Investigation of Solar Flare Classification to Identify Optimal Performance
Kakde, Aditya (University of Petroleum and Energy Studies)
Sharma, Durgansh (University of Petroleum and Energy Studies)
Kaushik, Bhavana (University of Petroleum and Energy Studies)
Arora, Nitin (Indian Institute of Technology Roorkee)

Date:	2021
Abstract:	When an intense brightness for a small amount of time is seen in the sun, then we can say that a solar flare emerged. As solar flares are made up of high energy photons and particles, thus causing the production of high electric fields and currents and therefore results in the disruption in space-borne or ground-based technological system. It also becomes a challenging task to extract its important features for prediction. Convolutional Neural Networks have gain a significant amount of popularity in the classification and localization tasks. This paper has given stress on the classification of the solar flares emerged on different years by stacking different convolutional layers followed by max pooling layers. From the reference of Alexnet, the pooling layer employed in this paper is the overlapping pooling. Also two different activation functions that are ELU and CReLU have been used to investigate how many number of convolutional layers with a particular activation function provides with the best results on this dataset as the size of the dataset in this domain is always small. The proposed investigation can be further used in a novel solar prediction systems.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Computer vision ; Classification and clustering ; Applications
Published in:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 20 Núm. 1 (2021) , p. 28-41 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v20-n1-Kakde
DOI: 10.5565/rev/elcvia.1274

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