Proceedings of the 21th ACM SIGKDD International 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