In this paper, a fuzzy information fusion method of fault diagnosis based on evidence similarity measure is presented. First, because of the fuzziness of information received by sensors, membership functions are introduced to describe the fault template mode in model database and features extracted from sensor observations; then the degrees of matching between them are obtained using random set...

Current longitudinal image registration methods rely on the assumption that image appearance between time-points remains constant or changes uniformly within intensity classes. This assumption, however, is not valid for magnetic resonance imaging of brain development. Registration methods developed to align images with non-uniform appearance change either (i) locally minimize some global simila...

One of the problems addressed by the Tracker project is the extraction of the key issues discussed at meetings through the analysis of transcripts. Whilst the task of topic extraction is an easy task for humans it has proven difficult task to automate given the unstructured nature of our transcripts. This paper proposes a new approach to transcript segmentation based on the Utterance Cosine Sim...

recommender systems (rs) provide personalized recommendation according to user need by analyzing behavior of users and gathering their information. one of the algorithms used in recommender systems is user-based collaborative filtering (cf) method. the idea is that if users have similar preferences in the past, they will probably have similar preferences in the future. the important part of col...

We show how to consider similarity between features for calculation of similarity of objects in the Vec­ tor Space Model (VSM) for machine learning algorithms and other classes of methods that involve similarity be­ tween objects. Unlike LSA, we assume that similarity between features is known (say, from a synonym dictio­ nary) and does not need to be learned from the data. We call the proposed...

Cosine similarity is a term-vector-based measure of similarity that has been used widely in information retrieval research. In this study, we collect user judgments of web document similarity in order to investigate the correlation between cosine similarity and users’ perception of similarity on web documents. Experimental results demonstrate that it is hard to deduce that cosine similarity cor...

The degree of similarity or dissimilarity between the objects under study plays an important role. In vector space, especially, the cosine similarity measure is often used in information retrieval, citation analysis, and automatic classification. However, it scarcely deals with trapezoidal fuzzy information and multicriteria decision-making problems. For this purpose, a cosine similarity measur...

OBJECTIVE In pattern recognition and medical diagnosis, similarity measure is an important mathematical tool. To overcome some disadvantages of existing cosine similarity measures of simplified neutrosophic sets (SNSs) in vector space, this paper proposed improved cosine similarity measures of SNSs based on cosine function, including single valued neutrosophic cosine similarity measures and int...

Similarity measure is an important tool in pattern recognition and fault diagnosis. This paper proposes two cotangent similarity measures for single-valued neutrosophic sets (SVNSs) based on cotangent function. Then, the weighted cotangent similarity measures are introduced by considering the importance of each element. Moreover, by the comparison between the cotangent similaritymeasures of SVN...

Dis) similarity measure is a significant component of vector model. In content based image retrieval the compatibility of (dis)similarity measure and representation technique is very important for effective and efficient image retrieval. In order to find a suitable dis-similarity measure for a particular representation technique experimental comparison is needed. This paper highlights some of t...