IMars: Intelligent Municipality Augmented Reality Service for Efficient Information Dissemination based on Deep Learning algorithm in Smart City of Jeddah

• 出版年: Procedia Computer Science
• 第一著者: 2-s2.0-85081165787
• フォーマット: Conference paper
• 言語: English
• 出版事項: Elsevier B.V. 2019
• オンライン･アクセス: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081165787&doi=10.1016%2fj.procs.2019.12.091&partnerID=40&md5=625310b422d0cc66644d9dcd86e70692

A smart city is one of a huge transformation in these millennia, where it combines latest and advanced technology to overcome the daily basis problem of humans. Artificial intelligence with augmented reality has been highlighted as the potential solution along with IOT and another system to support the smart city system. The smarter Jeddah is part of Saudi Vision 2030 where Saudi government tends aims to transform this second biggest city in the kingdom to be a smart city with a ton of applications that connected with IOT and they can monitor people inside the city in real time. The proposed system focused on providing the resident of the Jeddah city with smart apps that can help them on conducting their daily basis activities (tourism) or even in an emergency situation. The geo-location data and other information will be used as a feeder for deep learning to make the system learn and getting smarter to disseminate information to the user based on their needs, location, emergency factors, etc. Resident can access the municipality public facilities such as Jeddah waterfront location and see what the event happens inside. They also can find the location of other attractive location such as Al-Fakieh aquarium and observe the content of aquarium facilities with virtual reality technology. In term of emergency cases: accident, car stopping and another incident; the proposed apps will be able to inform the police patrol nearby or nearest workshop for tyre or car workshop. This app is capable for wide range utilization to support program of the Ministry of Municipal and Rural Affairs to apprehend the smart city integrated application. The simulation test has confidently proved that deep learning algorithm overcome other classifier Naïve Bayes and Random forest with 67.5% recognition rate. The future work can be integrated with IOT sensor based system to enhance the municipality service in smart city. © 2019 The Authors. Published by Elsevier B.V.