python

ENHANCEMENT OF MEDICAL MRI IMAGES BASED ON THE ATANAGAN-BALEANU FRACTAL OPERATOR

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.

Launching mvps for it startups: python/django/postgresql in location-tracking applications

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.

MATHEMATICAL MODELLING OF THE IMPACT OF CHEMOTHERAPY ON THE STATE OF A CANCEROUS TUMOR BASED ON FRACTIONAL CALCULUS

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.

ENHANCEMENT OF MEDICAL MRI IMAGES BASED ON FRACTAL OPERATORS

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.

DEVELOPMENT OF SOFTWARE AND ALGORITHMIC EQUIPMENT FOR PREDICTION OF RIVER WATER POLLUTION USING FRACTAL ANALYSIS METHODS

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.

INTRACRANIAL HEMORRHAGE SEGMENTATION USING NEURAL NETWORK AND RIESZ FRACTIONAL ORDER DERIVATIVE-BASED TEXTURE ENHANCEMENT

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.

Information System for the Educational Center

The goal of the research of the qualification thesis is the development of an intelligent information system of the educational centre using the Python programming language and the Django framework, the SQLite database system, and the chatbot with artificial intelligence ChatGPT. The developed system should facilitate the interaction of different types of users with learning/training centres in order to acquire new necessary skills. As you know, from now on every day more and more educational centres are opening, which provide their services for the study of this or that ability.

Information technology for gender recognition by voice

Gender recognition from voice is a challenging problem in speech processing. This task involves extracting meaningful features from speech signals and classifying them into male or female categories. In this article, was implemented a gender recognition system using Python programming. I first recorded voice samples from both male and female speakers and extracted Mel-frequency cepstral coefficients (MFCC) as features. Then trained, a Support Vector Machine (SVM) classifier was on these features and evaluated its performance using accuracy, precision, recall, and F1-score metrics.

Analysis of artificial intelligence methods for rail transport traffic noise detection

Nowadays, many cities all over the world suffer from noise pollution. Noise is an invisible danger that can cause health problems for both people and wildlife. Therefore, it is essential to estimate the environmental noise level and implement corrective measures. There are a number of noise identification techniques, and the choice of the most appropriate technique depends upon the information required and its application. Analyzing audio data requires three key aspects to be considered such as time period, amplitude, and frequency.