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 Oommen, Julia; Ramasamy, Gobi Title A name given to the resource Advancing Astronomical Science - Machine Learning-Based Classification of Variable Stars for Scientific Innovation and Research 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 Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ETCC65847.2025.11108518" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETCC65847.2025.11108518</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015753339?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015753339?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 Oommen J., Department of Computer Science, Christ (Deemed to be University), Bangalore, India; Ramasamy G., Department of Computer Science, Christ (Deemed to be University), Bangalore, India Description An account of the resource Variable star classification is an important part of astrophysics and gives astrophysicists a way of studying stellar evolution, structure and dynamics. Due to the availability of large scale surveys such as Gaia DR3, machine learning techniques are used in automation of the classification process. In this study, RF, SVM, MLP and XGBoost (XGBClassifier) models are evaluated for classification of variable stars. The data set used in this work was collected from Gaia DR3 using Astroquery and the ability of these models is evaluated for different star classes. The result shows that the XGBoost had the best accuracy of 91% compared to RF (89.98%), MLP (88%) and SVM (83%). A comparison of various metrics such as precision, recall and F1-score of each method is also provided to address their strengths and weaknesses. This work further emphasises the need of sophisticated machine learning techniques in astrophysical data analysis and discusses problems of certain kinds of variable star classification. KeywordsGaia, variable stars, Classification, Machine Learning, Random Forest, XGBoost, Multi-Layer Perceptron, Support Vector Machine. 2025 IEEE. Subject The topic of the resource Classification; Gaia; Machine Learning; Multi-Layer Perceptron; Random Forest; Support Vector Machine; variable stars; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152476-0; 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