144,436 research outputs found
Mining Target-Oriented Fuzzy Correlation Rules to Optimize Telecom Service Management
To optimize telecom service management, it is necessary that information
about telecom services is highly related to the most popular telecom service.
To this end, we propose an algorithm for mining target-oriented fuzzy
correlation rules. In this paper, we show that by using the fuzzy statistics
analysis and the data mining technology, the target-oriented fuzzy correlation
rules can be obtained from a given database. We conduct an experiment by using
a sample database from a telecom service provider in Taiwan. Our work can be
used to assist the telecom service provider in providing the appropriate
services to the customers for better customer relationship management.Comment: 10 pages, 7 table
Valuing information from mesoscale forecasts
The development of meso-gamma scale numerical weather prediction (NWP) models requires a substantial investment in research, development and computational resources. Traditional objective verification of deterministic model output fails to demonstrate the added value of high-resolution forecasts made by such models. It is generally accepted from subjective verification that these models nevertheless have a predictive potential for small-scale weather phenomena and extreme weather events. This has prompted an extensive body of research into new verification techniques and scores aimed at developing mesoscale performance measures that objectively demonstrate the return on investment in meso-gamma NWP. In this article it is argued that the evaluation of the information in mesoscale forecasts should be essentially connected to the method that is used to extract this information from the direct model output (DMO). This could be an evaluation by a forecaster, but, given the probabilistic nature of small-scale weather, is more likely a form of statistical post-processing. Using model output statistics (MOS) and traditional verification scores, the potential of this approach is demonstrated both on an educational abstraction and a real world example. The MOS approach for this article incorporates concepts from fuzzy verification. This MOS approach objectively weighs different forecast quality measures and as such it is an essential extension of fuzzy methods
Fuzzy set methods for object recognition in space applications
Progress on the following tasks is reported: (1) fuzzy set-based decision making methodologies; (2) feature calculation; (3) clustering for curve and surface fitting; and (4) acquisition of images. The general structure for networks based on fuzzy set connectives which are being used for information fusion and decision making in space applications is described. The structure and training techniques for such networks consisting of generalized means and gamma-operators are described. The use of other hybrid operators in multicriteria decision making is currently being examined. Numerous classical features on image regions such as gray level statistics, edge and curve primitives, texture measures from cooccurrance matrix, and size and shape parameters were implemented. Several fractal geometric features which may have a considerable impact on characterizing cluttered background, such as clouds, dense star patterns, or some planetary surfaces, were used. A new approach to a fuzzy C-shell algorithm is addressed. NASA personnel are in the process of acquiring suitable simulation data and hopefully videotaped actual shuttle imagery. Photographs have been digitized to use in the algorithms. Also, a model of the shuttle was assembled and a mechanism to orient this model in 3-D to digitize for experiments on pose estimation is being constructed
On the estimation of three parameters lognormal distribution based on fuzzy life time data
Countless statistical tools are available to extract information from data. Life time modeling is considered as one of the most prominent fields of statistics, which is evident from the developments made in this field in the last few decades. Almost every statistic for life time analysis is based on precise life time observations, however, life time is not a precise measurement but more or less fuzzy. Therefore, in addition to classical statistical tools, fuzzy number approaches to describe life time data are more suitable. In order to incorporate fuzziness of the observations, fuzzy estimators for the three parameter lognormal distribution were suggested. The proposed estimators cover stochastic variation as well as fuzziness of the observations
An information systems assessment framework for agile manufacturing
Turmoil in the business environment is driving manufacturing companies to
become agile. Agility means the capability of operating profitably in a
competitive environment of continuous and unpredictable changes with
information systems regarded as one of its main enablers. The research
presented in this work focuses on the assessment of information systems to
support agile manufacturing. The assessment framework is constructed upon a
series of competitive bases (six) and agility attributes (32) identified in
the literature. Other issues included cover the characteristics of the
business environment and the evolution/development and infrastructure of
information systems. The framework is validated through a survey research
instrument and the responses analysed with statistical tests (descriptive
statistics, factor analysis, linear regression and reliability). The results
of the statistical analysis enabled us to determine the attributes identified
as predictors for the set of questions linking information systems and agile
manufacturing. Due to the fuzzy nature of assessment of information systems,
the complete framework can be presented as a hierarchy where techniques like
AHP and fuzzy language sets are applicable. The findings will enable the
researchers to clearly identify the trends adopted by manufacturing companies
in the utilisation of information systems to gain competitive edge in support
of the concept of agile manufacturing
An Improved Semantic Similarity Algorithm on Hownet
Semantic similarity algorithm is one of the basic researches in the field of natural language processing. This algorithm is widely used in information retrieval, machine translation based on examples and other fields. In this paper, based on the basis of HowNet lexical semantic similarity algorithm, introduced the concept of fuzzy mathematics degree of membership, the fixed weighting factor assigned into a coefficient of variation based on statistics through experimental verification of the results of this improved contribution
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