Background and Objectives: Evaluating the performance of clinical units is critical for effective management of health settings. Certain assessment of clinical variables for performance analysis is not always possible, calling for use of uncertainty theory. This study aimed to develop and evaluate an integrated independent component analysis-fuzzy-data envelopment analysis approach to accurate ...

This paper focuses on Probabilistic Complex Event Processing (PCEP) in the context of real world event sources of data streams. PCEP executes complex event pattern queries on the continuously streaming probabilistic data with uncertainty. The methodology consists of two phases: Efficient Generic Event Filtering (EGEF) and probabilistic event sequence prediction paradigm. In the first phase, a N...

The adjective "fuzzy" seems to be a very popular and very frequent one in the contemporary studies concerning the logical and set-theoretical foundations of mathematics. The main reason of this quick development is, in our opinion, easy to be understood. The surrounding us world is full of uncertainty, the information we obtain from the environment, the notions we use and the data resulting fro...

The practical impact of treatment of epistemic uncertainty on decision making was illustrated on two kinds of decisions from chemical regulation. First, regulatory strategies derived from a simplified decision model based on toxicity and persistence showed that regulated level of exposure is more conservative (safe) when uncertainty has been given a non-probabilistic treatment. Persistence and ...

Fuzzy control systems have had various applications in a wide range of science and engineering in recent years. Since an unstable control system is typically useless and potentially dangerous, stability is the most important requirement for any control system (including fuzzy control system). Conceptually, there are two types of stability for control systems: Lyapunov stability (a special case ...

Type-2 fuzzy set theory is one of the most powerful tools for dealing with the uncertainty and imperfection in dynamic and complex environments. The applications of type-2 fuzzy sets and soft computing methods are rapidly emerging in the ecological fields such as air pollution and weather prediction. The air pollution problem is a major public health problem in many cities of the world. Predict...

Using Multi-Criteria Decision-Making (MCDM) to solve complicated decisions often includes uncertainty, which could be tackled by utilizing the fuzzy sets theory. Type-2 fuzzy sets consider more uncertainty than type-1 fuzzy sets. These fuzzy sets provide more degrees of freedom to illustrate the uncertainty and fuzziness in real-world production projects. In this paper, a new multi-criteria ana...

in applications there occur different forms of uncertainty. the twomost important types are randomness (stochastic variability) and imprecision(fuzziness). in modelling, the dominating concept to describe uncertainty isusing stochastic models which are based on probability. however, fuzzinessis not stochastic in nature and therefore it is not considered in probabilisticmodels.since many years t...

This paper reviews research in relation with modelling uncertainty within Decision Support Systems (DSS) from 2000 to 2011. It specifically addresses software that has been built or prototyped with the purpose of supporting actual decision making, which is able to explicitly deal with uncertainty (widely understood) on the corresponding model parameters and/or data. The main DSS features analys...