scholarly journals Optimal Cellular Automata Technique for Image Segmentation

2020 ◽  
Vol 9 (3) ◽  
pp. 1474-1478
Keyword(s):  
Cellular Automata ◽  
Search Algorithm ◽  
Early Stage ◽  
Gravitational Search Algorithm ◽  
Cluster Center ◽  
Cellular Automata Model ◽  
Delay In Diagnosis ◽  
Global Consistency ◽  
Optimal Value ◽  
Consistency Error

Leukemia death secured 10 thplace among the most dangerous death in the world. The main reason is due to the delay in diagnosis which in turn delayed the treatment process. Hence it becomes an exigent requirement to diagnose leukemia in its early stage. Segmentation of WBC is the initial phase of leukemia detection using image processing.This paper aims to extract WBC from the image background. There exists various techniques for WBC segmentation in the literature. Yet, they provides inaccurate results.Cellular Automata can be effectively implemented in image processing. In this paper, we have proposed an Optimal Cellular Automata approach for image segmentation.In this approach, the optimal value for alive cells is obtained through particle swarm Optimization with Gravitational Search Algorithm (PSOGSA). The optimal value have fed in to the cellular automata model and get the segmented image. The results are validated based on the parameters likeRand Index (RI), Global Consistency Error (GCE), and Variation of Information (VOI). The Experimental results of proposed technique shows better results when compared to the previously proposed techniques namely, Hybrid K-Means with Cluster Center Estimation, Region Splitting and Clustering Technique and Cellular Automata. The proposed technique outperformed all other techniques.

scholarly journals A Point Cloud Simplification Method Based on Modified Fuzzy C-Means Clustering Algorithm with Feature Information Reserved

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yang Yang ◽  
Ming Li ◽  
Xie Ma
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Principal Component ◽  
Cluster Center ◽  
Normal Vector ◽  
Fuzzy C Means ◽  
Feature Information ◽  
Point Cloud Simplification

To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed. Firstly, the normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. Thirdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm. Finally, the point cloud is simplified according to point cloud feature information and simplified parameters. The point cloud test data are simplified using the new algorithm and traditional algorithms; then, the results are compared and discussed. The results show that the new proposed algorithm can not only effectively improve the precision of point cloud simplification but also reserve the accuracy of part features.

scholarly journals A symmetry based anomaly detection in brain using cellular automata for computer aided diagnosis

2018 ◽  
Vol 14 (1) ◽  
pp. 471
Author(s):  
Nisha V M ◽  
L Jeganathan
Keyword(s):  
Cellular Automata ◽  
Early Stage ◽  
Computer Aided Diagnosis ◽  
Brain Images ◽  
Cellular Automata Model ◽  
Computing Power ◽  
Diagnosis System ◽  
Proposed Model ◽  
Computer Aided ◽  
Aided Diagnosis

Computer aided diagnosis (CAD) is an advancing technology in medical imaging. CAD acts as an additional computing power for doctors to interpret the medical images which leads to a more accurate diagnosis of the disease.CAD system increases the chances of detection of brain lesions by assisting the physicians in decreasing the observational oversight in the early stage of diseases.This paper focuses on the development of a cellular automata based model to find the anomaly prone areas in human brains.Because of the bilateral symmetric nature of human brain, a symmetry based cellular automata model is proposed.An algorithm is designed based on the proposed model to detect the anomaly prone areas in brain images. The proposed model can be a standalone model or it can be incorporated to a sophisticated computer aided diagnosis system. By incorporating asymmetry information into a computer aided diagnosis system, enhances its performance in identifying the anomalies exists in bilaterally symmetrical brain images.

scholarly journals Spatial cellular robot in orbital truss collision-free path planning

2020 ◽  
Vol 11 (2) ◽  
pp. 233-250
Author(s):  
Ye Dai ◽  
Zhaoxu Liu ◽  
Yunshan Qi ◽  
Hanbo Zhang ◽  
Bindi You ◽  
...  
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Early Stage ◽  
Gravitational Search Algorithm ◽  
Space Station ◽  
Optimal Solution ◽  
Ant Colony ◽  
Triangular Prism ◽  
Exploration Area

Abstract. Aiming at the problem of moving path planning of a cellular robot on trusses in space station, a triangular prism truss is taken as the research object, and an optimized ant colony algorithm incorporating a gravitational search algorithm is proposed. The innovative use of the hierarchical search strategy which limits the exploration area, the use of gravity search algorithm to get the optimal solution of truss nodes, and further transform it into the initial value of pheromone in ant colony algorithm, can effectively prevent the algorithm from falling into the local optimal solution in the early stage, and make the optimization algorithm have a faster convergence speed. This paper proposes a heuristic function including the angle between the targets, which can effectively avoid blind search in the early stage and improve the ability of path search. The simulation results show that the path and planning time of the cellular robot can be effectively reduced when choosing truss path.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Analysis of Optimal Power Flow Using Gravitational Search Algorithm

2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Power ◽  
Gravitational Search
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