This study aims to classify solar flares using machine learning techniques based on vector magnetic field data from 8,874 solar records collected between May 2010 and December 2019. It Utilizes Scikit-Learn toP implement machine learning methods in Python. Achieved an accuracy of 99.4% of predicting a solar flare using key features.
Developed a cross-platform mobile application that helps users decide on movies to watch. Connects with TMDB API to fetch movie information and utilizes Firebase as backend. Also implemented friendly polling feature. 
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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