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 Agrawal, Kanchi; Kavitha, R.; Kumar, Dalvin Vinoth Title A name given to the resource Depression Severity Prediction Among Higher Education Students Using Neural Network Model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Proceedings of the 5th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2026;pp.908-913 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSADL67539.2026.11451970" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSADL67539.2026.11451970</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036563271?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036563271?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 Agrawal K., Christ University, Department of Statistics and Data Science, Bengaluru, India; Kavitha R., Christ University, Department of Statistics and Data Science, Bengaluru, India; Kumar D.V., Christ University, Department of Statistics and Data Science, Bengaluru, India Description An account of the resource Depression significantly affects students' mental health and academic performance, highlighting the need for effective early detection methods. This study investigates machine learning approaches for automated classification of depression severity using responses from the Patient Health Questionnaire-9 (PHQ-9). Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and hybrid models combining structured PHQ-9 scores with descriptive text responses were evaluated. The experimental results show that the LSTM model achieved the highest classification precision (90%), demonstrating its ability to capture sequential relationships between items in PHQ-9. The findings indicate that sequence-based models are well suited to assess the severity of depression in student populations. Integrating such predictive models into digital mental health screening systems may support the early identification of at-risk students and enable timely, data-driven interventions in academic settings. 2026 IEEE. Subject The topic of the resource Depression Detection; Mental Health Screening; PHQ-9; Student Mental Health; Text Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156883-2; 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