摘要
The brain of humans and other organisms is affected in various ways through the electromagneticfield(EMF)radiations generated by mobile phones and cell phone towers.Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF.Cellular level analysis is used to measure and detect the effect of mobile radiations,but its utilization seems very expensive,and it is a tedious process,where its analysis requires the preparation of cell suspension.In this regard,this research article proposes optimal broadcast-ing learning to detect changes in brain morphology due to the revelation of EMF.Here,Drosophila melanogaster acts as a specimen under the revelation of EMF.Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF.The geometrical characteristics of the brain image of that is microscopic segmented are analyzed.Analysis results reveal the occur-rence of several prejudicial characteristics that can be processed by machine learn-ing techniques.The important prejudicial characteristics are given to four varieties of classifiers such as naïve Bayes,artificial neural network,support vector machine,and unsystematic forest for the classification of open or nonopen micro-scopic image of D.melanogaster brain.The results are attained through various experimental evaluations,and the said classifiers perform well by achieving 96.44%using the prejudicial characteristics chosen by the feature selection meth-od.The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity,where the machine learning techniques produce an effective framework for image processing.
