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Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012 pp 645–657Cite as

A Neuro-Fuzzy Approach to Diagnose and Classify Learning Disability

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 236))

Abstract

The aim of this study is to compare two supervised artificial neural network models for diagnosing a child with learning disability. Once diagnosed, then a fuzzy expert system is applied to correctly classify the type of learning disability in a child. The endeavor is to support the special education community in their quest to be with the mainstream. The initial part of the paper gives a comprehensive study of the different mechanisms of diagnosing learning disability. Models are designed by implementing two soft computing techniques called Single-Layer Perceptron and Learning Vector Quantization. These models classify a child as learning disabled or nonlearning disabled. Once diagnosed with learning disability, fuzzy-based approach is used further to classify them into types of learning disability that is Dyslexia, Dysgraphia, and Dyscalculia. The models are trained using the parameters of curriculum-based test. The paper proposes a methodology of not only detecting learning disability but also the type of learning disability.

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Authors and Affiliations

  1. Department of Computer Science, University of Mumbai, Mumbai-98, India

    Kavita Jain & Pooja Manghirmalani Mishra

  2. Department of Mathematics, Jai Hind College, Mumbai-20, India

    Sushil Kulkarni

Authors
  1. Kavita Jain
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  2. Pooja Manghirmalani Mishra
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  3. Sushil Kulkarni
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Corresponding author

Correspondence to Kavita Jain .

Editor information

Editors and Affiliations

  1. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    B. V. Babu

  2. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  5. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  6. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Kanad Ray

  7. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Umesh Gupta

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Jain, K., Mishra, P.M., Kulkarni, S. (2014). A Neuro-Fuzzy Approach to Diagnose and Classify Learning Disability. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_69

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  • DOI: https://doi.org/10.1007/978-81-322-1602-5_69

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1601-8

  • Online ISBN: 978-81-322-1602-5

  • eBook Packages: EngineeringEngineering (R0)

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