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Precise prediction of risk for dyslexia among children’s in earlier stages is a significant long-term aim in the field of cognitive computing. Producing such accurate results for detection of dyslexia from a dataset which consist of low-quality dataset and the presence of vague information is the toughest challenge among researchers. This paper aims at developing an evolving model to handle the impreciseness in the detection of dyslexia more intelligently. In this work, each instance is described in a neutrosophic domain by defining a membership degree of truthiness, indeterminacy, and falsity. These instances are neutrosophically clustered by applying Neutrosophic C-Means clustering (NCM) which forms four different clusters namely dyslexia, no dyslexia, control/revision and hyperactivity or other issues. The outlier and noise are the special categories of indeterminacy which often occurs in real datasets are promptly discovered and clustered. NCM is optimized by introducing Artificial Bacterial Foraging (ABF), especially when there is vagueness or imprecision in the selection of cluster centroids. With the merits of global searching, ABF selects more promising clustering during cluster re-computation. The interpreted results confirm that the role played by proposed ONCMC-ABF algorithm produces better results in the prediction of dyslexia with the low-quality dataset.

journal of king saud university - computer and information sciences

Evolving Optimized Neutrosophic C means clustering using Behavioral Inspiration of Artificial Bacterial Foraging (ONCMC-ABF) in the Prediction of Dyslexia