Assessing dyslexia with machine learning: a pilot study utilizing Google ML Kit

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dc.authorid0000-0001-8382-8417
dc.contributor.authorEroğlu, Güneten_US
dc.contributor.authorHarb, Mhd Raja Abouen_US
dc.date.accessioned2024-01-29T06:25:05Z
dc.date.available2024-01-29T06:25:05Z
dc.date.issued2023-12-19
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Makine Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Mechanical Engineeringen_US
dc.description.abstractIn this study, we explore the application of Google ML Kit, a machine learning development kit, for dyslexia detection in the Turkish language. We collected face-tracking data from two groups: 49 dyslexic children and 22 typically developing children. Using Google ML Kit and other machine learning algorithms based on eye-tracking data, we compared their performance in dyslexia detection. Our findings reveal that Google ML Kit achieved the highest accuracy among the tested methods. This study underscores the potential of machine learning-based dyslexia detection and its practicality in academic and clinical settings.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationEroğlu, G. & Harb, M. R. A. (2023). Assessing dyslexia with machine learning: a pilot study utilizing Google ML Kit. Paper presented at the 2023 Medical Technologies Congress (TIPTEKNO), 1-4. doi:10.1109/TIPTEKNO59875.2023.10359236en_US
dc.identifier.doi10.1109/TIPTEKNO59875.2023.10359236
dc.identifier.endpage4
dc.identifier.isbn9798350328967
dc.identifier.isbn9798350328974
dc.identifier.issn2687-7783
dc.identifier.issn2687-7775
dc.identifier.scopus2-s2.0-85182734971
dc.identifier.scopusqualityN/A
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/5889
dc.identifier.urihttp://dx.doi.org/10.1109/TIPTEKNO59875.2023.10359236
dc.indekslendigikaynakScopusen_US
dc.institutionauthorHarb, Mhd Raja Abouen_US
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartof2023 Medical Technologies Congress (TIPTEKNO)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDyslexiaen_US
dc.subjectDyslexia detectionen_US
dc.subjectEye movement dataen_US
dc.subjectEyezenithen_US
dc.subjectFace-tracking dataen_US
dc.subjectGoogle ML Kiten_US
dc.subjectSupervised machine learningen_US
dc.subjectEye movementsen_US
dc.subjectFace recognitionen_US
dc.subjectLearning algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectSupervised learningen_US
dc.subjectDyslexiumen_US
dc.subjectDyslexium detectionen_US
dc.subjectEye movement datumen_US
dc.subjectFace trackingen_US
dc.subjectGoogle+en_US
dc.subjectSupervised machine learningen_US
dc.subjectTracking dataen_US
dc.subjectEye trackingen_US
dc.titleAssessing dyslexia with machine learning: a pilot study utilizing Google ML Kiten_US
dc.typeConference Objecten_US

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