Alberto Palacios Pawlovsky

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Breast cancer is the world's second most frequent type of cancer and in Japan it is the third most frequent one. The prognosis of its recurrence, after a first treatment, is very important to increase the survival rate of a patient. This work shows the application of the k-Nearest Neighbors (kNN) method to prognosis breast cancer and also proposes a method(More)
We have been investigating on the generation of input pairs for combinational digital circuits that cause the maximum power consumption on them. This work shows the results we obtained using genetic algorithms (GA). We show a GA that runs almost in the same time that speed up simulated annealing algorithms and give better results than them for half of the(More)
— Low power and reliable thermal design are very important in developing state-of-the-art circuits. This work shows the results obtained with a hybrid of simulating annealing (SA) and a genetic algorithm (GA) and with a hybrid of SA and an evolutionary algorithm (EA) in the generation of inputs pairs that cause the maximum number of switching gates in(More)
—We have studied the effect that a cooling scheme has on a simulated annealing (SA) and evolutionary algorithm (EA) hybrid (SA-EA) method that searches for the input pair that causes the maximum number of switching gates in a com-binational circuit. We tested our approach with twelve cooling schemes and then chose one to test the influence of the initial(More)
This work shows a detailed description of the kNN method when using it for breast cancer diagnosis. We show the relation of the number of neighbors to the accuracy and also the change of accuracy with the change of the percentage of the data used for diagnosis. We also show details about the variation of the maximum and minimum values of the accuracy with(More)
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