A Novel Algorithm of Computing Similarity Degree between Chinese Articles Based on Tolerance Granular Computing Model

2011 ◽  
Author(s):  
Rao Fen ◽  
Li Xiangjun ◽  
Liu Tao ◽  
Qiu Taorong ◽  
Tao Qiuping
Keyword(s):  
Granular Computing ◽  
Similarity Degree ◽  
Computing Model ◽  
Novel Algorithm

scholarly journals PERFORMANCE ANALYSIS OF GRANULAR COMPUTING MODEL IN SOFT COMPUTING PARADIGM FOR MONITORING OF FETAL ECHOCARDIOGRAPHY

2019 ◽  
Vol 2019 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Sathesh A
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Granular Computing ◽  
Fetal Heart ◽  
Fetal Echocardiography ◽  
Detection Accuracy ◽  
Second Trimester ◽  
Data Set ◽  
Computing Model ◽  
Computing Paradigm

The monitoring of fetal heart being essential in the second trimester of the prenatal periods. The abnormalities in the child heart rate has to be identified in the early stages, so as to take essential remedies for the babies in the womb, or would enable the physician to be ready for he complication on the delivery and the further treatment after the baby is received. The traditional methodologies being ineffective in detecting the abnormalities leading to fatalities, paves way for the granular computing based fuzzy set, that requires only a limited set of data for training, and helps in the eluding of the unwanted data set that are far beyond the optimal. Further the methods performance is analyzed to evident the improvement in the fetal heart rate detection in terms of prediction accuracy and the detection accuracy.

A New Granular Computing Model Based on Algebraic Structure

2019 ◽  
Vol 28 (1) ◽  
pp. 136-142 ◽  
Author(s):  
Linshu CHEN ◽  
Jiayang WANG ◽  
Weicheng WANG ◽  
Li LI
Keyword(s):  
Algebraic Structure ◽  
Granular Computing ◽  
Computing Model ◽  
Model Based

Estimation of the Dispersion Coefficient in Natural Rivers Using a Granular Computing Model

2017 ◽  
Vol 143 (5) ◽  
pp. 04017001 ◽  
Author(s):  
Roohollah Noori ◽  
Behzad Ghiasi ◽  
Hossien Sheikhian ◽  
Jan Franklin Adamowski
Keyword(s):  
Granular Computing ◽  
Dispersion Coefficient ◽  
Computing Model ◽  
Natural Rivers

A Novel Model for Mining Frequent Patterns Based on Embedded Granular Computing

2018 ◽  
Vol 26 (05) ◽  
pp. 741-769 ◽  
Author(s):  
Gang Fang ◽  
Jiale Wang ◽  
Hong Ying
Keyword(s):  
Data Mining ◽  
Data Structure ◽  
Granular Computing ◽  
Search Space ◽  
Frequent Patterns ◽  
Information Granule ◽  
Memory Space ◽  
Computing Model ◽  
Model Based ◽  
Mining Model

For mining frequent patterns, it is very expensive for the Apriori mining model to read the database repeatedly, and a highly condensed data structure made the FP-growth mining model cost larger memory. In order to avoid the disadvantages of these data mining model, this paper proposes a novel data mining model for discovering frequent patterns, called a data mining model based on embedded granular computing, which is different from the Apriori model and the FP-growth model. The data mining model adopts efficiently dividing and conquering from granular computing, which can construct adaptively different hierarchical granules. To form the data mining model, an embedded granular computing model is proposed in this paper. The granular computing model is used in discovering frequent patterns, on the one hand, it avoids reading the database repeatedly via constructing the extended information granule, and lessen the calculated amount of support; on the other hand, it reduces the memory requirements by the attribute granule, where the search space can compress the memory space of data structure that make the method of generating the candidate become simple relatively; and it can divide the overlarge computing task into several easy operations via the attribute granule, namely, the embedded granular computing model could short the size of the search space from a super state to several sub-states. All experimental results show that the data mining model based on embedded granular computing is more reasonable and efficient than these classical models for mining frequent patterns under these different types of datasets. Otherwise, an extra discussion describes the performance trend of the model by a group of experiments.

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