Implications of Fuzziness for the Practical Management of High-Stakes Risks

Jablonowski, Mark

doi:10.2991/ijcis.2010.3.1.1

Cited by 5 publications

(3 citation statements)

References 18 publications

(9 reference statements)

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“…Given the risks posed to the population, this is considered a relevant problem by authorities and citizens alike. 19 Thus, SIMAT is made up by four specialized subsystems, one Atmospheric Monitoring Mobile Unit, and a Calibration Standards Transfer Lab.…”

Section: Time Series Of Air Pollution Data In Mexico Citymentioning

confidence: 99%

Polluants Time-Series Prediction Using the Gamma Classifier

López-Yáñez¹,

Argüelles-Cruz²,

Camacho-Nieto³

et al. 2011

IJCIS

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In this work we predict time series of air pollution data taken in Mexico City and the Valley of Mexico, by using the Gamma Classifier which is a novel intelligent associative mathematical model, coupled with an emergent coding technique. Historical and current data about the concentration of specific pollutants, in the form of time series, were used. The pollutants of interest are: carbon monoxide (CO), ozone (O 3), sulfur dioxide (SO 2), and nitrogen oxides (NO x , including both nitrogen monoxide, NO, and nitrogen dioxide, NO 2).

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Section: Time Series Of Air Pollution Data In Mexico Citymentioning

confidence: 99%

Polluants Time-Series Prediction Using the Gamma Classifier

López-Yáñez¹,

Argüelles-Cruz²,

Camacho-Nieto³

et al. 2011

IJCIS

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“…"A fundamental operation in data mining is the partitioning of a set of objects represented by data into homogeneous groups or clusters" [3]. The analysis of the process of groups making (clustering analysis) is fundamental nowadays for understanding the complex processes of various substantive areas such as medicine [4][5][6][7][8][9], chemical industry [10][11][12], engineering [13][14][15][16], image processing [17][18][19], business [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] and others.…”

Section: Introductionmentioning

confidence: 99%

Approximation to the theory of affinities to manage the problems of the grouping process

Gil‐Lafuente

Klimova

2014

ComSIS

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Perhaps more than ever, new economic and enterprise needs have increased the interest and utility of the methods of the grouping process based on the theory of uncertainty. Numerous studies made in this field until recently have contributed with a great number of techniques and algorithms that proved their efficiency in resolving challenging problems that arise in an economic management framework of modern companies. A fuzzy grouping (clustering) process is a key phase of knowledge acquisition and reduction complexity regarding different groups of objects. In this study, we consider some elements of the theory of affinities and uncertain pretopology which form a significant support tool for a fuzzy clustering process. A Galois lattice is also introduced in order to provide a clearer vision of the results we obtained in a previous process. We implement this useful set of mathematical elements to make an homogeneous grouping process of the economic regions of Russian Federation and Ukraine. The obtained results give us a large situational panorama of a regional economic situation of two countries as well as the key guidelines to follow in a decision-making process. We offer the calculus for two different countries to demonstrate the significant differences that could be obtained taking into account the minimum changes of an input data basis. The mathematical method is very sensible to any changes that the economic situation of the regions can have. In this study, we pretend to give an alternative method of the grouping process for any modern company that performs its activity under uncertainty.

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“…It is difficult to express trust using traditional mathematical theory since trust can be subjective and uncertain. Fuzzy theory can thus be used to handle uncertainty effectively and has been widely used in many fields such as natural science, social science and management [15][16][17][18][19][20] .…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Comprehensive Evaluation Method in Trust Quantification

Ma¹,

He²,

Shuai³

2011

IJCIS

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Trust can play an important role for the sharing of resources and information in open network environments. Trust quantification is thus an important issue in dynamic trust management. By considering the fuzziness and uncertainty of trust, in this paper, we propose a fuzzy comprehensive evaluation method to quantify trust along with a trust quantification algorithm. Simulation results show that the trust quantification algorithm that we propose can effectively quantify trust and the quantified value of an entity's trust is consistent with the behavior of the entity.

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Implications of Fuzziness for the Practical Management of High-Stakes Risks

Cited by 5 publications

References 18 publications

Polluants Time-Series Prediction Using the Gamma Classifier

Polluants Time-Series Prediction Using the Gamma Classifier

Approximation to the theory of affinities to manage the problems of the grouping process

Application of Fuzzy Comprehensive Evaluation Method in Trust Quantification

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