Interval-valued intuitionistic fuzzy number uses membership degree and non-member ship degree to express a decision-maker’s hesitation, which make decision-maker express his/her opinion in uncertainty decision-making environment easily. The comparison between some intervalvalued intuitionistic fuzzy numbers can not be undertaken because of its limitation. In application such as weak consistency...

the sensitivity analysis for multi-attribute decision making (madm) problems is important for two reasons: first, the decision matrix as the source of the results of a decision problem is inaccurate because it sorts the alternatives in each criterion inaccurately. second, the decision maker may change his opinions in a time period because of changes in the importance of the criteria and in the ...

Inherent characteristics of mobile devices make resource discovery in mobile grid challenging. Adding to the complexity is heterogeneity and hence multi-attribute management of these devices. Decentralized resource discovery using DHTs incur maintenance overheads when changes in attributes are continuous and rapid. The paper proposes a novel dual heap non-DHT overlay (DEEPG) for query resolutio...

In laboratory studies, praising children's effort encourages them to adopt incremental motivational frameworks--they believe ability is malleable, attribute success to hard work, enjoy challenges, and generate strategies for improvement. In contrast, praising children's inherent abilities encourages them to adopt fixed-ability frameworks. Does the praise parents spontaneously give children at h...

Multi-Attribute Generalization is an algorithm for attribute-oriented induction in relational databases using domain generalization graphs. Each node in a domain generalization graph represents a diier-ent way of summarizing the domain values associated with an attribute. When generalizing a set of attributes, we show how a serial implementation of the algorithm generates all possible combinati...

Discovering Attribute-Value Association (AVA) is of fundamental importance in knowledge discovery. Market Basket Analysis is an archetypical application. However, most existing algorithms rely only on frequency counts directly obtained from data at the surface and thus cannot reveal deeper knowledge, i.e. the AVAs governed by hidden factors inherent in the data. This paper proposes a new method...

The liver is well known as that organ which is obligately required for the intrahepatocyte development of the pre-erythrocytic stages of the malaria-causative agent Plasmodium. However, largely neglected is the fact that the liver is also a central player of the host defense against the morbidity- and mortality-causing blood stages of the malaria parasites. Indeed, the liver is equipped with a ...

This paper presents an algorithm for jointly learning a set of mid-level attributes from an image ensemble by locating clusters of dependent attributes. Human describable attributes are an active research topic due to their ability to transfer between domains, human understanding, and improvement to identification performance. Joint learning may allow for enhanced attribute classification when ...

The sensitivity analysis for multi-attribute decision making (MADM) problems is important for two reasons: First, the decision matrix as the source of the results of a decision problem is inaccurate because it sorts the alternatives in each criterion inaccurately. Second, the decision maker may change his opinions in a time period because of changes in the importance of the criteria and in the ...

When attempting to portray the attractiveness of a brand vis-à-vis its competitors, an ad may make global claims about superiority or specific claims about one or more attributes. A special case of latter is the piecemeal ad in which the advertised brand is compared to a competitor on one attribute, a different competitor on a second attribute, another competitor on a third attribute, and so on...