Modified Kernel-based Intuitionistic Fuzzy C-means Clustering
Method Using DNA Genetic Algorithm

Zang, Wenke; Ren, Liyan; Jiang, Zhenni; Liu, Xiyu

doi:10.3923/jse.2017.172.182

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…GA is widely used in pattern recognition, function optimization, and other fields with its unique global optimization ability. 'e FCM algorithm based on genetic optimization can effectively solve the local optimal problem and improve the robustness of the algorithm [19].…”

Section: Feature Extraction Image Segmentationmentioning

confidence: 99%

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Image Processing Method Based on FGCA and Artificial Neural Network

Yuan

2022

Scientific Programming

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With the aggravation of human sub-health problems, people’s demand for medical assistance is increasing. In the face of an endless stream of diseases, doctors use medical image analysis to intuitively obtain the morphological information of the affected part of the disease, which is convenient for doctors to make a more accurate assessment of the disease. The processing of medical images is essential for the treatment of people’s diseases and subsequent observation and recovery. Therefore, it is necessary to pay attention to the development and innovation of image processing. With the continuous increase of diseases and the innovation and development of technology, the existing medical image processing technology still has problems such as noise and low image contrast. The application of fuzzy genetic clustering algorithms and artificial neural networks can help image processing be more accurate and perfect. In view of the above problems, this paper has carried out data analysis and research on image processing methods based on fuzzy genetic clustering algorithm (FGCA) and artificial neural network (ANN). The research results have shown that in the case of no noise and 5% salt and pepper noise, the FGCA segmentation coefficient is the largest, at 0.9756 and 0.9758, respectively, and the segmentation entropy is the smallest, at 0.0885 and 0.0925, respectively. The liver CT (Computer Tomography) image segmentation method based on DeepLab V3+ has the highest PA, Mlou value, and Dice coefficient, which are 88%, 95%, and 94%, respectively, which has laid the foundation for the innovation and development of image processing methods.

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Section: Feature Extraction Image Segmentationmentioning

confidence: 99%

“…Given a small batch of samples, there are K samples. 'e corresponding mean and variance are formulas ( 18) and (19), respectively:…”

Section: Batch Normalization (Bn)mentioning

confidence: 99%

Image Processing Method Based on FGCA and Artificial Neural Network

Yuan

2022

Scientific Programming

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“…Finally, the IFCM clustering algorithm is known to be based on the traditional FCM algorithm by adding intuitionistic features to membership and objective functions. The fuzziness level is minimized when a total of 62 countries are divided into two clusters with an accuracy rate of 95.2% using the IFCM clustering algorithm [32]. As a result of the analyzed characteristics, the countries are classified into two clusters: 59 countries in Cluster 1, and the remaining countries in Cluster 2.…”

Section: The Ifcm Clustering Analysismentioning

confidence: 99%

Using Intuitionistic Fuzzy C-Means Clustering Algorithms to Model Covid-19 Cases for Countries in the Worldwide

Ince¹,

Şentürk

2023

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Every day, the number of newly confirmed cases of coronavirus (COVID-19) rises in many countries. It is critical to adjust policies and plans in order to investigate the relationships between the distributions of the spread of this virus in other countries. During this study, the intuitionistic fuzzy c-means (IFCM) clustering method is used to compare and cluster the distributions of COVID-19 spread in 62 countries. Using the IFCM clustering algorithm, the study aims to cluster the countries that use environmental, economic, social, health, and related measurements that affect disease spread to implement policies that regulate disease spread. As a result, countries that have similar factors can take proactive measures to address the pandemic. The data are obtained for 62 countries, and six different feature variables (factors associated with the spread of COVID-19) are determined. The data are obtained for 62 countries, and six variables with different characteristics (linked to the spread of COVID-19) are identified. In this study, the IFCM clustering algorithm is used to determine the dynamic behavior of COVID-19 based on real-world data for multiple countries and Turkey around the world. Data analysis is performed through MATLAB 2018a and R programs. The clustering results revealed that the distribution of dissemination in Brazil, India, and the United States was nearly identical and distinct from that of the 59 other countries.

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Automatic Density Peaks Clustering Using DNA Genetic Algorithm Optimized Data Field and Gaussian Process

Zang

Ren

Zhang

et al. 2017

Int. J. Patt. Recogn. Artif. Intell.

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Clustering by fast search and finding of Density Peaks (called as DPC) introduced by Alex Rodríguez and Alessandro Laio attracted much attention in the field of pattern recognition and artificial intelligence. However, DPC still has a lot of defects that are not resolved. Firstly, the local density [Formula: see text] of point [Formula: see text] is affected by the cutoff distance [Formula: see text], which can influence the clustering result, especially for small real-world cases. Secondly, the number of clusters is still found intuitively by using the decision diagram to select the cluster centers. In order to overcome these defects, this paper proposes an automatic density peaks clustering approach using DNA genetic algorithm optimized data field and Gaussian process (referred to as ADPC-DNAGA). ADPC-DNAGA can extract the optimal value of threshold with the potential entropy of data field and automatically determine the cluster centers by Gaussian method. For any data set to be clustered, the threshold can be calculated from the data set objectively rather than the empirical estimation. The proposed clustering algorithm is benchmarked on publicly available synthetic and real-world datasets which are commonly used for testing the performance of clustering algorithms. The clustering results are compared not only with that of DPC but also with that of several well-known clustering algorithms such as Affinity Propagation, DBSCAN and Spectral Cluster. The experimental results demonstrate that our proposed clustering algorithm can find the optimal cutoff distance [Formula: see text], to automatically identify clusters, regardless of their shape and dimension of the embedded space, and can often outperform the comparisons.

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Modified Kernel-based Intuitionistic Fuzzy C-means Clustering Method Using DNA Genetic Algorithm

Cited by 3 publications

References 18 publications

Image Processing Method Based on FGCA and Artificial Neural Network

Image Processing Method Based on FGCA and Artificial Neural Network

Using Intuitionistic Fuzzy C-Means Clustering Algorithms to Model Covid-19 Cases for Countries in the Worldwide

Automatic Density Peaks Clustering Using DNA Genetic Algorithm Optimized Data Field and Gaussian Process

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