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We discuss the integration of software verification and validation activities (as defined by the IEEE Std. 1012) within the Unified Process. We compare and contrast these two process frameworks, and identify the aspects of verification and validation that are directly supported, partially supported or not supported by the Unified Process.

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This chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on artificial intelligence using Bayesian Networks, Influence Diagrams and principles of traditional statistics. The model research is based on a repository of 10,000 medical records at a hospital in Peru. The model aims to identify the factors that are causes of morbidity in newborns, is based on data mining techniques and developed using the CRISP-DM methodology.