For a long time, machine learning is an application spanning from a wide variety of subjects – from vehicles to data extraction. When you take an image of your cell phone, the picture is a little tangy. It’s simple. It often happens that people take random pictures using phones, which may end up in a corner of the frame. This work blends computer study with tools for photo editing. It will explore the options of how to automatically create photos with aesthetic pleasure through machine learning and how to create a portrait cutting tool. It also explores how to use machine learning to incorporate a streamlined function. Finally, the tools will be compared to other automated machine cropping tools.
Diabetes Retinopathy(DR) occurs due to an injury to the retina, ultimately steering towards sightlessness. DR does not provide any early clues or signs and unnoticeably associates a new blood vessel towards the back portion of the eye, resulting in clots of blood in the eye, bleeding of eyes and distorted vision. The conventional methods are failed to produce the maximum classification accuracy. Therefore, this article is focused on implementation of hybrid logistic regression (HLR)-based machine learning model for classification of DR.Initially, histogram equalization is used to enhance the region of DR image. Then, segmentation of microaneurysmsis performed by using image morphological operations. Further, features extracted using gray level co-occurrence matrix (GLCM), which shows the internal relationship of DR disease. Then, selection of features is carried out using the Gaussian Mixture Model (GMM). Finally, HLR model is applied to perform the multi class classification operation. Simulation results shows that the proposed method resulted in superior performance as compared to state of art approaches
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