Cloud computing is the on-demand access to computer resources such as applications, servers, data storage, development tools, networking capabilities, and other resources that are held in a remote data center maintained by a cloud services provider(CSP) and accessible over the internet. The limited resources and higher time consumption are the major issues faced by cloud users. The appropriate selection of virtual machines (VMs) is used to maximize cloud resource usage and reduce task execution time for the clients (patients, physicians, etc.). In this article, we have introduced an efficient cloud-based healthcare services paradigm (HCS). The opposition-based Laplacian equilibrium optimizer (O-LEO) algorithm is used to select optimal VMs in which the maximization of resource utilization and minimization of task execution time is performed. Additionally, the boosted support vector machine (SVM) effectively predicts chronic kidney disease (CKD) thereby ensuring better prediction results. The CloudSim platform is used as the implementation platform of the proposed method. The overall time taken by the O-LEO-based CloudSim is less than the standard Cloud Sim model to create three cloudlets which improve the system efficiency by 6%. When compared with the existing techniques, both the O-LEO and boosted SVM classifier outperforms superior performances.

The aim of each enterprise is to achieve high revenue from the business and to stay in a high position from their competitors. To archive high revenue and high position from competitors the need of understanding the business consumers is a crucial one. However the firm business is completely dependent on the consumers the efficient analysis of consumers within the enterprises makes to achieve the business to high position. To perform effective consumer analysis, in this study different machine learning is studied and experimented. ML classifiers make to understand in-depth analysis about the consumer data and further enables to plan wise decision strategies to enhance the business revenue and consumer satisfaction intelligently. The use of different ML classifiers is to sort out how the customer prediction outcome changes accordingly to the ML classifier is applied. This makes to find the best ML classifier for the consumer dataset applied in this study. The experimental procedure is performed using different ML classifiers and the outcome achieved is captured and projected using various validity scores. This work applies different ML classifiers like K-NN, C4.5, Random Forest, Random Tree, LR, MLP and NB for customer analysis. The empirical results illustrate the C4.5 model achieves better accuracy prediction compare to other ML classifiers and also compared with the time complexity NB model works efficiently with running time.

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In recent years, augmented reality & virtual reality (AR/VR) has been seen as a technology with huge potential to give companies an operational and competitive advantage. But despite the use of new technologies, companies still face challenges and cannot immediately achieve performance. In addition, companies must adopt attractive technologies and analyse the areas where these technologies can be adopted, which emphasizes the importance of establishing appropriate e-government practices. This study explores how AR/VR governance is applied to support the development of sustainable AR/VR applications and analyse the negative impacts based on application domains. The study illustrates what practices are used to obtain information that helps engage technologies by overcoming obstacles with recommended actions that lead to desired results. The research helps to identify the most important scopes and limitations of AR/VR in e-governance.