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dc.contributor.authorNorhidayah, Mohd Rozi
dc.contributor.authorRazaidi, Hussin
dc.contributor.authorMuammar, Mohamad Isa
dc.contributor.authorSyed Muhammad Mamduh, Syed Zakaria
dc.contributor.authorAfzan, Kamaruddin
dc.contributor.authorRizalafande, Che Ismail
dc.contributor.authorMohd Nazrin, Md Isa
dc.contributor.authorSiti Zarina, Md Naziri
dc.date.accessioned2022-04-15T00:39:27Z
dc.date.available2022-04-15T00:39:27Z
dc.date.issued2021-12
dc.identifier.citationInternational Journal of Nanoelectronics and Materials, vol.14 (Special Issue), 2021, pages 245-252en_US
dc.identifier.issn1985-5761 (Printed)
dc.identifier.issn1997-4434 (Online)
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/74981
dc.descriptionLink to publisher's homepage at http://ijneam.unimap.edu.myen_US
dc.description.abstractHigher output was needed as technology advance to meet human needs and industry demands. Fruits Artificial Intelligence Segregation (FAIS) is a project that uses image processing to detect and differentiate between various types of fruits. This paper proposes an OpenCV python, and the Convolution Neural Network (CNN) is used to complete the segregation of multiple fruits. The code extracts the fruit's characteristics and separates them based on their color and shape once placed in front of the camera to implement liveness detection. This paper shows the accuracy and reliability of the Fruits Artificial Intelligence Segregation (FAIS) system based on the number of datasets.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.subject.otherArtificial Intelligenten_US
dc.subject.otherOpen VCen_US
dc.subject.otherTensor flowen_US
dc.subject.otherRaspberry Pien_US
dc.subject.otherimage recognitionen_US
dc.titleDevelopment of Fruits Artificial Intelligence Segregationen_US
dc.typeArticleen_US
dc.identifier.urlhttp://ijneam.unimap.edu.my
dc.contributor.urlshidee@unimap.edu.myen_US


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