Bipolar neutrosophic sets are the extension of neutrosophic sets and are based on the idea of positive and negative preferences of information. Projection measure is a useful apparatus for modeling real life decision making problems. In the paper, we have defined projection, bidirectional projection and hybrid projection measures between bipolar neutrosophic sets and the proposed measures are t...

The aim of this paper is to investigate intuitionistic fuzzy multiple attribute group decision making problems where the attribute values provided by experts are expressed in intuitionistic fuzzy numbers, and the weight information about the experts is to be determined. We present a new method to derive the weights of experts and rank the preference order of alternatives based on projection mod...

In many areas of medicine, visualization researchers can help by contributing to task simplification, abstraction or complexity reduction. As these approaches, can allow a better workflow in medical environments by exploiting easier communication through visualization, it is important to question their reliability and their reproducibility. Therefore, within this short paper, we investigate how...

Emotion is one of the important factors that cause the system performance degradation. By analyzing the similarity between channel effect and emotion effect on speaker recognition, an emotion compensation method called emotion attribute projection (EAP) is proposed to alleviate the intraspeaker emotion variability. The use of this method has achieved an equal error rate (EER) reduction of 11.7%...

The paper develops two new methods for solving multiple attribute decision making problems with interval – valued neutrosophic assessments. In the decision making situation, the rating of alternatives with respect to the predefined attributes is described by linguistic variables, which can be represented by interval valued neutrosophic sets. We assume that the weight of the attributes are not e...

Detecting outliers is an important task in many applications. Since most applications possess high dimensional data, traditional outlier detecting methods will become inefficient in such cases. To solve the problem, we propose the concept of outlying reduction by extending attribute reduction in rough set theory. Additionally, we define the key knowledge attribute subspace (KKAS), which can pro...

In view of the multi-attribute decision-making problem that the attribute values are grey multi-source heterogeneous data, a decision-making method based on kernel and greyness degree is proposed. The definitions of kernel and greyness degree of an extended grey number in a grey multi-source heterogeneous data sequence are given. On this basis, we construct the kernel vector and greyness degree...