Development of Web Based Courseware for Artificial Neural Networks

Year 2019, Volume: 32 Issue: 4, 1138 - 1148, 01.12.2019

Mehmet Bılen , Ali Hakan Isık , Tuncay Yıgıt

https://doi.org/10.35378/gujs.473450

Cited By: 1

Abstract

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. 

Keywords

Artificial Neural Networks, E-Learning, Courseware

References

• Sağıroğlu, Ş., Beşdok, E., &Erler, M. Mühendislikte yapa yzeka uygulamaları-1: Yapay sinir ağları. Kayseri: UfukKitapKırtasiye-Yayıncılık, (2003).
• Simon, H. Neural networks: a comprehensive foundation. New Delhi: Prentice-Hall of India, (2008).
• H. Blanton, An introduction to neural networks for technicians, engineers, and other non-PhDs,Proceedings of the 1997 Artificial Neural Networks in Engineering Conference, ANNIE'97, St.Louis, MO, (1997).
• Fulcher, J. Experience With Teaching a Graduate Neural Networks Course. Computer Science Education, 3(3), 297-314. doi:10.1080/0899340920030308, (1992).
• Fulcher, J. Laboratory Support for The Teaching of Neural Networks. International Journal of Electrical Engineering Education,35(1), 29-36. doi:10.1177/002072099803500103, (1998).
• 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. doi:10.1002/cae.10042, (2003).
• MATLAB [Computer software]. Natick, MA: MathWorks, (2006).
• Statistica [Computer software]. Tulsa, OK: Statsoft, (1997).
• Mathematica [Computer software]. Champaign (IL): Wolfram Research, (1997).
• NeuroSolutions [Computer software]. NeuroDimension, (2015).
• NeuroSolutions [Computer software]. NeuroDimension, (2015).
• Manic, M., Wilamowski, B., & Malinowski, A. (n.d.). Internet based neural network online simulation tool. IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02. doi:10.1109/iecon.2002.1182851, (2002).
• Venayagamoorty, G. K. Teaching Neural Network Concepts and Their Learning Techniques. Proceedings of the 2004 American Society for Engineering Education Midwest Section Conference, 20 September-1 October. Pittsburg,(CD-ROM), (2004).
• Bayındır, R., Sesveren, Ö. YSA Tabanlı Sistemler için Görsel Bir Arayüz Tasarımı. PamukkaleÜniversitesiMühendislikFakültesiMühendislikBilimleriDergisi, 14, 101-109., (2008).
• Ugur, A., &Kinaci, A. C. A web-based tool for teaching neural network concepts. Computer Applications in Engineering Education,18(3), 449-457. doi:10.1002/cae.2018, (2009).
• Deperlioglu, O., &Kose, U. An educational tool for artificial neural networks. Computers & Electrical Engineering,37(3), 392-402, (2011).
• Yegnanarayana, B. Artificial neural networks. New Delhi: Prentice-Hall of India, (2006).
• Bache, K., &Lichman, M. (n.d.). Uci Machine Learning Reposity : Balance and Scale Data Set. Retrieved December 02, 2017, from http://archive.ics.uci.edu/ml/index.php doi:10.1016/j.compeleceng.2011.03.010, (2017).