Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition

Khatibi, Vahid; Montazer, Gholam Ali

doi:10.1016/j.artmed.2009.03.002

Cited by 137 publications

(55 citation statements)

References 23 publications

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“…Using predefined disease feature categories, the detected bacteria are compared with most similar feature to enable a medical diagnosis. The training data (from [19] for Case 1 and from [20] for Case 2) are applied to the FCMAC classifier, presented in Section 2, for learning process with 1000 iterations. When the learning process is completed, the test data are fed into the FCMAC for classification.…”

Section: Resultsmentioning

confidence: 99%

“…The information about the bacterial samples includes F = {Domical shape, Single microscopic shape, Double microscopic shape, Flagellum} and V = {Bacillus coli, Shigella, Salmonella, Klebsiella}. The samples for classification are shown in Table 2 [19]. …”

Section: Medical Sample A) Case 1: Bacteriamentioning

confidence: 99%

“…2 [19]. Four bacterial cell characteristic values are shown in Table 1 [19]. F and V represent bacterial feature sets and species, respectively.…”

Section: Medical Sample A) Case 1: Bacteriamentioning

confidence: 99%

See 2 more Smart Citations

Medical Sample Classifier Design Using Fuzzy Cerebellar Model Neural Networks

Yeh

Lin

et al. 2016

APH

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Section: Resultsmentioning

confidence: 99%

Section: Medical Sample A) Case 1: Bacteriamentioning

confidence: 99%

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Medical Sample Classifier Design Using Fuzzy Cerebellar Model Neural Networks

Yeh

Lin

et al. 2016

APH

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“…Through these functions, the A-IFS can depict the vagueness and uncertainty of things more comprehensively. In the past few decades, the intuitionistic fuzzy set theory has received great attention and applied to many practical fields, including decision making, 5-9 pattern recognition, [10][11][12] medical diagnosis, 13,14 clustering analysis, 15,16 etc. Aggregation techniques, [17][18][19] clustering algorithms, 22 distance measures, [23][24][25] similarity measures, 26,27 and correlation measures 28,29 for intuitionistic fuzzy information have been researched widely.…”

Section: Introductionmentioning

confidence: 99%

Derivative and Differential Operations of Intuitionistic Fuzzy Numbers

Lei

Zhang

2014

Int. J. Intell. Syst.

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The innovative idea of Atanassov's intuitionistic fuzzy sets (A-IFSs) is to get a more comprehensive and detailed description of the ambiguity and uncertainty by introducing a membership function and a nonmembership function. Each element in an A-IFS is expressed by an ordered pair, which is called an intuitionistic fuzzy number (IFN). In this paper, we first describe the change values of IFNs when considering them as variables and classify these change values based on the basic operations for IFNs. Second, we depict the convergences of sequences of IFNs by the subtraction and division operations. Moreover, we develop some intuitionistic fuzzy functions (IFFs) and study in detail their continuities, derivatives, and differentials. C 2014 Wiley Periodicals, Inc.

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“…And the IFS is a generalization of the concept of a fuzzy set. IFS theory has been applied in different areas such as pattern recognition [24] and decision-making problems [25]. For the sake of understanding in the following sections, the basic concept of IFS is reviewed as below.…”

Section: Intuitionistic Fuzzy Numbersmentioning

confidence: 99%

An intuitionistic fuzzy OWA-TOPSIS method for collaborative network formation considering matching characteristics

Wang

Liu

et al. 2017

Scientia Iranica

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Abstract：Collaborative network (CN) as a new emerging paradigm can rapidly answer market demands by effective enterprise collaboration and coordination. Nowadays, it has become a potential solution for different organizations to manage their businesses effectively. Thus, selecting a suitable partner combination is critical to CN success. Matching characteristic is very important for partner combination selection in the CN formation, while it is neglected in the existing research. This paper is to propose a method and model for partner combination selection of CN considering matching utility. Firstly, the matching factors are developed from four aspects, supply capability, goal, culture and technology. And then a hybrid approach is designed to integrate intuitionistic fuzzy Ordered Weighted Averaging (IFOWA) operators into the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) procedure. And matching utility combination method amongst multi-partners is advanced to establish the partner combination model. Moreover, a decision support system is applied in a practical enterprise to illustrate the advantage of the proposed method. Finally a sensitivity analysis is conducted to investigate the robustness of solutions ranking to changes in matching factor. The result shows 2 that ranking the solutions for forming CN is relatively sensitive to its matching factor.

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Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition

Cited by 137 publications

References 23 publications

Medical Sample Classifier Design Using Fuzzy Cerebellar Model Neural Networks

Medical Sample Classifier Design Using Fuzzy Cerebellar Model Neural Networks

Derivative and Differential Operations of Intuitionistic Fuzzy Numbers

An intuitionistic fuzzy OWA-TOPSIS method for collaborative network formation considering matching characteristics

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