Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms
المؤلفون المشاركون
Moonis, Majaz
Karaca, Yeliz
Cattani, Carlo
Bayrak, Şengül
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-04-11
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Multifractal denoising techniques capture interest in biomedicine, economy, and signal and image processing.
Regarding stroke data there are subtle details not easily detectable by eye physicians.
For the stroke subtypes diagnosis, details are important due to including hidden information concerning the possible existence of medical history, laboratory results, and treatment details.
Recently, K-means and fuzzy C means (FCM) algorithms have been applied in literature with many datasets.
We present efficient clustering algorithms to eliminate irregularities for a given set of stroke dataset using 2D multifractal denoising techniques (Bayesian (mBd), Nonlinear (mNold), and Pumping (mPumpD)).
Contrary to previous methods, our method embraces the following assets: (a) not applying the reduction of the stroke datasets’ attributes, leading to an efficient clustering comparison of stroke subtypes with the resulting attributes; (b) detecting attributes that eliminate “insignificant” irregularities while keeping “meaningful” singularities; (c) yielding successful clustering accuracy performance for enhancing stroke data qualities.
Therefore, our study is a comprehensive comparative study with stroke datasets obtained from 2D multifractal denoised techniques applied for K-means and FCM clustering algorithms.
Having been done for the first time in literature, 2D mBd technique, as revealed by results, is the most successful feature descriptor in each stroke subtype dataset regarding the mentioned algorithms’ accuracy rates.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Karaca, Yeliz& Cattani, Carlo& Moonis, Majaz& Bayrak, Şengül. 2018. Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms. Complexity،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1136464
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Karaca, Yeliz…[et al.]. Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms. Complexity No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1136464
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Karaca, Yeliz& Cattani, Carlo& Moonis, Majaz& Bayrak, Şengül. Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms. Complexity. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1136464
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1136464
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر