Image segmentation-oriented tolerance granular computing model

2008 ◽  
Author(s):  
Zhongzhi Shi ◽  
Zheng Zheng ◽  
Zuqiang Meng
Keyword(s):  
Image Segmentation ◽  
Granular Computing ◽  
Computing Model

scholarly journals A Complex Chained P System Based on Evolutionary Mechanism for Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Xiyu Liu ◽  
Lin Wang ◽  
Jianhua Qu ◽  
Ning Wang
Keyword(s):  
Image Segmentation ◽  
Real Life ◽  
Membrane System ◽  
Global Search ◽  
P System ◽  
Evolutionary Mechanism ◽  
Modified Particle Swarm Optimization ◽  
Computing Model ◽  
Distributed Parallel Computing ◽  
Clustering Problems

A new clustering membrane system using a complex chained P system (CCP) based on evolutionary mechanism is designed, developed, implemented, and tested. The purpose of CCP is to solve clustering problems. In CCP, two kinds of evolution rules in different chained membranes are used to enhance the global search ability. The first kind of evolution rules using traditional and modified particle swarm optimization (PSO) clustering techniques are used to evolve the objects. Another based on differential evolution (DE) is introduced to further improve the global search ability. The communication rules are adopted to accelerate the convergence and avoid prematurity. Under the control of evolution-communication mechanism, the CCP can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. This proposed CCP is compared with four existing PSO clustering approaches on eight real-life datasets to verify the validity. The computational results on tested images also clearly show the effectiveness of CCP in solving image segmentation problems.

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
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