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Fractal and multifractional-based predictive optimization model for stroke subtypes? classification

dc.contributor.authorKaraca, Yeliz
dc.contributor.authorMoonis, Majaz
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorID56389tr_TR
dc.date.accessioned2020-12-31T11:29:38Z
dc.date.available2020-12-31T11:29:38Z
dc.date.issued2020
dc.departmentÇankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractNumerous natural phenomena display repeating self-similar patterns. Fractal is used when a pattern seems to repeat itself. Fractal and multifractal methods have extensive applications in neurosciences in which the prevalence of fractal properties like self-similarity in the brain, equipped with a complex structure, in medical data analysis at various levels of observation is admitted. The methods come to the fore since subtle details are not always detected by physicians, but these are critical particularly in neurological diseases like stroke which may be life-threatening. The aim of this paper is to identify the self-similar, significant and efficient attributes to achieve high classification accuracy rates for stroke subtypes. Accordingly, two approaches were implemented. The first approach is concerned with application of the fractal and multifractal methods on the stroke dataset in order to identify the regular, self-similar, efficient and significant attributes from the dataset, with these steps: a) application of Box-counting dimension generated BC_stroke dataset b) application of Wavelet transform modulus maxima generated WTMM_stroke dataset. The second approach involves the application of Feed Forward Back Propagation (FFBP) for stroke subtype classification with these steps: (i) FFBP algorithm was applied on the stroke dataset, BC_stroke dataset and WTMM_stroke dataset. (ii) Comparative analyses were performed based on accuracy, sensitivity and specificity for the three datasets. The main contribution is that the study has obtained the identification of self-similar, regular and significant attributes from the stroke subtypes datasets by following multifarious and integrated methodology. The study methodology is based on the singularity spectrum which provides a value concerning how fractal a set of points are in the datasets (BC_stroke dataset and WTMM_stroke dataset). The experimental results reveal the applicability, reliability and accuracy of our proposed integrated method. No earlier work exists in the literature with the relevant stroke datasets and the methods employed. Therefore, the study aims at pointing a new direction in the relevant fields concerning the complex dynamic systems and structures which display multifractional nature.en_US
dc.description.publishedMonth7
dc.identifier.citationKaraca, Yeliz; Moonis, Majaz; Baleanu, Dumitru (2020). "Fractal and multifractional-based predictive optimization model for stroke subtypes? classification", Chaos Solitons & Fractals, Vol. 136.en_US
dc.identifier.doi10.1016/j.chaos.2020.109820
dc.identifier.issn0960-0779
dc.identifier.issn1873-2887
dc.identifier.urihttp://hdl.handle.net/20.500.12416/4425
dc.identifier.volume136en_US
dc.language.isoenen_US
dc.relation.ispartofChaos Solitons & Fractalsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBox-Counting Methoden_US
dc.subjectFeedforward Neural Networksen_US
dc.subjectFractal Dimensionen_US
dc.subjectMultifractalsen_US
dc.subjectStroke Subtypesen_US
dc.subjectWavelet Transform Modulus Maximaen_US
dc.titleFractal and multifractional-based predictive optimization model for stroke subtypes? classificationtr_TR
dc.titleFractal and Multifractional-Based Predictive Optimization Model for Stroke Subtypes? Classificationen_US
dc.typeArticleen_US
dspace.entity.typePublication

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