Design and Development of Machine Learning and Evolutionary Computation Methods for Risk Factors Identification in Early Childhood Disability
Abstract
Timely identification of social and emotional
disorders is crucial for the immediate welfare and future
well-being of young children. The present study encounters
challenges in identifying cheap air jordan 11 shoes and establishing risk factors
associated with early childhood impairment. Consequently, the
overall system performance is substantially reduced. In order
to tackle the aforementioned challenges, this study proposes the
utilisation of the Cuckoo Search Optimisation with Adaptive
Network-based Fuzzy Inference System (CSO+ANFIS)
technique. The aim is to effectively generate and identify risk
factors associated with early childhood disability. This study
employs the CSO method to select the most important attributes
and determines the optimal objective function based on the
highest fitness values. The ANFIS technique focuses on
identifying important risk variables by analysing the hidden
layer and fuzzy inference values. The experimental results have
shown that the suggested CSO+ANFIS technique surpasses the
current paradigm in terms of accuracy and sensitivity metrics.
Keywords Early Childhood Disability, Risk Factors, CSO+ANFIS.
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