Publication:
Application of Machine Learning Methods for Solving Problems of Body Part Identification to Compare the Desired Level of Pain

dc.contributor.authorKlimonov, K. Y.
dc.contributor.authorSadikov, R. I.
dc.date.accessioned2024-11-29T15:25:07Z
dc.date.available2024-11-29T15:25:07Z
dc.date.issued2021
dc.description.abstract© 2021 IEEE.The purpose of this work is to write an application that detects a part of the body using a neural network and gives out the level of pain when tattooing this limb. According to statistics, in every country from 10 to 20 percent of people believe that a tattoo is painful, so they are afraid to do it. The app will help them choose the least painful place. We use an application that basically contains a convolutional neural network and the Tkinter and ImageAI library.
dc.format.extentС. 454-457
dc.identifier.citationKlimonov, K. Y. Application of Machine Learning Methods for Solving Problems of Body Part Identification to Compare the Desired Level of Pain / Klimonov, K.Y., Sadikov, R.I. // Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021. - 2021. - P. 454-457. - 10.1109/ElConRus51938.2021.9396446
dc.identifier.doi10.1109/ElConRus51938.2021.9396446
dc.identifier.urihttps://www.doi.org/10.1109/ElConRus51938.2021.9396446
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85104765925&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000669709800102
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/23992
dc.relation.ispartofProceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021
dc.titleApplication of Machine Learning Methods for Solving Problems of Body Part Identification to Compare the Desired Level of Pain
dc.typeConference Paper
dspace.entity.typePublication
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relation.isOrgUnitOfPublication.latestForDiscovery010157d0-1f75-46b2-ab5b-712e3424b4f5
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