Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Varghese, Nisha; Justin, Jesvin K; Kezya Steffyn, S. Title A name given to the resource Celestial Image Classification Using Attention And Boosting Mechanism Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SPACE65882.2025.11170481" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SPACE65882.2025.11170481</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018457544?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018457544?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Varghese N., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Justin J.K., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Kezya Steffyn S., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India Description An account of the resource Astronomical image classification is vital in the comprehension of celestial objects, but deep learning models are severely challenged by the lack of labeled datasets. The novelty of the study is two-fold - the development of the dataset and a hybrid learning method that combines both transformer-based feature extraction and gradient-boosted decision trees to improve classification performance for celestial image classification. This study is a comparison of CNNs, transformers, and hybrid models in nebulae, galaxy, and star cluster classification using the dataset collected from the Hubble Space Telescope image archive. Through progressive data augmentation, the dataset was augmented from 603 images to 4,500 high-diversity training samples to enhance model generalization. This research explores various architectures, including ResNet-50, DenseNet-121, EfficientNetV2-S, DeiT (Data-Efficient Image Transformer), and hybrid models like DeiT-RF (Data-Efficient Image Transformer - Random Forest) and DeiT-XGBoost (DXg). DXg brings a novel fusion mechanism in which DeiT learns high-level spatial representations, adaptive dimensionality reduction fine-tunes feature selection, and XGBoost best classifies celestial objects. Such a unique combination of transformers and gradient boosting improves interpretability without sacrificing state-of-the-art performance. 2025 IEEE. Subject The topic of the resource Astronomical Image Classification; Convolutional Neural Networks (CNNs); Data Augmentation; Deep Learning; Dimensionality Reduction; Ensemble Learning; Hybrid Models; Transformers; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151552-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online