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The autonomic management of large-scale distributed systems now allows performance improvement, availability, and security, while simultaneously reducing the effort and skills required of system administrators. One way that systems can support these abilities is by relying on a continuous monitoring service to keep track of the states of the targeted systems. However, it is challenging to achieve...
Effective software architecture evaluation methods are essential in today's system development for mission critical systems. We have previously developed MEMS and a set of test statistics for evaluating middleware architectures, which proven an effective assessment of important quality attributes and their characterizations. We have observed it is common that many system performance response data...
The volatility of crude oil market and its chain effects to the world economy augmented the interest and fear of individuals, public and private sectors. Previous statistical and econometric techniques used for prediction, offer good results when dealing with linear data. Nevertheless, crude oil price series deal with high nonlinearity and irregular events. The continuous usage of statistical and...
Support vector machine (SVM) is a new method based on statistical learning theory. It has been successfully used to solve nonlinear regression and time series problems. However, SVM has rarely been applied to software reliability prediction. In this study, an SVM-based model for software reliability forecasting is proposed. In addition, the parameters of SVM are determined by Genetic Algorithm (GA)...
Software fault prediction models play an important role in software quality assurance. They identify software subsystems (modules,components, classes, or files) which are likely to contain faults. These subsystems, in turn, receive additional resources for verification and validation activities. Fault prediction models are binary classifiers typically developed using one of the supervised learning...
In this work we present an instance of an architecture for the development of robust evolving predictive models. The architecture provides a conceptual framework for the development of such models while at the same time it provides mechanisms for the minimisation of effort needed for the development and maintenance of the models. These mechanisms deal with the model and parameter selection, model...
Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have been associated with the development of serious complications after the procedure. There is a growing need for understanding the risk of these complications, which has led to the development of statistical risk evaluation models. Conformal Predictors are a recently developed set of...
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