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dc.contributor.authorDewalegama, M. P-
dc.contributor.authorde Zoysa, A.D.S-
dc.contributor.authorKodikara, L. M-
dc.contributor.authorDissanayake, D.M.J.C-
dc.contributor.authorKuruppu, T. A-
dc.contributor.authorRupasinghe, S-
dc.date.accessioned2022-07-15T05:44:46Z-
dc.date.available2022-07-15T05:44:46Z-
dc.date.issued2022-06-27-
dc.identifier.citationM. P. Dewalegama, A. D. S. de Zoysa, L. M. Kodikara, D. M. J. C. Dissanayake, T. A. Kuruppu and S. Rupasinghe, "Deep Learning-Based Smart Infotainment System for Taxi Vehicles," 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2022, pp. 1-6, doi: 10.1109/HORA55278.2022.9799964.en_US
dc.identifier.isbn978-1-6654-6835-0-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2773-
dc.description.abstractNowadays, people are more intent to use IoT to ease their day-to-day work. As a result of that, the transportation industry is being adopted to more IoT-based approaches rather than traditional methods. When it comes to taxi services, companies need to keep up the competition with their rivals. Along with the high demand, there are also being reporting number of problems around the taxi industry daily. Taking some of the most common problems into consideration, such as “Smart Infotainment System” will help to resolve most of those problems. Deep learning models such as CNN (Convolutional Neural Network), YOLO and SKLearn used to develop the proposed system. As a result of that, the reputation of the taxi service will be increased, and the taxi service consumers will be able to get a comfortable and safer journey to the end of the day.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA);-
dc.subjectDeep Learning-Baseden_US
dc.subjectSmart Infotainment Systemen_US
dc.subjectTaxi Vehiclesen_US
dc.titleDeep Learning-Based Smart Infotainment System for Taxi Vehiclesen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/HORA55278.2022.9799964en_US
Appears in Collections:Department of Computer Science and Software Engineering
Research Papers - Dept of Computer Science and Software Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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