Zavvari, A. and Islam, M.T. and Anwar, R. and Abidin, Z.Z. (2015) Solar flare M-class prediction using artificial intelligence techniques. Journal of Theoretical and Applied Information Technology, 74 (1). pp. 63-67. ISSN 1992-8645,
Full text not available from this repository.Abstract
Currently, astronomical data have increased in terms of volume and complexity. To bring out the information in order to analyze and predict, the artificial intelligence techniques are required. This paper aims to apply artificial intelligence techniques to predict M-class solar flare. Artificial neural network, support vector machine and naïve bayes techniques are compared to define the best prediction performance accuracy technique. The dataset have been collected from daily data for 16 years, from 1998 to 2013. The attributes consist of solar flares data and sunspot number. The sunspots are a cooler spot on the surface of the sun, which have relation with solar flares. The Java-based machine learning WEKA is used for analysis and predicts solar flares. The best forecasted performance accuracy is achieved based on the artificial neural network method.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Artificial intelligence techniques; Naïve bayes; Neural networkSolar flare; Support vector machine |
| Subjects: | Q Science > Q Science (General) Q Science > QC Physics T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Science > Department of Physics |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 19 Sep 2018 03:08 |
| Last Modified: | 19 Sep 2018 03:08 |
| URI: | http://eprints.um.edu.my/id/eprint/19281 |
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