| Title |
Pervasive computing of adaptable recommendation system for head-up display in smart transportation |
| ID_Doc |
42321 |
| Authors |
Abu-Khadrah, A; Jarrah, M; Alrababah, H; Alqattan, ZNM; Akbar, H |
| Title |
Pervasive computing of adaptable recommendation system for head-up display in smart transportation |
| Year |
2022 |
| Published |
|
| DOI |
10.1016/j.compeleceng.2022.108204 |
| 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. |
| Author Keywords |
Computer vision; Deep learning; Recommendation system; Smart city; Smart transportation; Pervasive computing; Head -up display; Human assistance; Computation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000829289700008 |
| WoS Category |
Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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