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WordNet-like Lexical Databases (WLDs) group English words into sets of synonyms called “synsets.” Synsets are utilized for several applications in the field of text mining. However, they were also open to criticism because although, in theory, not all the members (i.e. word senses) of a synset represent the meaning of that synset with the same degree, in practice, in WLDs they are considered as members of the synset identically. Correspondingly, the fuzzy version of synonym sets, called fuzzy-synsets were proposed. But, to the best or our knowledge. In this study, we present an algorithm for constructing fuzzy version of WLDs of any language, given a corpus of documents and a word-sense-disambiguation system of that language. A theoretical proof is also proposed for the validity of results of the proposed algorithm. Then, inputting the open-American-online-corpus (OANC) and UKB word-sense-disambiguation to the algorithm, we construct and publish online the fuzzified version English WordNet (FWN), and apply them in a Sentiment Analysis problem.

Nowadays renewable energy sources, such as wind and solar, whether independently or integrated with other resources, are considered in power system, specifically self-scheduling, bidding and offering strategy problems. However, the uncertain nature of these sources has turned out the greatest challenge for their owners, which makes the bidding and offering in the restructured electricity market more complicated because wind energy generation may cause penalty fees for its generation mismatches. Hence, the primary objective of this paper is to suggest a novel bidding strategy framework based on fuzzy random variable for a wind-thermal system in the electricity market for the first time. The uncertainties associated with day ahead energy, spinning reserve market prices and imbalance prices, are characterized by random fuzzy variables and the uncertainties associated with wind power outputs are modeled as a LR fuzzy numbers. The proposed self-scheduling model maximizes the expected profits while it controls the risk by providing different possibility and probability levels for decision makers.A mathematical modeling approach is applied in this research by using a mixed-integer non-linear programming model which is implemented in Lingo software in a case study of thermal generation unit to investigate the efficiency of the proposed model. A sensitivity analysis is applied to validate the performance of the proposed model. Numerical results reveal that taking advantage of wind power generation alongside with thermal generation will substantially increase the profitability of the integrated generation company.

WordNet-like Lexical Databases (WLDs) group English words into sets of synonyms called “synsets.” Synsets are utilized for several applications in the field of text mining. However, they were also open to criticism because although, in theory, not all the members (i.e. word senses) of a synset represent the meaning of that synset with the same degree, in practice, in WLDs they are considered as members of the synset identically. Correspondingly, the fuzzy version of synonym sets, called fuzzy-synsets were proposed. But, to the best or our knowledge. In this study, we present an algorithm for constructing fuzzy version of WLDs of any language, given a corpus of documents and a word-sense-disambiguation system of that language. A theoretical proof is also proposed for the validity of results of the proposed algorithm. Then, inputting the open-American-online-corpus (OANC) and UKB word-sense-disambiguation to the algorithm, we construct and publish online the fuzzified version English WordNet (FWN), and apply them in a Sentiment Analysis problem. [ABSTRACT FROM AUTHOR]

The purpose of this study is to consider and modeling E-Commerce variables based on FAHP, FTOPSIS and Possibility Theory to create a new approach in decision-making techniques. Thus, modeling Market Orientations and Innovation Orientation indicators as the E-Satisfaction factors and Fulfillment & Reliability, Responsiveness and Security indicators as the E-Trust factors is done for selected banks. Data collected from customers and experts via distributing questionnaires and the data were analyzed by FAHP, FTOPSIS and Possibility Theory techniques. The results show that E-Satisfaction, Innovation Orientation and Security are the important indicators in E-Loyalty and Mellat Bank takes the highest rank in covering these indicators.