Solar flare M-class prediction using artificial intelligence techniques
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 neu...
| Main Authors: | , , , |
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| Format: | Article |
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Asian Research Publishing Network
2015
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| _version_ | 1825721269270609920 |
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| author | Zavvari, A. Islam, M.T. Anwar, R. Abidin, Z.Z. |
| author_facet | Zavvari, A. Islam, M.T. Anwar, R. Abidin, Z.Z. |
| author_sort | Zavvari, A. |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:47:49Z |
| format | Article |
| id | um.eprints-19281 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:47:49Z |
| publishDate | 2015 |
| publisher | Asian Research Publishing Network |
| record_format | dspace |
| spelling | um.eprints-192812018-09-19T03:08:36Z http://eprints.um.edu.my/19281/ Solar flare M-class prediction using artificial intelligence techniques Zavvari, A. Islam, M.T. Anwar, R. Abidin, Z.Z. Q Science (General) QC Physics TK Electrical engineering. Electronics Nuclear engineering 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. Asian Research Publishing Network 2015 Article PeerReviewed 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, https://ukm.pure.elsevier.com/en/publications/solar-flare-m-class-prediction-using-artificial-intelligence-tech |
| spellingShingle | Q Science (General) QC Physics TK Electrical engineering. Electronics Nuclear engineering Zavvari, A. Islam, M.T. Anwar, R. Abidin, Z.Z. Solar flare M-class prediction using artificial intelligence techniques |
| title | Solar flare M-class prediction using artificial intelligence techniques |
| title_full | Solar flare M-class prediction using artificial intelligence techniques |
| title_fullStr | Solar flare M-class prediction using artificial intelligence techniques |
| title_full_unstemmed | Solar flare M-class prediction using artificial intelligence techniques |
| title_short | Solar flare M-class prediction using artificial intelligence techniques |
| title_sort | solar flare m class prediction using artificial intelligence techniques |
| topic | Q Science (General) QC Physics TK Electrical engineering. Electronics Nuclear engineering |
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