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 Sambandam, Rakoth Kandan; Vetriveeran, Divya; Jenefa, J.; Vinodha, D.; Thaiyalnayaki, S. Title A name given to the resource Unlocking Insights into Mental Well-Being: A Deep Learning Approach to Depression 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 Lecture Notes in Networks and Systems;Volume;1200;pp.535-545 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9926-8_41" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9926-8_41</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006734568?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006734568?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 Sambandam R.K., Department of CSE, SOET, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Vetriveeran D., Department of CSE, SOET, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Jenefa J., Department of CSE, SOET, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Vinodha D., Department of CSE, SOET, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Thaiyalnayaki S., Department of CSE, School of Computing, Bharath Institute of Higher Education and Research (Deemed to be University), Tamil Nadu, Chennai, India Description An account of the resource Depression, a prevalent global mental health disorder, poses significant challenges for timely diagnosis and effective intervention. Our research leverages the power of deep learning to develop a novel approach to depression detection, with the ultimate goal of enhancing early diagnosis and improving mental well-being. To evaluate the effectiveness of our method, we carried out an examination on a sizable dataset consisting of real-world data from individuals with and without diagnosed depression. The report discusses the performance metrics to examine the proposed technique effectiveness in depression detection. Additionally, this proposed work emphasizes the ethical and privacy considerations surrounding mental health data. The findings of this research indicate promising results in early depression detection, offering the potential to revolutionize mental health care by facilitating timely interventions. We discuss the implications of our approach in terms of supporting mental health professionals, improving mental health care accessibility, and addressing the pressing global mental health crisis. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Deep learning; Depression; Healthcare; Mental well-being; Optimization Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981979925-1; 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