Mathematical Theory of Evidence called also Dempster-Shafer Theory (DST) is known as a foundation for reasoning when knowledge is expressed at various levels of detail. Though much research effort has been committed to this theory since its foundation, many questions remain open. One of the most important open questions seems to be the relationship between frequencies and the Mathematical Theor...

This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). takes into account the behaviour each pixel with its neighbours. It consists in calculating quadratic distance between value I (x, y) point all neighbouring pixels. Local is used to determine mass function defined DempsterShafer theory. The two classes studied are : fuzzy part focused pa...

In Dempster–Shafer evidence theory (DST), some classical combination rules can be used to fuse the multiple pieces of evidence, respectively abstracted from different attributes (features) so as increase accuracy multiattribute classification decision making. However, most them have not yet considered interdependence among evidence. The newly proposed maximum likelihood evidential reasoning (MA...

This paper investigates the issues of combination and normalization of interval-valued belief structures within the framework of Dempster–Shafer theory (DST) of evidence. Existing approaches are reviewed, examined and critically analysed. They either ignore the normalization or separate it from the combination process, leading to irrational or suboptimal interval-valued belief structures. A new...

Due to the construction and network infrastructure of wireless sensor network (WSN) are known to be vulnerable to variety of attacks. In order to ensure its functionality especially in malicious environments, security mechanisms are essential. Several works have been done to secure WSN, but identification of insider attacker has not been given much attention. In the WSN system the malicious nod...

Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a much higher computational burden. A lot of work has been done to reduce the time complexity information fusion with Dempster's rule, which is pointwise multiplication two zeta transforms, and optimal general algorithms have found get complete definition these transfor...

Context-sensing for context-aware HCI challenges the traditional sensor fusion methods with dynamic sensor configuration and measurement requirements commensurate with human perception. The Dempster-Shafer theory of evidence has uncertainty management and inference mechanisms analogous to our human reasoning process. Our Sensor Fusion for Contextaware Computing Project aims to build a generaliz...

Dempster-Shafer evidence theory is an efficient method to process uncertain, incomplete and vague information in data fusion. The combination of conflicting bodies of evidence has been one of the most important issues in Dempster-Shafer evidence theory since the example of counter-intuitive results produced by Dempster’s rule was found. Based on the framework of the Dempster-Shafer evidence the...

How to manage conflict is still an open issue in Dempster-Shafer evidence theory. The correlation coefficient can be used to measure the similarity of evidence in Dempster-Shafer evidence theory. However, existing correlation coefficients of belief functions have some shortcomings. In this paper, a new correlation coefficient is proposed with many desirable properties. One of its applications i...