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Algorithmic Complexity-Based Fractional-Order Derivatives in Computational Biology

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dc.contributor.author Karaca, Yeliz
dc.contributor.author Baleanu, Dumitru
dc.date.accessioned 2024-01-29T13:46:56Z
dc.date.available 2024-01-29T13:46:56Z
dc.date.issued 2023
dc.identifier.citation Karaca, Y.; Baleanu, D. "Algorithmic Complexity-Based Fractional-Order Derivatives in Computational Biology", Advances in Mathematical Modelling, Applied Analysis and Computation,ICMMAAC 2021, Proceedings, pp.55-89, 2023. tr_TR
dc.identifier.isbn 978-981190178-2
dc.identifier.uri http://hdl.handle.net/20.500.12416/7033
dc.description.abstract Fractional calculus approach, providing novel models through the introduction of fractional-order calculus to optimization methods, is employed in machine learning algorithms. This scheme aims to attain optimized solutions by maximizing the accuracy of the model and minimizing the functions like the computational burden. Mathematical-informed frameworks are to be employed to enable reliable, accurate, and robust understanding of various complex biological processes that involve a variety of spatial and temporal scales. This complexity requires a holistic understanding of different biological processes through multi-stage integrative models that are capable of capturing the significant attributes on the related scales. Fractional-order differential and integral equations can provide the generalization of traditional integral and differential equations through the extension of the conceptions with respect to biological processes. In addition, algorithmic complexity (computational complexity), as a way of comparing the efficiency of an algorithm, can enable a better grasping and designing of efficient algorithms in computational biology as well as other related areas of science. It also enables the classification of the computational problems based on their algorithmic complexity, as defined according to the way the resources are required for the solution of the problem, including the execution time and scale with the problem size. Based on a novel mathematical informed framework and multi-staged integrative method concerning algorithmic complexity, this study aims at establishing a robust and accurate model reliant on the combination of fractional-order derivative and Artificial Neural Network (ANN) for the diagnostic and differentiability predictive purposes for the disease, (diabetes, as a metabolic disorder, in our case) which may display various and transient biological properties. Another aim of this study is benefitting from the concept of algorithmic complexity to obtain the fractional-order derivative with the least complexity in order that it would be possible to achieve the optimized solution. To this end, the following steps were applied and integrated. Firstly, the Caputo fractional-order derivative with three-parametric Mittag-Leffler function (α,β,γ) was applied to the diabetes dataset. Thus, new fractional models with varying degrees were established by ensuring data fitting through the fitting algorithm Mittag-Leffler function with three parameters (α,β,γ) based on heavy-tailed distributions. Following this application, the new dataset, named the mfc_diabetes, was obtained. Secondly, classical derivative (calculus) was applied to the diabetes dataset, which yielded the cd_diabetes dataset. Subsequently, the performance of the new dataset as obtained from the first step and of the dataset obtained from the second step as well as of the diabetes dataset was compared through the application of the feed forward back propagation (FFBP) algorithm, which is one of the ANN algorithms. Next, the fractional order derivative model which would be the most optimal for the disease was generated. Finally, algorithmic complexity was employed to attain the Caputo fractional-order derivative with the least complexity, or to achieve the optimized solution. This approach through the application of fractional-order calculus to optimization methods and the experimental results have revealed the advantage of maximizing the model’s accuracy and minimizing the cost functions like the computational costs, which points to the applicability of the method proposed in different domains characterized by complex, dynamic and transient components. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1007/978-981-19-0179-9_3 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Caputo Fractional-Order Derivative tr_TR
dc.subject Classical Derivatives tr_TR
dc.subject Complex Systems tr_TR
dc.subject Computational And Nonlinear Dynamics tr_TR
dc.subject Computational Complexity tr_TR
dc.subject Data Analysis tr_TR
dc.subject Data Fitting tr_TR
dc.subject Data-Driven Fractional Biological Modeling tr_TR
dc.subject Dynamic Biological Models tr_TR
dc.subject Fractional Calculus And Complexity tr_TR
dc.subject Fractional-Order Derivatives tr_TR
dc.subject Integer-Order Derivatives tr_TR
dc.subject Mathematical Biology tr_TR
dc.subject Mittag-Leffler Functions tr_TR
dc.subject Multilayer Perceptron Algorithm tr_TR
dc.subject Neural Networks tr_TR
dc.subject Nonlinearity tr_TR
dc.subject Uncertainty tr_TR
dc.title Algorithmic Complexity-Based Fractional-Order Derivatives in Computational Biology tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal Advances in Mathematical Modelling, Applied Analysis and Computation,ICMMAAC 2021 tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 415 tr_TR
dc.identifier.startpage 55 tr_TR
dc.identifier.endpage 89 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümü tr_TR


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