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 Thakur, Utkarsh; Shanthan, Hubert; Arputharaj, Vijay Title A name given to the resource A Hybrid Ensemble Model Combining Machine Learning and Fuzzy Logic for Robust Stress Level Detection 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 the 9th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2025;pp.1956-1963 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECA66444.2025.11382770" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECA66444.2025.11382770</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034886621?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034886621?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 Thakur U., Christ (Deemed To Be University), Department of Computer Science, Central Campus, Karnataka, Bangalore, India; Shanthan H., Christ (Deemed To Be University), Department of Computer Science, Central Campus, Karnataka, Bangalore, India; Arputharaj V., Christ (Deemed To Be University), Department of Computer Science, Central Campus, Karnataka, Bangalore, India Description An account of the resource Accurate stress detection remains challenging because stress manifests differently across diverse populations. This research presents a novel hybrid approach that combines four machine learning classifiers - XGBoost, Random Forest, Support Vector Machine, and Logistic Regression - with an enhanced fuzzy logic system through weighted voting. The study employed a comprehensive dataset comprising over 1,100 samples containing more than 45 features that capture psychological, academic, social, physical, and environmental stress indicators. The results demonstrate that the ensemble achieves 87.27% accuracy with statistically significant improvements over individual models (p0.001). The approach maintains strong performance when 10% noise is injected or missing data is simulated, and provides well-calibrated probability estimates that could support clinical decision-making. 2025 IEEE. Subject The topic of the resource fuzzy logic; hybrid ensemble; machine learning; robustness; Stress detection; weighted voting Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159929-4; 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