A new approach to define and assign statistical parameters to Bayesian inference nodes derived from fuzzy logic technology is proposed. First to develop an intelligent medical diagnostic system, the individual membership function can be pre-defined by matching separately the adapted high-order polynomial, S-type or quasi-Gaussian function with plot of collected clinical diagnostic data. Consequ...

This paper presents an approach for fuzzy Bayesian games, based on the notion of fuzzy probabilities to estimate unknown types of agents in strategic situations. For that, we consider the notion of fuzzy probability introduced by Buckley and Eslami, who use fuzzy numbers to represent uncertain and imprecise probabilities, without, however, applying the fuzzy probability theory, but considering ...

This paper describes an approach to acquire qualitative and quantitative knowledge from verbally stated models in complex, probabilistic domains. This work is part of the development of an intelligent environment, MEDICUS2, that supports modelling and diagnostic reasoning in the domains of environmental medicine and human genetics. These domains are two yet new subdomains of medicine receiving ...

Ensuring adequate use of the computing resources for highly fluctuating availability in multi-user computational environments requires effective prediction models, which play a key role in achieving application performance for large-scale distributed applications. Predicting the processor availability for scheduling a new process or task in a distributed environment is a basic problem that aris...

The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph...

In this paper, Artificial Neural Networks (ANNs) are used to model the effect of atmospheric air-plasma treatment on fabric surfaces with various structures. In order to reduce the complexity of the models and increase the knowledge and comprehension of the underlying process, a fuzzy sensitivity variation criterion is used to select the most relevant parameters which are taken as inputs of the...

As an associative memory neural network model, the cerebellar model articulation controller (CMAC) has attractive properties of fast learning and simple computation, but its rigid structure makes it difficult to approximate certain functions. This research attempts to construct a novel neural fuzzy CMAC, in which Bayesian Ying-Yang (BYY) learning is introduced to determine the optimal fuzzy set...

the main purpose of this paper is to present a fuzzy multi-criteria decision making (fmadm) model for appropriate location selection of a health center. therefore, we identify sixteen criteria and sub-criteria for selecting a health center location. these criteria and sub-criteria have been obtained from literature reviews and practical interviews. this paper proposes a method which combines th...

We introduce a conservative fuzzy logic extension of the Naive Bayesian classification algorithm. The extension generalizes the algorithm such that the examples are described by a fuzzy set of attributes, instead of a classical set. Thus, an example possesses each attribute to a degree in [0, 1]. We present a new classification algorithm usable with fuzzy sets that is (a) fast, (b) is able to w...