RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      • 무료
      • 기관 내 무료
      • 유료
      • KCI등재

        Developmental dyslexia detection using machine learning techniques : A survey

        Shahriar Kaisar 한국통신학회 2020 ICT Express Vol.6 No.3

        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.

      • KCI등재

        Integrating oversampling and ensemble-based machine learning techniques for an imbalanced dataset in dyslexia screening tests

        Shahriar Kaisar,Abdullahi Chowdhury 한국통신학회 2022 ICT Express Vol.8 No.4

        Developmental Dyslexia is a learning disorder often discovered in school-aged children who face difficulties while reading or spelling words even though they may have average or above-average levels of intelligence. This ultimately results in anger, frustration, low self-esteem, and other negative feelings. Early detection of Dyslexia can be highly beneficial for dyslexic children as their learning needs can be properly addressed. Researchers have used several testing techniques for early discovery where the data is collected from reading and writing tests, online games, Magnetic reasoning imaging (MRI) and Electroencephalography (EEG) scans, picture and video recording. Several Machine learning techniques have also been used in this regard recently. However, existing works did not focus on the problem of the imbalanced dataset where the percentage of dyslexic participants is much higher compared to non-dyslexic participants, which is expected to be the case for pre-screening among a random population. This paper addresses the imbalanced dataset obtained from dyslexia pre-screening tests and proposes an oversampling and ensemble-based machine learning technique for the detection of Dyslexia. Simulation results show that the proposed approach improves the detection accuracy of the minority class, i.e., dyslexic patients from 80.61% to 83.52%.

      연관 검색어 추천

      이 검색어로 많이 본 자료

      활용도 높은 자료

      해외이동버튼