# Solar Active Regions Detection Via 2D Circular Kernel Time Series Transformation, Entropy and Machine Learning Approach

arXiv: https://arxiv.org/abs/2306.08270 · HTML: https://mlsanity.com/paper/2306.08270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Flare Prediction Using Photospheric and Coronal Image Data](https://mlsanity.com/paper/1708.01323/) (2017)
- [Enhancing SDO/HMI images using deep learning](https://mlsanity.com/paper/1706.02933/) (2017)
- [A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks](https://mlsanity.com/paper/2107.08399/) (2021)
- [Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing](https://mlsanity.com/paper/2210.06901/) (2022)
- [A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission](https://mlsanity.com/paper/1906.01062/) (2019)
- [Automatic Detection of Occulted Hard X-ray Flares Using Deep-Learning Methods](https://mlsanity.com/paper/2101.11550/) (2021)
- [A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science](https://mlsanity.com/paper/2306.01991/) (2023)

## Abstract (arXiv)

This study proposes an enhancement to the existing method for detecting Solar Active Regions (ARs). Our technique tracks ARs using images from the Atmospheric Imaging Assembly (AIA) of NASA's Solar Dynamics Observatory (SDO). It involves a 2D circular kernel time series transformation, combined with Statistical and Entropy measures, and a Machine Learning (ML) approach. The technique transforms the circular area around pixels in the SDO AIA images into one-dimensional time series (1-DTS). Statistical measures (Median Value, Xmed; 95th Percentile, X95) and Entropy measures (Distribution Entropy, DisEn; Fuzzy Entropy, FuzzyEn) are used as feature selection methods (FSM 1), alongside a method applying 1-DTS elements directly as features (FSM 2). The ML algorithm classifies these series into three categories: no Active Region (nARs type 1, class 1), non-flaring Regions outside active regions with brightness (nARs type 2, class 2), and flaring Active Regions (ARs, class 3). The ML model achieves a classification accuracy of 0.900 and 0.914 for Entropy and Statistical measures, respectively. Notably, Fuzzy Entropy shows the highest classification accuracy (AKF=0.895), surpassing DisEn (AKF=0.738), X95 (AKF=0.873), and Xmed (AKF=0.840). This indicates the high effectiveness of Entropy and Statistical measures for AR detection in SDO AIA images. FSM 2 captures a similar distribution of flaring AR activities as FSM 1. Additionally, we introduce a generalizing characteristic of AR activities (GSA), finding a direct agreement between increased AR activities and higher GSA values. The Python code implementation of the proposed method is available in supplementary material.