Keywords:Cloud computing, Cloud computing business framework, quality of service, quality of experience.Abstract: This paper discusses the adoption of cloud computing in education. It emphasizes the view that cloud computing is vital in the education sector because of its ability to reduce the overall costs of IT infrastructure installation and maintenance, improvement of efficiency, and the sharing of IT resources among students. The flexibility of cloud computing and its reliability makes it more appropriate for use in the educational environment. The Leeds Beckett University cloud project utilizes the SAS Educational Value-Added Assessment System, which gives lecturers the opportunity to deliver accurate content to students while monitoring their progress. Contemporary educational institutions should look forward to improve their research and education through cloud computing.

Abstract-The paper discusses the issues of risk analysis of Business Intelligence on the basis of Cloud platforms. The study gives an account on various aspects of the issue such as benefits and risks, financial appliance, and a factual process of data analysis. The paper attempts to address the issue in terms of empirical knowledge as long as numerous organizations face difficulties concerning appropriate application of Business Intelligence in the Cloud environment for purposes of risk forecasting and assessment.

Globally, COVID-19 already emerged in around 170 million confirmed cases of infected people and, as of May 31, 2021, affected more than 3.54 million deaths. This pandemic has given rise to numerous public health and socioeconomic issues, emphasizing the significance of unraveling the epidemic's history and forecasting the disease's potential dynamics. A variety of mathematical models have been proposed to obtain a deeper understanding of disease transmission mechanisms. Machine Learning (ML) models have been used in the last decade to identify patterns and enhance prediction efficiency in healthcare applications. This paper proposes a model to predict COVID-19 patients admission to the intensive care unit (ICU). The model is built upon robust known classification algorithms, including classic Machine Learning Classifiers (MLCs), an Artificial Neural Network (ANN) and ensemble learning. This model's strength in predicting COVID-19 infected patients is shown by performance analysis of various MLCs and error metrics. Among other used ML models, the ANN model resulted in the highest accuracy, 97.9% over other models. Mean Squared Error showed that the ANN method had the lowest error (0.0809). In conclusion, this paper could be beneficial to ICU staff to predict ICU admission based on COVID-19 patients' clinical characteristics.

Abstract:This paper explores security issues of storage in the cloud and the methodologies that can be used to improve the security level. This study is concluded with a discussion of current problems and the future direction of cloud computing. Big data analysis can also be classified into memory level analysis, business intelligence (BI) level analysis, and massive level analysis. This research paper is based on cloud computing security and data storage issues that organizations face when they upload their data to the cloud in order to share it with their customers. Most of these issues are acknowledged in this paper, and there is also discussion of the various perspectives on cloud computing issues.