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- W291661581 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." @default.
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- W291661581 date "2014-01-01" @default.
- W291661581 modified "2023-09-27" @default.
- W291661581 title "A Neuro-Fuzzy Approach to Diagnose and Classify Learning Disability" @default.
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- W291661581 doi "https://doi.org/10.1007/978-81-322-1602-5_69" @default.
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