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The present paper deals with the problem of an assessment of symptoms in medical diagnosis. A unified interpretation of symptoms is necessary to estimate their significance in a diagnosis. Yet, even if they are properly defined, different evaluations of them based on experts' knowledge or statistical estimation are possible. The present study aims at combining evaluations that may originate from an expert or can be found from statistical features of the data, as well as those determined for ‘easy’ and ‘difficult’ diagnostic cases. A model of diagnostic inference is proposed in the framework of the Dempster–Shafer theory extended for fuzzy focal elements. The basic probability assignment defined in this theory estimates weights of symptoms. Two basic probability assignments can be created and then combined. In this way weights of symptoms represent knowledge common for two kinds of data or obtained from an expert and from data. Thus, a combination of heuristics and data mining results becomes possible. An algorithm of the basic probability assignment calculation is suggested and tested for medical data: a database from the internet and individually gathered data. [ABSTRACT FROM AUTHOR]

In today's competitive environment, rapid advances in economic globalization and information technology have forced many organizations to anticipate and respond to increasing volatility and competitive pressures. Numerous researches have focused on developing techniques on the analysis, design and management of whole logistics chains in particular in the domain of data mining, data analysis and data classification. While the data mining techniques and computational intelligence techniques are good independently for solving specific tasks, they can be synergized through the formation of an integrated and unified model, which can take advantage of the goods and offset the flaws of the two techniques. In this paper, an intelligent information infrastructure, which is characterized by its ability to encompass a rich collection of knowledge representation formalisms for dealing with a logistics information flow problem, is presented. Such intelligent information infrastructure includes a two-step strategy that embraces the combination of online analytical processing and neural networks to support knowledge discovery. In addition, extensible markup language is used to support the overall infrastructure, in order to facilitate the seamless data interchange within an enterprise. [ABSTRACT FROM AUTHOR]