Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3323
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dc.contributor.authorKahawanugoda, A-
dc.contributor.authorGnanarathna, K-
dc.contributor.authorMeegoda, N-
dc.contributor.authorMonarawila, R-
dc.contributor.authorSamarasinghe, P-
dc.contributor.authorLindamulage, A.G-
dc.date.accessioned2023-03-08T04:01:02Z-
dc.date.available2023-03-08T04:01:02Z-
dc.date.issued2022-12-09-
dc.identifier.citationA. Kahawanugoda, K. Gnanarathna, N. Meegoda, R. Monarawila, P. Samarasinghe and A. G. Lindamulage, "Development of Low Resource Machine Learning Models for Child Cognitive Ability Assessments," 2022 4th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 2022, pp. 72-77, doi: 10.1109/ICAC57685.2022.10025049.en_US
dc.identifier.issn979-8-3503-9809-0-
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/3323-
dc.description.abstractAutomated cognitive assessment tools are state-of-the-art in assessing cognition development. Due to the low availability of resources, building automated cognitive ability evaluation tools is challenging. This study focuses on developing machine learning models using a limited amount of data to assess Reasoning IQ, Knowledge IQ, Mental Chronometry and Attention-levels of Sinhala-speaking children between the age of 7 to 9 years. Our solution includes Sinhala speech recognition systems, image classification models, gaze estimation, blink count detection and facial expression recognition models to evaluate the above four cognitive ability measuring factors. Open domain speech recognition has been used to evaluate complex Sinhala child verbal responses and limited vocabulary responses were assessed using an end-to-end speech recognition system, respectively achieving 40.1% WER and 97.14% accuracy. Additionally, the image classification models for handwritten Sinhala letter recognition and two shape recognition models have gained 97%, 89% and 99% accuracy. The linear regression model for attention level evaluation that utilizes the inputs from a combination of eye-gaze estimation, facial expression recognition and blink rate detection models has gained 85% accuracy.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2022 4th International Conference on Advancements in Computing (ICAC);-
dc.subjectDevelopmenten_US
dc.subjectLow Resourceen_US
dc.subjectMachine Learning Modelsen_US
dc.subjectChild Cognitiveen_US
dc.subjectAbility Assessmentsen_US
dc.titleDevelopment of Low Resource Machine Learning Models for Child Cognitive Ability Assessmentsen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC57685.2022.10025049en_US
Appears in Collections:4th International Conference on Advancements in Computing (ICAC) | 2022
Department of Information Technology
Research Papers - Dept of Computer Science and Software Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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