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      KCI등재 SCOPUS

      Developmental dyslexia detection using machine learning techniques : A survey

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      https://www.riss.kr/link?id=A107280975

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      다국어 초록 (Multilingual Abstract)

      Developmental dyslexia is a learning disability that occurs mostly in children during their early childhood. Dyslexic children face difficulties while reading, spelling and writing words despite having average or above-average intelligence. As a conse...

      Developmental dyslexia is a learning disability that occurs mostly in children during their early childhood. Dyslexic children face difficulties while reading, spelling and writing words despite having average or above-average intelligence. As a consequence, dyslexic children often suffer from negative feelings, such as low self-esteem, frustration, and anger. Therefore, early detection of dyslexia is very important to support dyslexic children right from the start. Researchers have proposed a wide range of techniques to detect developmental dyslexia, which includes game-based techniques, reading and writing tests, facial image capture and analysis, eye tracking, Magnetic reasoning imaging (MRI) and Electroencephalography (EEG) scans. This survey paper critically analyzes recent contributions in detecting dyslexia using machine learning techniques and identify potential opportunities for future research.

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      참고문헌 (Reference)

      1 "What is dyslexia?"

      2 M. Rauschenberger, "Towards language independent detection of dyslexia with a web-based game" 1-10, 2018

      3 K. Spoon, "Towards detecting dyslexia in children’s handwriting using neural networks" 1-5, 2019

      4 M.N. Benfatto, "Screening for dyslexia using eye tracking during reading" 11 (11): 2016

      5 L. Rello, "Screening dyslexia for English using hci measures and machine learning" 80-84, 2018

      6 H. Perera, "Review of the role of modern computational technologies in the detection of dyslexia" Springer 1465-1475, 2016

      7 H. Perera, "Review of eeg-based pattern classification frameworks for dyslexia" 5 (5): 4-, 2018

      8 Y. Lakretz, "Probabilistic graphical models of dyslexia" 1919-1928, 2015

      9 P. Pło´nski, "Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia" 38 (38): 900-908, 2017

      10 Z. Rezvani, "Machine learning classification of dyslexic children based on eeg local network features" bioRxiv 1-23, 2019

      1 "What is dyslexia?"

      2 M. Rauschenberger, "Towards language independent detection of dyslexia with a web-based game" 1-10, 2018

      3 K. Spoon, "Towards detecting dyslexia in children’s handwriting using neural networks" 1-5, 2019

      4 M.N. Benfatto, "Screening for dyslexia using eye tracking during reading" 11 (11): 2016

      5 L. Rello, "Screening dyslexia for English using hci measures and machine learning" 80-84, 2018

      6 H. Perera, "Review of the role of modern computational technologies in the detection of dyslexia" Springer 1465-1475, 2016

      7 H. Perera, "Review of eeg-based pattern classification frameworks for dyslexia" 5 (5): 4-, 2018

      8 Y. Lakretz, "Probabilistic graphical models of dyslexia" 1919-1928, 2015

      9 P. Pło´nski, "Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia" 38 (38): 900-908, 2017

      10 Z. Rezvani, "Machine learning classification of dyslexic children based on eeg local network features" bioRxiv 1-23, 2019

      11 R.U. Khan, "Machine learning and dyslexia:Diagnostic and classification system (dcs) for kids with learning disabilities" 7 (7): 97-100, 2018

      12 A. Frid, "Features and machine learning for correlating and classifying between brain areas and dyslexia"

      13 H. Perera, "Eeg signal analysis of writing and typing between adults with dyslexia and normal controls" 5 (5): 62-, 2018

      14 T. Asvestopoulou, "Dyslexml: Screening tool for dyslexia using machine learning"

      15 "Dyslexia in Australia"

      16 S.S.A. Hamid, "Dyslexia adaptive learning model: student engagement prediction using machine learning approach" Springer 372-384, 2018

      17 "Dyslexia"

      18 Z. Cui, "Disrupted white matter connectivity underlying developmental dyslexia : a machine learning approach" 37 (37): 1443-1458, 2016

      19 L. Rello, "Detecting readers with dyslexia using machine learning with eye tracking measures" 1-8, 2015

      20 S.S.A. Hamid, "A study of computerbased learning model for students with dyslexia" 284-289, 2015

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2017-08-01 평가 SCOPUS 등재 (기타) KCI등재
      2017-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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