Cloud computing is a rapidly growing technology due to its highly flexible uses and applications. It also has other features such as simplicity, quick data access and reduced data storage costs. Consequently, it has been widely used by many organizations. This widespread use of cloud computing among organizations causes many security issues. Moreover, cloud computing layers are likely to be jeopardized by many security risks such as privileged user access, data location, data segregation, and data recovery. This paper aims to prepare an ample debate of a literature review-based studies that provided important insights to researchers in the scope of security cloud computing. The researcher applied a relevant set of keywords. These keywords are limited to the title, abstract and keywords search archives published between 2010 and June 2017. The database search returned a total of 308 publications. In addition, we conducted backward-forward searches from the reference lists of relevant, quality previous works on the security framework in public cloud computing studies. Then, the researcher filtered the publications to only full text access articles that were written in English only. Finally, this study obtained a total of 53 publications. The findings of this paper address many important points such as authentication, data segregation, and encryption which are considered as the top concerns in security cloud computing. In addition, most of authentication layer is considered password as a prime criterion in determining authorizes user.
Performance evaluation can promote the continuous improvement of the laboratories in a college. It is necessary to take into account the scientific evaluation method during the process of the performance evaluation. In this paper, a performance evaluation method based on the fusion of the decision tree and BP neural network is presented. In detail, the decision tree model is used to select performance evaluation indexes with high weight. The BP neural network was adopted aiming to reduce the impact of assessment prediction of classification by non-core factors. First, the data were pre-processed by trapezoidal membership function. Then, the decision tree was generated by the C4.5 algorithm to select the evaluation indexes with high weight. Then, the BP neural network was trained with as many samples as possible by evaluation indexes; it possesses experts’ experience which can be used to predict the performance evaluation results. The method overcomes the shortages of the separate model, eliminates the disturbance of human factors and improves the accuracy of the evaluation. Experiments show that the model is feasible and effective in performance evaluation of college laboratories. The outcomes of this work can provide a scientific evaluation method for people such as researchers, college administrators and laboratory managers. Also, this paper will help them to improve the management of laboratories and provide them with decision references for constructing the laboratories.
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