Ibb University

About: Ibb University is a education organization based out in Ibb, Yemen. It is known for research contribution in the topics: Band gap & Crystallite. The organization has 367 authors who have published 705 publications receiving 6941 citations.

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

Abstract: Smart cities contain intelligent things which can intelligently automatically and collaboratively enhance life quality, save people's lives, and act a sustainable resource ecosystem. To achieve these advanced collaborative technologies such as drones, robotics, artificial intelligence, and Internet of Things (IoT) are required to increase the smartness of smart cities by improving the connectivity, energy efficiency, and quality of services (QoS). Therefore, collaborative drones and IoT play a vital role in supporting a lot of smart-city applications such as those involved in communication, transportation, agriculture,safety and security, disaster mitigation, environmental protection, service delivery, energy saving, e-waste reduction, weather monitoring, healthcare, etc. This paper presents a survey of the potential techniques and applications of collaborative drones and IoT which have recently been proposed in order to increase the smartness of smart cities. It provides a comprehensive overview highlighting the recent and ongoing research on collaborative drone and IoT in improving the real-time application of smart cities. This survey is different from previous ones in term of breadth, scope, and focus. In particular, we focus on the new concept of collaborative drones and IoT for improving smart-city applications. This survey attempts to show how collaborative drones and IoT improve the smartness of smart cities based on data collection, privacy and security, public safety, disaster management, energy consumption and quality of life in smart cities. It mainly focuses on the measurement of the smartness of smart cities, i.e., environmental aspects, life quality, public safety, and disaster management.

User profiling approaches for demographic recommender systems

Abstract: Many DRSs are available in our daily life and many online services will be more personalized if demographic data is taken into account.Unipolar or bipolar similarity measures can be used for categorical attributes profile.Treating age as a fuzzy variable improves the system performance and reflects the real life case.Results of the unified profiling approaches are almost similar with minor differences.Single-attribute profiling approach brings to light the advantage of each attribute of the profile. Many of our daily life decisions rely on demographic data, which is a good indicator for closeness of people. However, the lack of these data for many online systems let them search for explicit or implicit alternatives. Among many, collaborative filtering is the alternative solutions especially for e-commerce applications where many users are reluctant to disclose their demographic data. This paper explores, discusses and examines many user-profiling approaches for demographic recommender systems (DRSs). These approaches span many alternatives for profiling users in terms of the attribute types, attribute representations, and the profiling way. We present layout, description, and appropriate similarity computation methods for each one of them. A detailed comparison between these different approaches is given using many experiments conducted on a real dataset. The pros and cons of each approach are illustrated for more advantage that may open a window for future work.