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 Ensemble Learning and Vision Transformers 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 International Conference on Electronics, Computing and Communication Technologies, CONECCT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CONECCT65861.2025.11306644" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CONECCT65861.2025.11306644</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032701371?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032701371?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 plays a crucial role in understanding the universe, but deep learning models often stumble when faced with scarce labeled data. In our work, we address this gap in two key ways: first, by building a richly varied dataset from just 600 Hubble Space Telescope images and, through targeted augmentation, expanding it to 4,500 distinct training examples; and second, by introducing a hybrid learning strategy that marries transformer-driven feature extraction with gradient-boosted decision trees. We used benchmark standard convolutional architectures (ResNet-50, DenseNet-121) alongside the Data-Efficient Image Transformer (DeiT) and two novel hybrids-DeiT-RF and DeiT-XGBoost (DXg). In DXg, DeiT captures complex spatial patterns, an adaptive dimensionality reduction layer hones in on the most informative features, and XGBoost delivers the final classification. This fusion not only boosts accuracy across nebulae, galaxies, and star clusters but also enhances interpretability by revealing which transformer-derived features most influence the model's decisions. 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: 979-833150236-2; 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