Galyna Kondratenko

The spread of infectious diseases under wartime and resource-constrained conditions creates a need for analytical tools that support both population-level epidemiological monitoring and preliminary patient-level image analysis. This study develops and evaluates a web-based analytical framework incorporating two complementary modules: infectious-disease forecasting and chest X-ray image analysis. For epidemiological forecasting, ARIMA, deep neural network (DNN), and long short-term memory (LSTM) models were evaluated to compare statistical, feed-forward neural-network, and recurrent neural-network approaches to one-month-ahead prediction using Ukrainian epidemiological data from 2020 to 2023. For chest X-ray analysis, YOLOv8 and YOLOv11 nano and small configurations were compared in a proof-of-concept experiment involving five pulmonary categories. The two analytical modules were integrated at the application level within a common web-based interface providing data upload, model selection, analysis, and result visualization. Under the adopted one-month-ahead hold-out evaluation, DNN and LSTM generally provided lower forecasting errors than the ARIMA baseline. In the image-analysis experiment, YOLOv11s produced the highest validation mAP@0.5:0.95 among the evaluated configurations, reaching 0.74. However, the public chest X-ray dataset had been augmented by its provider before acquisition, and the present image-analysis evaluation used training and validation partitions without an independent test subset for final performance assessment. Therefore, the reported image-analysis metrics characterize only the adopted validation partition, may overestimate performance on independent data, and should not be interpreted as estimates of clinical or external generalization. In addition, the manually prepared bounding-box annotations were not independently verified by a qualified clinical expert. Accordingly, the image-analysis component should be regarded as a proof-of-concept computational evaluation rather than a clinically validated diagnostic system. The developed prototype demonstrates the technical feasibility of providing epidemiological forecasting and preliminary chest X-ray image analysis within a common application environment for emergency and resource-constrained settings.

This paper aims to improve the accuracy of infectious disease forecasting using machine learning methods. The main results of this work are an analysis of infectious diseases spread in Ukraine during the time span from December 2016 to January 2024 and a performance comparison of different neural network architectures in the scope of time series forecasting. The following steps were taken: analysis of current forecasting methods, selection of neural network architectures, dataset preprocessing, and model testing. The developed system can be an effective tool for rational management decisions to ensure the epidemiological well-being and biosecurity of the population.

This article presents a comprehensive approach to the structural-parametric optimization of Mamdani-type fuzzy control systems (FCS) using a set of bioinspired methods. The proposed methodology integrates optimization of the number of linguistic terms (LT), rule base (RB) synthesis, selection of membership function (MF) types, parametric tuning, as well as the adjustment of fuzzy inference engine (FIE) operations and defuzzification methods in the most rational sequence. The approach is validated through its application to a FCS for an unmanned aerial vehicle (UAV). Experimental results demonstrate that the greatest improvements in control accuracy are achieved through the parametric optimization, with reductions in the objective function of up to 27.5%. The final optimization stage yielded only minor gains (5.6%), indicating its lower impact. The proposed optimization strategy enables the development of high-performance fuzzy systems with simplified implementations and reduced computational costs, making it suitable for embedded control applications in robotics and autonomous systems.

This paper presents the results of time series forecasting using machine learning methods and mobile technologies. The main results of this work are a comparison of the effectiveness of different neural network architectures in predicting the spread of infectious diseases in Ukraine for the period from December 2016 to January 2024. To achieve the goal, the following tasks were solved: the current state of the time series forecasting was analyzed; existing analogs of the systems were analyzed; the necessary neural network architectures were selected as one of the machine learning methods; a dataset for infectious diseases spread was analyzed and normalized; conducted testing. The developed system can be an effective tool for rational management decisions to ensure the epidemiological well-being and biosecurity of the population.

Over recent years, bioinspired swarm techniques have gained significant popularity for addressing real- world engineering optimization challenges. One promising application of these methods is developing and optimizing of intelligent systems, specifically fuzzy control systems. This paper examines research issues and performs a comparative analysis of bioinspired swarm methods for parameter optimization in fuzzy control systems. It compares various hybrid modifications of particle swarm optimization and grey wolf optimization techniques, specifically adapted for fuzzy system parameter optimization, against traditional search methods. As a case study, the paper uses the parametric optimization of a Takagi- Sugeno fuzzy control system designed for a quadrotor-type unmanned aerial vehicle (UAV). The simulation results confirm the effectiveness of the presented swarm bioinspired optimization techniques, taking into account both the performance of the UAV's fuzzy control system and the computational costs involved.

This paper is related to the recognition of certain components in images using machine learning methods and mobile technologies. The main result of this work is a developed system for recognizing the presence of a mask on the face using an image, which provides all the necessary information in real-time about the presence or absence of a mask on the face. When the program is turned off, statistics about the presence/absence of the mask will be recorded in the database. To achieve the goal, the following tasks were solved: the current state of the task of recognizing the presence of a mask on a person’s face was analysed; existing analogs of the systems were analysed; the necessary neural network architecture was selected as one of the machine learning methods; developed a system for recognizing the presence of a mask on the face using the necessary libraries; a user graphical interface, a database model for recording statistics and additional functionality have been developed; conduct testing. Practical application has a fairly wide range, in particular, the developed intelligent system is intended for use in the subway, industrial enterprises, state institutions, educational institutions, offices, and other public places. The developed system recognizes and records statistics about the presence of a mask on a person’s face using neural networks.

This paper is related to the study of the features of the neural technologies’ application, in particular, ResNet neural networks for the classification of objects in photographs. The work aims to increase the accuracy of recognition and classification of objects in photographs by using various models of the ResNet neural network. The paper analyzes the features of the application of the corresponding models in comparison with other architectures of deep neural networks and evaluates their efficiency and accuracy in the classification of objects in photographs. The process of data formation for training neural networks, their processing and sorting is described. A web application and a mobile application for recognizing and classifying objects in a photo were also developed. A system for classifying objects, in particular airplanes in photographs, was developed using neural network technologies. It gives a recognition and classification accuracy of about 95%. Research results of ResNet models are of great practical importance, as they can improve the classification accuracy of various images. Features of ResNet, such as the use of skip connections or residual connections, make it effective in the relevant tasks. The results of the study will help to implement ResNet in various fields, including medicine, automatic pattern recognition and other areas where the classification of objects in photographs is an important task.

The paper is devoted to modern aspects of mobile robotics, namely to the universal robotic platforms that can be used in various technological environments and conditions to perform different technical tasks at industrial facilities and enterprises. These platforms are capable of moving on horizontal surfaces with complex terrain as well as climbing vertically on sheer walls and ceilings and are the universal autonomous means of carrying out complex operations in hard-to-reach and dangerous places for humans. One of the biggest challenges in operating these robotic platforms is proper adhesion control when moving on inclined planes. In this study, the authors performed the design and study of the intelligent adhesion control system that provides efficient and reliable fastening and movement of the platform on inclined surfaces of various types. This system is based on fuzzy control principles that allows determining the necessary adhesion force of the robotic platform for its safe and efficient use at various angles of the surface inclination. The system’s performance is verified by computer simulation.

This paper is devoted to the analysis of the specific focuses, directions, and peculiarities of the Strategy of Artificial Intelligence (AI) Development in Ukraine. The main paper’s components are an analysis of the current state of the justification, development, and governmental approval of the National Strategy of AI in Ukraine; key elements and main priority areas of AI implementation according to IAIP-project “Strategy for AI Development in Ukraine”; proposals for AI development in short- and long-term perspectives and features of the AI implementation in Ukraine during the current wartime. Special attention is paid to such focuses in AI research and development as (a) the design of AI systems based on cognitive and conscience conceptions; (b) new solutions in intelligent robotic systems for ground, underwater and aerial applications; (c) AI perspectives in the marine industry; (d) prospective AI implementation in education; (e) linguistic competency of AI systems.