| dc.contributor.advisor | Öğrenci, Arif Selçuk | en_US |
| dc.contributor.author | Öğrenci, Arif Selçuk | |
| dc.date.accessioned | 2019-06-27T08:04:11Z | |
| dc.date.available | 2019-06-27T08:04:11Z | |
| dc.date.issued | 2012 | |
| dc.identifier.isbn | 9781467352062 | |
| dc.identifier.isbn | 9781467352055 | |
| dc.identifier.issn | 2380-8586 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12469/905 | |
| dc.identifier.uri | https://doi.org/10.1109/CINTI.2012.6496787 | |
| dc.description.abstract | In this paper we describe a framework for an expert system that tries to predict effects of an accident based on past data using supervised learning employing artificial neural networks. For this purpose sensory data events are post processed in order to generate a reasonable mapping between input and output parameters in case an event is detected automatically or manually. The framework is intended to be used to take actions for reducing the effects of the accident on traffic congestion and to inform necessary parties to intervene in a timely fashion. | en_US] |
| dc.language.iso | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | N/A | en_US |
| dc.title | Complex event post processing for traffic accidents | en_US |
| dc.type | conferenceObject | en_US |
| dc.identifier.startpage | 341 | en_US |
| dc.identifier.endpage | 345 | |
| dc.relation.journal | 13th IEEE International Symposium On Computatıonal Intelligence And Informatics (CINTI 2012) | en_US |
| dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
| dc.identifier.wos | WOS:000319991600058 | en_US |
| dc.identifier.doi | 10.1109/CINTI.2012.6496787 | en_US |
| dc.identifier.scopus | 2-s2.0-84876921932 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
