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Theory, Analyses and Predictions of Multifractal Formalism and Multifractal Modelling for Stroke Subtypes’ Classification

dc.contributor.authorKaraca, Yeliz
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorMoonis, Majaz
dc.contributor.authorZhang, Yu-Dong
dc.contributor.authorID56389tr_TR
dc.date.accessioned2023-02-09T08:25:24Z
dc.date.available2023-02-09T08:25:24Z
dc.date.issued2020
dc.departmentÇankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractFractal and multifractal analysis interplay within complementary methodology is of pivotal importance in ubiquitously natural and man-made systems. Since the brain as a complex system operates on multitude of scales, the characterization of its dynamics through detection of self-similarity and regularity presents certain challenges. One framework to dig into complex dynamics and structure is to use intricate properties of multifractals. Morphological and functional points of view guide the analysis of the central nervous system (CNS). The former focuses on the fractal and self-similar geometry at various levels of analysis ranging from one single cell to complicated networks of cells. The latter point of view is defined by a hierarchical organization where self-similar elements are embedded within one another. Stroke is a CNS disorder that occurs via a complex network of vessels and arteries. Considering this profound complexity, the principal aim of this study is to develop a complementary methodology to enable the detection of subtle details concerning stroke which may easily be overlooked during the regular treatment procedures. In the proposed method of our study, multifractal regularization method has been employed for singularity analysis to extract the hidden patterns in stroke dataset with two different approaches. As the first approach, decision tree, Naïve bayes, kNN and MLP algorithms were applied to the stroke dataset. The second approach is made up of two stages: i) multifractal regularization (kulback normalization) method was applied to the stroke dataset and mFr_stroke dataset was generated. ii) the four algorithms stated above were applied to the mFr_stroke dataset. When we compared the experimental results obtained from the stroke dataset and mFr_stroke dataset based on accuracy (specificity, sensitivity, precision, F1-score and Matthews Correlation Coefficient), it was revealed that mFr_stroke dataset achieved higher accuracy rates. Our novel proposed approach can serve for the understanding and taking under control the transient features of stroke. Notably, the study has revealed the reliability, applicability and high accuracy via the methods proposed. Thus, the integrated method has revealed the significance of fractal patterns and accurate prediction of diseases in diagnostic and other critical-decision making processes in related fields.en_US
dc.identifier.citationKaraca, Yeliz...et al. (2020). "Theory, Analyses and Predictions of Multifractal Formalism and Multifractal Modelling for Stroke Subtypes’ Classification", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 20th International Conference on Computational Science and Its Applications, ICCSA 2020, Cagliari, 1 July 2020through 4 July 2020, Vol. 12250, pp. 410-425.en_US
dc.identifier.doi10.1007/978-3-030-58802-1_30
dc.identifier.endpage425en_US
dc.identifier.issn0302-9743
dc.identifier.startpage410en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/6165
dc.identifier.volume12250en_US
dc.language.isoenen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFractal Patternen_US
dc.subjectFractional Brownian Motionen_US
dc.subjectHurst Exponenten_US
dc.subjectKnn Algorithmen_US
dc.subjectMultifractal Formalismen_US
dc.subjectMultifractal Regularizationen_US
dc.subjectMultilayer Perceptron Algorithmen_US
dc.subjectNaïve Bayes Algorithmen_US
dc.subjectPrediction Algorithmsen_US
dc.subjectSelf-Similar Processen_US
dc.subjectStrokeen_US
dc.titleTheory, Analyses and Predictions of Multifractal Formalism and Multifractal Modelling for Stroke Subtypes’ Classificationtr_TR
dc.titleTheory, Analyses and Predictions of Multifractal Formalism and Multifractal Modelling for Stroke Subtypes’ Classificationen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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