This page has only limited features, please log in for full access.
This’s Dr. Rayan Nouh. Received his B.S. degree from King Abdulaziz University in 2008, his MSc degree from Sheffield University UK in 2010, and his Ph.D. In Computer Science Engineering (CSE) from Dankook University in Seoul South Korea 2019. He has working experience in Samsung Mobile Institute (SMI) as a senior researcher in computer science and engineering for Korean New Deal (KND) national strategy a great transformation project. His research interests in deep learning, hybrid methods, personalize recommender systems and context awareness technology. In 2019 joined Umm Al Qura University as a postdoctoral fellow in Hajj intelligent transportation system and traffic big data analytics. Moreover, he currently working in the National Transformation Program (NTP) government of Saudi Arabia as part of its Vision 2030
The Internet of vehicles (IoV) is a rapidly emerging technological evolution of Intelligent Transportation System (ITS). This paper proposes SafeDrive, a dynamic driver profile (DDP) using a hybrid recommendation system. DDP is a set of functional modules, to analyses individual driver’s behaviors, using prior violation and accident records, to identify driving risk patterns. In this paper, we have considered three synthetic data-sets for 1500 drivers based on their profile information, risk parameters information, and risk likelihood. In addition, we have also considered the driver’s historical violation/accident data-set records based on four risk-score levels such as high-risk, medium-risk, low-risk, and no-risk to predict current and future driver risk scores. Several error calculation methods have been applied in this study to analyze our proposed hybrid recommendation systems’ performance to classify the driver’s data with higher accuracy based on various criteria. The evaluated results help to improve the driving behavior and broadcast early warning alarm to the other vehicles in IoV environment for the overall road safety. Moreover, the propoed model helps to provide a safe and predicted environment for vehicles, pedestrians, and road objects, with the help of regular monitoring of vehicle motion, driver behavior, and road conditions. It also enables accurate prediction of accidents beforehand, and also minimizes the complexity of on-road vehicles and latency due to fog/cloud computing servers.
Rayan Nouh; Madhusudan Singh; Dhananjay Singh. SafeDrive: Hybrid Recommendation System Architecture for Early Safety Predication Using Internet of Vehicles. Sensors 2021, 21, 3893 .
AMA StyleRayan Nouh, Madhusudan Singh, Dhananjay Singh. SafeDrive: Hybrid Recommendation System Architecture for Early Safety Predication Using Internet of Vehicles. Sensors. 2021; 21 (11):3893.
Chicago/Turabian StyleRayan Nouh; Madhusudan Singh; Dhananjay Singh. 2021. "SafeDrive: Hybrid Recommendation System Architecture for Early Safety Predication Using Internet of Vehicles." Sensors 21, no. 11: 3893.
The main focus of the paper is to propose a smart recommender system based on the methods of hybrid learning for personal well-being services, called a smart recommender system of hybrid learning (SRHL). The essential health factor is considered to be personal lifestyle, with the help of a critical examination of various disciplines. Integrating the recommender system effectively contributes to the prevention of disease, and it also leads to a reduction in treatment cost, which contributes to an improvement in the quality of life. At the same time, there exist various challenges within the recommender system, mainly cold start and scalability. To effectively address the inefficiencies, we propose combined hybrid methods in regard to machine learning. The primary aim of this learning method is to integrate the most effective and efficient learning algorithms to examine how combined hybrid filtering can help to improve the cold star problem efficiently in the provision of personalized well-being in regard to health food service. These methods include: (1) switching among content-based and collaborative filtering; (2) identifying the user context with the integration of dynamic filtering; and (3) learning the profiles with the help of processing and screening of reflecting feedback loops. The experimental results were evaluated by using three absolute error measures, providing comparable results with other studies relative to machine learning domains. The effects of using the hybrid learning method are gathered with the help of the experimental results. Our experiments also show that the hybrid method improves accuracy by 14.61% of the average error predicted in the recommender systems in comparison to the collaborative methods, which mainly focus on the computation of similar entities.
Rayan M. Nouh; Hyun-Ho Lee; Won-Jin Lee; Jae-Dong Lee. A Smart Recommender Based on Hybrid Learning Methods for Personal Well-Being Services. Sensors 2019, 19, 431 .
AMA StyleRayan M. Nouh, Hyun-Ho Lee, Won-Jin Lee, Jae-Dong Lee. A Smart Recommender Based on Hybrid Learning Methods for Personal Well-Being Services. Sensors. 2019; 19 (2):431.
Chicago/Turabian StyleRayan M. Nouh; Hyun-Ho Lee; Won-Jin Lee; Jae-Dong Lee. 2019. "A Smart Recommender Based on Hybrid Learning Methods for Personal Well-Being Services." Sensors 19, no. 2: 431.