Search Results for author:
Found 17 papers, 7 papers with code
FFAD: A Novel Metric for Assessing Generated Time Series Data Utilizing Fourier Transform and Auto-encoder
While the Fr\'{e}chet Inception Distance (FID) serves as the standard metric for evaluating generative models in image synthesis, a comparable metric for time series data is notably absent.
Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions
We trained three well-known deep learning architectures--AlexNet, VGG16, and ResNet34 using transfer learning and compared and evaluated the overall performance of our models using true skill statistics (TSS) and Heidke skill score (HSS) and computed recall scores to understand the prediction sensitivity in central and near-limb regions for both X- and M-class flares.
Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks
In this work, we developed an attention-based deep learning model as an improvement over the standard convolutional neural network (CNN) pipeline to perform full-disk binary flare predictions for the occurrence of $\geq$M1. 0-class flares within the next 24 hours.
Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations
This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $\geq$M-class solar flares and evaluating its efficacy on both central (within $\pm$70$^\circ$) and near-limb (beyond $\pm$70$^\circ$) events, showcasing qualitative assessment of post hoc explanations for the model's predictions, and providing empirical findings from human-centered quantitative assessments of these explanations.
Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting
This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model.
Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods
This paper contributes to the growing body of research on deep learning methods for solar flare prediction, primarily focusing on highly overlooked near-limb flares and utilizing the attribution methods to provide a post hoc qualitative explanation of the model's predictions.
Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting
We utilized an equal weighted average ensemble of two base learners' flare probabilities as our baseline meta learner and improved the capabilities of our two base learners by training a logistic regression model.
Measuring Class-Imbalance Sensitivity of Deterministic Performance Evaluation Metrics
In this paper, we introduce an intuitive evaluation framework that quantifies metrics' sensitivity to the class imbalance.
Improving Solar Flare Prediction by Time Series Outlier Detection
While a number of machine-learning methods have been proposed to improve flare prediction, none of them, to the best of our knowledge, have investigated the impact of outliers on the reliability and those models' performance.
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods
We incorporated 24 Feature Subset Selection (FSS) algorithms, including multivariate and univariate, supervised and unsupervised, wrappers and filters.
Multiscale IoU: A Metric for Evaluation of Salient Object Detection with Fine Structures
General-purpose object-detection algorithms often dismiss the fine structure of detected objects.
Towards Synthetic Multivariate Time Series Generation for Flare Forecasting
We utilize a flare forecasting benchmark dataset, named SWAN-SF, and design two verification methods to both quantitatively and qualitatively evaluate the similarity between the generated minority and the ground-truth samples.
How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events
We present a case study of solar flare forecasting by means of metadata feature time series, by treating it as a prominent class-imbalance and temporally coherent problem.
Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data
This is in particular prevalent in interdisciplinary research where the theoretical aspects are sometimes overshadowed by the challenges of the application.
Toward Filament Segmentation Using Deep Neural Networks
Despite the known challenges in the identification and characterization of filaments by the existing module, which in turn are inherited into any other module that intends to learn from such outputs, Mask R-CNN shows promising results.
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission
We provide a large image parameter dataset extracted from the Solar Dynamics Observatory (SDO) mission's AIA instrument, for the period of January 2011 through the current date, with the cadence of six minutes, for nine wavelength channels.
Evaluating Preprocessing Strategies for Time Series Prediction Using Deep Learning Architectures
We propose a novel approach to combine state-of-the-art time series data processing methods, such as symbolic aggregate approximation (SAX), with very recently developed deep neural network architectures, such as deep recurrent neural networks (DRNN), for time series data modeling and prediction.
