Catálogo de publicaciones - libros

This paper describes combination rule of normal degrees in human body in automated medical diagnosis system. The normal degree is defined in a framework of fuzzy logic. Physician usually examines whether a patient is either normal or abnormal for a disease. The normal degree is calculated in automated medical diagnosis system. The practical examples of medical images and blood test are described. In it, it is shown that union or inter-section operators are introduced for calculating normal degrees on MR meniscal tear images and blood test for diabetes.

It is very challenging and interesting task in medicine to find a set of representative symptoms for the disease, i.e., the set of symptoms that best characterizes a disease. In this paper we propose a method for constructing such representative symptoms for a particular disease, based on the data taken from the patient-records. The method uses a closure operator on the base of which a (one-sided) fuzzy concept lattice is defined.

In this paper, we study and discuss the direct products of fuzzy connectives, negations and implications based on t-norms and t-conorms and give a necessary and sufficient condition for their direct decomposability.

The cancellativity A ⊕_T B = A ⊕_T C ⇒ B = C means that the equation A ⊕_T X = D has unique solution. The cancellation law for sum of fuzzy quantities based on the strongest t-norm T _M holds for arbitrary fuzzy interval A , see e.g. [ 2 ], [ 7 ], [ 8 ]. For the weakest t-norm T _D the cancellation law holds only for very special fuzzy intervals. Based on our results from [ 1 ] and Zagrodny’s results [ 10 ] we will present conditions for validity of the cancellation law for addition based on a continuous Archimedean t-norm.

Triangular norms were introduced in the framework of probabilistic metric spaces. However, they are widely applied in several other fields, e.g., in fuzzy set theory, fuzzy logic, and their applications. Therefore, the problem of compact representation of continuous t-norms using generating functions is of permanent interest.

In this paper, we describe a technique for extracting patterns to a XML data flow; then, we show how such patterns can be developed into an ontology of classes. Also, we discuss the impact of different fuzzy representation techniques for XML data on the outcome of our procedure. One might wonder why all this is needed, since the semantics of XML data could in principle be satisfactorily represented via their associated XML schemata ComplexTypes. Unfortunately it turns out that standard XML schema definitions need to cover a wide repertoire of possible attributes. For this reason, optional elements are widely used, thus decreasing the expressiveness of XML schemata as descriptors of the content of single instances. Our approach relies on comparing fuzzy encodings of XML fragments. This comparison will allow us to define “typical” sets of attributes, that we shall consider hints to possible meaningful classes. Then, we shall evaluate fuzzy overlapping between candidate cluster heads in order to define a tentative class hierarchy. Our fuzzy modelling assumes that a domain expert has associated an importance degree in the [0, 1] interval to vocabulary elements (i.e. tag names). As we shall see in the remainder of the paper, this burden is not excessive, since this importance assessment only needs to be carried out once, looking at the schema. At run time, each incoming XML fragment is mapped into a fuzzy set whose elements are the tag names [ 3 ]. Each element membership is computed by aggregating the vocabulary importance values of the tags lying on the path from it to the root.

In this paper we introduce $$ \mathcal{A}\mathcal{L}\mathcal{C}\mathcal{Q}_F^ + $$ , a fuzzy description logic with extended qualified quantification. The proposed language allows for the definition of fuzzy quantifiers of the absolute and relative kind by means of piecewise linear functions on ℕ and ℚ ∩ [0, 1] respectively. These quantifiers extends the usual (qualified) ∃, ∀ and number restriction. The semantics of quantified expressions is defined by using method GD [ 4 ], that is based on recently developed measures of the cardinality of fuzzy sets.

Many applications deal with data which are not easy to accommodate in conventional data models. This paper shows how the advanced features of modern programming platforms allow the transparent implementation of lazy typing capabilities which can then be easily used by programmers. As a first approximation to the problem, we show how this capabilities can be used to develop applications with fuzzy types.

The aim of this paper is to describe the method of induction of generalized annotated programs called IGAP what is a special case of inductive fuzzy logic programming for monotonely classified data. This method is based on the multiple use of two valued ILP and the syntactical equivalence of fuzzy logic programs and a restricted class of generalized annotated programs. Finally we compare our method with several fuzzy ILP methods.

In this work we propose a flexible approach to evaluate association rules on XML documents. In particular we describe evaluation techniques in order to assign a satisfiability degree to structural association rules , which allow one to evaluate the similarity of the XML document with respect to a given structure, and to value association rules which allow one to capture the similarity between the information contained in the XML document and the required information.