The SWAN-SF dataset is now fully preprocessed, optimized, and ready for binary classification tasks. Our team is excited to release the enhanced version of the SWAN-SF dataset across all five partitions.

Accurate solar flare prediction is crucial for mitigating risks to astronauts, space equipment, and satellite communication. Our study enhances prediction accuracy using advanced preprocessing and a novel deep learning-based classifier called ContReg on the SWAN-SF dataset, outperforming previous methods.

FlaPLeT is an open-source, full-stack web platform that enables end-to-end machine learning workflows for solar flare prediction through an interactive user interface. Its modular design also provides a general blueprint for AI-driven web services, such as large language model applications, where asynchronous task execution is essential.

Study solar flares and other phenomena using data from ISRO's Aditya-L1 space observatory

This repository contains code and data for predicting solar flare energy ranges using machine learning, based on NASA's RHESSI mission data. It includes preprocessing of FITS files into a unified CSV dataset and implements models like Gradient Boosting, Random Forest, and Decision Tree classifiers, achieving accuracies up to 87%.

These notebooks provide a comprehensive workflow, from start to finish, for processing and analyzing the SWAN-SF dataset. They include detailed steps for reading the dataset files, performing full preprocessing, and executing classification.

By analyzing the results of this project, we can identify the most effective data preprocessing techniques and classifiers, ultimately leading to the development of a highly accurate solar flare prediction model.

On March 7, 2012, active region 11429 produced an X5.4 flare (00:02 UTC).

CONTREX, a novel contrastive representation learning approach for multivariate time series data.