Fuzzy Set Theory and Its Applications

Honda, Nakaji; Ohsato, Ario

doi:10.2333/jbhmk.13.2_64

Cited by 3 publications

(4 citation statements)

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“…The membership function is defined as a curve which shows the pattern of every input data coordinates into its membership value (membership degree) which ranged from 0 to 1 interval (Honda & Ohsato, 1986). One of the function that can be applied is the triangle curve.…”

Section: Fuzzy Membership Functionmentioning

confidence: 99%

A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN

Sugiyarto

Eliyanto

Irsalinda

et al. 2021

JTAM

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Sentiment analysis is an analysis with an objective to identify like, dislike, comments, opinion, or feedback on certain content which will be categorized into positive, negative, or neutral. In general selection, sentiment analysis widely known to be used to predict the winner on election process. This method tries to dig the people sentiment on their governor candidates during election, whether it’s positive, negative, or neutral opinion. The output of the positive sentiment is related to people acceptance towards one of the election nominee. That statement usually applied as a base reference for determining the result of the election process. In sentiment analysis, the importance of its fuzzy logics must be considered. Each of the people statement is assumed to have the level of positive, negative, or neutral percentage. The concept of fuzzy logic is developed and applied on one of this text mining method. This research is focusing on comparison analysis and fuzzy logic application in sentiment analysis method. Two method which discussed in this research are Fuzzy Naïve Bayes and Sentiment Fuzzy with convolutional neural network. This research is applied on PILKADA of Solo and Medan district case study. The data of the people opinion are acquired from twitter and collected on September 2020 to December 2020. The two methods which mentioned before are implemented on the acquired data and the output of these method application then compared. The conclusion of this research suggest that different approach will resulting in different output.

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Section: Fuzzy Membership Functionmentioning

confidence: 99%

A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN

Sugiyarto

Eliyanto

Irsalinda

et al. 2021

JTAM

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“…The function of the fuzziness is called a measure of fuzziness. Fuzziness can be applied in civil engineering discipline with linguistic variables, whose values are not numbers but words or sentences in a natural or artificial language as (Zimmermann, 2011) very cold, cold, warm, hot and very hot for temperature; low, moderate and high for corrosion rate; under reinforced, balanced and over-reinforced for reinforced concrete section; no damage, slight damage, moderate damage, severe damage and destructive damage for damage assessment of earthquake effect on structures with unclear boundaries.…”

Section: Literature Review 21 Fuzziness and Fuzzy Set Theorymentioning

confidence: 99%

“…The a-cut set is the crisp set of the elements whose degree of membership is greater than or equal to a, (Lee, 2004;Zimmermann, 2011)…”

Section: Operation Of Fuzzy Interval and A-cut Intervalmentioning

confidence: 99%

Fuzzy uncertainty and its applications in reinforced concrete structures

Woju¹,

Balu

2020

JEDT

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Purpose The aim of this paper is mainly to handle the fuzzy uncertainties present in structures appropriately. In general, uncertainties of variables are classified as aleatory and epistemic. The different sources of uncertainties in reinforced concrete structures include the randomness, mathematical models, physical models, environmental factors and gross errors. The effects of imprecise data in reinforced concrete structures are studied here by using fuzzy concepts. The aim of this paper is mainly to handle the uncertainties of variables with unclear boundaries. Design/methodology/approach To achieve the intended objective, the reinforced concrete beam subjected to flexure and shear was designed as per Euro Code (EC2). Then, different design parameters such as corrosion parameters, material properties and empirical expressions of time-dependent material properties were identified through a thorough literature review. Findings The fuzziness of variables was identified, and their membership functions were generated by using the heuristic method and drawn by MATLAB R2018a software. In addition to the identification of fuzziness of variables, the study further extended to design optimization of reinforced concrete structure by using fuzzy relation and fuzzy composition. Originality/value In the design codes of the concrete structure, the concrete grades such as C16/20, C20/25, C25/30, C30/37 and so on are provided and being adopted for design in which the intermediate grades are not considered, but using fuzzy concepts the intermediate grades of concrete can be recognized by their respective degree of membership. In the design of reinforced concrete structure using fuzzy relation and composition methods, the optimum design is considered when the degree of membership tends to unity. In addition to design optimization, the level of structural performance evaluation can also be carried out by using fuzzy concepts.

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“…Zadeh's [1] fuzzy set theory, established in 1965, is the best explanation for interacting with sources of uncertainty. In 1986, Honda and Ohsato [2] have discussed some fuzzy set concepts and applications. A graph is a simple way to communicate data and the relationship between various types of substances.…”

Section: Introductionmentioning

confidence: 99%

Perfect Fuzzy Soft Tripartite Graphs and Their Complements

Kalaiarasi

Mahalakshmi

Kausar

et al. 2022

Discrete Dynamics in Nature and Society

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Fuzzy soft graphs are efficient numerical tools for simulating the uncertainty of the real world. A fuzzy soft graph is a perfect fusion of the fuzzy soft set and the graph model that is widely used in a variety of fields. This paper discusses a few unique notions of perfect fuzzy soft tripartite graphs (PFSTG), as well as the concepts of complement of perfect fuzzy soft tripartite graphs (CPFSTGs). Because soft sets are most useful in real-world applications, the newly developed concepts of perfect soft tripartite fuzzy graphs will lead to many theoretical applications by adding extra fuzziness in analysing. We look at some of their properties and come up with a few results that are related to these concepts. Furthermore, we investigated some fundamental theorems and illustrated an application of size of perfect fuzzy soft tripartite graphs in employee selection for an institution using the perfect fuzzy soft tripartite graph.

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Fuzzy Set Theory and Its Applications

Cited by 3 publications

References 0 publications

A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN

A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN

Fuzzy uncertainty and its applications in reinforced concrete structures

Perfect Fuzzy Soft Tripartite Graphs and Their Complements

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