Kannan, Balakrishnan; David Julie, M(Springer, May 15, 2011)
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Abstract:
Learning disability (LD) is a neurological condition
that affects a child’s brain and impairs his ability to
carry out one or many specific tasks. LD affects about 10%
of children enrolled in schools. There is no cure for learning
disabilities and they are lifelong. The problems of children
with specific learning disabilities have been a cause of
concern to parents and teachers for some time. Just as there
are many different types of LDs, there are a variety of tests
that may be done to pinpoint the problem The information
gained from an evaluation is crucial for finding out how the
parents and the school authorities can provide the best
possible learning environment for child. This paper proposes
a new approach in artificial neural network (ANN) for
identifying LD in children at early stages so as to solve the
problems faced by them and to get the benefits to the students,
their parents and school authorities. In this study, we
propose a closest fit algorithm data preprocessing with
ANN classification to handle missing attribute values. This
algorithm imputes the missing values in the preprocessing
stage. Ignoring of missing attribute values is a common
trend in all classifying algorithms. But, in this paper, we use
an algorithm in a systematic approach for classification,
which gives a satisfactory result in the prediction of LD. It
acts as a tool for predicting the LD accurately, and good
information of the child is made available to the concerned
Description:
Neural Comput & Applic (2012) 21:1757–1763
DOI 10.1007/s00521-011-0619-1
Kannan, Balakrishnan; Julie, David M(Springer, September 24, 2013)
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Abstract:
Learning Disability (LD) is a neurological condition
that affects a child’s brain and impairs his ability to
carry out one or many specific tasks. LD affects about 15 %
of children enrolled in schools. The prediction of LD is a
vital and intricate job. The aim of this paper is to design an
effective and powerful tool, using the two intelligent methods
viz., Artificial Neural Network and Adaptive Neuro-Fuzzy
Inference System, for measuring the percentage of LD that
affected in school-age children. In this study, we are proposing
some soft computing methods in data preprocessing for
improving the accuracy of the tool as well as the classifier.
The data preprocessing is performed through Principal Component
Analysis for attribute reduction and closest fit algorithm
is used for imputing missing values. The main idea in
developing the LD prediction tool is not only to predict the
LD present in children but also to measure its percentage
along with its class like low or minor or major. The system
is implemented in Mathworks Software MatLab 7.10.
The results obtained from this study have illustrated that the
designed prediction system or tool is capable of measuring
the LD effectively
Description:
Soft Comput (2014) 18:1093–1112
DOI 10.1007/s00500-013-1129-0