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With the recent progress of information technology, the use of networkedinformation systems has rapidly expanded. Electronic commerce and electronicpayments between banks and companies, and online shopping and social networkingservices used by the general public are examples of such systems. Therefore, inorder to maintain and improve the dependability of these systems, we areconstructing a failure database from past failure cases. When importing newfailure cases to the database, it is necessary to classify these casesaccording to failure type. The problems are the accuracy and efficiency of theclassification. Especially when working with multiple individuals, unificationof classification is required. Therefore, we are attempting to automateclassification using machine learning. As evaluation models, we selected themultilayer perceptron (MLP), the convolutional neural network (CNN), and therecurrent neural network (RNN), which are models that use neural networks. As aresult, the optimal model in terms of accuracy is first the MLP followed by theCNN, and the processing time of the classification is practical.

Attempt to Predict Failure Case Classification in a Failure Database by using Neural Network Models