This paper presents a novel fingerprint classifier fusion algorithm using Dempster-Shafer theory concomitant with update rule. The proposed algorithm accurately matches fingerprint evidences and also efficiently adapts to dynamically evolving database size without compromising accuracy or speed. We experimentally validate our approach using three fingerprint recognition algorithms based on minu...

The research described in this paper addresses issues of designing a computationally effective decision support system which can assist a decision maker in making an optimal choice between several discrete alternatives. A new hybrid approach to multi-attribute decision making under uncertainty incorporating Neural Networks and the Dempster-Shafer theory of evidence is introduced. A neural netwo...

Dempster-Shafer Evidence Theory(DST) enables multi-sensor data fusion, which makes it possible to infer the context. In this paper, we propose about how to use multi-sensor data fusion to infer context in the dynamic circumstances. Dynamic circumstance means a changing of the situation or the surrounding itself, and particularly signifies that there is a changeable factor in a specific environm...

In this paper we are presenting a work concerning the classification and recognition of off-line signatures. Signatures form a special class of handwriting in which legible letters or words may be impossible to exhibit but we can extract some features with the help of some parameters. Our proposed fusion methodology for improving the classification and recognition performance of classifiers is ...

The factors influencing dam safety are complex and their impact on dam safety is fuzzy and uncertain. With the introduction of the Dempster-Shafer theory, BP neural network and Jousselme distance function into dam safety monitoring, a new method for dam safety evaluation is explored. Firstly, the basic probability assignment of the factors influencing dam safety is determined by BP neural netwo...

1 Faculty of Electronics and Telecommunications, Multimedia Dept. Bd. V. Parvan 2, 300223 Timisoara, Romania, e-mail: [email protected], [email protected] 2 Mathematics, Image and Applications Laboratory, University of La Rochelle Avenue Michel Crepeau 17042 La Rochelle, France, e-mail: [email protected], [email protected] Abstract – Detection of moving objects has been widel...

It is critical to build an effective prediction model to improve the accuracy of financial distress prediction. Some existing literatures have demonstrated that single classifier has limitations and combination of multiple prediction methods has advantages in financial distress prediction. In this paper, we extend the research of multiple predictions to integrate with rough set and Dempster–Sha...

Stochastic Multicriteria Acceptability Analysis (SMAA) is a family of methods to aid decision makers (DMs) in discrete decision making problems. SMAA methods are capable of handling preference information of various types. The imprecise preferences of multiple DMs can for example be represented by a joint probability distribution for criteria weights. Dempster-Shafer theory with Analytic Hierar...

We develop a new approach for decision making with Dempster-Shafer theory of evidence by using uncertain information represented in the form of interval numbers. We suggest the use of different types of uncertain aggregation operators in the problem. Then, we obtain new aggregation operators such as the belief structure uncertain ordered weighted averaging (BSUOWA) operator and the BS uncertain...