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Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.

<p class="0abstract">There are several reasons why most of the universities implement E-learning. The extent of E-learning programs is being offered by the higher educational institutes in the UAE are evidently expanding. However, very few studies have been carried out to validate the process of how E-learning is being accepted and employed by university students. The study involved a sample of 365 university students. To describe the acceptance process, the Structural Equation Modeling (SEM) method was used. On the basis of the technology acceptance model (TAM), the standard structural model that involved E-learning Computer Self-Efficacy, Social Influence, Enjoyment, System Interactivity, Computer Anxiety, Technical support, Perceived Usefulness, Perceived Ease of Use, Attitude, and Behavioral Intention to Use e-learning, was developed. The findings showed that TAM served as a suitable theoretical tool to comprehend the acceptance of e-learning by users. The most critical construct to explain the causal process employed in the model was E-learning Computer Self-Efficacy, Social Influence, Enjoyment, System Interactivity, Computer Anxiety, Technical support, Perceived Usefulness, Perceived Ease of Use, Attitude, followed by Behavioral Intention to Use e-learning. Practical implications are offered by the outcomes for decision makers, professionals and developers in how effective E-learning systems can be implemented properly.</p>

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