Internet of vehicles and autonomous systems with AI for medical things

dc.contributor.author Ghazal, Taher M.
dc.contributor.author Said, Raed A.
dc.contributor.author Taleb, Nasser
dc.date.accessioned 2022-02-04T07:05:55Z
dc.date.available 2022-02-04T07:05:55Z
dc.date.copyright © 2021
dc.date.issued 2021
dc.description This article is not available at CUD collection. The version of scholarly record of this article paper is published in Soft Computing (2021), available online at: https://doi.org/10.1007/s00500-021-06035-2 en_US
dc.description.abstract The current world faces a considerable traffic rate on roads due to the increase in various types of vehicles. It caused emergency vehicles to delay and increasing the patients' health risk factor. Internet of vehicle-based artificial neural network (IoV-ANN) has been proposed for effective health autonomous system in medical things. The proposed IoV-ANN provides a secure network to monitor and track the vehicle's motion using the global positioning system. It consists of an autonomous system which is enabled with an artificial neural network (ANN). ANN model has three layers. First layers collect the data using IoV sensors. Second or hidden layers process the sensor data, predict the road's traffic condition and reroute the emergency vehicle to an exact route. IoV-ANN helps to reduce road congestion in this article to enhance the timely functioning of an emergency vehicle. ANN categorizes the congestion networks of traffic. Traffic restrictions such as changing the queue gap in the road signals and the alternative roads are altered automatically due to congestion. It allows the government to develop ideas for alternate routes to enhance traffic control. The output layer gives commands to the driver autonomously. The simulation analysis of the proposed method proved that the system could work independently. The IoV-ANN achieves the highest performance rate of (97.89%), with a reduced error rate (9.12%) traffic congestion rate (10.31%), travel period (32 s), vehicle detection rate (93.61%), classification accuracy (95.02%), MAPE (8.4%), throughput rate (93.50%) when compared to other popular methods. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. en_US
dc.identifier.citation Ghazal, T. M., Said, R. A., & Taleb, N. (2021). Internet of vehicles and autonomous systems with AI for medical things. Soft Computing, https://doi.org/10.1007/s00500-021-06035-2 en_US
dc.identifier.issn 14327643
dc.identifier.uri https://doi.org/10.1007/s00500-021-06035-2
dc.identifier.uri http://hdl.handle.net/20.500.12519/506
dc.language.iso en en_US
dc.publisher Springer Science and Business Media Deutschland GmbH en_US
dc.relation Authors Affiliations : Ghazal, T.M., Skyline University College, Sharjah, United Arab Emirates, Universiti Kebangsaan Malaysia (UKM), Bandar Baru Bangi, Malaysia; Said, R.A., Canadian University Dubai, Dubai, United Arab Emirates; Taleb, N., Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseries Soft Computing;
dc.rights License to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holder © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
dc.rights.holder Copyright : © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
dc.rights.license License Number: 5240790500126 License date: Feb 02, 2022
dc.rights.uri https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=7e78a46e-b213-4584-a7bb-433422aff704
dc.subject Artificial neural network en_US
dc.subject Autonomous system en_US
dc.subject Emergency en_US
dc.subject Global positioning system en_US
dc.title Internet of vehicles and autonomous systems with AI for medical things en_US
dc.type Article en_US
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