-Internet of Medical Things (IOMT) is playing vital role in healthcare industry to increase the accuracy, reliability and productivity of electronic devices. Researchers are contributing towards a digitized healthcare system by interconnecting the available medical resources and healthcare services. As IOT converge various domains but our focus is related to research contribution of IOT in healthcare domain. This paper presents the peoples contribution of IOT in healthcare domain, application and future challenges of IOT in term of medical services in healthcare. We do hope that this work will be useful for researchers and practitioners in the field, helping them to understand the huge potential of IoT in medical domain and identification of major challenges in IOMT. This work will also help the researchers to understand applications of IOT in healthcare domain. This contribution will help the researchers to understand the previous contribution of IOT in healthcare industry.
Social network analysis has gained importance in the current era of online networks. Companies nowadays are using the social network analysis tools and techniques to enhance their business positions. Analyzing the social networks provide the massive amount of useful information for the business. In this paper, we have analyzed the online social networks of leading mobile phone company Huawei. The importance of any individual (Node) can be determined by the participation and behavior of the node in the OSN. Centrality measure is the key factor to visualize any social network. Centrality in term includes Density, Closeness Centrality, Degree Centrality, Eigenvector Centrality and some other metrics such as Coefficient Clustering, Page Rank. These metrics can be used to determine the most important node (key player) in a network. Visualization of the social network is a complicated task as it is expanded every minute as thousands of users join the social network daily. We have put an effort to analyze the social network and determine the influential node with the calculation of above-mentioned metrics. The overall aim of our research is to improve the business by identifying the most influential node. The experimental results and a detailed quantitative analysis show that this is the more efficient and effective way to detect the influential nodes in an online social network.
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