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 Sajid, Sadiya; Das, Shreyashi; Kokatnoor, Sujatha Arun Title A name given to the resource Machine Learning and Ensemble Models for Hazardous Asteroids Prediction 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 Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.145-155 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4883-2_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4883-2_12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010645162?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010645162?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 Sajid S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Das S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India Description An account of the resource The prediction of hazardous asteroids near Earth is critical for planetary defense and avoiding any possible impacts. This study investigates the use of five ensemble models, XGBoost, Gradient Boost, CatBoost, Voting Classifier, and Random Forest, as well as four standalone machine learning models, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and Decision Tree, to improve the prediction accuracy of identifying potentially hazardous asteroids. With 92% accuracy and 91% precision, Random Forest performed better than other models. It was the preferred choice for predicting hazardous asteroids because of its capacity to handle the hugedatasetwith efficiency and its ability tomanage non-linear data patterns. Additionally, XGBoost and CatBoost providedhigh accuracy at lowcomputational costs, making them suitable for real-time monitoring. KNN, on the other hand, did not perform well, and SVM's high processing time made it less useful. In particular, Random Forest ensemble modelperformed better at predicting hazardous asteroids. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Asteroids prediction; CatBoost; Decision trees; Gradient boost; Hazardous objects; K-Nearest Neighbors (KNN); Logistic regression; Nearest Earth Objects (NEOs); Random forest; Support Vector Machine (SVM); Voting classifier; XGBoost Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964882-5; 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