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In this paper, java programming new automated detection framework is built java programming detect if java task help eye has normal or high eye force. Our smart framework is in accordance with image processing and laptop learning thoughts java programming extract five facets, solely from java assignment help frontal eye image: java project help pupil/iris diameter or radius ratio, java assignment help mean redness level MRL and red area percent RAP of java task help sclera, and lines of java project help contour of java assignment help sclera area, height. Table 1 shows java programming comparability between java task help existing scientific methods and java project help proposed framework. Once java assignment help five features are extracted from java assignment help frontal eye images, neural network NN is applied in order java programming train and test java task help extracted features and obtain java programming risk assessment result for intraocular force normal or high IOP. The proposed work doesn’t without delay measure java task help IOP value in millimeter of mercury; rather, it determines whether java assignment help user/patients IOP is at java programming risky level high or not, further serving as an initial IOP risk assessment framework that can help many americans, particularly people with family historical past of IOP and glaucoma, java programming deliver an early warning if java assignment help ir IOP is beyond java task help normal range. If java task help proposed preliminary screening framework resulted in high IOP, java task help affected person must seek/visit java project help clinic/doctor for extra examinations/consultations.