Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494506
Ismail Baaj, Jean-Philippe Poli, W. Ouerdane, N. Maudet
In this paper, we explore the min-max inference mechanism of any rule-based system of $n$ if-then possibilistic rules. We establish an additive formula for the output possibility distribution obtained by the inference. From this result, we deduce the corresponding possibility and necessity measures. Moreover, we give necessary and sufficient conditions for the normalization of the output possibility distribution. As application of our results, we tackle the case of a cascade of two if-then possibilistic rules sets and establish an input-output relation between the two min-max equation systems. Finally, we associate to the cascade construction an explicit min-max neural network.
{"title":"Min-max inference for Possibilistic Rule-Based System","authors":"Ismail Baaj, Jean-Philippe Poli, W. Ouerdane, N. Maudet","doi":"10.1109/FUZZ45933.2021.9494506","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494506","url":null,"abstract":"In this paper, we explore the min-max inference mechanism of any rule-based system of $n$ if-then possibilistic rules. We establish an additive formula for the output possibility distribution obtained by the inference. From this result, we deduce the corresponding possibility and necessity measures. Moreover, we give necessary and sufficient conditions for the normalization of the output possibility distribution. As application of our results, we tackle the case of a cascade of two if-then possibilistic rules sets and establish an input-output relation between the two min-max equation systems. Finally, we associate to the cascade construction an explicit min-max neural network.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121451307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494402
H. Phan, Van-Hieu Bui, N. Nguyen, D. Hwang
From the end of 2019, numerous comments and opinions relating to the COVID-19 pandemic have been posted on Twitter. The number of opinions rapidly increased since the countries began implementing social isolation and reduction. In these comments, users often express different emotions regarding COVID-19 signs and symptoms, the majority of which are sadness and fear sentiments. It is important to determine the symptom effect level for the emotions of symptomatic persons based on their opinions. However, no study analyzes the tweets' sentiment related to the COVID-19 topic to predict the symptoms effect level. Therefore, in this study, we present a method to predict the symptoms effect level based on the sentiment analysis of symptomatic persons according to the following steps. First, the sentiments in tweets are analyzed by using a combination of the text representation model and convolutional neural network. Second, a topic modeling model is built based on the latent Dirichlet allocation algorithm to group symptoms into small clusters that conform to sadness and fear sentiments. Finally, the symptom effect level is predicted based on the probability distribution of the symptoms in each sentiment cluster. Experiments using tweets promise that the proposed method achieves significant results toward the accuracy and obtained information.
{"title":"Tweet Sentiment Analysis for Predicting the Symptoms Effect Level Regarding COVID-19","authors":"H. Phan, Van-Hieu Bui, N. Nguyen, D. Hwang","doi":"10.1109/FUZZ45933.2021.9494402","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494402","url":null,"abstract":"From the end of 2019, numerous comments and opinions relating to the COVID-19 pandemic have been posted on Twitter. The number of opinions rapidly increased since the countries began implementing social isolation and reduction. In these comments, users often express different emotions regarding COVID-19 signs and symptoms, the majority of which are sadness and fear sentiments. It is important to determine the symptom effect level for the emotions of symptomatic persons based on their opinions. However, no study analyzes the tweets' sentiment related to the COVID-19 topic to predict the symptoms effect level. Therefore, in this study, we present a method to predict the symptoms effect level based on the sentiment analysis of symptomatic persons according to the following steps. First, the sentiments in tweets are analyzed by using a combination of the text representation model and convolutional neural network. Second, a topic modeling model is built based on the latent Dirichlet allocation algorithm to group symptoms into small clusters that conform to sadness and fear sentiments. Finally, the symptom effect level is predicted based on the probability distribution of the symptoms in each sentiment cluster. Experiments using tweets promise that the proposed method achieves significant results toward the accuracy and obtained information.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122112050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494485
T. R. Razak, Chao Chen, J. Garibaldi, Christian Wagner
The use of Hierarchical Fuzzy Systems (HFS) has been well acknowledged as a good approach in reducing the complexity and improving the interpretability of fuzzy logic systems (FLS). Over the past years, many fuzzy logic toolkits have been made available for type-1, interval type-2 and general type-2 fuzzy logic systems under different programming languages. However, it is still challenging for people, especially for those who are not expert in fuzzy systems or programming, to build models based on HFSs. The main reason could be the lack of practical tools and examples of using HFSs. This paper presents a step-by-step guide to the implementation of an HFS with the open-source toolbox, FuzzyR, utilising the R Programming Language.
{"title":"Designing the Hierarchical Fuzzy Systems Via FuzzyR Toolbox","authors":"T. R. Razak, Chao Chen, J. Garibaldi, Christian Wagner","doi":"10.1109/FUZZ45933.2021.9494485","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494485","url":null,"abstract":"The use of Hierarchical Fuzzy Systems (HFS) has been well acknowledged as a good approach in reducing the complexity and improving the interpretability of fuzzy logic systems (FLS). Over the past years, many fuzzy logic toolkits have been made available for type-1, interval type-2 and general type-2 fuzzy logic systems under different programming languages. However, it is still challenging for people, especially for those who are not expert in fuzzy systems or programming, to build models based on HFSs. The main reason could be the lack of practical tools and examples of using HFSs. This paper presents a step-by-step guide to the implementation of an HFS with the open-source toolbox, FuzzyR, utilising the R Programming Language.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123051055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494388
Juan Moreno García, L. Jiménez, Jun Liu, L. Rodriguez-Benitez
One of the major problems of concern to the nowadays society is pollution, which can be of many types: acoustic, environmental, thermal, etc. Among these, noise pollution causes serious problems for citizens because it is continuous for a large part of the day, due to the fact that it is mostly caused by traffic. On the other hand, large cities provide a large amount of data obtained daily thanks to the sensorisation resulting from the concept of “smart cities”, which makes it possible to display information from the sensorised areas and to alert the institutions of the problems and, for citizens, to know the situation of noise pollution based on data in order to be able to make the relevant complaints and denunciations to the institutions. A universally understandable way of displaying the information contained in the captured data is the generation of linguistic descriptions that synthesise the information residing in the data. This paper presents a method for generating linguistic descriptions based on the noise pollution data captured by noise measurement stations. A method for generating descriptions of a day will be presented that considers the daily periods in which the data taken from the stations are structured (daytime, evening, night-time and full day). In order to test the proposed method, available data from the city of Madrid have been used to generate descriptions that allow the influence of Covid-19 on noise pollution to be analysed.
{"title":"Generation of linguistic descriptions for daily noise pollution in urban areas","authors":"Juan Moreno García, L. Jiménez, Jun Liu, L. Rodriguez-Benitez","doi":"10.1109/FUZZ45933.2021.9494388","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494388","url":null,"abstract":"One of the major problems of concern to the nowadays society is pollution, which can be of many types: acoustic, environmental, thermal, etc. Among these, noise pollution causes serious problems for citizens because it is continuous for a large part of the day, due to the fact that it is mostly caused by traffic. On the other hand, large cities provide a large amount of data obtained daily thanks to the sensorisation resulting from the concept of “smart cities”, which makes it possible to display information from the sensorised areas and to alert the institutions of the problems and, for citizens, to know the situation of noise pollution based on data in order to be able to make the relevant complaints and denunciations to the institutions. A universally understandable way of displaying the information contained in the captured data is the generation of linguistic descriptions that synthesise the information residing in the data. This paper presents a method for generating linguistic descriptions based on the noise pollution data captured by noise measurement stations. A method for generating descriptions of a day will be presented that considers the daily periods in which the data taken from the stations are structured (daytime, evening, night-time and full day). In order to test the proposed method, available data from the city of Madrid have been used to generate descriptions that allow the influence of Covid-19 on noise pollution to be analysed.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"10 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123540222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494418
Z. Suraj
In this paper, we present an approach to construct a concurrent algorithm that supports real-time decision making based on the knowledge extracted from empirical data. The data is represented by a decision table in the Pawlak sense, while the concurrent algorithm is represented as a weighted priority fuzzy Petri net. This idea overcomes the difficulties that arise when field experts are entrusted with determining the values of net parameters. In the proposed approach, we assume that the decision tables contain conditional attribute values that are obtained from measurements made by sensors in real time. The Petri net built within the presented conception allows for the fastest possible identification of objects in decision tables in order to make the right decision. The sensor output values are transmitted over the net at the maximum possible speed. We achieve this effect thanks to the appropriate implementation of all true and acceptable rules generated from a given decision table.
{"title":"A Hybrid Approach to Approximate Real-time Decision Making","authors":"Z. Suraj","doi":"10.1109/FUZZ45933.2021.9494418","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494418","url":null,"abstract":"In this paper, we present an approach to construct a concurrent algorithm that supports real-time decision making based on the knowledge extracted from empirical data. The data is represented by a decision table in the Pawlak sense, while the concurrent algorithm is represented as a weighted priority fuzzy Petri net. This idea overcomes the difficulties that arise when field experts are entrusted with determining the values of net parameters. In the proposed approach, we assume that the decision tables contain conditional attribute values that are obtained from measurements made by sensors in real time. The Petri net built within the presented conception allows for the fastest possible identification of objects in decision tables in order to make the right decision. The sensor output values are transmitted over the net at the maximum possible speed. We achieve this effect thanks to the appropriate implementation of all true and acceptable rules generated from a given decision table.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129708039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494438
Cleo Pau, Temur Kutsia
We consider the problem of solving approximate equations between logic terms. The approximation is expressed by proximity relations. They are reflexive and symmetric (but not necessarily transitive) fuzzy binary relations. The equations are solved by variable substitutions that bring the sides of equations “close” to each other with respect to a predefined degree. We consider unification and matching equations in which mismatches in function symbol names, arity, and in the argument order are tolerated (i.e., the approximate equations are formulated over so called fully fuzzy signatures). This work generalizes on the one hand, class-based proximity unification to fully fuzzy signatures, and on the other hand, unification with similarity relations over a fully fuzzy signature by extending similarity to proximity.
{"title":"Proximity-Based Unification and Matching for Fully Fuzzy Signatures","authors":"Cleo Pau, Temur Kutsia","doi":"10.1109/FUZZ45933.2021.9494438","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494438","url":null,"abstract":"We consider the problem of solving approximate equations between logic terms. The approximation is expressed by proximity relations. They are reflexive and symmetric (but not necessarily transitive) fuzzy binary relations. The equations are solved by variable substitutions that bring the sides of equations “close” to each other with respect to a predefined degree. We consider unification and matching equations in which mismatches in function symbol names, arity, and in the argument order are tolerated (i.e., the approximate equations are formulated over so called fully fuzzy signatures). This work generalizes on the one hand, class-based proximity unification to fully fuzzy signatures, and on the other hand, unification with similarity relations over a fully fuzzy signature by extending similarity to proximity.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128770377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494523
S. Zadrożny, J. Kacprzyk, Mateusz Dziedzic
We propose a new approach to database querying, termed context seeking querying, which involves context that is crucial for information interpretation and understanding yet practically not considered in querying. We present a justification, formalization and two algorithms for the new queries.
{"title":"A Concept of Context-Seeking Queries","authors":"S. Zadrożny, J. Kacprzyk, Mateusz Dziedzic","doi":"10.1109/FUZZ45933.2021.9494523","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494523","url":null,"abstract":"We propose a new approach to database querying, termed context seeking querying, which involves context that is crucial for information interpretation and understanding yet practically not considered in querying. We present a justification, formalization and two algorithms for the new queries.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129053749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494521
Penugonda Ravikumar, R. U. Kiran, N. Unnam, Y. Watanobe, K. Goda, V. Devi, P. K. Reddy
Time series classification is an important model in data mining. It involves assigning a class label to a test instance based on the training data with known class labels. Most previous studies developed time series classifiers by disregarding the fuzzy nature of events (i.e., events with similar values may belong to different classes) within the data. Consequently, these studies suffered from performance issues, including decreased accuracy and increased memory, runtime, and energy requirements. With this motivation, this paper proposes a novel fuzzy nearest neighbor classifier for time series data. The basic idea of our classifier is to transform the very large training data into a relatively small representative training data and use it to label a test instance by employing a new fuzzy distance measure known as Ravi. Experimental results on real world benchmark datasets demonstrate that the proposed classifier outperforms the current parameter-free time series classifiers and also the popular deep learning techniques.
{"title":"A Novel Parameter-Free Energy Efficient Fuzzy Nearest Neighbor Classifier for Time Series Data","authors":"Penugonda Ravikumar, R. U. Kiran, N. Unnam, Y. Watanobe, K. Goda, V. Devi, P. K. Reddy","doi":"10.1109/FUZZ45933.2021.9494521","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494521","url":null,"abstract":"Time series classification is an important model in data mining. It involves assigning a class label to a test instance based on the training data with known class labels. Most previous studies developed time series classifiers by disregarding the fuzzy nature of events (i.e., events with similar values may belong to different classes) within the data. Consequently, these studies suffered from performance issues, including decreased accuracy and increased memory, runtime, and energy requirements. With this motivation, this paper proposes a novel fuzzy nearest neighbor classifier for time series data. The basic idea of our classifier is to transform the very large training data into a relatively small representative training data and use it to label a test instance by employing a new fuzzy distance measure known as Ravi. Experimental results on real world benchmark datasets demonstrate that the proposed classifier outperforms the current parameter-free time series classifiers and also the popular deep learning techniques.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127833541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494345
H. Sadłowska, A. Kochański, P. Grzegorzewski
Hydroforming is a relatively new technology of forming and profiling. So far, the application of this method has been limited by the costs of die production. The cost of the dies and the long production start-up time made this method economically viable for the production of hundreds of products. The approach change to the tool design for profile shaping techniques has allowed to develop the new hydroforming method perfectly suited to low-volume or even unit production. In traditional solutions, the die is rigid and does not deform during the expansion of the profile. In the newly patented RTH (Rapid Tube Hydroforming) method, the die undergoes controlled deformation during the process. The specificity of the granular materials used for the production of the dies makes modeling the behavior of the die during the expansion of the profile a remarkable problem. This contribution presents considerations on the fuzzy inference method used to model the technological process. As a result, it was possible to more accurately determine the importance of individual die parameters (geometry and material properties), and thus better predict the final shape of the formed profile. The main goal is to understand the effect of shaped profile on the matrix and to recognize the influence of granular material in the matrix under the compaction conditions of the expanded profile on its final geometry.
{"title":"Multi-Phase Fuzzy Modeling in the Innovative RTH Hydroforming Technology","authors":"H. Sadłowska, A. Kochański, P. Grzegorzewski","doi":"10.1109/FUZZ45933.2021.9494345","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494345","url":null,"abstract":"Hydroforming is a relatively new technology of forming and profiling. So far, the application of this method has been limited by the costs of die production. The cost of the dies and the long production start-up time made this method economically viable for the production of hundreds of products. The approach change to the tool design for profile shaping techniques has allowed to develop the new hydroforming method perfectly suited to low-volume or even unit production. In traditional solutions, the die is rigid and does not deform during the expansion of the profile. In the newly patented RTH (Rapid Tube Hydroforming) method, the die undergoes controlled deformation during the process. The specificity of the granular materials used for the production of the dies makes modeling the behavior of the die during the expansion of the profile a remarkable problem. This contribution presents considerations on the fuzzy inference method used to model the technological process. As a result, it was possible to more accurately determine the importance of individual die parameters (geometry and material properties), and thus better predict the final shape of the formed profile. The main goal is to understand the effect of shaped profile on the matrix and to recognize the influence of granular material in the matrix under the compaction conditions of the expanded profile on its final geometry.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"350 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494404
M. Ojeda-Hernández, I. P. Cabrera, P. Cordero, Emilio Muñoz-Velasco
Two alternative definitions of closure system in complete fuzzy lattices are introduced, first as a crisp set and then as a fuzzy one. It is valuated in a complete Heyting algebra and follows the classical definition on complete lattices. The classical bijection between closure systems and fuzzy closure operators is preserved. Then, the notion is compared with the most used definition given by Bělohlávek on the fuzzy powerset lattice.
{"title":"On (fuzzy) closure systems in complete fuzzy lattices","authors":"M. Ojeda-Hernández, I. P. Cabrera, P. Cordero, Emilio Muñoz-Velasco","doi":"10.1109/FUZZ45933.2021.9494404","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494404","url":null,"abstract":"Two alternative definitions of closure system in complete fuzzy lattices are introduced, first as a crisp set and then as a fuzzy one. It is valuated in a complete Heyting algebra and follows the classical definition on complete lattices. The classical bijection between closure systems and fuzzy closure operators is preserved. Then, the notion is compared with the most used definition given by Bělohlávek on the fuzzy powerset lattice.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120962887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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