No abstract

Bayesian Network (BN) is a classification technique widely used in Artificial Intelligence. Its structure is a Direct Acyclic Graph (DAG) used to model the association of categorical variables. However, in cases where the variables are numerical, a previous discretization is necessary. Discretization methods are usually based on a statistical approach using the data distribution, such as division by quartiles. In this article we present a discretization using a heuristic that identifies events called peak and valley. Genetic Algorithm was used to identify these events having the minimization of the error between the estimated average for BN and the actual value of the numeric variable output as the objective function. The BN has been modeled from a database of Bit's Rate of Penetration of the Brazilian pre-salt layer with 5 numerical variables and one categorical variable, using the proposed discretization and the division of the data by the quartiles. The results show that the proposed heuristic discretization has higher accuracy than the quartiles discretization.

Generation of membership functions is an important step in construction of fuzzy systems. Since membership functions reflect what is known about the variables involved in a problem, when they are correctly modeled the system will behave in the manner that is expected in the context of the problem being addressed. Since their creation, type-1 membership functions have been used in domains characterized by uncertainty. Nevertheless, use of type-2 membership functions has been expanding over recent years because they are considered more appropriate for this application. Both types of membership function can be generated with the aid of automatic methods that implement generation of membership functions from data. These methods are convenient for situations in which it is not possible to obtain all the information needed from an expert or when the problem in question is complex. The aim of this study is to present a review of the most important automatic methods for generation of membership functions, both type 1 and interval type-2, highlighting the principal characteristics of each approach.

This paper presents the study and expansion of a dynamic simulation model for aging and death (Hargrove 1998), which contemplates the representation of physiological capacity and the generation of events of risk in the life of an individual. The study identified the most influential parameters in the results of the simulations and a health impact and recovery module was included. The simulation model incorporates a fuzzy module to treat uncertainty. The expansion conducted allowed adapting the results of the simulation to real mortality curves. The reproduction of mortality curves allowed the study of populations with similar characteristics as well as the factors that could influence their development. This is interesting principally because it is possible to calibrate parameters with risk values for diseases that have high associated costs for both public and private health plans.

Para corresponder ao desafio de compreender e explicar como o processo deconstrução do conhecimento acontece, este estudo buscou analisar ecomparar o processo de construção do conhecimento em dois cenários: escritacolaborativa tradicional e escrita colaborativa apoiada por computador (editorcolaborativo Equitext). Com esse intuito foi feita uma revisão bibliográfica sobreo tema, que mostrou a existência de uma relação direta entre o pensamentocrítico e a colaboração com o aprendizado profundo. Partiu-se de duaspremissas. A primeira foi de que os sistemas de aprendizagem colaborativaapoiados por computador e, especificamente, os editores colaborativosdeveriam favorecer o pensamento crítico e, consequentemente, o aprendizadoprofundo. A segunda, que os editores colaborativos deveriam oferecermelhores condições para o processo de construção colaborativa de um textoem relação ao processo de escrita tradicional. Foram então projetados numsegundo momento os experimentos que permitissem a coleta de dados nosdois cenários diferentes. Conseguiu-se demonstrar quais resultados, emtermos de desenvolvimento de pensamento crítico, podem ser obtidos emambientes de aprendizagem colaborativa, especificamente em cenários deescrita colaborativa.