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Keystroke dynamics analysis using machine learning methods

Shabliy Nataliya, Lupenko Serhii, Lutsyk Nadiia, Yasniy Oleh, Malyshevska Olha

Applied Computer Science

2021

Vol. 17, no 4

75--83

The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network.

Manufacturing lead time prediction for extrusion tools with the use of Neural Networks

Sajko Nika, Kovacic Simon, Ficko Mirko, Palcic Iztok, Klancnik Simon

Management and Production Engineering Review

2020

Vol. 11, No. 3

48--55

Due to fast-paced technical development, companies are forced to modernise and update their equipment, as well as production planning methods. In the ordering process, the customer is interested not only in product specifications, but also in the manufacturing lead time by which the product will be completed. Therefore, companies strive towards setting an appealing but attainable manufacturing lead date. Manufacturing lead time depends on many different factors; therefore, it is difficult to predict. Estimation of manufacturing lead time is usually based on previous experience. In the following research, manufacturing lead time for tools for aluminium extrusion was estimated with Artificial Intelligence, more precisely, with Neural Networks. The research is based on the following input data; number of cavities, tool type, tool category, order type, number of orders in the last 3 days and tool diameter; while the only output data are the number of working days that are needed to manufacture the tool. An Artificial Neural Network (feed-forward neural network) was noted as a sufficiently accurate method and, therefore, appropriate for implementation in the company