Current Issue : October - December Volume : 2014 Issue Number : 4 Articles : 5 Articles
Airport classification is a common need in the air transport field due to several purposesââ?¬â?such as resource allocation, identification\nof crucial nodes, and real-time identification of substitute nodesââ?¬â?which also depend on the involved actorsââ?¬â?¢ expectations. In this\npaper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the\nconcept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance\namong airportsââ?¬â?which corresponds to a measure of similarityââ?¬â?has in fact an intrinsic fuzzy nature due to the airport specific\ncharacteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained\nresults are in line with expectations....
The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their\nbusiness. Good management of these risks is a key factor to increase profitability.Therefore, every bank needs to predict the credit\nrisks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This\npaper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The\nproposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we\nhave used immune memory to remember good B cells during the cloning process.We have designed two forms of memory: simple\nmemory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data\nto show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results\nobtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in\ndetecting credit risks....
The passivity for discrete-time stochastic T-S fuzzy systems with time-varying delays is investigated. By constructing appropriate\nLyapunov-Krasovskii functionals and employing stochastic analysis method and matrix inequality technique, a delay-dependent\ncriterion to ensure the passivity for the considered T-S fuzzy systems is established in terms of linear matrix inequalities (LMIs) that\ncan be easily checked by using the standard numerical software. An example is given to show the effectiveness of the obtained result....
In recent years, imperialist competitive algorithm (ICA), genetic algorithm (GA), and hybrid fuzzy classification systems have been\nsuccessfully and effectively employed for classification tasks of data mining. Due to overcoming the gaps related to ineffectiveness\nof current algorithms for analysing high-dimension independent datasets, a new hybrid approach, named HYEI, is presented to\ndiscover generic rule-based systems in this paper. This proposed approach consists of three stages and combines an evolutionarybased\nfuzzy system with two ICA procedures to generate high-quality fuzzy-classification rules. Initially, the best feature subset is\nselected by using the embedded ICA feature selection, and then these features are used to generate basic fuzzy-classification rules.\nFinally, all rules are optimized by using an ICA algorithm to reduce their length or to eliminate some of them. The performance\nof HYEI has been evaluated by using several benchmark datasets from the UCI machine learning repository. The classification\naccuracy attained by the proposed algorithm has the highest classification accuracy in 6 out of the 7 dataset problems and is\ncomparative to the classification accuracy of the 5 other test problems, as compared to the best results previously published....
We introduce the notions of totally continuous functions, totally semicontinuous functions, and semitotally continuous functions\nin double fuzzy topological spaces. Their characterizations and the relationship with other already known kinds of functions are\nintroduced and discussed....
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