A System for Traffic Events Detection Using Fuzzy C-Means

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dc.contributor.authorEndo Pérez, Hayder
dc.contributor.authorLeiva Mederos, Amed Abel
dc.contributor.authorGálvez Lio, Daniel
dc.contributor.authorHurtado, Luis Ernesto
dc.contributor.authorGarcía Duarte, Doymer
dc.contributor.authorAuguste Atemezing, Ghislain
dc.contributor.departmentUniversidad Central “Marta Abreu” de las Villas. Centro de Investigaciones de la Informática, Grupo de Web Semánticaen_US
dc.contributor.departmentUniversidad Central “Marta Abreu” de las Villas. Centro de Investigaciones de la Informática, Grupo de Web Semánticaen_US
dc.contributor.departmentUniversidad Central “Marta Abreu” de las Villas. Centro de Investigaciones de la Informática, Grupo de Web Semánticaen_US
dc.contributor.departmentUniversidad Central “Marta Abreu” de las Villas. Centro de Investigaciones de la Informática, Grupo de Web Semánticaen_US
dc.contributor.departmentMondeca. Paris, Franciaen_US
dc.coverage.spatialEstados Unidosen_US
dc.date.accessioned2022-02-01T16:47:26Z
dc.date.available2022-02-01T16:47:26Z
dc.date.issued2021-02-06
dc.description.abstractSystems for traffic events administration are important tools in the prediction of disasters and management of that of the movement flow in diverse contexts. These systems are generally developed on non-fuzzy grouping algorithms and ontologies. However, the results of the implementation do not always give high precision scores due to different factors such as data heterogeneity, the high number of components used in their architecture and to the mixture of highly specialized and diverse domain ontologies. These factors do not ease the implementation of the systems able to predict with higher reliability traffic events. In this work, we design a system for traffic events detection that implements a new ontology called trafficstore and leverages the fuzzy c-means algorithm. The indexes evaluated on the fuzzy c-means algorithm demonstrates that the implemented system improves its efficiency in the grouping of traffic events.en_US
dc.identifier.citationCitar según la fuente original: Pérez H.E., Mederos A.L., Lio D.G., Hurtado L.E., Duarte D.G., Atemezing G.A. (2021) A System for Traffic Events Detection Using Fuzzy C-Means. In: Villazón-Terrazas B., Ortiz-Rodríguez F., Tiwari S., Goyal A., Jabbar M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_6en_US
dc.identifier.doiDOI: 10.1007/978-3-030-91305-2_6en_US
dc.identifier.isbn978-3-030-91305-2en_US
dc.identifier.urihttps://dspace.uclv.edu.cu/handle/123456789/13286
dc.language.isoen_USen_US
dc.relation.conferenceThird Iberoamerican Conference and Second Indo-American Conference, KGSWC 2021, Kingsville, Texas, USA, November 22–24, 2021, Proceedingsen_US
dc.rightsEste documento es Propiedad Patrimonial de Springer, Cham y se socializa en este Repositorio gracias a la política de acceso abierto del Congreso Third Iberoamerican Conference and Second Indo-American Conference, KGSWC 2021, Kingsville, Texas, USA, November 22–24, 2021, Proceedings.en_US
dc.rights.holderSpringer, Chamen_US
dc.subjectSemantic Weben_US
dc.subjectFuzzy C Meansen_US
dc.subjectIoTen_US
dc.subjectTraffic Event Detectionen_US
dc.subject.otherWeb Semánticaen_US
dc.subject.otherLógica Difusaen_US
dc.subject.otherTráfico de Eventosen_US
dc.subject.otherMétodos de Detecciónen_US
dc.subject.otherDiseño de Sistemasen_US
dc.titleA System for Traffic Events Detection Using Fuzzy C-Meansen_US
dc.typeProceedingsen_US

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