Software metrics provide a means for software practitioners to assess the quality of their software. Ideally, this information should be available earlier in the software development lifecycle, since changes are much more expensive to incorporate in the later stages. Design level metrics offer an elegant way of capturing this information. Research in software design metrics has focused primarily on procedural and object oriented software. However, such metrics are currently not available for Aspect Oriented Software Development (AOSD), which is an emerging paradigm. Aspect Oriented Programming (AOP) is an approach that allows programmers to modularize crosscutting concerns that are scattered across multiple modules. Separation of concerns through aspects has the advantages of increased reliability, adaptability and better reuse. The objective of this paper is to propose suitable metrics for the Aspect Oriented Design (AOD) and to develop a tool that will automatically select a better design based on the proposed metrics. In this paper, class and sequence diagrams are used to represent an AOD. The proposed design level metrics are applied to two alternative designs of an illustrative case study. The tool selects the design that better suits stakeholder requirements, based on logical inferences obtained from these metrics regarding the quality of the Aspect Oriented software.
Automated detection of retinal hemorrhages in fundus image [2] is crucial step towards early detection or screening is difficult among large population. A novel splat feature classification method is introduced to detect retinal hemorrhages. Classification is been achieved through supervised learning approaches. The performance of sensitivity and specificity is been improved while processing with retinal hemorrhages than with lesions. An area under receiver operating characteristics curve (ROC) of 0.96 can be achieved at splat level and 0.87 at image level.
General Terms : Supervised Classification
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