The internet of medical things (IoMT) is playing a substantial role in improving the health and providing medical facilities to people around the globe. With the exponential growth, IoMT is having a huge influence in our everyday life style. Instead of going to the hospital, patient clinical related data is remotely observed and processed in a real time data system and then is transferred to the third party for future use such as the cloud. IoMT is intensive data domain with a continuous growing rate which means that we must secure a large amount of sensitive data without being tampered. Blockchain is a temper proved digital ledger which provides us peer-to-peer communication. Blockchain enables communication between non-trusting members without any intermediary. In this paper we first discuss the technology behind Blockchain then propose IoMT based security architecture employing Blockchain to ensure the security of data transmission between connected nodes.

Human respiratory infections caused by coronaviruses can range from mild to deadly. Although there are numerous studies on coronavirus disease 2019 (COVID-19), few have been published on its Omicron variant. In order to remedy this deficiency, this study undertook a bibliometric analysis of the publishing patterns of studies on the Omicron variant and identified hotspots. Automated transportation, environmental protection, improved healthcare, innovation in banking, and smart homes are just a few areas where machine learning has found use in tackling complicated problems. The sophisticated Scopus database was queried for papers with the term “Omicron” in the title published between January 2020 and June 2022. Microsoft Excel 365, VOSviewer, Bibliometrix, and Biblioshiny from R were used for a statistical analysis of the publications. Over the study period, 1917 relevant publications were found in the Scopus database. Viruses was the most popular in publications for Omicron variant research, with 150 papers published, while Cell was the most cited source. The bibliometric analysis determined the most productive nations, with USA leading the list with the highest number of publications (344) and the highest level of international collaboration on the Omicron variant. This study highlights scientific advances and scholarly collaboration trends and serves as a model for demonstrating global trends in Omicron variant research. It can aid policymakers and medical researchers to fully grasp the current status of research on the Omicron variant. It also provides normative data on the Omicron variant for visualization, study, and application.

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COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea, Japan, Saudi Arabia, and Pakistan. Significant environmental and non-environmental features were taken as the input dataset, and confirmed COVID-19 cases were taken as the output dataset. A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables. The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases. However, age and the human development index had a negative influence on the cases. The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases. During training, the binary classification model was highly accurate, with a Root Mean Square Error (RMSE) of 0.91. Likewise, the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate (RMSE = 0.95239) when predicting the number of confirmed COVID-19 cases in an area. However, dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches, like Artificial Intelligence (AI). In this study, an SSLPNN model has been trained to fit public health associated data into an appropriate class, allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected

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