4926227
An Approach Based on Similarity Measure to Multiple Attribute Decision Making with Trapezoid Fuzzy Linguistic Variables
In this paper, we investigate the multiple attribute decision making problems under fuzzy linguistic environment. We introduce the concept of trapezoid fuzzy linguistic variable and some operational laws of trapezoid fuzzy linguistic variables. We develop a similarity measure between two trapezoid fuzzy linguistic variables. Based on the similarity measure and the ideal point of attribute values, we develop an approach to ranking the decision alternatives in multiple attribute decision making with trapezoid fuzzy linguistic variables. We finally illustrate the developed approach with a practical example.
44744332
The continuous ordered weighted geometric operator and its application to decision making
The aim of this paper is to develop a continuous ordered weighted geometric (C-OWG) operator, which is based on the continuous ordered weighted averaging (C-OWA) operator recently introduced by the author and the geometric mean. We study some desirable properties of the C-OWG operator, and present its application to decision making with interval multiplicative preference relation, and finally, an illustrative example is pointed out.
15852916
Induced uncertain linguistic OWA operators applied to group decision making
The ordered weighted averaging (OWA) operator was developed by Yager [IEEE Trans. Syst., Man, Cybernet. 18 (1998) 183]. Later, Yager and Filev [IEEE Trans. Syst., Man, Cybernet.--Part B 29 (1999) 141] introduced a more general class of OWA operators called the induced ordered weighted averaging (IOWA) operators, which take as their argument pairs, called OWA pairs, in which one component is used to induce an ordering over the second components which are exact numerical values and then aggregated. The aim of this paper is to develop some induced uncertain linguistic OWA (IULOWA) operators, in which the second components are uncertain linguistic variables. Some desirable properties of the IULOWA operators are studied, and then, the IULOWA operators are applied to group decision making with uncertain linguistic information.
11036293
Virtual reality for neuropsychological diagnosis and rehabilitation: a survey
Recently the considerable potential of virtual reality has been recognized for the scientific study, diagnosis and treatment in the field of mental healthcare. It provides a unique opportunity to provide a natural interface and to mimic actual challenges faced by impaired subjects in their lives. In spite of certain limitations, some work has emerged which provides guidelines for future research efforts. A shortlist of such applications includes assessment and treatment of phobias; obsessive compulsive disorders, posttraumatic stress disorders and hyperactive attention deficit syndromes. We highlight these recent and ongoing research projects and compare this unique paradigm with conventional nonVR techniques by discussion of potential benefits and possible shortcomings. Due to the nature of research, ethical and human factors cannot be over emphasized. We also describe the limitations of VR in the field of practical psychology as media hype has over sold its potential. With recent advances in technology, it is not unrealistic to hope that VR will soon be a part of a clinician's armoury.
5894989
A Hybrid Relevance-Feedback Approach to Text Retrieval
Relevance feedback (RF) has been an effective query modification approach to improving the performance of information retrieval (IR) by interactively asking a user whether a set of documents are relevant or not to a given query concept. The conventional RF algorithms either converge slowly or cost a user's additional efforts in reading irrelevant documents. This paper surveys several RF algorithms and introduces a novel hybrid RF approach using a support vector machine (HRFSVM), which actively selects the uncertain documents as well as the most relevant ones on which to ask users for feedback. It can efficiently rank documents in a natural way for user browsing. We conduct experiments on Reuters-21578 dataset and track the precision as a function of feedback iterations. Experimental results have shown that HRFSVM significantly outperforms two other RF algorithms.
13917211
Web page classification with heterogeneous data fusion
Web pages are more than text and they contain much contextual and structural information, e.g., the title, the meta data, the anchor text,etc., each of which can be seen as a data source or are presentation. Due to the different dimensionality and different representing forms of these heterogeneous data sources, simply putting them together would not greatly enhance the classification performance. We observe that via a kernel function, different dimensions and types of data sources can be represented into acommon format of kernel matrix, which can be seen as a generalized similarity measure between a pair of web pages. In this sense, a kernel learning approach is employed to fuse these heterogeneous data sources. The experimental results on a collection of the ODP database validate the advantages of the proposed method over traditional methods based on any single data source and the uniformly weighted combination of them.
1661848
Efficient constraint processing for location-aware computing
For many applications, such as friend finder, buddy tracking, and location mapping in mobile wireless networks or information sharing and cooperative caching in mobile ad hoc networks, it is often important to be able to identify whether a given set of moving objects is close to each other or close to a given point of demarcation. To achieve this, continuously available location position information of thousands of mobile objects must be correlated against each other to identify whether a fixed set of objects is in a certain proximity relation, which, if satisfied, would be signaled to the objects or any interested party. In this paper, we state this problem, referring to it as the location constraint matching problem and present and evaluate solutions for solving it. We introduce two types of location constraints to model the proximity relations and experimentally validate that our solution scales to the processing of hundreds of thousands of constraints and moving objects.
16217697
Large-sample performance of blind and Group-blind multiuser detectors: a perturbation perspective
In blind and group-blind multiuser detection, different detectors can be designed using either the sample data covariance matrix directly or its eigencomponents. Due to finite-sample effect in practice, their performance deviates from the corresponding optimum. A perturbation technique is developed rigorously and systematically to analyze those detectors in this work. Subject to the assumption that the first-order perturbation dominates, corresponding results can be applied to a practical system of a given sample size. In particular, performance of the following typical detectors is studied for either flat or estimated multipath channels: direct-matrix-inversion (DMI) blind minimum mean-square error (MMSE) detector, subspace blind MMSE detector, direct zero-forcing (ZF) detector, subspace ZF detector, and group-blind hybrid detector. Simulation examples further verify various analytical results.