Background Social networking sites are widely used by university students. This study investigated the purposes for which social networking sites are used and their effects on learning, social interaction, and sleep duration. Material and methods A cross-sectional study was conducted among 300, 17–29-year-old female students at Prince Sattam bin Abdul Aziz University. A questionnaire was used to collect data. Chi-squared (Fisher’s exact test) test was used to analyze the data. Results The results showed that 97% of the students used social media applications. Only 1% of them used social media for academic purposes. Whereas 35% of them used these platforms to chat with others, 43% of them browsed these sites to pass time. Moreover, 57% of them were addicted to social media. Additionally, 52% of them reported that social media use had affected their learning activities, 66% of them felt more drawn toward social media than toward academic activities, and 74% of them spent their free time on social media platforms. The most popular applications (i.e., based on usage) were Snapchat (45%), Instagram (22%), Twitter (18%), and WhatsApp (7%). Further, 46% and 39% of them reported going to bed between 11 pm and 12 am and between 1 am and 2 am, respectively. Finally, 68% of them attributed their delayed bedtime to social media use, and 59% of them reported that social media had affected their social interactions. Conclusions A majority of the participants reported prolonged use of social networking sites for nonacademic purposes. These habitual behaviors can distract students from their academic work, adversely affect their academic performance, social interactions, and sleep duration, and lead to a sedentary lifestyle and physical inactivity, which in turn can render them vulnerable to non-communicable diseases and mental health problems.

In this article, we have built a prototype of a decentralized IoT based biometric face detection framework for cities that are under lockdown during COVID-19 outbreaks. To impose restrictions on public movements, we have utilized face detection using three-layered edge computing architecture. We have built a deep learning framework of multi-task cascading to recognize the face. For the face detection proposal we have compared with the state of the art methods on various benchmarking dataset such as FDDB and WIDER FACE. Furthermore, we have also conducted various experiments on latency and face detection load on threelayer and cloud computing architectures. It shows that our proposal has an edge over cloud computing architecture.

This paper deals with the necessary steps to be followed in upgrading the traditional e-Learning system to the new Adaptive e-Learning system. The framework, which is an important part of the implementation, is also covered in the paper. Before implementing the adaptive learning system, the existing system needs to be reviewed. A survey was conducted to find practical feedback from both the users: students and instructors to recognize their belief about the current e-Learning system. Results are discussed to frame the strategy to implement an adaptive learning system in which the issues of the current system can be addressed. To confirm the feedback, a report from the university LMS has also been taken into account and talked about. There are many open sources available in the market for the implementation of the adaptive e-learning. Along with the conceptual model, various popular LMS has also been discussed. A detailed comparison has also been exercised to identify their features. This paper can help the readers to derive all the components of a conceptual model for an adaptive e-Learning system and choose a suitable LMS for the actual implementation of the model.

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