Abstract:In the present work, an artificial neural network (ANN) model was developed to predict frictional performance of a polymeric composite. The experimental dataset at different applied loads (30-100 N), sliding speeds (300-700 r/min), and up to 10 min of sliding duration was used to train the model. The ANN model was trained with a large volume of experimental data (7389 sets). In addition to that, fibre mat orientation was considered in ANN development. Various configurations with different functions of training were used to find the optimal model. As a result of this work, single-layered models with large number of neurons showed high accuracy, up to 90 per cent in prediction, when trained with the Levenberg-Marqurdt function.

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User authentication depends largely on the concept of passwords. However, users find it difficult to remember alphanumerical passwords over time. When user is required to choose a secure password, they tend to choose an easy, short and insecure password. Graphical password method is proposed as an alternative solution to text-based alphanumerical passwords. The reason of such proposal is that human brain is better in recognizing and memorizing pictures compared to traditional alphanumerical string. Therefore, in this paper, we propose a conceptual framework to better understand the user performance for new high-end graphical password method. Our proposed framework is based on hybrid approach combining different features into one. The user performance experimental analysis pointed out the effectiveness of the proposed framework.

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