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Multifractional Gaussian Process Based on Self-similarity Modelling for MS Subgroups' Clustering with Fuzzy C-Means

dc.contributor.authorKaraca, Yeliz
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorID56389tr_TR
dc.date.accessioned2022-06-21T07:30:25Z
dc.date.available2022-06-21T07:30:25Z
dc.date.issued2020
dc.departmentÇankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractMultifractal analysis is a beneficial way to systematically characterize the heterogeneous nature of both theoretical and experimental patterns of fractal. Multifractal analysis tackles the singularity structure of functions or signals locally and globally. While Holder exponent at each point provides the local information, the global information is attained by characterization of the statistical or geometrical distribution of Holder exponents occurring, referred to as multifractal spectrum. This analysis is time-saving while dealing with irregular signals; hence, such analysis is used extensively. Multiple Sclerosis (MS), is an auto-immune disease that is chronic and characterized by the damage to the Central Nervous System (CNS), is a neurological disorder exhibiting dissimilar and irregular attributes varying among patients. In our study, the MS dataset consists of the Expanded Disability Status Scale (EDSS) scores and Magnetic Resonance Imaging (MRI) (taken in different years) of patients diagnosed with MS subgroups (relapsing remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS)) while healthy individuals constitute the control group. This study aims to identify similar attributes in homogeneous MS clusters and dissimilar attributes in different MS subgroup clusters. Thus, it has been aimed to demonstrate the applicability and accuracy of the proposed method based on such cluster formation. Within this framework, the approach we propose follows these steps for the classification of the MS dataset. Firstly, Multifractal denoising with Gaussian process is employed for identifying the critical and significant self-similar attributes through the removal of MS dataset noise, by which, mFd MS dataset is generated. As another step, Fuzzy C-means algorithm is applied to the MS dataset for the classification purposes of both datasets. Based on the experimental results derived within the scheme of the applicable and efficient proposed method, it is shown that mFd MS dataset yielded a higher accuracy rate since the critical and significant self-similar attributes were identified in the process. This study can provide future direction in different fields such as medicine, natural sciences and engineering as a result of the model proposed and the application of alternative mathematical models. As obtained based on the model, the experimental results of the study confirm the efficiency, reliability and applicability of the proposed method. Thus, it is hoped that the derived results based on the thorough analyses and algorithmic applications will be assisting in terms of guidance for the related studies in the future.en_US
dc.identifier.citationKaraca, Yeliz; Baleanu, Dumitru (2020). "Multifractional Gaussian Process Based on Self-similarity Modelling for MS Subgroups' Clustering with Fuzzy C-Means", COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT II, Vol. 12250, pp. 426-441.en_US
dc.identifier.doi10.1007/978-3-030-58802-1_31
dc.identifier.endpage441en_US
dc.identifier.isbn9783030588021
dc.identifier.isbn9783030588014
dc.identifier.issn0302-9743
dc.identifier.startpage426en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/5688
dc.identifier.volume12250en_US
dc.language.isoenen_US
dc.relation.ispartofCOMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT IIen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFractional Brownian Motionen_US
dc.subjectFractional Gaussian Processen_US
dc.subjectHolder Regularityen_US
dc.subjectMultifractal Analysisen_US
dc.subjectMsfuzzy C-Meansen_US
dc.subjectClassificationen_US
dc.subjectDiscrete Variationsen_US
dc.subjectRegularityen_US
dc.subjectSelf-Similarityen_US
dc.titleMultifractional Gaussian Process Based on Self-similarity Modelling for MS Subgroups' Clustering with Fuzzy C-Meanstr_TR
dc.titleMultifractional Gaussian Process Based on Self-Similarity Modelling for Ms Subgroups' Clustering With Fuzzy C-Meansen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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