Early Diagnosing and Identifying Tool for Specific Learning Disability using Decision Tree algorithm

Devi, A.; Kavya, G.; Therese, M. Julie; Gayathri, R.

doi:10.1109/icirca51532.2021.9544617

Cited by 6 publications

(1 citation statement)

References 7 publications

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“…In 2021, Devi and other researchers published tests conducted with 40 students, using a web application for diagnosis of dyscalculia, among other difficulties, containing questions elaborated based on the Woodcock-Johnson Test (WJT). In that study, the machine learning technique was Decision Tree Algorithm (DEVI et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Random Forest multiclasse: a diagnostic study of mathematical learning difficulties

Augusto

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This work is dedicated to all mothers, heroines in their homes, fighters for their families and their children. To the mothers who multiply themselves in daily activities, who build inexplicable relationships of affection, which are the basis of their home, but who never stop dreaming, believing that they are capable of, in addition to the glory of motherhood, still realizing personal dreams and desires to collaborate. I do thank my family, specially my husband Renato and my kids Rebecca and Giovani, for the support and for understanding the absent hours. My thanks for my mother Neusa that ever inspired me with her strength for living. I also thank to MSc. Giulia Paiva, from UFMG, for sharing her experience, work and knowledge without what this work could not be done. A special thank for Professor Dr.José Augusto Baranauskas, for all orientation and corrections that improved my work and comprehension. I would also like to thank Professor Dr.Vitor Geraldi Haase, from UFMG, for leading me to Giulia, becoming possible the partnership for this work, and therefore for all background transferred by publications I have read. Futhermore, I would like to thank Professor Dr. Galera from Psicobiology Departament -USP, who was responsible to connect me to UFMG colleagues. I kindly thank for docent group from DCM -USP who taught and lead me to new concepts and a whole new world of opportunities. I also thanks for all staff support.

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Section: Discussionmentioning

confidence: 99%

Random Forest multiclasse: a diagnostic study of mathematical learning difficulties

Augusto

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Automated systems for diagnosis of dysgraphia in children: a survey and novel framework

Kunhoth,

Al-Maadeed,

Kunhoth

et al. 2024

IJDAR

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Learning disabilities, which primarily interfere with basic learning skills such as reading, writing, and math, are known to affect around 10% of children in the world. The poor motor skills and motor coordination as part of the neurodevelopmental disorder can become a causative factor for the difficulty in learning to write (dysgraphia), hindering the academic track of an individual. The signs and symptoms of dysgraphia include but are not limited to irregular handwriting, improper handling of writing medium, slow or labored writing, unusual hand position, etc. The widely accepted assessment criterion for all types of learning disabilities including dysgraphia has traditionally relied on examinations conducted by medical expert. However, in recent years, artificial intelligence has been employed to develop diagnostic systems for learning disabilities, utilizing diverse modalities of data, including handwriting analysis. This work presents a review of the existing automated dysgraphia diagnosis systems for children in the literature. The main focus of the work is to review artificial intelligence-based systems for dysgraphia diagnosis in children. This work discusses the data collection method, important handwriting features, and machine learning algorithms employed in the literature for the diagnosis of dysgraphia. Apart from that, this article discusses some of the non-artificial intelligence-based automated systems. Furthermore, this article discusses the drawbacks of existing systems and proposes a novel framework for dysgraphia diagnosis and assistance evaluation.

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Advanced Library Management System using IoT

Devi¹,

Mirudhula²,

Devi³

2021

2021 Fifth International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Early Diagnosing and Identifying Tool for Specific Learning Disability using Decision Tree algorithm

Cited by 6 publications

References 7 publications

Random Forest multiclasse: a diagnostic study of mathematical learning difficulties

Random Forest multiclasse: a diagnostic study of mathematical learning difficulties

Automated systems for diagnosis of dysgraphia in children: a survey and novel framework

Advanced Library Management System using IoT

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