Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15105
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dc.contributor.authorHaider, Sami Ahmed-
dc.contributor.authorRamesh, Janjhyam Venkata Naga-
dc.contributor.authorRaina, Vikas-
dc.contributor.authorMaaliw, Renato R-
dc.contributor.authorSoni, Mukesh-
dc.contributor.authorNasurova, Kamolakhon-
dc.contributor.authorPatni, Jagdish Chandra-
dc.contributor.authorSingh, Pavitar Parkash-
dc.date.accessioned2024-04-08T04:11:10Z-
dc.date.available2024-04-08T04:11:10Z-
dc.date.issued2024-
dc.identifier.citationpp. 1-1en_US
dc.identifier.issn0098-3063-
dc.identifier.urihttps://doi.org/10.1109/TCE.2024.3369399-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15105-
dc.description.abstractIn the context of Secure Artificial Intelligence for 6G Consumer Electronics, accurately predicting vehicle behavior in dynamic traffic scenarios is a significant challenge in intelligent transportation. To avoid sending all raw data to a centralized cloud server, this study presents an artificial intelligence (AI) based distributed machine learning framework (AICEML) that can run on local edge devices. This method protects user privacy while minimizing transmission and processing delays. Accurate predictions are maintained despite the presence of many cars because to AICEML’s use of the model on edge devices, which incorporates edge-enhanced attention and graph convolutional neural network features to swiftly collect and transmit vehicle interaction information. Each edge device can adapt its neural network type and scale based on its computing capabilities, accommodating various application scenarios. Experimental results using the NGGSIM dataset demonstrate AICEML’s superiority, achieving precision, recall, and F1 scores of 0.9391, 0.9557, and 0.9473, respectively. With a 1-second prediction horizon, it maintains 91.21% accuracy and low time complexity even as the number of vehicles increases. This framework holds promise for enhancing intelligent transportation systems in the 6G era while prioritizing security and efficiency. IEEEen_US
dc.language.isoenen_US
dc.publisherIEEE Transactions on Consumer Electronicsen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subject6G Consumer Electronicsen_US
dc.subjectBehavioral Sciencesen_US
dc.subjectCommunication Efficiencyen_US
dc.subjectComputational Modelingen_US
dc.subjectData Modelsen_US
dc.subjectEdge Computingen_US
dc.subjectHidden Markov Modelsen_US
dc.subjectNeural Networksen_US
dc.subjectSecure Artificial Intelligenceen_US
dc.subjectServersen_US
dc.subjectTrainingen_US
dc.subjectVehicle Behavior Predictionen_US
dc.titleSecure Artificial Intelligence for Precise Vehicle Behavior Prediction in 6G Consumer Electronicsen_US
dc.typeArticleen_US
Appears in Collections:Journal Articles

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