python

This study explores the application of a fractional diffusion equation in diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) analysis, aiming to validate and extend previous research based on an open-access dataset. A fractional-order model using the Mittag-Leffler function is implemented and validated by reproducing results presented in existing literature. The method is then applied to an open-access Connectome Diffusion Microstructure Dataset (CDMD) to analyze real brain imaging data.

This article describes the use of the Atangana-Baleanu fractal operator for the task of enhancing textures in medical MRI images. It provides a detailed explanation of the mathematical framework of the Atangana-Baleanu fractal differential. A numerical approach for calculating the fractal differential using the finite difference method is considered. Based on the approximated solution, approximation coefficients are determined.

In this study, we develop and evaluate performance of a location-tracking application built using a Python/Django/PostgreSQL stack. Through rigorous experimentation and its analysis, we scrutinize the efficiency of WebSocket connections in facilitating real-time commun­ication and data retrieval. By comparing our findings with the previous research results available on an ASP.NET stack, we contribute to understanding of the technologies for stack selection and the strategies used for optimization of the location-tracking applications.

The article is dedicated to constructing difference approximations of fractal operators in a mathematical model of the impact of chemotherapy on the state of a cancerous tumor, based on fractional calculus using the Caputo derivative. A mathematical model of stem cells and chemotherapy is presented. Numerical algorithms for implementing fractional-order mathematical models have been developed using the Atangana-Toufik method. The UML diagram of the software application and its development process are described.

This article presents the research of texture enhancement algorithms on medical images. Medical MRI brain scans contain large areas with low level grey colors that carry useful information for doctors. Texture improvement allow to highlight large grey areas on images for future detailed recognition. Based on the study of existing texture enhancement methods, it was determined that fractal operators are effective for processing medical images.

This paper explores the application of the ARFIMA fractal model for prediction of the dynamics of river water pollution based on BOD measure. The study begins by conducting a review of related works in the field of water quality analysis. At this stage also a suitable dataset is selected, that is used to train the ARFIMA, one of the machine learning models. GPH semi-parametric algorithm is applied for estimating the fractal differentiation parameter of the ARFIMA.

This paper explores the application of the U-Net architecture for intracranial hemorrhage segmentation, with a focus on enhancing segmentation accuracy through the incorporation of texture enhancement techniques based on the Riesz fractional order derivatives. The study begins by conducting a review of related works in the field of computed tomography (CT) scan segmentation. At this stage also a suitable dataset is selected.