Faculty of Computing

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Subject: search.filters.subject.convolutional neural network

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    LexAyudha : Personalized AI-Driven Rehabilitation for Adolescents with Dyslexia and Dyscalculia
    (Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Silva, U; Madusanka, I; Thalangama, T; Dissanayake, T; Thelijjagoda, S; Vidanaralage, A. J
    Dyslexia and dyscalculia, the most common learning disabilities, produce a considerably challenging environment for adolescents and lead to frustration, disengagement, and reduced self-esteem. While assistive technologies with influential functionalities exist, they lack personalization for effective and supportive learning. LexAyudha is an AI-powered platform addressing these gaps by integrating proven medical methodologies such as chromatic variation, Touch Math, and multisensory teaching strategies. Advanced AI technologies like Convolutional and Recurrent Neural Networks have been used in LexAyudha to dynamically adjust reading content, visual layouts, and lesson plans in the gamified app based on students' performances to cater for their requirements. Moreover, a novel emotion recognition algorithm even adjusts difficulty levels of activities and voice output with altered audio features to ensure a stress-free learning process and a stimulating environment. Initial findings based on the user performances tests conducted with the dyslexic and dyscalculia adolescents in Sri Lanka, represents significant improvements in reading fluency, comprehension, and motivation, showing that adaptive learning with AI has the potential to revolutionize learning for dyslexic and dyscalculia students. The research identifies the potential of rehabilitation with AI-driven technology as a flexible and scalable solution for personalized education in dyslexia and dyscalculia.
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    DiverseMind: An Integrated Framework for Children with Multi-Dimensional Challenges as Slow Learners
    (Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Jayasundara, H; Neewin, S; Kiriwaththuduwa, C; Herath, R; Krishara, J; Thelijjagoda, S
    Education systems worldwide struggle to support slow learners, who face difficulties in traditional classrooms due to learning challenges in writing, mathematics, attention, and memory. Slow learners, characterized by an Intelligence Quotient (IQ) between 70 and 85, require additional time and adaptive learning methods to grasp concepts effectively. However, existing educational frameworks lack comprehensive screening and targeted interventions. This research introduces "DiverseMind", an integrated framework designed to identify and assist slow learners among Grade 4 primary school children in Sri Lanka using advanced Machine Learning (ML) algorithms, image processing, and multi-model architecture. The system evaluates academic abilities through four key assessments of writing skills, mathematical proficiency, attention span, and short-term memory. A Convolutional Neural Network (CNN) based model, achieving a training accuracy of 98% combined with a Python-based weighted condition function, classifies writing accuracy, while Decision Tree (DT) classifiers analyze mathematical capabilities with 98% accuracy. Attention span is assessed using facial landmark detection, gaze tracking, and emotion recognition, where the CNN model trained on 28,709 images achieved a training accuracy of 80%. Short-term memory is evaluated through ML driven cognitive tasks, with the DT model achieving 99% accuracy. In addition to comprehensive assessments and interventions, the system provides a dedicated dashboard for the teachers to monitor the student progress. By integrating gamification and AI-driven learning analytics, "DiverseMind"promotes inclusive education and bridges the gap in support for slow learners, ensuring they receive the necessary resources to reach their full potential.