Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0193456" target="_blank" >RIV/00216305:26220/26:0193456 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10771968" target="_blank" >https://ieeexplore.ieee.org/document/10771968</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TCE.2024.3509624" target="_blank" >10.1109/TCE.2024.3509624</a>
Alternative languages
Result language
angličtina
Original language name
Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products
Original language description
Product 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
ISSN
0098-3063
e-ISSN
1558-4127
Volume of the periodical
71
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
2309-2321
UT code for WoS article
001511069500038
EID of the result in the Scopus database
2-s2.0-85210773967