Prediction of Content Error in Cloud Computing based on Perceptron Neural Network and Radial Basis Function (RBF)
Cloud computing is a general term for referring to anything that requires the provision of services hosted on the Internet. With advance in cloud computing, data error prediction has become an important factor in cloud computing, so that predicting cloud errors is the most important barrier to the speed and development of cloud computing software. The purpose of the study was to predict content error in cloud computing based on perceptron neural network and RBF. In this research, a data set of 10,000 records has been used, including 23 features that were generally divided into three categories, properties related to public security and properties related to the content of the data and the characteristics required for cloud storage. In this research, the KFOLD method was selected as the allocation of training and testing the data. The method of data selection was random. The software used in this research was MATLAB, 2015. The results showed that the performance of the RBF system was better than the other method in the two predictive systems.