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Detection of Dangerous Web Pages Based on the Analysis of Suicidal Content Using Machine Learning Algorithms

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dc.contributor.authorLyovkin, M.
dc.contributor.authorFrolov, A. A.
dc.contributor.authorPerminov, E.
dc.date.accessioned2024-11-29T15:26:13Z
dc.date.available2024-11-29T15:26:13Z
dc.date.issued2021
dc.description.abstract© 2021 IEEE.Nowadays, the task of preventing suicide is one of the priorities in the health sector. Therefore, it is important to identify people prone to suicide at an early stage. This article discusses the possibility of real-time detection of visited websites containing suicidal statements. The classification of web pages is based on the analysis of the text contained on it. This work can be divided into two parts: creating a browser extension and the server. The extension collects information about the content of the web pages visited by the user and transmits it to the server. The page classification process takes place on the server. In the final part of this work, a comparison of the effectiveness of detecting suicidal websites using various machine learning algorithms is presented.
dc.format.extentС. 513-516
dc.identifier.citationLyovkin, M. Detection of Dangerous Web Pages Based on the Analysis of Suicidal Content Using Machine Learning Algorithms / Lyovkin, M., Frolov, A.A., Perminov, E. // Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021. - 2021. - P. 513-516. - 10.1109/ElConRus51938.2021.9396529
dc.identifier.doi10.1109/ElConRus51938.2021.9396529
dc.identifier.urihttps://www.doi.org/10.1109/ElConRus51938.2021.9396529
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85104736977&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000669709800115
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/24009
dc.relation.ispartofProceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021
dc.titleDetection of Dangerous Web Pages Based on the Analysis of Suicidal Content Using Machine Learning Algorithms
dc.typeConference Paper
dspace.entity.typePublication
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relation.isOrgUnitOfPublication.latestForDiscovery010157d0-1f75-46b2-ab5b-712e3424b4f5
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