Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/2218
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Raina, Ajeet Singh | - |
dc.contributor.author | Çakar, Tuna | - |
dc.contributor.author | Ertuğrul, Seyit | - |
dc.contributor.author | Arslan, Şuayip | - |
dc.contributor.author | Sayar, Alperen | - |
dc.date.accessioned | 2024-01-25T08:13:24Z | - |
dc.date.available | 2024-01-25T08:13:24Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Sayar, A., Arslan, S., Raina, A. S., Ertugrul, S., & Cakar, T. (2023). GRAFRAUD: Fraud detection using graph databases and neural networks. In 2023 4th International Informatics and Software Engineering Conference. IEEE. pp. 1-4. | en_US |
dc.identifier.isbn | 979-8-3503-1803-6 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11779/2218 | - |
dc.identifier.uri | https://doi.org/10.1109/IISEC59749.2023.10391038 | - |
dc.description | Index tarihi :19 Ocak 2024 | en_US |
dc.description.abstract | The issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and RedisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, RedisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle Databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of RedisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle Database to construct and deploy real-time systems that can efficiently detect fraudulent activities. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | RedisGraph | en_US |
dc.subject | Complex data structures | en_US |
dc.subject | Financial transactions | en_US |
dc.subject | Decision making | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Highperformance | en_US |
dc.subject | Fraud detection | en_US |
dc.subject | Real-time | en_US |
dc.subject | Anomalies | en_US |
dc.subject | Large-scale environments | en_US |
dc.subject | Neural networks | en_US |
dc.title | Grafraud: Fraud Detection Using Graph Databases and Neural Networks | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/IISEC59749.2023.10391038 | - |
dc.identifier.scopus | 2-s2.0-85184668054 | en_US |
dc.authorid | Tuna Çakar / 0000-0001-8594-7399 | - |
dc.description.PublishedMonth | Kasım | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Ulusal - Kurum Öğretim Elemanı | en_US |
dc.identifier.endpage | 4 | en_US |
dc.identifier.startpage | 1 | en_US |
dc.department | Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.relation.journal | 2023 4th International Informatics and Software Engineering Conference | en_US |
dc.institutionauthor | Çakar, Tuna | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.openairetype | Conference Object | - |
item.grantfulltext | embargo_20400101 | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.02. Department of Computer Engineering | - |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
Files in This Item:
File | Description | Size | Format | |
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Neural_Networks.pdf Until 2040-01-01 | Proceedings Paper | 3.73 MB | Adobe PDF | View/Open Request a copy |
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