Date of Award:

12-2024

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Soukaina Filali Boubrahimi

Committee

Soukaina Filali Boubrahimi

Committee

Shah Muhammad Hamdi

Committee

Mahdi Nasrullah Al-Ameen

Abstract

Solar flares are powerful eruptions of energy from the Sun that can cause disruptions to technology here on Earth, like communication systems, GPS, and power grids. To help manage these risks, it’s important to accurately identify and classify these solar flares before they cause problems. In our research, we focused on improving how we classify solar flares by looking at large sets of complex data collected over time. We used several techniques to find the most important factors that help us tell different types of solar flares apart. Each method has its strengths, so instead of relying on just one, we developed a new approach that combines the results from multiple methods. This combined approach, called an ensemble method, gives us a more accurate and reliable way to classify solar flares. By better understanding which factors are most important in predicting solar flares, our method can help improve the accuracy of space weather warnings and reduce the chances of unexpected disruptions to our technology. Our findings show that this new approach significantly improves the accuracy of solar flare classification, providing valuable insights that can help us better protect our technology and daily life from the impacts of solar activity.

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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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