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    AI-Powered Mobile Application for Supporting Mental Wellness in Children
    (Institute of Electrical and Electronics Engineers, 2026-06-26) Pathiraja G.P.K; Navarathne N.M.D.C; Navodya D.P.D.D.; Dharmapriya R.A.P.; Vidanaralage, A.J; Vidanaralage, A.J
    Children often struggle to express their emotions verbally, making early detection of mental health issues challenging and limiting timely intervention. This research proposes an AI-powered mobile application designed to support children's mental well-being through multimodal emotional analysis and personalized interventions. The significance of this study lies in addressing the lack of accessible, child-friendly, and culturally relevant digital mental health solutions, particularly for Sinhala-speaking users, while reducing dependence on subjective expert interpretation. The proposed system integrates four main components, including a culturally grounded therapeutic story generator using a GRU-based deep learning model, an emotion analysis module for children's drawings utilizing DenseNet121 and YOLOv8 with colour and spatial feature extraction, an emotion-aware music recommendation and adaptive puzzle game powered by EfficientNet-based facial emotion recognition, and a Sinhala voice-first mood prediction system using fine-tuned XLM-RoBERTa.Experimental results demonstrate strong performance, including improved contextual coherence and adaptive therapeutic storytelling capabilities, 79% accuracy in drawing-based emotion classification, 87% accuracy in emotion-aware music recommendation, and 96% accuracy in Sinhala text-based mood prediction, along with improved user engagement through personalized storytelling and adaptive feedback mechanisms. The system further enhances interpretability by generating parent-friendly emotional reports using large language models. In conclusion, this research contributes a comprehensive, multimodal, and culturally adaptive AI solution for supporting children's mental health, enabling early emotional detection, improved communication, and scalable intervention through an engaging mobile platform.
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    PublicationOpen Access
    A User-oriented Ensemble Method for Multi-Modal Emotion Recognition
    (SLAAI - International Conference on Artificial Intelligence, 2019-12-12) Iddamalgoda, N; Thrimavithana, P; Fernando, H; Ratnayake, T; Priyadarshana, Y. H. P. P; Aththidiye, R; Kasthurirathna, D
    Emotions play a vital role in mental and physical activities of human lives. One of the biggest challenges in Human-Computer Interaction is emotion recognition. With the resurgence in the fields of Artificial Intelligence and Machine learning, a considerable number of studies have been carried out in order to address the challenge of emotion recognition. The individual heterogeneity of expressing emotions is a key problem that needs to be addressed in accurately detecting the emotional state of an individual. The purpose of this work is to propose a novel ensemble method to predict the emotions using a multimodal approach. The presented multimodal approach with the modalities of facial expressions, voice variations and, speech and social media content, are used to identify seven emotional states: anger, fear, disgust, happiness, sadness, surprise and neutral emotion. In this study, for the facial expression-based emotion recognition and voice variation-based emotion recognition, Deep Neural Network models have been used, and for emotion recognition using speech and social media content, Multinomial Naïve Bayesian algorithm is used. The mentioned three modalities were integrated using a novel ensemble method that captures the heterogeneity of individuals in how they express their emotions. The proposed ensemble method was evaluated with respect to real states of human emotions of a sample user group and the experimental results suggest that the suggested ensemble method may be more accurate in recognizing emotions. Accurate recognition of emotions may have myriad applications in domains such as healthcare, advertising and human resource management.