Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products

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dc.contributor.authorRaghupathy, Bala Krishnancs
dc.contributor.authorVairavasundram, Subramaniyaswamycs
dc.contributor.authorGanesan, Manikandancs
dc.contributor.authorNamachivayam, Rajesh Kumarcs
dc.contributor.authorKotecha, Ketancs
dc.contributor.authorHerencsár, Norbertcs
dc.date.accessioned2024-12-10T13:55:32Z
dc.date.available2024-12-10T13:55:32Z
dc.date.issued2024-11-29cs
dc.description.abstractProduct tracking applications utilize the Internet of Things and cyber-physical systems to identify permitted or unauthorized user intrusions into the system. Classical machine learning algorithms cannot detect every risk in an environment that evolves constantly and where new abnormalities are visible. This article investigates the potential of quantum machine learning (QML) for real-time product purchase monitoring and intrusion detection using an enhanced quantum convolutional neural network (EQCNN) with signature-based detection over a massive volume of search space data (qubits). We suggest a three-stage technique to effectively handle the sensitive content: Pre-processing, EQCNN-based feature extraction, and syntactic pattern recognition. Signature-based identification is a feature of the EQCNN architecture that helps detect particular patterns linked to goods purchases or invasions. The model can minimize product tracking mistakes by utilizing the QML-based EQCNN with signature-based detection, resulting in a more efficient supply chain.en
dc.formattextcs
dc.format.extent13cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE TRANSACTIONS ON CONSUMER ELECTRONICS. 2024, 13 p.en
dc.identifier.doi10.1109/TCE.2024.3509624cs
dc.identifier.issn0098-3063cs
dc.identifier.orcid0000-0002-9504-2275cs
dc.identifier.other193456cs
dc.identifier.researcheridA-6539-2009cs
dc.identifier.scopus23012051100cs
dc.identifier.urihttps://hdl.handle.net/11012/249750
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE TRANSACTIONS ON CONSUMER ELECTRONICScs
dc.relation.urihttps://ieeexplore.ieee.org/document/10771968cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0098-3063/cs
dc.subjectConvolutional Neural Networken
dc.subjectQuantum Machine Learningen
dc.subjectRFID Product Trackingen
dc.subjectSBDen
dc.subjectSignature-Based Detectionen
dc.titleEnhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Productsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-193456en
sync.item.dbtypeVAVen
sync.item.insts2024.12.10 14:55:32en
sync.item.modts2024.12.10 13:32:08en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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