Alqattan, Zakaria N.M., Akbar, Habibullah and Abu-Khadrah, Ahmed (2022) Pervasive computing of adaptable recommendation system for head-up display in smart transportation. Computers and Electrical Engineering, 102. p. 108204. ISSN 00457906
pii/S0045790622004451 - Published Version
Download (2kB)
Abstract
Pervasive computing aims to simplify our lives by efficiently managing information in different fields such as transportation, and healthcare. Smart transportation has become an integral part of our modern society and is attractive for pervasive computing. Head-Up Display (HUD) assists users in locating and identifying objects and humans by establishing volatile contact with them. HUD is aided by computer vision (CV) techniques and used in smart transportation for human assistance. An Adaptable Recommendation System (ARS) using an analytical CV (ACV) in smart transportation is introduced to improve the swiftness in detecting objects in a multi-layer smart city environment. The proposed system is backhauled using deep, short-term memory networks to identify and verify the layers' correctness in detecting the target with a reduced time factor. The application's design concentrates on enlightening HUD for end-user recommendations. The HUD applications with the recommended system achieve less time, error, and computations.
Affiliation: | Skyline University College |
---|---|
SUC Author(s): | Jarrah, Muath and Alrababah, Hamza |
All Author(s): | Alqattan, Zakaria N.M., Akbar, Habibullah and Abu-Khadrah, Ahmed |
Item Type: | Article |
Subjects: | B Information Technology > BA Information Systems B Information Technology > BC Digital Logic B Information Technology > BJ Computer Science B Information Technology > BW Computer Networks |
Divisions: | Skyline University College > School of IT Skyline University College > School of Business |
Depositing User: | Mr Mosys Team |
Date Deposited: | 11 Dec 2023 06:05 |
Last Modified: | 11 Dec 2023 06:05 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/585 |
Publisher URL: | https://doi.org/10.1016/j.compeleceng.2022.108204 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/27887 |
Related URLs: |
|
Actions (login required)
Statistics for this ePrint Item |