Pervasive computing of adaptable recommendation system for head-up display in smart transportation. (September 2022) - Abu-Khadrah, Ahmed; Jarrah, Muath; Alrababah, Hamza; Alqattan, Zakaria N.M.; Akbar, Habibullah
Highlights: Designing an ARS using an ACV system for detecting objects using pervasive computing in smart transportation. Evaluating backhauled using deep, short-term memory networks to identify and verify the layers' correctness in detecting the target. The experimental analysis has been performed, and the proposed method achieves better detection accuracy, less detection time and error, and improved precision compared to other existing systems. 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. Graphical 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. This technology is aided by computer vision (CV) techniques and used in smart transportation for human assistance. This article introduces an Adaptable Recommendation System (ARS) using an analytical CV (ACV) in smart transportation. This ARS can 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. Image, graphical abstract