{"id":14940,"date":"2024-01-16T13:36:26","date_gmt":"2024-01-16T12:36:26","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=14940"},"modified":"2024-01-16T13:36:26","modified_gmt":"2024-01-16T12:36:26","slug":"using-deep-learning-to-detect-anomalies-in-traffic-flow","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/using-deep-learning-to-detect-anomalies-in-traffic-flow\/","title":{"rendered":"Using Deep Learning to Detect Anomalies in Traffic Flow"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Manuel M\u00e9ndez, Alfredo Ibias and Manuel N\u00fa\u00f1ez<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">Uncertainty is an ever present challenge in data analysis. In particular, it is important to detect, as precisely as possible, unforeseen phenomena. In this paper we study the usefulness of two deep learning based methods (CNN auto-encoder and BiLSTM auto-encoder) to detect anomalies in situations that can be defined in terms of time series. In order to evaluate our approaches, we consider traffic flow data and perform experiments in two orthogonal scenarios: a guided scenario (training only with data considered as \u2018normal\u2019 after a na\u00efve labelling) and a basic scenario. Our results show that if we train the models using only the considered \u2018normal\u2019 data, the obtained models do not achieve good results because none of them are able to detect all type of abnormal data correctly. In contrast, both models can detect all type of time series anomalies when we consider the basic scenario.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-21743-2_24\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>In: 14th Asian Conference on Intelligent Information and Database Systems, 2022.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[14],"class_list":["post-14940","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-intelligence"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.8 (Yoast SEO v27.8) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Using Deep Learning to Detect Anomalies in Traffic Flow - 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