Dempster-Shafer theory provides a sensor fusion framework that autonomously accounts for obstacle occlusion in dynamic, urban environments. However, to discern static and moving obstacles, the Dempster-Shafer approach requires manual tuning of parameters dependent on the situation and sensor types. The proposed methodology utilizes a deep fully convolutional neural network to improve the robust...

This paper presents an integrated approach which utilizes multi criteria decision making (MCDM) alongside with Dempster Shafer Theory of evidence (DST) for the selection of landfill sites. It employs a two stage analysis for selecting landfill sites in urban region of Zanjan. The first-stage analysis makes use of the maps in Geospatial information system (GIS) in conjunction with different vari...

Supplier selection is a multi-criterion decision making problem under uncertain environments. Hence, it is reasonable to hand the problem in fuzzy sets theory (FST) and Dempster Shafer theory of evidence (DST). In this paper, a new MCDM methodology, using FST and DST, based on the main idea of the technique for order preference by similarity to an ideal solution (TOPSIS), is developed to deal w...

In this era of big data, Multi-source Domain Adaptation (MDA) becomes more and popular is employed to make full use available source data collected from several different, but related domains. Although multiple domains provide much information, the processing domain shifts challenging, especially in learning a common domain-invariant representation for all Moreover, it counter-intuitive treat e...

Dempster-Shafer evidence theory is an efficient mathematical tool to deal with uncertain information. In that theory, basic probability assignment (BPA) is the basic element for the expression and inference of uncertainty. Decision-making based on BPA is still an open issue in Dempster-Shafer evidence theory. In this paper, a novel approach of transforming basic probability assignments to proba...

Modern multitarget-multisensor tracking systems involve the development of reliable methods for the data association and the fusion of multiple sensor information, and more specifically the partioning of observations into tracks. This paper discusses and compares the application of Dempster-Shafer Theory (DST) and the Dezert-Smarandache Theory (DSmT) methods to the fusion of multiple sensor att...

In this paper, one studies the famous well-known and challenging Tweety Penguin Triangle Problem (TPTP or TP2) pointed out by Judea Pearl in one of his books. We first present the solution of the TP2 based on the fallacious Bayesian reasoning and prove that reasoning cannot be used to conclude on the ability of the penguin-bird Tweety to fly or not to fly. Then we present in details the counter...

Dempster-Shafer theory is widely applied to uncertainty modelling and knowledge reasoning due to its ability of expressing uncertain information. However, some conditions, such as exclusiveness hypothesis and completeness constraint, limit its development and application to a large extend. To overcome these shortcomings in Dempster-Shafer theory and enhance its capability of representing uncert...

Finding defects in software is a challenging and time and budget consuming task. Minimizing these adverse effects using software defect prediction models via guiding testers with defective parts of software system is an attractive research area. Previous research emphasized the value of these tools with a mean probability of detection of 71 percent and mean false alarm rates of 25 percent. This...

Multi-classifier combination based on Dempster-Shafer theory of evidence has demonstrated it’s superior performance. In the approach based on Dempster-Shafer theory, the basic probability assignments for evidence are usually derived from classifiers’ global performance. However, our study discovered that while using classifiers’ global performance as basic probability assignments doesn’t necess...