A Traffic Flow Status Recognition Method by Combining Fuzzy Logic and Rough Set Theory

Logistics ◽  
2009 ◽  
Author(s):  
Ruimin Li ◽  
Shilin Pu ◽  
Jiangang Lu ◽  
Qixin Shi
Keyword(s):  
Fuzzy Logic ◽  
Traffic Flow ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Recognition Method ◽  
Status Recognition

scholarly journals Topology in the Alternative Set Theory and Rough Sets via Fuzzy Type Theory

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 432 ◽  
Author(s):  
Vilém Novák
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Rough Sets ◽  
Type Theory ◽  
Rough Set Theory ◽  
Basic Concepts ◽  
Alternative Set

In this paper, we will visit Rough Set Theory and the Alternative Set Theory (AST) and elaborate a few selected concepts of them using the means of higher-order fuzzy logic (this is usually called Fuzzy Type Theory). We will show that the basic notions of rough set theory have already been included in AST. Using fuzzy type theory, we generalize basic concepts of rough set theory and the topological concepts of AST to become the concepts of the fuzzy set theory. We will give mostly syntactic proofs of the main properties and relations among all the considered concepts, thus showing that they are universally valid.

Research on Information Technology with Character Pattern Recognition Method Based on Rough Set Theory

2014 ◽  
Vol 886 ◽  
pp. 519-523 ◽  
Author(s):  
Yong Li Liu
Keyword(s):  
Information Technology ◽  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Pattern Recognition Method ◽  
Two Dimensional ◽  
Recognition Method ◽  
Two Dimensional Image

Character Pattern recognition is widely used in the information technology field. This paper proposes a method of character pattern recognition based on rough set theory. By giving the characters two dimensional image, defining the location of the characteristic and abstracting the characteristic value, the knowledge table and table reduction can be ascertained. Then the decision rules can be deduced. Through the simulation of 26 English alphabets, the results illustrate this methods validity and correctness.

Rough Set Theory and Fuzzy Logic Based Warehousing of Heterogeneous Clinical Databases

2017 ◽  
Vol 25 (03) ◽  
pp. 385-408 ◽  
Author(s):  
R. Saravana Kumar ◽  
G. Tholkappia Arasu
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Second Phase ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Clinical Databases ◽  
Efficient Data

Large amounts of data about the patients with their medical conditions are presented in the Medical databases. Analyzing all these databases is one of the difficult tasks in the medical environment. In order to warehouse all these databases and to analyze the patient’s condition, we need an efficient data mining technique. In this paper, an efficient data mining technique for warehousing clinical databases using Rough Set Theory (RST) and Fuzzy Logic is proposed. Our proposed methodology contains two phases – (i) Clustering and (ii) Classification. In the first phase, Rough Set Theory is used for clustering. Clustering is one of the data mining techniques for warehousing the heterogeneous data bases. Clustering technique is used to group data that have similar characteristics in the same cluster and also to group the data that have dissimilar characteristics with other clusters. After clustering the data, similar objects will be clustered in one cluster and the dissimilar objects will be clustered under another cluster. The RST can be reduced the complexity. Then in the second phase, these clusters are classified using Fuzzy Logic. Normally, Classification with Fuzzy Logic is generated more number of rules. Since the RST is utilized in our work, the classification using Fuzzy can be done with less amount of complexity. The proposed approach is evaluated using various clinical related databases from heart disease datasets – Cleveland, Switzerland and Hungarian. The performance analysis is based on Sensitivity, Specificity and Accuracy with different cluster numbers. The experimentation results show that our proposed methodology provides better accuracy result.

A Novel Emotion Recognition Method Based on Ensemble Learning and Rough Set Theory

2011 ◽  
Vol 5 (3) ◽  
pp. 61-72 ◽  
Author(s):  
Yong Yang ◽  
Guoyin Wang
Keyword(s):  
Emotion Recognition ◽  
Ensemble Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Classical Method ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Audio Information

Emotion recognition is a very hot topic, which is related with computer science, psychology, artificial intelligence, etc. It is always performed on facial or audio information with classical method such as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning theory is a novelty in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method is proposed, which is based on selective ensemble feature selection and rough set theory. This method can meet the tradeoff between accuracy and diversity of base classifiers. Moreover, the proposed method is taken as an emotion recognition method and proved to be effective according to the simulation experiments.

A Novel Emotion Recognition Method Based on Ensemble Learning and Rough Set Theory

2012 ◽  
pp. 128-139 ◽  
Author(s):  
Yong Yang ◽  
Guoyin Wang
Keyword(s):  
Emotion Recognition ◽  
Ensemble Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Classical Method ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Selective Ensemble ◽  
Audio Information

Emotion recognition is a very hot topic, which is related with computer science, psychology, artificial intelligence, etc. It is always performed on facial or audio information with classical method such as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning theory is a novelty in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method is proposed, which is based on selective ensemble feature selection and rough set theory. This method can meet the tradeoff between accuracy and diversity of base classifiers. Moreover, the proposed method is taken as an emotion recognition method and proved to be effective according to the simulation experiments.

On Objective-Based Roughc-Regression

2015 ◽  
Vol 19 (1) ◽  
pp. 36-42
Author(s):  
Akira Sugawara ◽  
◽  
Yasunori Endo ◽  
Naohiko Kinoshita ◽  
Keyword(s):  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Pattern Recognition Method ◽  
Clustering Methods ◽  
Recognition Method ◽  
Numerical Examples

The pattern recognition method of clustering is a technique automatically classifying data into clusters. Among clustering methods,c-regression based on fuzzy set theory, called Fuzzyc-Regression (FCR), is proposed to get a linear dataset structure. The most recent clustering is based on rough set theory called rough clustering, which is less descriptive than fuzzy clustering. A typical rough clustering algorithm is Roughk-Regression (RKR). However, RKR has problems because it depends on initial values and has no optimum index, so we do not know whether a clustering result will be optimal. This paper proposes Roughc-Regression (RCR) based on the optimization of an objective function and demonstrates its effectiveness through numerical examples.

A Hybrid Protocol using Fuzzy Logic and Rough Set Theory for Target Coverage

2020 ◽  
Vol 13 ◽  
Author(s):  
Pooja Chaturvedi ◽  
A K Daniel
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Network Performance ◽  
Processing Time ◽  
Rough Set Theory ◽  
Set Cover ◽  
Target Coverage ◽  
Rule Set ◽  
Trust Calculation

Background: Target coverage is considered as a significant problem in the area of wireless sensor networks which usually aims at monitoring a given set of targets with the required confidence level so that network lifetime can be enhanced while considering the constraints of the resources. Objective: To maximize the lifetime of the sensor network and minimize the overhead involved in the scheduling approach, such that the pre specified set of targets is monitored for longer duration with the required confidence level. Methods: The paper uses a fuzzy logic system based on Mamdani inference in which the node status to remain in the active state is determined on the basis of coverage probability, trust values and node contribution. The rule set for determining the set cover is optimized by using the rough set theory which aims to determine the node validity for the trust calculation. Results: The results show that the proposed approach improved the network performance in terms of processing time, throughput and energy conservation by a factor of 50%, 74% and 74% respectively as compared to the existing approaches. Conclusion: The paper proposes a scheduling strategy of the nodes for target coverage as an enhancement to the energy efficient coverage protocol (EECP) on the basis of rough set theory. The rule set for determining the set cover is optimized by using the rough set theory so that the network performance is improved in terms of the processing time, throughput and energy consumption. Current and Future Developments: In this paper we have optimized the performance of EECP protocol by considering the concept of Rough Set Theory, which determined the validity of the nodes in the trust calculation. We have evaluated the performance through a number of simulations and the results show the comparative improvement in terms of the processing time, throughput and energy conservation. To utilize the unutilized nodes in the current round for the coverage enhancement is our future work. We also aim to study the effects of heterogeneity on the performance of the proposed protocol in future. Declaration: The authors declare that the research article is author’s original work and have not been sent/ published elsewhere.

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