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

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dc.contributor.author Karaca, Yeliz
dc.contributor.author Baleanu, Dumitru
dc.contributor.author Moonis, Majaz
dc.contributor.author Zhang, Yu-Dong
dc.date.accessioned 2023-02-09T08:25:24Z
dc.date.available 2023-02-09T08:25:24Z
dc.date.issued 2020
dc.identifier.citation Karaca, 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. tr_TR
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/20.500.12416/6165
dc.description.abstract Fractal 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. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1007/978-3-030-58802-1_30 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Fractal Pattern tr_TR
dc.subject Fractional Brownian Motion tr_TR
dc.subject Hurst Exponent tr_TR
dc.subject Knn Algorithm tr_TR
dc.subject Multifractal Formalism tr_TR
dc.subject Multifractal Regularization tr_TR
dc.subject Multilayer Perceptron Algorithm tr_TR
dc.subject Naïve Bayes Algorithm tr_TR
dc.subject Prediction Algorithms tr_TR
dc.subject Self-Similar Process tr_TR
dc.subject Stroke tr_TR
dc.title Theory, Analyses and Predictions of Multifractal Formalism and Multifractal Modelling for Stroke Subtypes’ Classification tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 12250 tr_TR
dc.identifier.startpage 410 tr_TR
dc.identifier.endpage 425 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümü tr_TR


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