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.

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.

As foods and personalized recommendation system has become one of the fastest growing applications services in the world, there is at the same time growth in the use of mobile and proximity commerce that utilizes new services model which connects the digital to the physical world (O2O). This provides an interactive way to communicate with consumers based on their location and preferences. Halal Food Product (HFP) has become widespread in the global market. Recently, with the increase in foreign residents in S. Korea, it has led to growth in demand for Halal Food. However, the current S. Korea retailers market is facing various challenges such as lack of data, targeting consumer performances, and offering promotion in interactive technologies. Furthermore, there is hardly any recommendation system in the (HFP) domain. In this paper, we design and propose an (O2O) recommendation system framework in a mobile environment to match (HFP) based on user preference. The inference engine is modeled by a rule based on user activities in-store, where we expect it can have great potential to enhance current S. Korea (HFP) food enterprise market.

Internet of Vehicle (IoV) is the evolution of Intelligent Transportation System (ITS). This paper proposes Driveshaft which is a dynamic driver profile (DDP) using a recommender system. It is an umbrella designing to provide a set of functional and module to analyze individual driver's behaviors using prior violation and accident recorder to identify driver risk patterns. Therefore, we have created three synthetic data-sets for 1500 users based on driver profile information, risk parameters information, risk likelihood into four risk score level high, medium, low risk, and none risk driver determined based on driver historical violation/accident records to predict current and future driver risk scores. In this paper proposed deep learning methods to analyze driver profiles based on dynamic driver profile, thus could laying a foundation for improving driver's driving behavior to make the IoV environment alert to send early warnings alarm to the vehicles for road safety. Moreover, the intention is to make the environment safe, here vehicle safety, pedestrian safety, and neighboring vehicle safety has ensured. Regular monitoring of vehicle motion, driver behavior, and road conditions enables accurate prediction of accidents earlier. We also aim to minimize complexity at vehicles and latency due to cloud servers by introducing a novel fog tier. The classification prediction used a deep learning algorithm for performs learning to improve the recommendation accuracy and performance using different criteria to evaluate the accuracy of MAE, MSE, and MAPE.