Diabetic retinopathy (DA) is an eye disease caused by retinal damage as a result of long-term diabetes mellitus. Microaneurysms (MA)are an indicator of DA and are small red spots formed on the retina caused by the ballooning of a weak blood artery.The DA is mainly classified between Proliferate diabetic retinopathy (PDR) and Non-proliferative diabetic retinopathy (NPDR). Non-proliferative is an earlier stage of DA. In our study we will classify the images into 5 stages based on their severity of DA taken from a dataset. The existing modelshave usedLogistic Regression (LR), Support Vector Machine (SVM), gradient boosting techniques such as XGBoost and Logistic Regression with Elastic-Net penalty (LR-EN), to classify wavelet features among the groups. In our project study we used theDeeplearning-based algorithmFast R-CNN (Region Convolutional Neural Network) to build the model and tested its accuracy in training as well as testing the same model with other Machine learning techniques like Decision Tree, k-nearest neighbors (k-NN) classifier, GaussianNaïve Bayes, Kernel-SVM. Our project study shows that Decision Tree had the best training accuracy with 99.31% whereas in case ofthe best predicted testing accuracy it is k-Nearest Neighbors (k-NN) Classifier with 71.29%.

All the cloud based applications work on serviceoriented architectures and collaborate with multiple components from other services to execute discreet application logic. In this environment there are a lot of Web services facilitated to the customer to make the systems. As the potential of the same Web service will change with respect to users' needs. On an average a user will be heavily relied on tools to aid their activities on the internet vice versa the Service provider are also dependent on the users profile and what services are being used in the system. A User Reputation model offers a solution to the Service providers in supporting their service decision based on the User Profile. This model takes usage ratings as data and produces a personalised score. We suggest a new Cumulative separation on the basis of Tags and popularity estimation method and showcase its enhanced filtration ability.