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.

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

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.

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