Annual Research Conference of SLIIT CITY UNI [ARCSCU]

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The Annual Research Conference of SLIIT City Uni (ARCSCU), organized by the academic departments of SLIIT City Uni, which provides a dynamic platform for undergraduate and postgraduate researchers, scholars, and professionals to share their work, engage in academic discourse, and foster innovation. With a focus on encouraging student participation, the conference features paper presentations, poster sessions, interactive workshops, and publication of selected research in conference proceedings

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Author: search.filters.author.Welarathna, J.HSubject: search.filters.subject.machine learning

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    PublicationOpen Access
    Explainable AI Powered Mental Health State Capturing Application to Support Students’ Mental Wellness and Academic Stress Mitigation
    (SLIIT City UNI, 2025-07-08) Welarathna, J.H; Nallaperunma, P.
    Mental health is a state of well-being that enables individuals to manage stress, work effectively, and contribute to society. However, reports show that serious mental health problems among students worldwide are increasing rapidly. A critical problem is that students often fail to recognize mental health issues or the sources of their academic stress, leading to silent suffering that escalates over time. A significant research gap exists as current assessments methods lack the ability to identify root causes of academic stress and provide explainable decisions for clinical use. This significant rise in many students’ mental health issues have indeed opened important discussions about its underlying causes, consequences, and the need for a comprehensive support system. Voices are an important part for identifying emotional expressions, as speech is the most vital channel of communication, enriched with emotions. The system analyzes emotional patterns in students' voices using Natural Language Processing (NLP) techniques to identify eight emotions and reveal the root causes of their mental health challenges and academic or non-academic stress. Additionally, Explainable AI (XAI) techniques are employed to provide a comprehensive analysis of these patterns, enhancing understanding and supporting managerial decision-making. The system achieves 93.46% accuracy using Random Forest algorithm with reliable confidence levels for clinical applications. It operates effectively in uncontrolled environments with language-independent features, ensuring adaptability across diverse student populations. While students typically seek support from counselors and healthcare professionals who base their decisions on clinical experience, this system offers an additional diagnostic tool to complement and validate professional evaluations. This research aims to better understand student mental health issues and contribute to improved students’ wellness and academic success.