Paola Verónica Britos

Requirements Elicitation in Data Mining for Business Intelligence Projects

There are data mining methodologies for business intelligence (DM-BI) projects that highlight the importance of planning an ordered, documented, consistent and traceable requirement’s elicitation throughout the entire project. However, the classical software engineering approach is not completely suitable for DM-BI projects because it neglects the requirements specification aspects of projects. This article focuses on identifying concepts for understand DM-BI project domain from DM-BI field experience, including how requirements can be educed by a proposed DM-BI project requirements elicitation process and how they can be documented by a template set.

Fuzzy Control For Improving Energy Management Within Indoor Building Environments

Research on energy efficiency in buildings has proved that the design and even the sophisticated facilities such as building energy management systems (BEMS), aiming to improve the indoor environmental conditions while minimizing the energy needs, are not sufficient enough due to users interference. Users are a dynamic part of the building, therefore they should be taken into account in the control strategy. Latest trends in designing intelligent building energy management systems (IBEMS) integrate a man machine interface that could store the users preferences and adapt the control strategy accordingly. The objective of the present paper is to present the advantages of the fuzzy control techniques together with a man machine interface based on a smart card terminal in satisfying the user preferences. A fuzzy controller is developed and the minimization of energy consumption is achieved by the use of a suitable cost function for the whole system. The overall control system including the cost function has been modeled and tested by using the software tool MATLAB/SIMULINK.

PREDICTION IN HEALTH DOMAIN USING BAYESIAN NETWORKS OPTIMIZATION BASED ON INDUCTION LEARNING TECHNIQUES

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

Work in progress - programming misunderstandings discovering process based on intelligent data mining tools

We present research work in progress that focuses on data mining tools used for helping teachers to apply a three step knowledge discovering process to diagnose studentspsila misunderstandings (and their causes) related to their programming errors.

Detection of Breast Lesions in Medical Digital Imaging Using Neural Networks

The purpose of this article is to present an experimental application for the detection of possible breast lesions by means of neural networks in medical digital imaging. This application broadens the scope of research into the creation of different types of topologies with the aim of improving existing networks and creating new architectures which allow for improved detection.

Learning by knowledge sharing in autonomous intelligent systems

Very few learning systems applied to problem solving have focused on learning operator definitions from the interaction with a completely unknown environment. In order to achieve better learning convergence, several agents that learn separately are allowed to interchange each learned set of planning operators. Learning is achieved by establishing plans, executing those plans in the environment, analyzing the results of the execution, and combining new evidence with prior evidence. Operators are generated incrementally by combining rote learning, induction, and a variant of reinforcement learning. The results show how allowing the communication among individual learning (and planning) agents provides a much better percentage of successful plans, plus an improved convergence rate than the individual agents alone.

Patterns in temporal series of meteorological variables using SOM & TDIDT

The purpose of the present article is to investigate if there exist any such set of temporal stable patterns in temporal series of meteorological variables studying series of air temperature, wind speed and direction an atmospheric pressure in a period with meteorological conditions involving nocturnal inversion of air temperature in Allen, Rio Negro, Argentina. Our conjecture is that there exist independent stable temporal activities, the mixture of which give rise to the weather variables; and these stable activities could be extracted by Self Organized Maps plus Top Down Induction Decision Trees analysis of the data arising from the weather patterns, viewing them as temporal signals.

Detecting Unusual Changes of Users Consumption

The points being approached in this paper are: the problem of detecting unusual changes of consumption in mobile phone users, the corresponding building of data structures which represent the recent and historic users’ behavior bearing in mind the information included in a call, and the complexity of the construction of a function with so many variables where the parameterization is not always known.

Pedagogical Protocols Selection Automatic Assistance

The preliminary results presented in this paper corresponds to a research project oriented to the search of the relationship between the predilection of students concerning learning style and the pedagogical protocols used by the human tutors (professors during the first courses of the Computer Engineering undergraduate Program) by using intelligent systems tools.

Optimizing Building's Environments Performance Using Intelligent Systems

This article describes an intelligent system architecture that based on neural networks, expert systems and negotiating agents technologies is designed to optimize intelligent buildings performance. By understanding a building as a dynamic entity capable of adapting itself not only to changing environmental conditions but also to occupants living habits, high standards of comfort and user satisfaction can be achieved. Results are promising and encourage further research in the field of artificial intelligence applications in building automation systems.