In recent years developments in deep learning and machine learning techniques have had a major impact on the healthcare sector especially in the early diagnosis of diseases. The MedMNIST dataset contains medical images used for various diseaes diagnosis, and this dataset is analyzed with deep learning methods, making it possible to make important medical decisions. However privacy and security concerns encountered during the processing of medical data restrict traditional data processing methods. Federated Learning ensures data security by allowing training to be done on local devices without data being collected on a central server. In this study a method that improves the medical image classification task is proposed by integrating the federated learning approach with the Particle Swarm Optimization(PSO) algorithm. The PSO algorithm is used to optimize model parameters contributing to increased classification accuracy. In the proposed method the Convolutional Neural Network(CNN) model is used for image classification and local training is performed by preserving the privacy of the data with the federated learning method. Experimental results demonstrate that the proposed method effectively addresses the challenges of federated learning by achieving accuracy levels comparable to centralized state-of-the-art approaches, despite being trained across distributed clients. Furthermore, the method enhances data privacy by ensuring that raw data remains local, mitigating the security risks inherent in conventional centralized training paradigms. The integration of the PSO algorithm with the federated learning method allows the model parameters to be optimized more efficiently, increasing accuracy and making the training process more efficient. This method provides a safe and effective solution for medical image classification and has great potential especially in cases where data privacy is critical.
The dynamics of consumption of various medical products by the world's population and high competition in the pharmaceutical market make it urgent for pharmaceutical chains to develop new marketing strategies to improve the loyalty of consumers of pharmaceutical products. The purpose of this work was to study the consumer preferences of customers of a selected pharmacy chain in the Ukrainian city of Lviv, considering the vagueness of the primary information about these preferences obtained during their survey and using the Matlab computer system to perform numerical analysis of this information. The authors identify the primary factors that characterize the loyalty of consumers of goods and services in the pharmaceutical industry and present the results of a survey based on the developed questionnaire of customers of the pharmacy chain Biomed. It is proposed to apply the theory of fuzzy sets and fuzzy logic to calculate the integral indicator "Consumer loyalty to the pharmacy chain". The paper builds a fuzzy model for assessing consumer behaviour and loyalty using the Fuzzy Logic Toolbox module of the MATLAB application package, which allows, based on data about the consumer, to calculate their degree of loyalty to the goods and services of the pharmacy chain Biomed. Based on the research conducted, the average overall level of customer loyalty is estimated, and a system of measures is proposed to increase consumer loyalty, which will contribute to improving the company's marketing decisions and increasing customer satisfaction with the pharmacy chain.
In this study, we leverage the rich club coefficient to improve the representation capabilities of existing Graph Neural Networks(GNNs) for molecular graph classification. To achieve this, we introduce a novel structural plugin that enhances node features, enabling them to capture the hierarchical structural information within the graph. Our experiments demonstrate that our solution can uplift the performance of existing GNNs.
For many problems of modeling the behavior of complex systems, it is necessary to apply optimization procedures. The most effective in this case are stochastic optimization methods. Given the significant number of such methods, the problem of studying their effectiveness on the class of optimization problems arises. The complexity of such research is due to the need to automate the collection of statistical data on the relevant indicators of the effectiveness of a particular method. Obviously, it is worth developing appropriate software for this.This article considers the stochastic method of the guiding cone. Its main and additional parameters are determined, which directly or indirectly affect the effectiveness of the application of this method. An approach to software development is proposed, with the help of which it is possible to study the effectiveness of the method during the parametric identification of discrete dynamic models. Using the developed software, a numerical experiment was conducted to test the computational complexity of the method.General criteria for the effectiveness of optimization methods are determined. A procedure for automating the comprehensive testing of the effectiveness of various optimization algorithms within one stochastic method with changes in various search parameters is proposed. At the same time, the conditions for their application are indicated. The general characteristics of the software being developed are determined, and the choice of the software environment for it is justified. The concept of interaction of the developed software with a complex of computer modeling of complex systems is proposed.For the numerical experiment, a test objective function with a complex functional relief was selected in order to confirm the high efficiency of the selected optimization method. Based on the results of the numerical experiment, conclusions were drawn regarding the conditions for the effectiveness of the application of individual algorithms for solving a specific optimization problem.Directions for further research were formulated.
The work is devoted to the study of the economic activity of enterprises in the agrarian sector of the national economy, since this industry is developing and provides the largest share of exports in the national economy, even in the crisis year of 2022 for Ukraine. The possibility of applying modern scientific approaches to the modeling of leading agricultural companies using the cluster analysis method, and the implementation of the conducted modeling in modern information systems such as Python, which allows processing large data sets if necessary, and presenting them in a convenient interface for users, are considered. According to the results of the research, the proposed algorithm for clustering the leading agricultural firms of the national economy, and the result of the modeling is the construction of a dendrogram, which determines the rating position of the studied economic entities.
