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This paper presents a proposal of an adaptive device for low-pulse detection on NIBP measurements using an adaptive decision tree algorithm (AdapTree) and a probabilistic methodology, besides featuring learning related to expert knowledge.

This research dealt with the unification of graphs applied to the problem of haplotype networks. From the low reliability found in networks generated by the traditional algorithms, it was necessary to introduce a new proposition to improve the outcome. For proper evaluation of the results obtained by the new algorithm, a formal-theoretical framework for generalization of haplotype networks related solutions, making use of parameterized functions easily represented through LISP-like functional languages. Seeking for real case application of the theory developed, a structure of simulation and testing were developed to build genetic code strings randomly or even parameterized. The generated tests have allowed to observe the expected improvement in the algorithm, especially for cases of low mutation. The framework for simulation and testing was extremely useful and easy to use, making itself a product to be distributed to the academic Biology community.

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