Hybrid Design using Counter Propagation Neural Network-Genetic Algorithm Model for the Anomaly Detection in Online Transaction

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dc.contributor.authorAmusan, D.G.
dc.contributor.authorOlabode, A.O.
dc.contributor.authorOjo, O.S.
dc.contributor.authorFolowosele, A.O.
dc.contributor.authorOyediran, M.O.
dc.date.accessioned2023-12-04T14:54:48Z
dc.date.available2023-12-04T14:54:48Z
dc.date.issued2019-09-20
dc.description.abstractIn e-commerce, credit card fraud is an evolving challenge. The increase in the number of credit card transactions provides more opportunity for fraudsters to steal credit card numbers and execute fraud. Fraud detection is a continuously evolving discipline to tackle ever changing tactics to commit fraud. Existing fraud detection systems have not been so much efficient to reduce fraud transaction rate. Improvement in fraud detection practices has become essential to maintain existence of payment system. This research designed hybrid of Counter Propagation Neural Network and genetic algorithm (CPNN-GA) for the detection of anomaly in any online transactions.en_US
dc.identifier.urihttp://repository.acu.edu.ng:8080/jspui/handle/123456789/360
dc.language.isoenen_US
dc.publisherInternational Journal of Advances in Scientific Research and Engineering (ijasre)en_US
dc.subjectAnomaly Detectionen_US
dc.subjectCounter propagation neural networken_US
dc.subjectCredit card frauden_US
dc.subjectGenetic algorithmen_US
dc.subjectModelen_US
dc.subjectOnline transactionsen_US
dc.titleHybrid Design using Counter Propagation Neural Network-Genetic Algorithm Model for the Anomaly Detection in Online Transactionen_US
dc.typeArticleen_US

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