Abstract

Recent advancements in neuroimaging have made new ways of breaking down the dynamic interplay of genes and environmental influences, which affect the structure of accessible brain development. The impact on language abilities and verbal short-term memory of pre-school children from socioeconomic status (SES). Children from lower SES show less linguistic abilities and communication skills compared to higher SES children. The challenges of children's vocabulary brain development difference between lower and higher SES continues sometimes grows with age. Therefore this paper, machine learning assisted vocabulary development framework (MLVDF), has proposed a multimodal assessment of young children's brains' intellectual ability and capabilities. The proposed methodology is focused on the estimation and comparison with more developed strategies of nonverbal abilities using similar video interfaces. Machine learning technologies are used to determine a minimum number of variables capable of predicting particular children's cognitive skills. Thus, the findings highlight the complex effect of verbal and cognitive abilities on children's language and life experience. The results are obtained by the analysis of SES in Children's brain development as Cognitive skills ratio is 86.6%, Verbal abilities ratio is 87.12%, Children's brain capability ratio is 87.6%, Increasing the memory ratio is 83.5%, Learning attitude ratio is 93.8%, Reduce the effect of socioeconomic status ratio is 84.25%, Reading efficiency ratio is 87.6%.

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