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 Article Faculty Publications -Articles 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 Govinda, Sangeetha; Vincent, Agnes Nalini; Babu, Merwa Ramesh Title A name given to the resource Novel artificial intelligence-based ensemble learning for optimized software quality 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 IAES International Journal of Artificial Intelligence;Volume;14;Issue;3;pp.1820-1828 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijai.v14.i3.pp1820-1828" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v14.i3.pp1820-1828</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008100220?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008100220?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 Govinda S., Department of Computer Science, Christ University, Bangalore, India; Vincent A.N., Faculty of Information Technology, AMITY Institute of Higher Education, Quatre Bornes, Mauritius; Babu M.R., Department of Computer Science, Bharathiar University, Coimbatore, India Description An account of the resource Artificial intelligence (AI) contributes towards improving software engineering quality; however, existing AI models are witnessed to deploy learning-based approaches without addressing various complexities associated with datasets. A literature review showcases an unequilbrium between addressing the accuracy and computational burden. Therefore, the proposed manuscript presents a novel AI-based ensemble learning model that is capable of performing an effective prediction of software quality. The presented scheme adopts correlation-based and multicollinearity-based attributes to select essential feature selection. At the same time, the scheme also introduces a hybrid learning approach integrated with a bio-inspired algorithm for constructing the ensemble learning scheme. The quantified outcome of the proposed study showcases 65% minimized defect density, 94% minimized mean time to failure, 62% minimized processing time of the algorithm, and 43% enhanced predictive accuracy. 2025, Institute of Advanced Engineering and Science. All rights reserved. Subject The topic of the resource Accuracy; Artificial intelligence; Defect density; Software engineering; Software quality Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20894872; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online