Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/2136
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dc.contributor.authorSubairu, S. O.-
dc.contributor.authorAlhassan, J. K.-
dc.contributor.authorAbdulhamid, S. M.-
dc.contributor.authorOjeniyi, J.A-
dc.date.accessioned2021-06-08T11:38:11Z-
dc.date.available2021-06-08T11:38:11Z-
dc.date.issued2020-
dc.identifier.citationhttps://www.ijmsat.com/archives/ijmsat-volume-1-issue-3en_US
dc.identifier.issn2708-0587-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/2136-
dc.description.abstractQuick Response Code technology has made so easy many human digital transactions such as payment, authentication, advertisement, web navigation and others. This technology, despite being widely accepted because of its ease of creation, deployment and usage, has been recently a tool of personal identification theft in the hands of fraudster. Researchers in the area of application of machine learning to cyber security may find it difficulty sourcing QR code dataset. In order to fill this identified gap, a model was developed which incorporate data engineering principle to formulate QR code dataset in the form implementable on machine learning algorithm for analysis.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Multidisciplinary Sciences and Advanced Technologyen_US
dc.relation.ispartofseriesVolume 1 Number 3;-
dc.subjectMachine Learning, Quick Response Code, Dataset, cyber security.en_US
dc.titleFormulation of Quick Response Code Dataset for Machine learning Analysis.en_US
dc.typeArticleen_US
Appears in Collections:Computer Science

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