Tuncay YİĞİT, Ali Hakan IŞIK, Mehmet BİLEN

Development of Web Based Courseware for Artificial Neural Networks

Artificial Neural Networks (ANN) are important data processing algorithms which are used for solving nonlinear problems. Through classical approaches, mathematical infrastructure and complex equations in ANN are difficult to understand. Interactive and multimedia-based courseware has the potential to overcome these difficulties. In this study, a web based educational courseware for ANN was developed to provide an effective and efficient learning environment so that the difficulties can be overcome. This interactive courseware was also enriched with animations and text-based course contents. In addition to this, the effects of ANN parameters’ changes were observed directly through graphical results. In this way, users can easily understand the fundamentals and working mechanism of ANN. Without using any commercial libraries, the courseware was developed with ASP.NET, an object-oriented programming language. The courseware supports file formats such as XML, TXT, and CSV so that it can co-operate with other software. “Balance and Scale” data set was used to evaluate the performance of the courseware. 0.9918 accuracy, 1 specificity and 1 sensitivity values were achieved. When this study is compared to previous studies, improvements in terms of visuality, understandability and interactivity can clearly be identified.

PDF

___

[1] Masters, T. Practical neural network recipes in C++, Morgan Kaufmann, (1993).
[2] Simon, H. Neural networks: a comprehensive foundation. New Delhi, Prentice-Hall of India, (2008).
[3] Blanton, H. “An introduction to neural networks for technicians, engineers, and other nonPhDs,” Proceedings of the 1997 Artificial Neural Networks in Engineering Conference, St.Louis, (1997).
[4] Fulcher, J. “Experience With Teaching a Graduate Neural Networks Course”, Computer Science Education, 3(3):297-314, (1992).
[5] Fulcher, J. “Laboratory Support for The Teaching of Neural Networks”, International Journal of Electrical Engineering Education, 35(1),:29-36, (1998).
[6] Roselló, E. G., Pérez-Schofield, J. B., Dacosta, J. G., & Pérez-Cota, M. “Neuro-Lab: A highly reusable software-based environment to teach artificial neural networks”, Computer Applications in Engineering Education, 11(2):93-102, (2003).
[7] MATLAB [Computer software]. Natick, MA: MathWorks, (2006).
[8] Statistica [Computer software]. Tulsa, OK: Statsoft, (1997).
[9] NeuroSolutions [Computer software]. NeuroDimension, (2015).
[10] Mathematica [Computer software]. Champaign (IL): Wolfram Research, (1997).
[11] JOONE [Computer software]. Paolo Marrone and the Joone team, (2015).
[12] Manic, M., Wilamowski, B., & Malinowski, A. “Internet based neural network online simulation tool”, 28th Annual Conference of the Industrial Electronics Society, Sevilla, 4(4):2870-2874, (2002).
[13] Venayagamoorty, G. K. “Teaching Neural Network Concepts and Their Learning Techniques”, Proceedings of the 2004 American Society for Engineering Education Midwest Section Conference, Pittsburg, (2004).
[14] Bayindir, R., Sesveren, O. “Design of Visual Interface for ANN Based Systems”, Pamukkale University Journal Of Engineering Sciences, 14:101-109, (2008).
[15] Ugur, A., Kinaci, A. C.” A web-based tool for teaching neural network concepts”, Computer Applications in Engineering Education, 18(3):449-457, (2009).
[16] Deperlioglu, O., Kose, U. “An educational tool for artificial neural networks”, Computers & Electrical Engineering, 37(3):392-402, (2011).
[17] Dragly, S. A., Hobbi Mobarhan, M., Våvang Solbrå, A., Tennøe, S., Hafreager, A., MaltheSørenssen, A., Fyhn, M., Hafting, T., Einevoll, G. T. “Neuronify: An Educational Simulator for Neural Circuits”, eNeuro, 4(2):17, (2017).
[18] Cope, A.J., Richmond, P., James, S.S. et al., “SpineCreator: a Graphical User Interface for the Creation of Layered Neural Models”, Neuroinform, 15-25, (2017).
[19] Yegnanarayana, B. Artificial neural networks. New Delhi: Prentice-Hall of India, (2006).
[20] Internet: Bache, K., &Lichman, M. Uci Machine Learning Reposity, Balance and Scale Data Set, http://archive.ics.uci.edu/ml/datasets/balance+scale , (2017).