ABSTRACT
A Smart City (SC) is a viable solution for green and sustainable living, especially with the current explosion in global population and rural-urban immigration. One of the fields that is not getting much attention in the Smart Economy (SE) is customer satisfaction. The SE is a component of SC that is concerned with using Information and Communication Technology (ICT) to improve stages of the traditional economy. In this paper, we propose a fog computing-based shopping recommendation system. Our simulations used Al-Madinah city as a case study. It aims to improve the customer shopping experience. Customers in shopping malls can connect to the system via Wi-Fi. Then the system recommends products to the shoppers according to their preferences. It optimizes shoppers' schedules using price, the distance between the shops, and the congestion. It also improves customers' savings by up to 30%. It also increases the shopping speed by up to 6.12% compared to the system proposed in the literature.
ABSTRACT
COVID-19 pandemic has changed the way we live our lives for the foreseen future. To date, there have been over 113 million reported cases and 2.5 million deaths worldwide. Many studies investigated the factors affecting the number of daily cases such as weather conditions, lockdown duration and other factors. In this study, we propose a COVID-19 analytical formula for factors contributing to the number of the new coronavirus daily cases. We have also calculated values of relative weights of those factors. We focus on the first wave data that are publically available. Seven countries were considered including the UK, Italy, Spain, Canada, South Korea, Germany and France. We considered the following factors: temperature, humidity, government expenditure, lockdown hours and the number of daily tests for COVID-19 performed. The weights were calculated based on the hypothesis that a high correlation between recorded data of a given pair of countries implies a high correlation of the pair's COVID-19 proposed analytical formula. The factors are calculated using the brute-force technique. Our results showed that in five out of the seven countries; temperature, humidity, and lockdown duration were the most dominant with values of 26%, 32% and 38%, respectively. In other countries, however, humidity, government expenditure and the daily performed tests for COVID-19 were the most effective factors, with relative values of 35%, 26%, and 28%.