Cataract is a condition of the opacity in the lenticular regions, which usually results in bad visual interpretation of the viewed object or any entity. Hence the timely detection of cataract is considered to be significant and can even contribute in the prevention from loss of fight that might occur if the cataract is left untreated. In this paper, detection of cataract disease is carried out based on the image processing technique. Color features, texture features and shape features are extracted separately. This study proposed a Novel Angular Binary Pattern (NABP) for the extraction of texture features. And after the extraction of features, the images are subjected to classification through the implementation of the proposed novel Kernel Based Convolutional Neural Networks. Results are obtained separately for all the three types of features. A comparison is carried out for the proposed work with existing works and based on the results obtained it can be seen that the proposed work comes up with the enhanced results than the traditional methods.
Software written using an object-oriented programming language is made up of classes, which are compile-time entities and objects, that represent the run-tlme instances of these classes. When the software evolves from one version to another, some of these classes might undergo changes. An important question concerning this evolution is whether the changed class needs to be retested. Research in the area of regression testing has focussed on techniques for optimizing the regression testing effort.We have proposed elsewhere a language-based approach to regression testing that exploits features specific to an object-oriented language to reason about the need to retest an evolved class. The basis for our research is the intuition that when a class can be shown to be equivalent to its original tested version, regression testing may not always be required. We have identified four types of equivalence between a class and its evolved counterpart. In this article, we discuss these equivalence categories and point out their impact on retest effort.
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