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- research-articleSeptember 2023
A modified fuzzy similarity measure for trapezoidal fuzzy number with their applications
The Journal of Supercomputing (JSCO), Volume 80, Issue 4Pages 4733–4759https://doi.org/10.1007/s11227-023-05608-2AbstractThe similarity measure (SM) of fuzzy numbers is vital in decision-making, ranking, and risk analysis, particularly when dealing with qualitative information and fuzzy mathematical models. Traditional set theory struggles with such applications due ...
- research-articleJuly 2022
A novel method to rank fuzzy numbers using the developed golden rule representative value
Applied Intelligence (KLU-APIN), Volume 52, Issue 9Pages 9751–9767https://doi.org/10.1007/s10489-021-02965-4AbstractRanking fuzzy numbers is an important subject of fuzzy set theory, which has been widely studied and applied to many practical problems. However, the previous fuzzy number ranking methods have some weaknesses, such as incomplete ranking objects, ...
- research-articleMarch 2020
An improved fuzzy risk analysis by using a new similarity measure with center of gravity and area of trapezoidal fuzzy numbers
Soft Computing - A Fusion of Foundations, Methodologies and Applications (SOFC), Volume 24, Issue 6Pages 3923–3936https://doi.org/10.1007/s00500-019-04160-7AbstractThis paper is to develop a new similarity measure of generalized trapezoidal fuzzy numbers (GTFNs). Firstly, a new method to calculate the center of gravity (COG) of GTFNs is put forward. Then, based on the drawbacks of existing similarity ...
- research-articleNovember 2020
Ranking Fuzzy Numbers by Similarity Measure Index
ICACS '20: Proceedings of the 4th International Conference on Algorithms, Computing and SystemsPages 12–16https://doi.org/10.1145/3423390.3423399Uncertainties included in soft classification exist in classical mathematics. However, in daily life, the extended fuzzy concept has much information and due to the large applications of fuzzy numbers, the ranking of numbers plays a very important role ...
- research-articleJanuary 2020
Fuzzy risk analysis under influence of non-homogeneous preferences elicitation in fiber industry
- Ahmad Syafadhli Abu Bakar,
- Ku Muhammad Naim Ku Khalif,
- Asma Ahmad Shariff,
- Alexander Gegov,
- Fauzani Md Salleh
Applied Intelligence (KLU-APIN), Volume 50, Issue 1Pages 157–168https://doi.org/10.1007/s10489-019-01508-2AbstractFuzzy risk analysis plays an important role in mitigating the levels of harm of a risk. In real world scenarios, it is a big challenge for risk analysts to make a proper and comprehensive decision when coping with risks that are incomplete, vague ...
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- research-articleMarch 2017
An improved similarity measure for generalized fuzzy numbers and its application to fuzzy risk analysis
Applied Soft Computing (APSC), Volume 52, Issue CPages 478–486https://doi.org/10.1016/j.asoc.2016.10.020Display Omitted A new method for similarity measure of the generalized trapezoidal fuzzy numbers.Proven properties of the new proposed method.Better performance in comparison with the previous methods.Application on fuzzy risk analysis.An example of ...
- research-articleMarch 2015
Fuzzy risk analysis using area and height based similarity measure on generalized trapezoidal fuzzy numbers and its application
Applied Soft Computing (APSC), Volume 28, Issue CPages 276–284https://doi.org/10.1016/j.asoc.2014.11.042HighlightsA new method of similarity measure of the generalized trapezoidal fuzzy numbers.Properties regarding the proposed new method.Comparison of this method with the existing methods.Application in a production system. In this paper, we have ...
- articleJune 2012
Fuzzy risk analysis based on ranking generalized fuzzy numbers with different left heights and right heights
Expert Systems with Applications: An International Journal (EXWA), Volume 39, Issue 7Pages 6320–6334https://doi.org/10.1016/j.eswa.2011.12.004In this paper, we present a new method for fuzzy risk analysis based on the proposed new fuzzy ranking method for ranking generalized fuzzy numbers with different left heights and right heights. First, we present a fuzzy ranking method for ranking ...
- articleMay 2012
Fuzzy risk analysis of flood disasters based on diffused-interior-outer-set model
Expert Systems with Applications: An International Journal (EXWA), Volume 39, Issue 6Pages 6213–6220https://doi.org/10.1016/j.eswa.2011.12.008Floods are indeed one of the most serious natural hazards for human societies, especially in China. In this paper, we firstly introduce the interior-outer-set model (IOSM) based on information diffusion theory in detail. Then taking consideration its ...
- articleAugust 2011
An improved fuzzy risk analysis based on a new similarity measures of generalized fuzzy numbers
Expert Systems with Applications: An International Journal (EXWA), Volume 38, Issue 8Pages 9179–9185https://doi.org/10.1016/j.eswa.2011.01.101This paper presents a novel method of fuzzy risk analysis based on a new similarity measure of generalized fuzzy numbers. This similarity measure considers many features of generalized fuzzy numbers such as the area, perimeter, height and geometric ...
- articleJuly 2011
Analyzing fuzzy risk based on similarity measures between interval-valued fuzzy numbers
Expert Systems with Applications: An International Journal (EXWA), Volume 38, Issue 7Pages 8612–8621https://doi.org/10.1016/j.eswa.2011.01.065In this paper, we present a new method for handling fuzzy risk analysis problems based on the proposed new similarity measure between interval-valued fuzzy numbers. First, we present a new similarity measure between interval-valued fuzzy numbers. It ...
- articleMarch 2011
Analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers
Expert Systems with Applications: An International Journal (EXWA), Volume 38, Issue 3Pages 2163–2171https://doi.org/10.1016/j.eswa.2010.08.002In this paper, we present a new method for analyzing fuzzy risk based on a new method for ranking generalized fuzzy numbers. First, we present a new method for ranking generalized fuzzy numbers. It considers the areas on the positive side, the areas on ...
- articleMarch 2010
A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers
Expert Systems with Applications: An International Journal (EXWA), Volume 37, Issue 3Pages 1920–1927https://doi.org/10.1016/j.eswa.2009.07.015At present, some researchers provide a type of fuzzy risk analysis algorithms for dealing with fuzzy risk analysis problems, where the values of the evaluating items are represented by trapezoidal fuzzy numbers. In those algorithms, the main operations ...
- articleApril 2009
Fuzzy risk analysis based on similarity measures between interval-valued fuzzy numbers and interval-valued fuzzy number arithmetic operators
Expert Systems with Applications: An International Journal (EXWA), Volume 36, Issue 3Pages 6309–6317https://doi.org/10.1016/j.eswa.2008.08.017In this paper, we present a new method for fuzzy risk analysis based on a new similarity measure between interval-valued fuzzy numbers and new interval-valued fuzzy number arithmetic operators. First, we present a new similarity measure between interval-...
- articleApril 2009
Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads
Expert Systems with Applications: An International Journal (EXWA), Volume 36, Issue 3Pages 6833–6842https://doi.org/10.1016/j.eswa.2008.08.015In this paper, we present a new method for fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. First, we present a new method for ranking generalized fuzzy numbers. The proposed method considers ...
- articleApril 2009
Fuzzy risk analysis based on ranking fuzzy numbers using α-cuts, belief features and signal/noise ratios
Expert Systems with Applications: An International Journal (EXWA), Volume 36, Issue 3Pages 5576–5581https://doi.org/10.1016/j.eswa.2008.06.112In this paper, we present a new approach for fuzzy risk analysis based on the ranking of fuzzy numbers. First, we propose a new method for ranking fuzzy numbers using the @a-cuts, the belief feature and the signal/noise ratios, where @a@?[0,1]. The ...
- articleMarch 2009
Fuzzy risk analysis based on interval-valued fuzzy numbers
Expert Systems with Applications: An International Journal (EXWA), Volume 36, Issue 2Pages 2285–2299https://doi.org/10.1016/j.eswa.2007.12.037In this paper, we present a new method for fuzzy risk analysis based on similarity measures between interval-valued fuzzy numbers. First, we present a new similarity measure between interval-valued fuzzy numbers. It combines the concepts of geometric ...
- articleJanuary 2009
A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
Expert Systems with Applications: An International Journal (EXWA), Volume 36, Issue 1Pages 589–598https://doi.org/10.1016/j.eswa.2007.09.033In this paper, we present a new method for fuzzy risk analysis based on similarity measures between generalized fuzzy numbers. First, we present a new similarity measure between generalized fuzzy numbers. It combines the concepts of geometric distance, ...
- articleMay 2008
Fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations
Expert Systems with Applications: An International Journal (EXWA), Volume 34, Issue 4Pages 2763–2771https://doi.org/10.1016/j.eswa.2007.05.009In this paper, we present a new method for fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations. First, we present a new method for ranking trapezoidal fuzzy numbers based on their shapes and deviations. Then, we use ...
- articleApril 2008
Fuzzy risk analysis based on measures of similarity between interval-valued fuzzy numbers
Computers & Mathematics with Applications (CMAP), Volume 55, Issue 8Pages 1670–1685https://doi.org/10.1016/j.camwa.2007.06.022In this paper, we present a new method for handling fuzzy risk analysis problems based on measures of similarity between interval-valued fuzzy numbers. First, we propose a similarity measure to calculate the degree of similarity between interval-valued ...