Browsing by Author "Moonis, Majaz"

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Citation Count: Karaca, Yeliz;...et.al. (2022). "Editorial: Special issue section on fractal ai-based analyses and applications to complex systems: Part iii", Fractals, Vol.30, No.5.
Editorial: Special issue section on fractal ai-based analyses and applications to complex systems: Part iii
(2022) Karaca, Yeliz; Baleanu, Dumitru; Moonis, Majaz; Zhang, Yu-Dong; Gervasi, Osvaldo; 56389
Citation Count: Karaca, Yeliz; Moonis, Majaz; Baleanu, Dumitru (2020). "Fractal and multifractional-based predictive optimization model for stroke subtypes’ classification", Chaos, Solitons and Fractals, Vol. 136.
Fractal and multifractional-based predictive optimization model for stroke subtypes’ classification
(2020) Karaca, Yeliz; Moonis, Majaz; Baleanu, Dumitru; 56389
Numerous 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. © 2020 Elsevier Ltd
Citation Count: Karaca, Yeliz; Moonis, Majaz; Baleanu, Dumitru (2020). "Fractal and multifractional-based predictive optimization model for stroke subtypes? classification", Chaos Solitons & Fractals, Vol. 136.
Fractal and multifractional-based predictive optimization model for stroke subtypes? classification
(2020) Karaca, Yeliz; Moonis, Majaz; Baleanu, Dumitru; 56389
Numerous 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.
Citation Count: Karaca, Yeliz...et al. Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems, Elsevier, p. 332, 2022.
Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems
(Elsevier, 2022) Karaca, Yeliz; Moonis, Majaz; Baleanu, Dumitru; Zhang, Yu-Dong; Gervasi, Osvaldo; 56389
Citation Count: Karaca, Yeliz...et al. (2021). "SPECIAL ISSUE SECTION ON FRACTAL AI-BASED ANALYSES AND APPLICATIONS TO COMPLEX SYSTEMS: PART I", FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY, Vol. 29, No. 5.
SPECIAL ISSUE SECTION ON FRACTAL AI-BASED ANALYSES AND APPLICATIONS TO COMPLEX SYSTEMS: PART I
(2021) Karaca, Yeliz; Baleanu, Dumitru; Moonis, Majaz; Muhammad, Khan; Zhang, Yu-Dong; Gervasi, Osvaldo; 56389
Citation Count: 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.
Theory, Analyses and Predictions of Multifractal Formalism and Multifractal Modelling for Stroke Subtypes’ Classification
(2020) Karaca, Yeliz; Baleanu, Dumitru; Moonis, Majaz; Zhang, Yu-Dong; 56389
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.