Wagner Meira Júnior

A construção da base de dados nacional em Terapia Renal Substitutiva (TRS) centrada no indivíduo: aplicação do método de linkage determinístico-probabilístico

Os sistemas de informação em saúde são ferramentas fundamentais para subsidiar a tomada de decisões e auxiliar a organização dos serviços, por meio do planejamento das ações e do acompanhamento e avaliação dos objetivos propostos. O sistema de saúde brasileiro tem larga experiência com a captura e o uso de informações. No entanto, Morais e Gómez (2007) fazem uma reflexão de que os atuais pressupostos, práticas e saberes de informação e informática em saúde não mais dão conta da complexidade do processo saúde/doença/cuidado, apontando algumas questões: a fragmentação dos indivíduos entre diversas bases de dados em saúde, o que acar-

Masks: Managing Anonymity While Sharing Knowledge to Servers

This work presents an architecture that allows users to enhance their privacy control over the computational environment. Web privacy is a topic that is raising, nowadays, many discussions. Usually, people do not know how their privacy can be violated or what can be done to protect it. Among the generated conflicts, we would like to show up the one that happens between privacy and personalization: by one side, users appreciate the idea of receiving personalized services and do not approve the collection, tracing and analysis of their actions; by the other side, personalization services need this type of information in order to profile their users. The architecture presented in this article helps users to understand better how their privacy can be invaded and, at the same time, gives them a better control of their privacy, through anonymity, without preventing them from receiving personalized services.

Methodology for fraud detection in electronic transactions

Due to the growing popularity of the Web, there is an increasing number of people who performs e-business transactions. On the other hand, this popularity has also attracted the attention of criminals, raising the number of fraud cases in Web and financial losses that reach billions of dollars per year. This paper proposes a methodology, based on the knowledge discovery process, to detect fraud in online payment systems. In order to evaluate this methodology we define the concept of economical efficiency and applied it to an actual dataset of one of the largest Latin American electronic payment systems. The results show a very good performance for our proposal, providing gains of up to 46.5% in comparison with the strategy currently employed.

Particle swarm optimization for multi-label classification

Multi-label classification learning first arose in the context of text categorization, where each document may belong to several classes simultaneously and has attracted significant attention lately, as a consequence of both the challenge it represents and its relevance in terms of application scenarios. In this paper, we propose a new hybrid approach, Multi Label K-Nearest Michigan Particle Swarm Optimization (ML-KMPSO), that is based on two strategies: Michigan Particle Swarm Optimization (MPSO) and ML-KNN. We evaluated the performance of ML-KMPSO using two real-world datasets and the results show that our proposal matches or outperforms well-established multi-label classification learning algorithms.

A Hybrid Particle Swarm Optimization Applied for Multi-Label Classification Problem

Multi-label learning first arose in the context of text categorization, where each document may belong to several classes simultaneously. In this paper, we propose a hybrid approach, called Multi Label K-Nearest Michigan Particle Swarm Optimization (ML-KMPSO). It is based on two strategies. The first strategy is the Michigan Particle Swarm Optimization (MPSO), which breaks the multi-label classification task into several binary classification problems. The second strategy is ML-KNN, which is complementary and takes into account the correlations among classes. We evaluated the performance of ML-KMPSO using two real-world benchmark datasets: Yeast gene functional analysis and natural scene classification. The experimental results show that ML-KMPSO produced results that match or outperform well-established multi-label learning algorithms.

A KDD-Based Methodology to Rank Trust in e-Commerce Systems

Due to the growing popularity of the Web, there is an increasing number of people who perform e-business transactions. On the other hand, this popularity has also attracted the attention of criminals, raising the number of frauds on the Web and associated financial losses, which reach billions of dollars per year. This paper proposes a KDD-based methodology to detect fraud in e-payment systems. In order to evaluate this methodology we defined the concept of economic efficiency and applied it to an actual dataset of one of the largest Latin American electronic payment systems. The results show a very good performance, providing gains of up to 46.5% in comparison with the strategy currently employed by the company.