Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494391
Lewis Veryard, H. Hagras, A. Conway, G. Owusu
In this paper, we present a novel Type-2 fuzzy multi-objective multi-chromosomal optimisation algorithm for capacity planning within telecommunication networks. The proposed system is compared to one of the most successful multi-objective optimisation algorithms which is NSGA-II. This comparison shows that in the capacity planning problems the proposed algorithm can produce a better solution front than NSGA-II in 80% - 93 % of cases. Additionally the use of Type-2 fuzzy logic produces a better solution front in 72% of cases when compared to using Type-1 fuzzy logic.
{"title":"A Type-2 Fuzzy Multi-Objective Multi-Chromosomal Optimisation for Capacity Planning within Telecommunication Networks","authors":"Lewis Veryard, H. Hagras, A. Conway, G. Owusu","doi":"10.1109/FUZZ45933.2021.9494391","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494391","url":null,"abstract":"In this paper, we present a novel Type-2 fuzzy multi-objective multi-chromosomal optimisation algorithm for capacity planning within telecommunication networks. The proposed system is compared to one of the most successful multi-objective optimisation algorithms which is NSGA-II. This comparison shows that in the capacity planning problems the proposed algorithm can produce a better solution front than NSGA-II in 80% - 93 % of cases. Additionally the use of Type-2 fuzzy logic produces a better solution front in 72% of cases when compared to using Type-1 fuzzy logic.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"38 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":"132609378","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/FUZZ45933.2021.9494469
M. Badurowicz, Paweł Karczmarek, J. Montusiewicz
In the presented paper the authors are showing the usage of fuzzy extensions of isolations forests for detecting road anomalies like potholes. Using the data acquired by the accelerometer in the smartphone and the proper smartphone application, the vibrations while driving over road were analyzed using multiple variants of extended isolation forests - n-ary (NIF), with fuzzy membership function (MIF), with k-means clustering (KIF), with two fuzzy clusters incorporated (CIF) or two fuzzy clusters and the distance to the cluster center (prototype) utilized (C2DIF). The presented research shows that in comparison to the state-of-the-art methods previously discussed by the authors, the accuracy and false positive rate have improved, while the sensitivity has been improved to reach 100%.
{"title":"Fuzzy Extensions of Isolation Forests for Road Anomaly Detection","authors":"M. Badurowicz, Paweł Karczmarek, J. Montusiewicz","doi":"10.1109/FUZZ45933.2021.9494469","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494469","url":null,"abstract":"In the presented paper the authors are showing the usage of fuzzy extensions of isolations forests for detecting road anomalies like potholes. Using the data acquired by the accelerometer in the smartphone and the proper smartphone application, the vibrations while driving over road were analyzed using multiple variants of extended isolation forests - n-ary (NIF), with fuzzy membership function (MIF), with k-means clustering (KIF), with two fuzzy clusters incorporated (CIF) or two fuzzy clusters and the distance to the cluster center (prototype) utilized (C2DIF). The presented research shows that in comparison to the state-of-the-art methods previously discussed by the authors, the accuracy and false positive rate have improved, while the sensitivity has been improved to reach 100%.","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":"130801736","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/FUZZ45933.2021.9494344
Namal Rathnayake, Tuan Linh Dang, Y. Hoshino
Performing an accurate and smooth trajectory of a quad-copter is a crucial aspect in autonomous controls due to its non-linearity and under-actuated characteristic. Adaptive Neuro-Fuzzy Inference System (ANFIS) is well-known for nonlinear controls. This paper focuses on comparing the performance of ANFIS based quad-copter systems to identify the best optimization algorithm. Two famous algorithms called Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) was used as the optimization algorithms and to tune the gains of the Fuzzy Inference Systems (FIS). The analysis was performed using two different simulations namely, altitude control and trajectory navigation. The final results were compared between traditional PID, conventional ANFIS, GA-ANFIS and PSO-ANFIS. PSO-ANFIS obtained the highest performance in our experiments.
{"title":"Performance Comparison of the ANFIS based Quad-Copter Controller Algorithms","authors":"Namal Rathnayake, Tuan Linh Dang, Y. Hoshino","doi":"10.1109/FUZZ45933.2021.9494344","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494344","url":null,"abstract":"Performing an accurate and smooth trajectory of a quad-copter is a crucial aspect in autonomous controls due to its non-linearity and under-actuated characteristic. Adaptive Neuro-Fuzzy Inference System (ANFIS) is well-known for nonlinear controls. This paper focuses on comparing the performance of ANFIS based quad-copter systems to identify the best optimization algorithm. Two famous algorithms called Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) was used as the optimization algorithms and to tune the gains of the Fuzzy Inference Systems (FIS). The analysis was performed using two different simulations namely, altitude control and trajectory navigation. The final results were compared between traditional PID, conventional ANFIS, GA-ANFIS and PSO-ANFIS. PSO-ANFIS obtained the highest performance in our experiments.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"46 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":"128744616","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/FUZZ45933.2021.9494513
João C. B. Oliveira, Ricardo Rios, E. Almeida, C. Sant'Anna, T. N. Rios
Source code quality plays a key role in software quality mainly due to its impact on software maintainability. Software engineers have been using source code metrics to support them to assess source code quality. Source code metrics quantify different source code characteristics. However, source code metric analysis still involves subjectivity. For instance, it is not trivial to decide whether a metric value is high or low. To reduce the eventual subjectivity of source code metrics analysis, several researchers are using Machine Learning algorithms. Therefore, in this paper, we designed a Fuzzy-based approach to extract characteristics and patterns present in source code versioning repositories in order to: i) assist the specialist in the interpretation of releases, especially when working with large volumes of source code; ii) from the release interpretation, specialists can improve the quality of the source code; and iii) monitor the evolution of the software as new releases are submitted to the repositories. We evaluated the proposed approach with the Linux Test Project repository, emphasizing the interpretability of large source code versioning repositories.
{"title":"Fuzzy Software Analyzer (FSA): A New Approach for Interpreting Source Code Versioning Repositories","authors":"João C. B. Oliveira, Ricardo Rios, E. Almeida, C. Sant'Anna, T. N. Rios","doi":"10.1109/FUZZ45933.2021.9494513","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494513","url":null,"abstract":"Source code quality plays a key role in software quality mainly due to its impact on software maintainability. Software engineers have been using source code metrics to support them to assess source code quality. Source code metrics quantify different source code characteristics. However, source code metric analysis still involves subjectivity. For instance, it is not trivial to decide whether a metric value is high or low. To reduce the eventual subjectivity of source code metrics analysis, several researchers are using Machine Learning algorithms. Therefore, in this paper, we designed a Fuzzy-based approach to extract characteristics and patterns present in source code versioning repositories in order to: i) assist the specialist in the interpretation of releases, especially when working with large volumes of source code; ii) from the release interpretation, specialists can improve the quality of the source code; and iii) monitor the evolution of the software as new releases are submitted to the repositories. We evaluated the proposed approach with the Linux Test Project repository, emphasizing the interpretability of large source code versioning repositories.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"14 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":"126832956","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/FUZZ45933.2021.9494472
Chao Chen, Yu Zhao, Christian Wagner, Direnc Pekaslan, J. Garibaldi
Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition approaches to modelling the interaction between the non-singleton input and the antecedent fuzzy sets enable the efficient handling of uncertainty without requiring changes in a system's rule base, with benefits both in terms of performance and interpretability. As thus far few current software toolkit support non-singleton fuzzy systems, this paper presents an extension of the FuzzyR toolbox, which is a freely available R package on CRAN, for non-singleton fuzzy logic systems. The updated toolbox enables a non-singleton model to be conveniently built from scratch, or for existing singleton fuzzy logic systems built using FuzzyR to be converted easily. Predefined operations include fuzzification of crisp inputs (e.g. into Gaussian membership functions), and a variety of composition approaches for computing rules' firing-strengths, based on the standard, centroid-based, and similarity-based methods. It is also possible to include user-defined options for these abovementioned methods, without the need to modify (or update) the FuzzyR toolbox itself. In this paper, detailed introductions for the new non-singleton features of the toolkit are presented, complete with code samples in R to facilitate adoption both within and beyond the community. Further, the paper presents a series of validation experiments, replicating a recent empirical analysis of non-singleton fuzzy logic systems in the context of time-series prediction with different levels of noise.
{"title":"An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems","authors":"Chao Chen, Yu Zhao, Christian Wagner, Direnc Pekaslan, J. Garibaldi","doi":"10.1109/FUZZ45933.2021.9494472","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494472","url":null,"abstract":"Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition approaches to modelling the interaction between the non-singleton input and the antecedent fuzzy sets enable the efficient handling of uncertainty without requiring changes in a system's rule base, with benefits both in terms of performance and interpretability. As thus far few current software toolkit support non-singleton fuzzy systems, this paper presents an extension of the FuzzyR toolbox, which is a freely available R package on CRAN, for non-singleton fuzzy logic systems. The updated toolbox enables a non-singleton model to be conveniently built from scratch, or for existing singleton fuzzy logic systems built using FuzzyR to be converted easily. Predefined operations include fuzzification of crisp inputs (e.g. into Gaussian membership functions), and a variety of composition approaches for computing rules' firing-strengths, based on the standard, centroid-based, and similarity-based methods. It is also possible to include user-defined options for these abovementioned methods, without the need to modify (or update) the FuzzyR toolbox itself. In this paper, detailed introductions for the new non-singleton features of the toolkit are presented, complete with code samples in R to facilitate adoption both within and beyond the community. Further, the paper presents a series of validation experiments, replicating a recent empirical analysis of non-singleton fuzzy logic systems in the context of time-series prediction with different levels of noise.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"26 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":"114775345","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/FUZZ45933.2021.9494506
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}
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