Internet of vehicles and autonomous systems with AI for medical things

Simple item page
dc.contributor.authorGhazal, Taher M.
dc.contributor.authorSaid, Raed A.
dc.contributor.authorTaleb, Nasser
dc.date.accessioned2022-02-04T07:05:55Z
dc.date.available2022-02-04T07:05:55Z
dc.date.copyright© 2021
dc.date.issued2021
dc.descriptionThis 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/s00500-021-06035-2en_US
dc.description.abstractThe 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.citationGhazal, 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/s00500-021-06035-2en_US
dc.identifier.issn14327643
dc.identifier.urihttps://doi.org/10.1007/s00500-021-06035-2
dc.identifier.urihttp://hdl.handle.net/20.500.12519/506
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relationAuthors 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.ispartofseriesSoft Computing;
dc.rightsLicense 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.holderCopyright : © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
dc.rights.licenseLicense Number: 5240790500126 License date: Feb 02, 2022
dc.rights.urihttps://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=7e78a46e-b213-4584-a7bb-433422aff704
dc.subjectArtificial neural networken_US
dc.subjectAutonomous systemen_US
dc.subjectEmergencyen_US
dc.subjectGlobal positioning systemen_US
dc.titleInternet of vehicles and autonomous systems with AI for medical thingsen_US
dc.typeArticleen_US
Files
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.01 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
International Business