Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/59731
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dc.contributor.authorNawer, Nafisaen
dc.contributor.authorParvez, Mohammad Zaviden
dc.contributor.authorIqbal Hossain, Muhammaden
dc.contributor.authorBarua, Prabal Dattaen
dc.contributor.authorRahim, Miaen
dc.contributor.authorChakraborty, Subrataen
local.source.editorEditor(s): Kevin Daimi, Abeer Al Sadoonen
dc.date.accessioned2024-05-23T01:06:25Z-
dc.date.available2024-05-23T01:06:25Z-
dc.date.issued2023-06-17-
dc.identifier.citationProceedings of the Second International Conference on Innovations in Computing Research (ICR’23), v.721, p. 165-174en
dc.identifier.isbn978-3-031-35307-9en
dc.identifier.isbn978-3-031-35308-6en
dc.identifier.urihttps://hdl.handle.net/1959.11/59731-
dc.description.abstract<p>Approximately 1 in 44 children worldwide has been identified as having Autism Spectrum Disorder (ASD), according to the Centers for Disease Control and Prevention (CDC). The term ‘ASD’ is used to characterize a collection of repetitive sensory-motor activities with strong hereditary foundations. Children with autism have a higher-than-average rate of motor impairments, which causes them to struggle with handwriting. Therefore, they generally perform worse on handwriting tasks compared to typically developing children of the same age. As a result, the purpose of this research is to identify autistic children by a comparison of their handwriting to that of typically developing children. Consequently, we investigated state-of-the-art methods for identifying ASD and evaluated whether or not handwriting might serve as bio-markers for ASD modeling. In this context, we presented a novel dataset comprised of the handwritten texts of children aged 7 to 10. Additionally, three pre-trained Transfer Learning frameworks: InceptionV3, VGG19, Xception were applied to achieve the best level of accuracy possible. We have evaluated the models on a number of quantitative performance evaluation metrics and demonstrated that Xception shows the best outcome with an accuracy of 98%.</p>en
dc.languageenen
dc.publisherSpringer, Chamen
dc.relation.ispartofProceedings of the Second International Conference on Innovations in Computing Research (ICR’23)en
dc.relation.ispartofseriesLecture Notes in Networks and Systemsen
dc.titleCNN-Based Handwriting Analysis for the Prediction of Autism Spectrum Disorderen
dc.typeConference Publicationen
dc.relation.conferenceThe Second International Conference on Innovations in Computing Research (ICR’23)en
dc.identifier.doi10.1007/978-3-031-35308-6_14en
local.contributor.firstnameNafisaen
local.contributor.firstnameMohammad Zaviden
local.contributor.firstnameMuhammaden
local.contributor.firstnamePrabal Dattaen
local.contributor.firstnameMiaen
local.contributor.firstnameSubrataen
local.profile.schoolSchool of Lawen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailmrahim@une.edu.auen
local.profile.emailschakra3@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference4th - 6th September, 2023en
local.conference.placeMadrid, Spainen
local.publisher.placeSwitzerlanden
local.format.startpage165en
local.format.endpage174en
local.series.issn2367-3370en
local.series.issn2367-3389en
local.peerreviewedYesen
local.identifier.volume721en
local.contributor.lastnameNaweren
local.contributor.lastnameParvezen
local.contributor.lastnameIqbal Hossainen
local.contributor.lastnameBaruaen
local.contributor.lastnameRahimen
local.contributor.lastnameChakrabortyen
dc.identifier.staffune-id:mrahimen
dc.identifier.staffune-id:schakra3en
local.profile.orcid0000-0003-0637-8445en
local.profile.orcid0000-0002-0102-5424en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/59731en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleCNN-Based Handwriting Analysis for the Prediction of Autism Spectrum Disorderen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsThe Second International Conference on Innovations in Computing Research (ICR’23), Madrid, Spain, 4th - 6th September, 2023en
local.search.authorNawer, Nafisaen
local.search.authorParvez, Mohammad Zaviden
local.search.authorIqbal Hossain, Muhammaden
local.search.authorBarua, Prabal Dattaen
local.search.authorRahim, Miaen
local.search.authorChakraborty, Subrataen
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/fae5dd49-2e01-4e1c-94cd-b27c16cf277cen
local.uneassociationYesen
dc.date.presented2023-09-04-
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2023en
local.year.presented2023en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/fae5dd49-2e01-4e1c-94cd-b27c16cf277cen
local.subject.for20204601 Applied computingen
local.date.start2023-09-04-
local.date.end2023-09-06-
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.date.moved2024-06-26en
Appears in Collections:Conference Publication
School of Law
School of Science and Technology
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