Minimal training time in supervised retinal vessel segmentation

In this paper, we perform comparative analysis between different classifiers using the same experimental setup for supervised retinal vessel segmentation. The aim of this paper is to find supervised classifier that can obtain good segmentation accuracy with minimal training time. Minimizing the training time is essential when dealing with biomedical images. The more samples introduced to a learning model, the better it can adapt to the unseen data. The results indicate a trade-off between accuracy and training time can be obtained in a classifier trained by a Neural Network. When tested with a publicly available database, the learning model only requires less than 2 minutes in the learning phase and achieves overall accuracy of 94.54%

An evaluation of COCHCOMO tool for change effort estimation in software development phase

Software changes are inevitable in any software project. Software project manager is required to make an effective decision when dealing with the software changes. One type of information that helps to make the decision is the estimation of the change effort produced by the changes. Reliable information of estimation on the change effort is significant to decide whether to accept or reject the changes. From software development perspective, the estimation has to take into account the inconsistent states of software artifacts across project lifecycle i.e., fully developed and partially developed. This research introduces a new change effort estimation tool (Constructive Change Cost Model or COCHCOMO) that is able to take into account the inconsistent states of software artifacts in its estimation process. This tool was developed based on our extended version of static and dynamic impact analysis techniques. Based on extensive experiments using several case studies have shown that an acceptable error rates result has been achieved