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Predicting the fault-proneness of program modules when the fault labels for modules are unavailable is a practical problem frequently encountered in the software industry. Because fault data belonging to previous software version is not available, supervised learning approaches can not be applied, leading to the need for new methods, tools, or techniques.(More)
Social media has become an important information source with the expanding internet and ideas shared by the people. The social media raw data which is quite disordered and messy can not be processed as it is and obtain adequate results. In this study, sentiment analysis has been performed by collecting data from the Twitter. To perform this analysis an(More)
The features of real-time dependable systems are availability, reliability, safety and security. In the near future, real-time systems will be able to adapt themselves according to the specific requirements and real-time dependability assessment technique will be able to classify modules as faulty or fault-free. Software fault prediction models help us in(More)
Software testing is a time-consuming and expensive process. Software fault prediction models are used to identify fault-prone classes automatically before system testing. These models can reduce the testing duration, project risks, resource and infrastructure costs. In this study, we propose a novel fault prediction model to improve the testing process.(More)
BACKGROUND Lemierre syndrome is a rare and potentially fatal entity characterized by septic emboli from thrombosis of the internal jugular vein after oropharyngeal infection. The etiologic agent is not always an anaerobic bacterium. We report a patient with a Lemierre syndrome variant who presented with thrombosis of both the right internal jugular vein and(More)
The primary role of the thyroid gland is to help regulation of the body's metabolism. The correct diagnosis of thyroid dysfunctions is very important and early diagnosis is the key factor in its successful treatment. In this article, we used four different kinds of classifiers, namely Bayesian, k-NN, k-Means and 2-D SOM to classify the thyroid gland data(More)