International Conference on Advancements in Computing [ICAC]

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/312

The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.

The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.

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Subject: search.filters.subject.Emotion Recognition

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Now showing 1 - 3 of 3
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    AI-Driven Behavioral Assessment and Intervention for ADHD
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dharmasena U.D.S.V; Manamperi R.S; Dilshani H.T.D.P; Halliyadda H.U.M.S.; Kasthuriarachchi, S; Samaraweera, M
    Attention Deficit Hyperactivity Disorder (ADHD) affects millions of children worldwide, causing cognitive, behavioral,and academic challenges. Traditional diagnostic methods rely on subjective assessments, leading to inconsistent evaluations and delayed interventions. This research addresses these limitations by developing an AI-driven gamified behavioral assessment and intervention platform that integrates machine learning, real-time emotion recognition, and adaptive learning techniques. The system features interactive games that dynamically adjust difficulty based on behavioral and emotional responses captured through facial expression analysis. Data collection includes gameplay metrics, parent-reported questionnaires, and expert evaluations to train AI models for ADHD classification and personalized intervention. The emotion recognition module achieved 89% accuracy using Convolutional Neural Networks, while reinforcement learning algorithms enabled real-time game adaptation. Classification models, including Random Forest (91.3% accuracy), demonstrated strong predictive capabilities. The platform provides continuous monitoring dashboards for caregivers and educators, enabling data-driven decision-making. Results indicate that AI-based behavioral assessments offer improved accuracy and flexibility compared to traditional methods, with emotion-adaptive gaming enhancing engagement. This research demonstrates the potential of AI-powered solutions to transform ADHD diagnosis and intervention through improved efficiency, personalization, and accessibility.
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    Unveiling EEG Emotional Patterns during Interactive Engagement Activities: A Performance Comparison of Machine Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Rathnayake, T; Subasinghe, S; Nawarathne, M; Sumathipala, P
    This paper presents a comparative evaluation of machine learning and deep learning models for emotion recognition from electroencephalography (EEG) signals recorded during interactive engagement activities. EEG data were collected using a Muse headband as participants performed engagement activities designed to elicit five emotions: Afraid, Happy, Calm, Neutral, and Sensitive. After preprocessing and feature extraction, eight machine learning and deep learning models were trained. Based on the experiment, Random Forest emerged as the best-performing classifier, achieving ~95.6% accuracy with balanced precision-recall, while gradient boosting and SVM also shown comparative results. The models demonstrated potential for real-time streaming despite slightly lower accuracy, highlighting scalability with larger datasets. These findings demonstrate the feasibility of consumer-grade EEG devices, enhanced with multimodal features, for robust emotion recognition in realistic engagement activities. The results underscore the effectiveness of multimodal fusion and engagement-based design, confirming that lightweight EEG systems can support practical, real-time affective computing applications in education, adaptive interfaces, and mental well-being monitoring.
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    Human-Ai Cooperative Driving Through Emotion-Aware Decision Making and Driver Personalization
    (Institute of Electrical and Electronics Engineers, 2025-12-09) Wickramasuriya L.H.N.Y; Rathnayaka D.B; Walpalage J.V.; Rathnayake, S
    Conventional in-vehicle safety systems often neglect real-time emotional monitoring, prediction, and passenger influence, leading to reactive rather than proactive interventions. This paper presents an emotion-aware cooperative driving system that combines facial emotion recognition, time-series emotion forecasting, and personalized music-based regulation. The system detects both driver and front-seat passenger emotions through a Vision Transformer (ViT) model, while a time-series model anticipates the driver's upcoming emotional state. A prioritization algorithm ensures driver emotions hold precedence, with passenger states considered when the driver is stable. Based on this prioritized emotional context, the system regulates the in-cabin atmosphere using music recommendations drawn from the driver's preferred artists via the Spotify API. Experimental results show robust real-time emotion classification (86.4% validation accuracy), proactive forecasting (81.6% predictive accuracy), and improved driver acceptance due to personalization. The proposed framework advances intelligent transportation by shifting from static monitoring toward predictive, human-centered, and non-intrusive emotional regulation, thereby enhancing both safety and user comfort.