Hybrid clustering algorithm ‘KCu’ for combining the features of K-means and CURE Algorithm for efficient outliers handling

Devi, Bharathi B.; Setty, Suresh Lakshmi Narasimha

doi:10.18280/ama_b.610204

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Clustering algorithms can be divided into partition-based (Jing et al, 2007;Zhu et al, 2009;Yu et al, 2012;Qian et al, 2017), hierarchy-based (Hall and Goldgof, 2011;Devi and Setty, 2018), density-based (Sunjana and Azizah, 2020;Yin et al, 2020), grid-based (Wang et al, 2019;Zhang et al, 2020), and model-based (Lee et al, 2018;Chrobak et al, 2020) algorithms. Due to the complexity of brain medical images, the boundaries and boundaries of different tissues in the brain are uncertain and unclear.…”

Section: Clustering Algorithmmentioning

confidence: 99%

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

Lee

et al. 2021

Front. Neurosci.

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Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization. Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise. Therefore, in practical applications, the segmentation of brain MRI images has difficulty obtaining high accuracy.Materials and Methods: The fuzzy clustering algorithm establishes the expression of the uncertainty of the sample category and can describe the ambiguity brought by the partial volume effect to the brain MRI image, so it is very suitable for brain MRI image segmentation (B-MRI-IS). The classic fuzzy c-means (FCM) algorithm is extremely sensitive to noise and offset fields. If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be obtained. Accordingly, considering the defects of MRI medical images, this study uses an improved multiview FCM clustering algorithm (IMV-FCM) to improve the algorithm’s segmentation accuracy of brain images. IMV-FCM uses a view weight adaptive learning mechanism so that each view obtains the optimal weight according to its cluster contribution. The final division result is obtained through the view ensemble method. Under the view weight adaptive learning mechanism, the coordination between various views is more flexible, and each view can be adaptively learned to achieve better clustering effects.Results: The segmentation results of a large number of brain MRI images show that IMV-FCM has better segmentation performance and can accurately segment brain tissue. Compared with several related clustering algorithms, the IMV-FCM algorithm has better adaptability and better clustering performance.

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Section: Clustering Algorithmmentioning

confidence: 99%

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

Lee

et al. 2021

Front. Neurosci.

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“…At present, the research direction of sleep quality mainly focuses on the study of sleep staging. With the continuous development of computer technology, many machine learning methods had been proposed and used in the medical applications (Bezdek, 1980(Bezdek, , 1981Hall et al, 1992;Ahmed et al, 2002;Pedrycz, 2002;Chen and Zhang, 2004;Chuang et al, 2006;Weijer and Gevers, 2006;Jing et al, 2007;Cleuziou et al, 2009;Gu and Zhou, 2009;Zhu et al, 2009;Krinidis and Chatzis, 2010;Hall and Goldgof, 2011;Ji et al, 2011;Jiang et al, 2012Jiang et al, , 2014Jiang et al, , 2020Yu et al, 2012;Chen et al, 2013;Gong et al, 2013;Thanh and Wu, 2013;Li et al, 2014;Elazab et al, 2015;Okita et al, 2015;Qian et al, 2015;Wang et al, 2015Wang et al, , 2019Zheng et al, 2015;Devi and Setty, 2018;Lee et al, 2018;Rosati et al, 2018;Cai et al, 2019;Gu et al, 2019;Chrobak et al, 2020;Kumar et al, 2020;Liu et al, 2020;Singh et al, 2020;Sunjana and Azizah, 2020;Yin et al, 2020;…”

Section: Introductionmentioning

confidence: 99%

Sleep Quality Detection Based on EEG Signals Using Transfer Support Vector Machine Algorithm

Wen

2021

Front. Neurosci.

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BackgroundIn recent years, with the acceleration of life rhythm and increased pressure, the problem of sleep disorders has become more and more serious. It affects people’s quality of life and reduces work efficiency, so the monitoring and evaluation of sleep quality is of great significance. Sleep staging has an important reference value in sleep quality assessment. This article starts with the study of sleep staging to detect and analyze sleep quality. For the purpose of sleep quality detection, this article proposes a sleep quality detection method based on electroencephalography (EEG) signals.Materials and MethodsThis method first preprocesses the EEG signals and then uses the discrete wavelet transform (DWT) for feature extraction. Finally, the transfer support vector machine (TSVM) algorithm is used to classify the feature data.ResultsThe proposed algorithm was tested using 60 pieces of data from the National Sleep Research Resource Library of the United States, and sleep quality was evaluated using three indicators: sensitivity, specificity, and accuracy. Experimental results show that the classification performance of the TSVM classifier is significantly higher than those of other comparison algorithms. This further validated the effectiveness of the proposed sleep quality detection method.

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The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021

Resta,

Costantiello

et al. 2024

BMC Public Health

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The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Furthermore, to control endogeneity we also created instrumental variable models for each component of the ESG model. Results show that HEAR is negatively associated to the E, S and G component within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.

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Hybrid clustering algorithm ‘KCu’ for combining the features of K-means and CURE Algorithm for efficient outliers handling

Cited by 3 publications

References 11 publications

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

Sleep Quality Detection Based on EEG Signals Using Transfer Support Vector Machine Algorithm

The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021

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