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 Shrivastava, Abhay; George, Jossy P; Paul Alapatt, Bosco Title A name given to the resource Geochemical Data Exploration using Machine Learning Methods 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 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.484-489 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICICV64824.2025.11085828" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICV64824.2025.11085828</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013073769?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013073769?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 Shrivastava A., Christ University, India; George J.P., Christ University, India; Paul Alapatt B., Christ University, India Description An account of the resource This study introduces a novel ensemble model combining Support Vector Machine (SVM) and Gradient Boosting algorithm (GBC). The model's performance is compared with the two single layered model namely K-Nearest Neighbors (KNN) and Gaussian Naive Bayes (GNB) on a publicly available dataset. Further, Performance is measured using standard metrics such as accuracy, precision, and recall. To have the excellence in detection of types of rocks based on its properties this research explores the stacking approach, contributing in the field of geological studies and also for future exploration making it effective and efficient in identification of mineral deposits. 2025 IEEE. Subject The topic of the resource Classification; Gaussian Nae Bayes; Geochemical exploration; Gradient Boosting algorithm; igneous rocks; KNN; Machine learning; Stacking and ensemble; SVM Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151175-3; 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