Visnyk of Kherson National Technical University
COMPARATIVE REVIEW OF LANGUAGE MODELS OF ARTIFICIAL INTELLIGENCE
Currently, artificial intelligence is spreading across various areas of human activity and is becoming more accessible to a wide range of users. In particular, various language models of artificial intelligence have become available to a wide range of users. In this regard, the current task is to determine a language model of artificial intelligence that will be convenient to use and will allow the user to solve his task at the proper level. This work considers language models of artificial intelligence. The following artificial intelligence language models are identified that are freely available and with which the user can interact via a chat bot: ChatGPT-4 Turbo (OpenAI), Copilot on GPT-4 (OpenAI), Gemini (Google), Claude (Anthropic), Jurassic-2 (AI21 Labs), DeepSeek (Hangzhou DeepSeek Artificial Intelligence Co., Ltd.).Among the studied language models of artificial intelligence, Claude, Jurassic-2, DeepSeek do not have access to search for information on the Internet, which makes it impossible to search for relevant current information. Among the studied AI language models that have access to searching for information on the Internet (ChatGPT-4 Turbo, Copilot on GPT-4, Gemini), only ChatGPT-4 Turbo did not have completely incorrect answers. However, it should be noted that it, like the other studied AI language models, was unable to give a completely correct answer to the prompt “Write a complete list of networks included in the international standard IEC 61158”. It correctly indicated only 10 of the 18 groups of networks that are in the standard, and incorrectly indicated 3 networks that are actually not in the standard (in this task, it was the fourth out of six in terms of correctness of the answer). It should be noted that for prompts that did not require access to the Internet, the most relevant answers were obtained from the AI language model Claude (Anthropic). It should also be noted that Claude (Anthropic), like other studied language models of artificial intelligence, was unable to give a completely correct answer to the prompt “Write a complete list of networks included in the international standard IEC 61158”. However, he correctly indicated 15 out of 18 groups of networks that are in the standard, and incorrectly indicated only 1 network that is not actually in the standard (in this task, he was the first out of six in terms of correctness of the answer). A comparative analysis of language models of artificial intelligence showed that there is no ideal one that provides the best or always correct answers in different tasks. But it can be noted that the model that provided the most relevant answers is ChatGPT-4 Turbo (OpenAI). The results of the research give reason to argue that any results obtained using existing language models of artificial intelligence require additional verification.
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IMPROVEMENT OF INFORMATION AND MEASURING TECHNOLOGIES IN WELDING AND INSTALLATION OF METAL STRUCTURES
This article considers an approach to improving information and measurement technologies during welding and installation of metal structures, as well as presents the results of development and testing of the proposed integrated system. Particular attention is paid to the development and application of artificial intelligence algorithms, such as machine learning and deep neural networks, to improve the accuracy, speed, and adaptability of information and measurement technologies. Their role in detecting deviations, predicting the development of defects and automatic adaptation of welding parameters to changes in the production environment is considered. The proposed integrated system combines a sensor subsystem, a data preprocessing module for cleaning and structuring information, a neural network analytical unit (CNN for visual control and XGBoost for parameter classification), as well as a feedback loop that provides adaptive adjustment of parameters in real time and prompt response to deviations. Thanks to the implementation of a full closed cycle – from data collection, pre-processing and analysis to automatic parameter adjustment – the impact of the human factor is minimized, process reliability is increased and the number of defects is significantly reduced. Such a cycle ensures continuous updating of data, their thorough checking, quick response and flexible adjustment of welding parameters in accordance with changes in the production environment. The use of the Python ecosystem (Pandas, NumPy, TensorFlow, Scikit-learn) for data analysis, the use of sensors for reading key parameters and adaptive control via the MQTT and PLC protocols ensure a 27 % reduction in defects and process stability. The presented approach combines modern machine learning methods with a complex IoT architecture in detail, emphasizing their interaction and opening up prospects for the further development of intelligent production systems that are capable of self-learning and adaptation in real time.
TAXONOMY AND ARCHITECTURAL CLASSIFICATION OF BLOCKCHAIN TECHNOLOGIES: OPPORTUNITIES AND FUTURE APPLICATIONS
Modern centralized computing services do not provide a sufficient level of decentralization, transparency, and scalability, which limits their use in open ecosystems. There is a growing need for a well-founded selection of a blockchain architecture for the development of an efficient computing power marketplace that combines security, performance, and economic viability. The subject of the present analytical study is the architectural characteristics and evaluation metrics of blockchain technologies for building decentralized platforms for computational resource exchange.The goal of the study is to systematize knowledge of blockchain architecture types, consensus algorithms, and performance metrics to justify the technological basis for the creation of a custom computing power marketplace.The tasks addressed in the paper include: classification of blockchain architectures by openness, consensus type, and scalability approach; comparative analysis of technical, functional, and economic metrics of leading platforms (Ethereum, Polkadot, Hyperledger Fabric, IOTA, Solana); review of existing marketplaces such as Akash, Golem, and iExec to identify their strengths and limitations; and formulation of requirements for a custom solution.As a result of the analytical review, a systematic typology of blockchain solutions was developed and architectural comparison metrics were generalized with respect to the blockchain trilemma (decentralization – scalability – security).A comparative analysis of five modern platforms (Ethereum, Hyperledger, Polkadot, Solana, IOTA) was conducted in terms of their applicability for a computing power marketplace.It was found that DAG-oriented architectures (e.g., IOTA) offer higher scalability potential but require improvements in security mechanisms. BFT-based platforms demonstrate high throughput but are limited in terms of open access.Principles for constructing a hybrid architecture based on edge computing were proposed, enabling efficient load balancing, reduced latency, and greater autonomy among participants in a distributed computational environment.Conclusions: The application of a typified approach to selecting blockchain architecture enables the avoidance of common limitations of existing systems and ensures high adaptability, efficiency, and transparency in the new computing power marketplace.
EVALUATION OF THE EFFECTIVENESS OF MEDICATION FOR TREMOR BASED ON ΔR AND EEG ANALYSIS
Effectiveness of tremor treatment in Parkinson’s disease and other tremor-related neurological disorders requires objective assessment, as visual scale evaluation is overly subjective. Development of a comprehensive approach combining analysis of movement parameters and neurophysiological indicators enhances the accuracy of therapeutic response monitoring and treatment personalization. Study objective – to objectively evaluate the effectiveness of tremor pharmacotherapy in patients with Parkinson’s disease and other tremor-related neurological disorders through comprehensive analysis of graphomotor activity parameters (ΔR) and electroencephalographic (EEG) patterns. Materials and methods. A systematic analysis of scientific literature and synthesis of current methodological approaches to tremor assessment in Parkinson’s disease and other tremor-related neurological disorders was conducted. The theoretical foundations and practical application of the ΔR indicator (deviation from ideal trajectory) in spirographic tests and EEG analysis methods were examined, with emphasis on assessment of inter-channel synchronization and signal coherence.The prospects for integrating these methods for objective evaluation of dopaminergic drug effectiveness were analyzed.Results. The ΔR indicator during spirographic test performance represents a sensitive tool for quantitative tremor assessment regarding objective documentation of changes under pharmacotherapy influence. EEG analysis enabled identification of neuronal activity synchronization patterns characteristic of Parkinson’s disease and other tremor- related neurological disorders, particularly increased coherence in the beta range (13–30 Hz) between motor cortex and frontal brain regions. Application of dopaminergic drugs results in reduction of this pathological synchronization with a characteristic time lag of 200–300 ms between EEG pattern changes and tremor reduction. The inter-channel synchronization phenomenon serves as a potential biomarker for differential diagnosis of various forms of parkinsonism and treatment effectiveness monitoring. Conclusions. Integrated analysis of ΔR and EEG signal patterns provides objective instrumentation for evaluating tremor pharmacotherapy effectiveness in Parkinson’s disease and other tremor- related neurological disorders. Such temporal coincidence indicates a probable causal relationship between brain functional activity normalization and motor function improvement. The comprehensive approach has significant potential for clinical practice implementation to personalize therapeutic strategies.
ANALYSIS OF NEURAL MODEL EFFECTIVENESS IN ULTRASOUND DIAGNOSTIC IMAGE PROCESSING
This article presents a comparative analysis of the effectiveness of using simple artificial neural networks (ANN) and convolutional neural networks (CNN) for processing ultrasound diagnostic images. The relevance of the study stems from the widespread use of ultrasound imaging in clinical practice, along with its inherent limitations such as low contrast, high noise levels, and dependency on both the equipment and operator’s experience. In the context of increasing volumes of medical data and the growing need for automated decision support systems, neural models offer a promising tool for enhancing the diagnostic value of ultrasound images. To achieve the research objective, an experimental simulation was conducted using an open-access dataset of breast ultrasound images available on the Kaggle platform. The original images were downscaled from 500 × 500 to 128 × 128 pixels to reduce computational load. Two neural network architectures were implemented: a basic ANN with a single hidden layer and a CNN with three convolutional layers. The models were evaluated based on classification accuracy, recall, precision, F1-score, inference speed, and interpretability.The results demonstrated that CNN significantly outperformed ANN across all major performance metrics. CNN achieved an average classification accuracy of 86.4 %, compared to 71.3 % for ANN. Furthermore, CNN provided more stable performance under varying image quality and noise conditions and enabled the use of Grad-CAM for highlighting the most influential regions in the decision-making process. While ANN was less sensitive to local textural features, it offered advantages in computational simplicity and inference speed (under 5 ms per image).The study concludes that CNN is more suitable for clinical settings where accuracy and reliability are critical, whereas ANN may be useful for preliminary filtering or in systems with limited computational resources. The article also emphasizes the importance of considering hardware-specific variations in image sources and highlights the future potential of hybrid, interpretable neural architectures in medical imaging.
ANALYSIS OF PARAMETERS OF GENETIC ALGORITHM FOR SOLVING TWO-DIMENSIONAL PACKING PROBLEM
The task of packaging is a topical one and is encountered in many industrial productions. It belongs to the class of NP-hard problems, so the use of an exact enumeration algorithm is possible only for small-dimensional problems.Metaheuristic methods are a popular tool for the approximate solution of complex combinatorial optimization problems.The article considers solving the problem of packing rectangles into a semi-infinite strip of fixed width using a genetic algorithm. The purpose of the article is to study the influence of genetic operators of crossover and selection on the efficiency of the algorithm. The paper provides a statement of the problem, presents the genetic operators of crossover and selection used in the algorithm. The solution of the packing problem is divided into two steps: determining the permutation for the packing sequence of rectangular elements using a genetic algorithm, and applying a decoding algorithm to place them in the strip. The BLF algorithm was used as a decoder. A computational experiment was carried out on test examples known from literary sources, as well as on two generated sets of rectangles. All test problems used in this study have an optimal solution with the minimum admissible length of the occupied part of the strip. The program is implemented using the Python programming language and the Deap package. Analysis of the experimental results showed that the best solutions to the problem of packing rectangles into a semi-infinite strip were obtained when using the PMX crossover operator and the tournament selection operator in the genetic algorithm. The genetic algorithm showed the worst result when using the UPMX crossover operator and roulette selection.
REVIEW OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE ALGORITHMS FOR FINANCIAL DATA ANALYSIS AND PROCESSING
The article analyses modern machine learning algorithms and their application for financial data processing, forecasting indicators and creating personalised recommendation systems. The effectiveness of various types of algorithms, in particular regression models, classification methods, neural networks and clustering, for solving specific financial tasks is investigated. Particular attention is paid to deep learning models, in particular recurrent neural networks (RNN), long short-term memory (LSTM) and generative adversarial networks (GAN), which demonstrate high efficiency in forecasting financial time series. The application of artificial intelligence in various sectors of the financial industry is considered, in particular in the banking sector for credit scoring, investment management, personal finance for budgeting, and corporate finance for forecasting performance. The potential of natural language processing models, in particular ChatGPT, for analysing financial texts and creating intelligent financial assistants capable of providing personalised recommendations in a natural dialogue mode is analysed. The key challenges of implementing artificial intelligence technologies in the financial sector are identified, including the ‘black box’ problem of algorithms, ensuring the quality of input data, compliance with regulatory requirements, and cybersecurity. Approaches to overcoming these challenges have been proposed and promising directions for the development of machine learning algorithms for the financial sector have been outlined, including the integration of federated learning, the development of explainable artificial intelligence models, and the creation of hybrid systems that combine symbolic and neural network approaches. The study demonstrates that the introduction of artificial intelligence technologies in the financial sector has significant potential for revolutionary changes in business processes, improving data analysis efficiency, and enhancing customer service quality.
DEVELOPMENT AND EVALUATION OF THE EFFECTIVENESS OF A HANDWRITTEN TEXT RECOGNITION MODEL BASED ON CONVOLUTIONAL NEURAL NETWORKS
The article is devoted to developing and evaluating the effectiveness of a handwritten text recognition model based on convolutional neural networks using the IAM Sentences dataset. The paper details the process of creating a model that combines modern deep learning methods to solve the complex task of converting handwritten information into digital format. A comprehensive analysis of scientific research in text recognition confirms the relevance of neural networks.The research methodology includes detailed pre-processing of the IAM Sentences dataset, which contains images of handwritten sentences with corresponding text labels. The data preparation includes metadata reading, filtering of false entries, image normalisation, and creating a dictionary of unique characters. Particular attention is paid to data augmentation methods using random brightness changes and scaling to improve model robustness.The architecture of the developed model is based on a combination of convolutional layers to extract spatial features from images and LSTM layers to capture sequential dependencies between characters. The Connectionist Temporal Classification (CTC) loss function allows the model to predict symbol sequences without explicit alignment between input and output, critical for processing variable-length handwritten text.The experiments’ results demonstrate the developed system’s high efficiency, achieving a CER of 11.04 % after training for 67 epochs. This indicator shows high character recognition accuracy, a competitive result for handwritten text recognition tasks. Analysis of the training curves via TensorBoard showed a steady improvement in metrics with minor fluctuations, confirming the correctness of the chosen architecture and training parameters.
VISUALIZATION OF LEONTIEV’S MATHEMATICAL MODEL ON THE DJANGO WEBSITE
The purpose of the work is to demonstrate a site created to visualize the processes of inter-industry connections according to Leontiev’s model. Since the time of Newton, the basis of a scientific answer to real, not entirely simple problems has always been the construction of a mathematical model, that is, a mathematical theory that describes some objects or processes of the real world. As a rule, a software implementation of this model is then built, with the help of which the results of this theory are numerically obtained.The correctness of the results of the theory must be verified experimentally. The analysis of economic processes and the adoption of appropriate decisions, the study of appropriate models in educational courses on mathematical modeling are always significantly improved by their visualization. And it is precisely such visualization that was implemented for the Leontiev’s model. In our case, the mathematical model is a mathematical problem from matrix algebra, to the solution of which this economic problem can be reduced thanks to the research of the American economist V. Leontiev. As a result of its solution, we get not only the numerical values of the characteristics, but also estimates of the profitability and profitability of the sectors of the economy and the opportunity to review their factors in detail. The development of the site is based on the Python framework for the development of Django web systems. It is a high-level Python web framework that opens up the possibility of creating well-secured websites and maintaining them, it takes care of most of the web development.Django provides developers with literally everything they need to develop a website from scratch – this is its core philosophy. To obtain data in the form of matrix table calculations and display graphs, the corresponding Python libraries NumPy and Chart.js were used. Other traditional web development tools were also used when creating the site: HTML (bootstrap), CSS, JavaScript (jQuery).This site was used in teaching the courses “Mathematical Modeling of Processes and Phenomena”, “Modeling of Economic Processes” and “Economic and mathematical methods and models” at Kherson State University and Kherson National Technical University.
THE CONCEPTUAL MODEL FOR IMPROVEMENT IN CONTROL METHODS OF COMPLEX SYSTEMS ON COMPUTER-ORIENTED TRAINING WITH INTENSIVE USE OF NETWORK INFORMATION TECHNOLOGIES ON THE BASIS OF ELEMENTS IN THE METHODOLOGY OF ARTIFICIAL INTELLIGENCE SYSTEMS
The topical issues for improvement of systems on computer-oriented training that play an important role in increasing the efficiency of modern educational processes are considered. The research was aimed at solving the topical task of developing such a conceptual model for improvement in control methods of complex systems on computer-oriented training with intensive use of network information technologies that create the basis for strengthening the stability in the functioning of these systems in conditions of increased probability for the emergence of destabilizing factors and situations. Based on the results of the conducted analysis, for the most appropriate approach to the development of a conceptual model for improvement in control methods of researching systems was choose using the elements in the methodology of artificial intelligence systems. The purpose of the research was achieved by entering in the control model for the system on computer-oriented training of the specialized additional components formalized in a number of interrelated intellectual scenarios with interactive possibilities. These scenarios were aimed at adaptive expert support for making optimized integrated decisions regarding the choice of work modes, types and protocols of activity, information support for the system on computer-oriented training and its users according to the types of available and predictive malfunction of the system. Six types of specialized interactive intellectual scenarios for subsystem on control of education processes in complex network computer-oriented system of distance training were entered for consideration, their functions and hierarchical subordination was determined. The specified scenarios make it possible to flexibly form optimal models for stabilization in the functioning of the system and activity of its users in freelance situations by following the criteria for minimizing information and time excess.
ADVANCED CONTROL STRATEGIES USING DEEP REINFORCEMENT LEARNING FOR IMPROVING ENERGY EFFICIENCY IN SMART BUILDINGS
This research investigates building control strategies for decreasing the energy consumption. A review of existing literature indicates that prior studies employed either complex, model-based approaches or relatively less efficient Q-learning techniques. To address these limitations, we propose a novel control method for Heating, Ventilation, and Air Conditioning (HVAC) systems in medium-sized office buildings, utilizing an intelligent reinforcement learning controller grounded in the model-free Proximal Policy Optimization algorithm (PPO). Our approach incorporates simulations conducted in the EnergyPlus environment, which allows for a precise and responsive depiction of HVAC system dynamics under various control conditions. A key aspect of the study is the regulation of supply air temperature. To support the implementation and enhancement of our control strategy, we developed a co-simulation framework using the Gymnasium library. This environment served as a robust foundation for testing and fine-tuning reinforcement learning models. The performance of the proposed deep reinforcement learning-based controller was benchmarked against a conventional control strategy that adjusts supply air temperature based on outdoor air conditions. Experimental results showed a significant 27.8 % reduction in energy use, all while preserving indoor comfort in terms of temperature, humidity, and carbon dioxide levels. This work highlights the potential of reinforcement learning to simplify the deployment of advanced control techniques in real-world building energy management systems. Unlike traditional optimization methods that require detailed mathematical models of the system, deep reinforcement learning infers optimal control actions by analyzing the relationship between system states and the outcomes of selected actions.
METHOD FOR DETECTING PSYCHOLOGICAL DIGITAL OVERLOAD BY ANALYZING TEXT DATA WITH DEEP LEARNING NEURAL NETWORK MODELS
The paper presents a method for detecting psychological digital overload, based on the use of deep learning neural network models, in particular the BERT architecture adapted for binary classification of text content. The research is aimed at solving the scientific and applied problem of automated analysis of the psycho-emotional state of users in the digital environment, which arises under the influence of prolonged interaction with digital technologies.The relevance of the proposed approach is due to the growing need for objective, scalable and interpretable means of identifying cognitive overload, which are able to function without expert intervention. The method is implemented as a seven-stage procedure that includes text preprocessing, neural network classification, thematic modeling, isolation of target objects of the subject area and the formation of a multi-level assessment of psychological digital overload with subsequent visualization of the results. The basis of the computational core is the BERT model, which forms contextualized representations of text sequences and provides high accuracy in detecting latent states of users based on the analysis of their digital speech. Semantic detailing is implemented by integrating thematic classification with key concept detection methods based on NER, TF-IDF and variance analysis, which allows determining information centers of cognitive load within text profiles. The model was tested on open corpora of social messages, where the values of Accuracy 0.83, Precision 0.87, Recall 0.88 and F1-score 0.87 were achieved for the main metrics. The obtained results demonstrate the suitability of the neural network approach for solving the problem of classifying psychological digital overload as a state manifested through language indicators. The method allows not only to carry out an integral assessment of the level of psychological digital overload, but also to localize its manifestations in thematic segments of the text, which forms the basis for interpreting the classification decision and making management decisions in the field of digital interaction.The results of the study can be applied to the development of information systems to support mental well-being, adapting digital services to the individual user load and monitoring psychological risks in communication-saturated environments. The proposed approach expands the tools of natural language analysis in the context of psycho-emotional diagnostics and contributes to the formation of a scientifically sound basis for the implementation of artificial intelligence methods in the field of digital hygiene.
METHOD OF EXECUTING DYNAMIC CALLS IN .NET USING SOURCE CODE GENERATION
The article discusses the development of a method for executing dynamic calls in .NET by generating code at the compilation. The traditional mechanism for making dynamic calls in .NET, based on the use of reflection, is analyzed. It is determined that the key disadvantage of such an approach is its performance, which necessitates the search for an alternative solution for making dynamic calls. A review of recent publications on the topic of optimizing dynamic calls in .NET is conducted. As a result of the analysis, it is determined that a number of existing solutions describe only the process of making a call, but do not consider the storage of generated functions and a structure that would allow for their reuse. A solution based on the generation of metadata at the compilation stage and the association of such metadata with corresponding static calls is considered. A solution is proposed that is also based on the generation of the metadata during compilation and their association with static calls. The advantage of the proposed solution over the existing one is the ability to make dynamic calls for objects cast to one of the basic types, which expands the list of scenarios for the proposed method’s application. This was achieved by using a metadata structure that is similar to the reflection. Metadata generation is implemented with Incremental Source Generators, which allows for efficient code generation. The method involves using a combination of two code generators, one of which is responsible for generating the metadata itself, and the other for generating code that combines such metadata into a single structure. To determine data types for which generation is required, it is proposed to use both metadata attributes and special methods that accept the data type as an argument. This allowed for the proposed method to be used for data types defined in the context of the current syntax tree and data types belonging to external libraries. The performance of the developed method was tested using the Benchmark.NET library, as a result of which it was found that the proposed method has better performance compared to reflection.
DEVELOPMENT OF A HYBRID INTELLIGENT SYSTEM ARCHITECTURE FOR AUTOMATED CONTROL OF THE NUMERICAL MODELING PROCESS
The article presents the architecture of a hybrid intelligent system designed for automated control of the process of numerical modeling of optical properties of multilayer structures. The main goal of the development is to increase the accuracy, stability and computational efficiency in modeling complex physical phenomena in heterogeneous environments. The proposed system combines classical physical and mathematical models (in particular, RCWA) with adaptive grid algorithms, machine learning modules and optimization components based on gradient methods.The architecture is implemented as a modular system that includes a physical core, error estimation, optimization, machine learning modules, as well as a control agent that coordinates the operation of all subsystems. During the modeling, a dynamic change in the discretization parameters is provided depending on the local features of the spectrum – in particular, in the zones of spectral resonances, the grid is automatically condensed, while in stable areas – its rarefaction. This allows achieving high accuracy without excessive load on computing resources. The system also provides a posteriori assessment of the modeling accuracy, which allows to identify areas with potentially high error and adaptively refine the calculation parameters. The results of numerical experiments indicate a reduction in the average deviation from the reference solution to less than 1.2 % compared to more than 4.5 % in the case of using a non-adaptive scheme. A mechanism for generating recommendations in JSON format is also implemented, which suggests optimal geometric configurations of the multilayer structure to enhance the resonant behavior, reduce reflection and improve spectral selectivity. The hybrid system has demonstrated resistance to variations in input parameters and flexibility in application to new physical problems. The proposed architecture opens up prospects for its application in multiphysics problems, integration with cloud platforms and implementation of parallel computing. It can be used in spectroscopy, optical sensor analysis, design of photonic structures and study of thin-film materials. The conclusions drawn confirm the effectiveness of the synergy of classical numerical methods and modern intelligent technologies in high-precision modeling tasks.
CONFIGURING THE STRUCTURE OF THE SERVERLESS SYSTEM FOR EFFICIENT DATA COLLECTION
Collecting and analyzing the vast amount of data in different formats and from different vendors was always important for modern software of all kinds. It brings us to the urge of creating a system that is flexible, scalable, and effective with capabilities such as fetching, processing, and storing this data. Modular abstraction helps in making the system generic and overcoming some limitations of data providers and vendors. Also helping to handle different formats and process high-volume workloads in parallel with a comprehensive error-handling strategy. Serverless architecture also plays a huge part in achieving desired results. The essential architectural elements are: AWS Lambda, S3, Event Bridge, SQS, and Athena. All of them are the core of building a fault-tolerant, efficient pipeline which can work with various domains, API’s and handle different regulations. Metadata-driven orchestration approach allows seamless vendor integration and support. Moreover, those methods allows the system to be more trackable, easy to test and secure, due to basic cloud solutions provided by certain cloud-provider, which is being used. Modular experimental estimation demonstrates the effectiveness of the system compared to alternative solutions that are currently widely used. It’s a new step for the collection of data from 3rd party resources as it’s also providing more versatility and configurability on top of discussed topics.This research is focused on the creation of an advanced pipeline for analyzing climate and environment-related datasets from various external providers (collection, organization, storage, transformation, analysis). The solution must be flexible and adapt to different user needs and extensions. It shouldn’t only focus on the initial state of the data collection process, but extend beyond that, to be prepared to effectively operate data with other external services. The system should be ready to handle a list of factors that may appear during the process of data collection. Such as: non-consistent data/ metadata, quotas, server downtime, dynamic schema evolution, and so on. For these reasons, it is advisable to propose a modular, event-driven, and serverless architecture that will lead to the orchestration of the entire workflow described.The system should allow the dynamic adjustments to data-fetching strategies based on relevant situation, real- time statistics and providers reliability. The architecture supports extensibility for future data sources and analytical modules, promoting long-term maintainability and adaptability. Integration with services like AWS Secrets Manager and MongoDB enhances security and centralized management of access credentials and vendor state. Furthermore, the use of Athena-based querying enables near real-time analytical capabilities over large, semi-structured datasets, supporting advanced insights generation. These design decisions contribute to a resilient, future-proof solution tailored to the needs of complex, large-scale data ecosystems.
DESIGN OF WEB SYSTEM WITH DATABASE FOR ONLINE HOTEL ROOM BOOKING BASED ON MVC ARCHITECTURE
The article presents an approach to the design and development of a web system with a database for online hotel booking based on the MVC architecture. Existing solutions in the field of online hotel booking are analyzed, which indicates that despite the presence of numerous systems, common problems remain the complexity of integration with payment services and external APIs, insufficient level of personalization, as well as optimization of performance during high load. Therefore, the aim of the work was to create an effective, user-friendly and secure web system with a database for online hotel booking based on the MVC architecture, which automates the hotel booking process and simplifies customer data management.To implement the user interface, the React library was used, which provides dynamic and adaptive display of information, facilitating user interaction with the system. The server part is built on the Node.js platform using the Express framework, which allows you to organize request routing, business logic processing and secure user authorization. The MySQL database management system is used to store data, which is responsible for the integrity, speed of access and structured storage of information about users, reservations, rooms and their statuses.The developed web system with a database for online hotel room booking takes into account the main functional requirements of modern online booking services: user registration, booking management, viewing available rooms and managing personal accounts. The MVC architectural approach used ensures the separation of logic, presentation and data work, which facilitates further support, expansion of functionality and scaling of the system.The proposed system has the potential for further development, in particular for the implementation of algorithms for personalized recommendations, expanding payment options and increasing data protection.
OVERVIEW OF DATA ANALYSIS METHODS IN THE CONTEXT OF PROJECT MANAGEMENT ACTIVITIES
The subject of this study is the data analysis methods that can be applied in the context of project management information systems to enhance the efficiency of managerial decision-making, optimize the utilization of available resources, forecast risks, and improve project outcomes. The aim of this work is to systematize and review the key data analysis methods that can be utilized in the development of project management information systems, as well as to identify their practical applications for addressing critical tasks in project management. This study addresses the following tasks: 1) Identifying the key areas of ap-plied data analysis within project management information systems; 2) Describing data analysis methods, their purposes, and examples of their application in the context of project management; 3) Classifying data analysis methods applicable to project management information systems by types of analysis (descriptive, diagnostic, predictive, prescriptive). The following meth-ods are applied: systems analysis, classification, as well as methods of descriptive, diagnostic, predictive, and prescriptive data analysis. Achieved results: the primary types and methods of data analysis applicable to project management have been sys-tematized; the tables that contain methods description for each type of analysis, their purposes, and examples of application in project management have been developed; have been identified key advantages of applying data analysis to address project management challenges such as risk forecasting, resource optimization, and improving team efficiency; practical examples of using data analysis methods for managerial decision-making have been described.Conclusions. Data analysis is a critical tool for enhancing the efficiency of project management. The use of techniques and methods of descriptive, diagnostic, predictive, and prescriptive analysis within project management information systems ena-bles managers to obtain valuable insights for informed decision-making, improve resource utilization, forecast risks, and en-hance project outcomes. The systematization of data analysis methods applicable to project management promotes their more effective application and integration into information systems, thereby improving project planning, resource utilization, risk mitigation, and ultimately increasing the quality of project execution.
CREATION OF INTELLECTUAL PROPERTY OBJECTS BASED ON THE USE OF COMPUTER TECHNOLOGIES IN FOOTWEAR PRODUCTION
Modern trends in the creation of intellectual property objects based on the use of computer mathematical modeling methods in light industry are considered. The results of research into the creation of new technologies for injection molding of shoe parts from polymer materials are presented. A method for manufacturing multi-colored soles by simultaneously injecting different polymer materials into a mold is proposed. A computer mathematical model of the flow of the melt of a polymer material in a mold is developed, which allows determining its design parameters for obtaining soles with specified properties. The process of tightening the upper of shoes with increased dimensional stability from leather raw materials that have undergone special treatment using an electroactivated medium and enzymes is studied. A technology for manufacturing comfortable shoes on an individual order is proposed, which includes the processes of 3D scanning of a human foot and 3D printing of overlays on the surface of the insole to take into account the anatomical features of the customer. An experimental setup has been developed to determine the pressure of the inner surface of the shoe on a person’s foot, which consists of a personal computer and an Arduino microcontroller with force sensors.The introduction of computer technologies for conducting theoretical scientific research in the footwear industry opens up opportunities for mathematical modeling of complex technological processes and predicting the operational properties of manufactured products. The production of designer shoes is accompanied by the creation of parts of complex geometric shape, the use of materials that are unconventional for footwear production. The use of innovative computer technologies in footwear production has become an impetus for the development and implementation of creative and patentable technological, engineering and design solutions.
THEORETICAL FOUNDATIONS OF CLOUD MIGRATION
The article presents an analysis of the theoretical foundations of cloud migration – the process of transitioning from traditional on-premises IT infrastructure to cloud-based solutions. It emphasizes that the current conditions of business operations and the public sector are marked by increasing demands for scalability, flexibility, and information security, which are often unattainable using outdated local systems. However, despite the numerous advantages of cloud technologies, the migration process is accompanied by a range of challenges related to data protection, compatibility, legal compliance, and cybersecurity risks. Based on the analysis of recent studies, including both international and Ukrainian scientific publications, the paper identifies three key aspects that must be considered during cloud transformation: (1) the shortcomings of traditional on-premises IT infrastructure, including high maintenance costs, limited scalability, technical complexity of support, and low resilience to failures; (2) the advantages of cloud-based solutions, such as flexible scalability, cost-effectiveness, the availability of service models like IaaS, PaaS, and SaaS, automated management, and compliance with international security standards; and (3) the challenges of securely transferring services and data, covering issues such as encryption, access control, system compatibility, and adherence to legal regulations. The article also focuses on modern practices for securing information in cloud environments, including penetration testing, microsegmentation, the use of multi-factor authentication, and risk management models. A systematic approach to planning and implementing cloud migration is proposed, taking into account the specifics of business processes, technical limitations, regulatory frameworks, and information security threats. The results of the study can serve as a foundation for developing digital transformation strategies across various industries, as well as an analytical basis for further scientific and applied developments in the field of cloud security and IT infrastructure optimization.
APPLICATION OF ARTIFICIAL INTELLIGENCE FOR PERSONALIZING DISTANCE LEARNING FOR SOFTWARE ENGINEERING STUDENTS
This article explores the potential of artificial intelligence (AI) in personalizing the process of distance learning for students majoring in Software Engineering. The relevance of the topic is driven by the increasing demand for effective digital solutions that ensure the quality of education in remote formats, which has become especially important in the post- pandemic era. The author analyzes the key challenges of distance learning, including the lack of an individualized approach, low student engagement, and difficulties in mastering complex topics independently.The paper examines practical aspects of implementing AI as a tool for adaptive learning, capable of providing personalized recommendations, explanations of educational material, automated knowledge assessment, and 24/7 support.Special attention is given to the practical experience of using a GPT-based chatbot in the teaching of the course “Fundamentals of Programming in C#”. It was found that AI integration helps increase student motivation, accelerates the acquisition of complex concepts, and supports the development of self-directed learning skills. The results of surveys and observations demonstrate a positive impact of AI on the quality of learning outcomes.In conclusion, the article substantiates the appropriateness of using artificial intelligence as a component of an adaptive model of distance learning in higher education institutions. The article may be useful for university instructors, educational platform developers, and professionals working on the modernization of digital educational infrastructure.The article also outlines directions for future research, including the development of ethical interaction frameworks between students and AI, the improvement of dynamic content adaptation mechanisms, and the creation of integrated solutions for various learning platforms tailored to the technical and cognitive needs of learners.
THE ROLE OF WEB COMPONENTS IN BUILDING MODERN INTERFACES: ADVANTAGES AND LIMITATIONS
The purpose of the article. The purpose of the article is to study the impact of web components on the creation of modern web interfaces, to analyze their role in ensuring modularity, compatibility, performance, availability and testability in the context of the software life cycle. The article also aims to develop strategies for overcoming the limitations associated with the implementation of web components to improve the quality of web applications.Scientific novelty. The article deepens the understanding of the role of web components based on W3C standards in meeting the functional and non-functional requirements of web development. The novelty lies in systematizing approaches to integrating web components with modern frameworks (React, Angular, Vue.js), analyzing their impact on all stages of the software life cycle, and developing practical recommendations for overcoming challenges such as the complexity of Shadow DOM testing, performance optimization through WebAssembly, and ensuring accessibility according to WCAG standards. Particular attention is paid to under-researched aspects, such as integration with DevOps practices and automated testing.Results. The study found that web components using Custom Elements, Shadow DOM, HTML Templates, and ES Modules help create modular and scalable interfaces, reducing design and development complexity. They provide isolation of styles and logic to prevent conflicts in large projects and compatibility with different frameworks, making integration easier. At the testing stage, web components simplify unit testing, but complicate unit testing due to the Shadow DOM, which requires specialized tools such as Playwright or Testing Library. Performance depends on rendering optimization, where WebAssembly and modular rendering reduce latency. Accessibility requires the use of ARIA attributes and semantic HTML, and SEO requires server-side rendering (SSR) or static generation (SSG). The comparative analysis showed the advantages of web components in modularity and reusability compared to traditional approaches but revealed the need for additional efforts for testing and accessibility.Conclusions. Web components are an effective tool for building modern web interfaces, providing modularity, encapsulation, and compatibility. They simplify software development, deployment, and support, but require a careful approach to testing, performance, and availability. The proposed strategies, such as the use of WebAssembly, automated testing tools (Playwright, axe-core), and SSR for SEO, allow overcoming the limitations. In 2025, web components remain relevant, integrating with new technologies such as Web 3.0 and AI, which opens prospects for their further development.
COMPUTER-INTEGRATED SYSTEM FOR CONTROLLING THE COMBUSTION PROCESS OF SOLID HOUSEHOLD WASTE IN AN INDUSTRIAL FURNACE
The issue of municipal solid waste (MSW) disposal remains critical in Ukraine, where over 10 million tons of waste are generated annually, with only 5–7 % recycled, leading to significant landfill accumulation. This poses environmental threats, including methane emissions, groundwater contamination, and land resource depletion. Amid the ongoing war and attacks on Ukraine’s energy infrastructure, MSW incineration with energy recovery emerges as a vital alternative energy source, capable of reducing waste volume by up to 90 % while generating heat or electricity. However, the incineration process requires precise control to minimize emissions of nitrogen oxides (NOx), carbon monoxide (CO), and other toxic compounds, particularly due to the variable composition of MSW (moisture content 20–40 %, organic content 50–70 %). This study aims to enhance MSW incineration by integrating secondary air and flue gas recirculation systems within a computer-integrated control system (CICS). The paper provides a detailed description of the industrial furnace design, which includes a grate, an afterburning chamber, a waste bunker with dosing gates, and systems for secondary air (fan, nozzles, valves) and flue gas recirculation (fan, heat exchanger, filter). The CICS enables adaptive regulation of combustion parameters (temperature 850–1000 °C, oxygen content 6–8 %) based on real-time data from temperature, oxygen, CO, and NOx sensors. Literature analysis confirms that secondary air reduces CO concentrations by 15 %, while flue gas recirculation decreases NOx emissions by 10–15 % and saves 8–10 % of gas cleaning costs. Future research directions include experimental validation of the proposed approach, implementation of IoT-based automation, and integration with CO2 capture technologies to enhance environmental safety and energy efficiency.
COMPARATIVE ANALYSIS OF APPROACHES AND METHODS FOR SENTIMENT ANALYSIS OF TEXT IN THE CONTEXT OF PROCESSING CITY RESIDENTS’ FEEDBACK
The article presents a comprehensive comparative analysis of contemporary approaches and methods used to determine the emotional tone of text, with a particular emphasis on Ukrainian-language content. The relevance of this study stems from the growing need for effective tools to process citizen feedback collected via digital platforms, mobile applications, and social media – all of which serve as valuable sources of information for improving urban governance and service delivery. The research aims to address both methodological and linguistic gaps in sentiment analysis for the Ukrainian language, which, unlike English, remains under-resourced in terms of lexicons, corpora, and pretrained models.The study systematizes sentiment analysis methods into four main paradigms: those relying on predefined lexicons, those governed by linguistic rules, data-driven learning techniques, and integrated models that combine multiple strategies.For each paradigm, the theoretical foundations, typical algorithms, linguistic tools, and practical application examples are discussed. Lexicon-based methods, such as those utilizing the NRC EmoLex dictionary and corpus tools like Sketch Engine, are noted for their simplicity and adaptability to low-resource environments. Rule-based systems, including VADER and LIWC, stand out for their ability to account for syntactic structure, intensifiers, and negations, offering better interpretability, albeit with limited language generalizability.The section on machine learning explores both traditional classification algorithms – including models based on decision boundaries, probabilistic inference, and tree-like structures – and modern deep learning architectures, such as multilayer neural networks. The maximum entropy approach is also examined as a representative of statistical modeling that requires minimal assumptions about input features.Particular attention is given to recent advancements in deep neural networks, namely convolutional neural networks (CNN), recurrent LSTM networks, and transformer-based architectures such as BERT, RoBERTa, and GPT. Empirical results from recent studies confirm the high effectiveness of transformer models in multilingual contexts, especially in sentiment analysis of Ukrainian texts, where models like XLM-RoBERTa and Ukr-RoBERTa have achieved accuracy levels exceeding 91 %.The final part of the article discusses hybrid models that combine the strengths of different paradigms to enhance robustness, accuracy, and adaptability to domain-specific data. The proposed classification framework, developed on the basis of the conducted research, provides a coherent overview of existing methods and serves as a methodological foundation for the development of intelligent decision-support systems in urban governance with active citizen participation.The conclusions outline directions for further research, including the localization of deep learning models for Ukrainian, integration of sentiment analysis with topic modeling and named entity recognition, and the application of semi-supervised and active learning techniques to improve outcomes in scenarios with limited annotated data. Overall, the proposed taxonomy not only reflects the current state of sentiment analysis technologies but also outlines future development trajectories in multilingual and socially oriented applications.
USING AN EVOLVING GRAPH NEURAL NETWORK FOR PRICE FORECASTING ON THE STOCK MARKET
Stock price forecasting is essential for making informed investment decisions in the financial market. Analyzing financial market movements and stock price behavior is extremely complex due to the dynamic, nonlinear, non-stationary, non-parametric, and chaotic nature of markets. Various approaches are used to analyze stocks for the purpose of financial market forecasting. Traditional methods based on time series information for stocks of one company do not take into account the relationships between stocks of other companies. The use of graph neural networks (GNN) for this purpose, in which time series relationships are represented as a graph structure and variables are represented as graph nodes, significantly improves the accuracy of forecasting. Existing forecasting methods usually assume that the structure of the relationship graph, which is described by the relationship matrix and determines the aggregation method of the graph neural network, is fixed by definition. Therefore, they cannot effectively account for dynamic changes in the relationship graphs. In this paper, the use of an evolving graph neural network is proposed for forecasting stock prices in the financial market.To obtain dynamic correlations between price movements in financial time series, a cluster-based relationship graph is constructed, the generation and evolution of the structure and parameters of which are implemented using a dendritic artificial immune network (DaiNet). For each generated cluster of the relationship graph, price coding is performed using transformers to determine price information. Then, messages from the relationship graph structure and input time sequences are aggregated based on the use of the attention layer of the time graph. The final forecast of the future price movement of each stock is performed at the last layer of the GNN using a multilayer perceptron. Experimental studies have shown that the use of dynamic graph construction and stock clustering based on DaiNet tracks the temporal nature of the relationships between stocks and improves forecasting performance.
OPERATIONAL MODEL OF A COLLAGE INFORMATION SYSTEM
An analysis of existing online technologies (Photoshop Online, Canva, Pixlr Editor) identified significant shortcomings in their models, primarily the absence of automatic linkage between image containers. This results in substantial time spent on manual adjustments and potential errors in the final collage compositions. These issues particularly disrupt compositional integrity when resizing one or several collage elements. The paper also analyzes known algorithmic models such as AutoCollage (automatic selection and arrangement of important image areas), Correlation-Preserving Photo Collage (grouping images based on semantic relationships), Aesthetic Photo Collage with Deep Reinforcement Learning (creating aesthetic compositions through reinforcement learning), and SoftCollage (differentiable collage composition based on probabilistic trees). The fundamental contribution of this research is the development and detailed description of algorithms implementing the handleMoving function, designed for the automatic adaptation of collage elements during interactive repositioning and resizing by users. Algorithms were developed for three primary structural components: CollageTree (hierarchical tree structure of the collage), CollageRow (horizontal arrangement of elements), and CollageColumn (vertical arrangement of elements). The logic behind these algorithms is thoroughly described, accompanied by flowcharts illustrating the algorithmic processes of movement, size recalculations, and automatic adaptation of elements based on user actions. Detailed analysis of the developed algorithms demonstrated high efficiency, even when scaling collages containing a significant number of images. The handleMoving function, developed in this study, can complement or extend the aforementioned algorithmic models, ensuring automatic maintenance of compositional integrity during interactive collage editing.
ANALYSIS OF TOOLS FOR OBTAINING CHIDAMBER AND KEMMERER METRICS FROM SOFTWARE PROJECTS DEVELOPED IN JAVA
Chidamber and Kemerer Metrics (CK Metrics) is a widely used set of metrics designed to measure properties in object- oriented software. The CK Metrics set includes six indicators, each of which provides representation into different aspects of software development and implementation. The object-oriented Java programming language still remains one of the main ones for creating both server-side software and native mobile applications for Android OS and desktop applications for stationary PCs. This paper provides an analysis of tools that can be used to obtain numerical values of metrics from the CK Metrics set from software projects written using the Java programming language. A description and a brief assessment of the usability of the following tools are provided: ck, ckjm, ckjm-extended, CodeMR,, Metrics Reloaded, Metrics Tree.The tools are mainly presented, which are distributed open source, with the exception of CodeMR, which has a limited free and paid full-featured version. The tools presented in the analysis can be divided into two groups in terms of user interaction: via the command line interface and as plugins for the IntelliJ integrated development environment, which can be de facto called the standard for developing software implemented in Java. Tools for interaction via the graphical user interface are not presented in this analysis.The results of the analysis will help to make the right choice of a tool for obtaining numerical values of metrics from the CK Metrics set, based on the specific requirements of the developer or researcher and achieve the desired result with less time spent on independently studying the capabilities of the corresponding tool.
APPROACH TO DETECTION AND CLASSIFICATION OF RADIO-CONTROLLED MODELS BY THEIR RADIO SIGNAL
The ongoing development of affordable embedded sensors, microcontrollers, and wireless communication technologies has led to a rapid rise in the popularity and use of unmanned aerial vehicles (UAVs), commonly known as drones, across various fields – including agriculture, environmental monitoring, reconnaissance, and defense. However, alongside these benefits, the potential for malicious or unauthorized drone usage poses significant risks to public safety, critical infrastructure, private property, and industrial facilities. Therefore, there is an increasing need for systems capable of detecting and classifying drones based on the analysis of their radio frequency (RF) communication signals. This paper explores an approach for monitoring and identifying drones by analyzing the RF signals generated by their communication modules. Specifically, the proposed system leverages the capabilities of a software-defined radio (SDR), which enables real-time signal reception, processing, and analysis without the need for specialized hardware. The implementation utilizes the GNU Radio framework, offering flexibility in designing custom signal processing workflows. The focus is placed on classifying drone communication signals based on their modulation type – particularly, orthogonal frequency-division multiplexing (OFDM). Signal classification is achieved by analyzing its energy profile and structural features, which vary depending on the communication protocol used. Machine learning algorithms are employed to train a model that can recognize and classify such signals in real-world environments. This enables the identification of the device type, behavior analysis, and, in some cases, estimation of the operator’s location – all without direct interaction with the drone. Overall, the study demonstrates the effectiveness of combining SDR technology with artificial intelligence methods for drone detection and classification, potentially forming the basis for early warning systems and countermeasures against unauthorized drone activity.
SOFTWARE MODULES COUPLING MODELLING IN SOFTWARE DEVELOPMENT PROCESS SIMULATIONS USING PROBABALISTIC DISTRIBUTION OF MODULE CHANGE FREQUENCIES
In today’s world, the software development productivity is not considered at the planning stage when choosing the architecture type for a future software project due to an inaccurate and overly complex process of estimating the necessary time and resources needed to build such a system. To enable research on the impact of different types of software architectures on productivity through simulation of corresponding software development processes, this study examines the approach to modeling the coupling between modules of a software system based on an analysis of the change history of 8 modular monolithic and microservice open-source projects. As a result of studying the modular structure and change history of the source code, it was found that there is a lognormal probability distribution both for the size of the software modules and their modification frequency, with linear dependence between them but different correlation parameters for different projects. The analysis of modules coupling based on the association rules mining from the history of their independent and simultaneous changes revealed a power function as the relationship between average values of both input and output coupling and the module change frequency, but the parameters of such a relationship also differed for different projects and were found to correlate with the number of modules in the software system being modeled. To optimize calculations during the simulation of the software development process, it was proposed to use independent module change frequencies instead of detailed modeling of each pair’s coupling, which involved investigating the parameters of the lognormal distribution of independent module change probabilities for each project and using linear regression to derive the coefficients of the dependence of the parameters of such a distribution on the number of modules in the software system. To further enhance computational efficiency of simulations, an algorithm for adjusting the generated module change frequencies was additionally proposed to increase the probability of at least one module modification being made at each iteration of simulation, as all change probabilities are independent and there is a non-zero chance of not making any changes to any module.
RESEARCH ON THE IMPACT OF CYBERSECURITY TOOLS INTEGRATION ON THE SECURITY OF AN ORGANIZATION’S IT INFRASTRUCTURE
The article addresses the pressing issue of ensuring cybersecurity in the context of increasing numbers of complex and multi-vector attacks on corporate IT infrastructures. It substantiates the need to transition from isolated security tools to a comprehensive, integrated approach that ensures the interaction between NGFW, EDR, SIEM, PAM, IAM, and MDM.It is noted that a significant portion of security breaches is caused by insufficient coordination between cybersecurity components, which hinders the early detection of complex attacks.Based on the analysis of current research and statistical data, typical vulnerabilities of IT systems, key attack vectors, and weaknesses in current security management practices are examined. An architectural model of multi-layered protection is proposed, enabling the creation of a unified information environment for correlating threat data, managing access, and providing real-time incident response. Special attention is given to the practical impact of integrated systems on enhancing infrastructure protection, reducing threat detection time, and improving incident response efficiency.The article also emphasizes the improvement in IT department productivity through the automation of routine tasks and centralized security management.Recommendations are proposed for implementing an integrated cybersecurity model that takes into account the current needs of organizations and the requirements of standards such as ISO/IEC 27001 and the NIST Cybersecurity Framework.The results of the study are valuable for IT managers, cybersecurity professionals, and organizational leadership seeking to strengthen their information security. The impact of technological integration on reducing human factor involvement in incident response and establishing a secure digital environment is separately highlighted.
THE PROCESS OF CREATING AN AUTHOR’S DATASET FOR RETRAINING MODELS OF NETWORK INTRUSION DETECTION SYSTEMS BASED ON DEEP LEARNING
The article describes the process of creating an author’s set of real network streaming data intended for training models of network intrusion detection systems (NIDS) based on deep learning methods. Given the limited availability of modern, complete, and diverse datasets, the authors have developed a comprehensive methodology for generating such a set in a controlled environment that simulates realistic corporate network conditions. To generate traffic, they used both legitimate actions of ordinary users (web browsing, file sharing, e-mail, etc.) and various cyber threats, including DDoS, DoS, reconnaissance, attacks on web applications, Brute-force, and others. Tools such as Metasploit, Hping3, Slowloris, Hydra, and others were used to simulate attacks, and legitimate traffic was created by automated activity from multiple nodes that mimicked the behavior of ordinary users. Particular attention is paid to adapting the data collection process to limited resources, without losing the quality or representativeness of the collected traffic. An approach to balancing the amount of legitimate and abnormal traffic is proposed, which allows the dataset to be effectively used for training, retraining, validation, and testing of NIDS models. In addition, emphasis is placed on the structuredness, labels, and meta- information in the collected data, which simplifies further processing and analysis. The developed dataset can become a valuable resource for the cybersecurity research community, especially in the context of developing adaptive and intelligent intrusion detection systems that can effectively recognize both known and new types of attacks. The obtained results confirm the reliability of the proposed methodology, the high quality of the collected traffic, and its relevance to modern network security challenges.
OPTIMIZING INFORMATION-EXTREMAL MACHINE LEARNING PARAMETERS FOR GROUND OBJECT RECOGNITION BY UNMANNED AERIAL VEHICLES
The article is devoted to the justification of an innovative approach to autonomous navigation of unmanned aerial vehicles (UAVs) based on machine learning using the information-extreme method. The main focus is on optimizing machine learning parameters to increase the accuracy of recognition of ground objects in a geospatial scene.The study determined the optimal values of the vector machine learning parameters, which allowed constructing geometric containers of recognition classes and forming effective decision rules based on them. Functional testing of the algorithm confirmed the accuracy of the system’s operation based on the training matrix, and the examination stage demonstrated the high accuracy of machine learning of an autonomous UAV. The key theoretical result is the justification of the information-extreme synthesis problem of the onboard system of an unmanned aerial vehicle, which consists in finding the global maximum of the information criterion for optimizing the training parameters in different zones of the geospatial scene. Additionally, a functional categorical machine learning model of the second level of depth is proposed, which can significantly improve the quality of recognition in a dynamic environment.A new functional categorical machine learning model of the second level of depth is proposed, which significantly improves the quality of recognition of complex objects in dynamic conditions. The effectiveness of the model lies in the combination of the information-extreme method with the categorical machine learning model. The practical significance of the results obtained is manifested in the possibility of their use for the development of intelligent UAV control systems, geospatial data processing and application in the military, cartographic and monitoring spheres.The work opens up new prospects for the creation of highly efficient autonomous navigation systems capable of operating in real time in a dynamic, changing environment.
RELIABILITY ASSESSMENT OF THE INDUSTRIAL ENTERPRISE PLATFORM SYSTEM
The presented study investigates the problem of ensuring the reliability of the control system for mobile robotic platforms (MRPs), which are commonly used in modern intralogistics systems of industrial enterprises. A standardized approach is proposed for assessing the reliability of MRP system components, taking into account structural redundancy, series- parallel architecture, and degradation processes in complex industrial environments. Using mathematical modeling, reliability functions were calculated for a mid-range MRP system with a payload capacity of 500 to 1000 kg. The Mean Time To Failure (MTTF) and failure rate were computed for key MRP components.As a result of the study, the obtained Mean Time Between Failures (MTBF) is 19,802 hours, or approximately 2.26 years of continuous operation. This MTBF is a high value for mid-range MRPs, which typically exhibit MTBF values in the range of 10,000–20,000 hours, and it is a suitable indicator for industrial-grade platforms.The overall failure rate was determined as λtotal = 5.0499 × 10−⁵ failures/hour, which corresponds to an average failure frequency of approximately once every 19,800 hours.The availability coefficient A ≈ 0.998991 indicates that the system will be operational 99.9 % of the time, meaning downtimes will be extremely rare. This aligns with the requirements for AGVs (Automated Guided Vehicles) operating on production lines.Based on the calculations, critical elements for redundancy were identified, as well as a reconfiguration algorithm for the control system considering changes in its reliability characteristics.The failure of individual components indicates that the least reliable component is the LiDAR, exhibiting the highest failure rate. The most reliable component is the redundant communication module (BLE or WiFi), with the lowest λ value.Both the chassis and the camera show high reliability, which is essential for long-term operation.The proposed approaches can be used to enhance the fault tolerance, adaptability, and overall efficiency of intralogistics systems within the frameworks of Industry 4.0 and 5.0.
RESEARCH ON THE ARCHITECTURE OF DISTRIBUTED DATABASES IN PROCUREMENT MANAGEMENT SYSTEMS OF B2B COMPANIES
This paper explores possible architectures of distributed databases as an innovative solution for procurement management systems in B2B (business-to-business) companies and provides a rationale for selecting a databases management system (DBMS). During the period of military aggression, Ukrainian companies face the need to transform their business models, with the active implementation of various managerial decisions. This, in turn, necessitates the improvement of corresponding information systems through the integration of local databases. Several distributed database architectures are proposed: client-server architecture with independent servers, client-server architecture with cooperating servers, and a hybrid architecture that includes cloud storage, local servers, and their integration, along with the use of CRM/ERP (Customer Relationship Management / Enterprise Resource Planning) systems and neural networks. An analysis of the most widely used database management systems is conducted, including PostgreSQL, Oracle Database, MySQL, Microsoft SQL Server, MongoDB, MariaDB, IBM DB2, and SAP HANA, in the context of their application in automated procurement management systems. Many modern database management systems demonstrate high adaptability to business processes of varying complexity. The presented results can serve as a methodological basis for the development of procurement information systems and for the adaptation of distributed technologies in the processes of corporate digital transformation.The scientific novelty lies in the substantiation of the feasibility of using a mixed (hybrid) client-server architecture for distributed databases in procurement management systems for B2B companies.
AUTOMATED PROCESS OF DETERMINING AREAS AND PLACES OF CONCENTRATION OF ROAD AND TRANSPORT ACCIDENTS IN THE URBAN TERRITORIAL COMMUNITY OF IVANO-FRANKIV
This article examines the pressing issue of increasing road traffic accident (RTA) rates in Ukraine, particularly the sharp rise in accidents involving fatalities and injuries in 2024 compared to previous years. According to national statistics, over 37 % of traffic-related fatalities are vulnerable road users such as pedestrians and cyclists. The most frequent causes of accidents include speeding, improper maneuvers, and violations of intersection rules. In light of these challenges, the identification of accident concentration areas is vital for improving road safety and allocating resources effectively.The authors propose a modern, automated approach to detecting accident-prone areas based on geospatial clustering of RTAs. The method uses the Haversine formula to calculate geographic distances between individual accidents and groups them into clusters based on configurable parameters such as proximity, number of incidents, presence in an urban area, and time range.A web application and REST API were developed using ASP.NET Core and Google Maps JavaScript API to visualize clusters and allow users to filter data by year, road conditions, lighting, weather, involvement of vulnerable users, and presence of fatalities. This platform provides an interactive and data-driven means of identifying high-risk zones.The approach overcomes limitations of earlier methods that relied on Excel and outdated scripting languages like VBA, which restricted scalability and integration with modern web and mobile solutions. The implemented system is extensible, allowing for integration with municipal traffic monitoring systems or national road safety platforms. This facilitates more timely, data-informed decision-making and proactive safety measures, especially in areas with high concentrations of vulnerable road users.
SOFTWARE TOOL FOR WEB CONTENT ACCESSIBILITY ANALYSIS FOR VISUALLY IMPAIRED USERS
The article addresses the issue of implementing modern principles of digital inclusivity in web content. It is shown that a key factor affecting the accessibility of a web resource for people with visual impairments is the contrast level of its web pages and their content, including elements of the graphical user interface. An original approach is proposed for automated contrast analysis of web content in accordance with the Web Content Accessibility Guidelines (WCAG) 2.1 standard, without accessing the Document Object Model (DOM) structure or HTML code of the web page, using only its graphical representation. To implement this approach, the following popular tools were used: programming language Python, OpenCV, and Tesseract Optical Character Recognition. To improve the performance of the developed software, principles of parallel computing were implemented at its core to enable distributed processing of designated text blocks across multiple threads. For this purpose, the concurrent.futures library was used, allowing the creation of threads that process different parts of the image – where text has been identified – in parallel. The developed module demonstrated its ability to effectively analyze the contrast of web page elements without access to their DOM structure, making it suitable for automated accessibility audits of images, PDF documents, web interfaces, and other graphical elements. This was confirmed during testing of the module on several real websites, such as the Medium platform (access mode: https:// medium.com/). Comparative testing was also conducted between the developed module and well-known alternatives, including Google Vision API, Adobe Acrobat OCR, and Tesseract with custom scripts. The results were analyzed based on a dataset consisting of various types of images of different sizes, according to the following criteria: image processing time, accuracy of text block and contrast detection, and operational stability.
ADVANTAGES AND DISADVANTAGES OF GENERATING CODE USING LARGE LANGUAGE MODELS IN MODERN IDE
In this paper we discuss ways to use large language models to generate program code. The focus is on IDEs and their built-in artificial intelligence for such tasks as creating projects, building software application architectures, and prototyping applications based on prompts. AI-generated code lacks traceability and often requires iterative refinement, challenges that existing IDEs are unable to address. Unlike human-created code, generated code does not integrate well with structured development workflows. As a result, developers struggle with AI hallucinations, when models generate plausible but incorrect code that is difficult to verify. This research has identified situations of incorrect output of LLMs as intent misuse, hallucination, missing items misuse, and redundancy misuse. Misuse of third-party APIs in generated code is a serious problem in software development. Although LLMs have demonstrated impressive code generation capabilities, their interaction with complex library APIs remains highly error-prone, potentially leading to software malfunctions and security vulnerabilities. The obtained results show that code autocomplete is more useful, as it makes it possible to generate code on demand in compliance with the specified restrictions and with a lower probability of errors. However, among the disadvantages of this approach is the lack of the ability to generate code in several files at once. The results obtained will contribute to the optimal choice of options for using AI and LLMs in application development. This work is aimed at improving the understanding of scenarios for using LLMs in working with program code.
SURVEY ON AI IN THE FUNCTIONAL SOFTWARE TESTING
The survey conducts a systematic review of the application of artificial intelligence (AI) in software quality control and testing, covering both functional and non-functional quality aspects across all testing stages. The study analyzes various AI techniques, including machine learning, deep learning, evolutionary algorithms, and natural language processing, while also examining AI-based testing tools. It highlights advancements, challenges, and research gaps, drawing on scientific publications from the last decade, supplemented by key foundational works.Functional software quality is defined by its compliance with requirements and correct execution of functions, verified through unit, integration, system, and acceptance testing. AI facilitates automation of test case generation, defect prediction, and oracle design. Non-functional quality encompasses attributes such as performance, reliability, security, and usability, which are more challenging to automate. The review emphasizes AI’s role in automating testing, particularly regression testing, to ensure product stability after changes.Key AI applications include test case generation and test automation using search-based algorithms (e.g., EvoSuite, Sapienz), deep learning, and large language models (LLMs) like Codex. AI is also utilized for defect prediction, code quality assessment, test selection, prioritization, and optimization of test execution in continuous integration environments.Tools like Diffblue Cover demonstrate industrial adoption of AI for generating compact test suites.Major challenges include the oracle problem (determining correct test outcomes), data dependency, the need for model transparency, and limited applicability in safety-critical domains. Future research directions include automating oracles, integrating AI with DevOps, developing human-in-the-loop hybrid systems, and establishing standards for AI in testing.The review calls for unified datasets and benchmarks to evaluate AI testing techniques, particularly in safety-critical sectors like healthcare and automotive.
DEVELOPMENT OF DECISION-MAKING MODELS AND ARTIFICIAL INTELLIGENCE SYSTEMS FOR ENTERPRISES
The article explores the processes of building decision-making models and implementing artificial intelligence systems in enterprises as tools for improving business process management. The purpose of the article is to highlight the specific features of developing decision-making models in business based on artificial intelligence. The study employed general scientific methods of cognition, such as analysis, synthesis, induction, deduction, comparison, generalization, and a systems approach. The findings show that in today’s dynamic business environment, modeling plays a central role in strategic decision-making, helping companies reduce risks and increase management efficiency. Traditional modeling methods, including statistical and linear approaches, remain fundamental tools for analysis and forecasting, but their capabilities are no longer sufficient under conditions of heightened uncertainty. It was found that modern approaches, such as scenario planning and artificial intelligence technologies, offer flexibility, adaptability, and a deeper level of analytics, significantly expanding the boundaries of traditional business modeling. The research reveals that artificial intelligence systems play a key role in transforming decision-making processes by providing tools for analyzing large volumes of data, forecasting trends, identifying hidden correlations, and personalizing customer experiences. These systems are capable not only of processing information in real time, but also of learning from collected data using machine learning algorithms and neural networks.This ability to self-adapt enables AI systems to predict the outcomes of various management decisions, conduct multifactor analysis, and automatically determine optimal action strategies under unpredictable conditions. The study concludes that the use of such systems provides enterprises with a strategic advantage, allowing them to become more flexible, innovative, and competitive. The practical value of the research lies in developing recommendations for implementing intelligent management systems to enhance the adaptability and efficiency of enterprises in the context of digital transformation.
INTEGRATING NLP LIBRARIES AND PRE-TRAINED MODELS FOR AUTOMATED TEXT COMPLEXITY ASSESSMENT
The rapid development of intelligent multi-agent systems has significantly influenced the field of Natural Language Processing (NLP), enabling more efficient and scalable text analysis methods. This study explores a modular approach to NLP, emphasising the integration of specialised libraries and models to enhance text processing capabilities. Modern NLP solutions streamline tasks like text complexity assessment by leveraging machine learning (ML) and deep learning techniques, particularly through pre-trained models and structured linguistic pipelines. Such integration allows for addressing not only syntactic and lexical aspects but also deeper semantic relationships within texts. The modular system proposed in this research allows for the seamless combination of various NLP components, ensuring adaptability to different analytical tasks. The study focuses on widely used NLP tools, including SpaCy for linguistic analysis and BERT (Bidirectional Encoder Representations from Transformers) for deep contextual understanding. The approach integrates traditional linguistic analysis with advanced neural network-based models to facilitate the assessment of text difficulty. The research presents a systematic approach to categorising texts of varying complexity, ranging from essential children’s stories to advanced legal documents.The research underscores the effectiveness of modular NLP systems in addressing the growing demand for automated text analysis. Combining structured linguistic features with deep-learning-based contextual embeddings enables accurate classification of text complexity, facilitating language learning and computational text processing applications. A key advantage of the modular approach is its flexibility and scalability, allowing researchers and developers to integrate customised NLP solutions for diverse applications. By adopting a modular approach, NLP continues to evolve, providing scalable and adaptable solutions for the ever-increasing challenges of text analysis.
THE PROCESS OF TRANSFORMING HIGH-LEVEL QUANTUM GATES INTO BASIS GATES FOR EXECUTION ON A REAL QUANTUM COMPUTER
The article provides a systematic study of the process of transpilation of quantum algorithms, which is a mandatory stage of preparation for the execution of programs on real quantum devices. The essence of the concept of basic gates is disclosed as an elementary set of unitary operations that are supported by specific hardware. It is substantiated why high-level quantum gates (such as Hadamard, Toffoli, Fredkin) cannot be implemented directly on most modern quantum processors due to hardware limitations and architectural features, including the topology of connections between qubits.As an example, the IBM Quantum platform is considered, where the basic set for superconducting processors of the Eagle type includes RZ, SX, X gates and a two-qubit ECR (Echoed Cross Resonance) operation. A step-by-step analysis of the process of transpilation in the Qiskit environment has been carried out, which includes unification of the circuit, decomposition of high-level valves into basic ones, transformation of logical qubits into physical ones, consideration of the connection map, insertion of SWAP-valves, as well as optimization of the circuit to reduce its depth and the number of operations. It explains how the transpiler automatically adapts the logic quantum circuit to the limitations of the real device using built-in libraries of valve schedules and optimization passes (in particular, the SABRE algorithm). An example of transpilation of the circuit with valves H, CX and T is given, which shows the transition to a fully hardware-compatible implementation. The advantages of using different levels of optimization (0–3) of the Qiskit transpiler are determined, the influence of optimization on the accuracy of results and noise resistance is considered. Particular attention is paid to the issues of effective programming, considering hardware limitations: reducing the number of multi-qubit valves, rational use of auxiliary qubits, limiting the use of barriers and the feasibility of manually adjusting the correspondences of qubits in the scheme.
The article explores personalized learning as a modern educational approach that is becoming a key component of the transformation of education in the context of digitalization. Personalized learning is considered as a method of organizing the educational process that takes into account individual characteristics, needs, and levels of preparedness of each learner. Special attention is given to the role of information systems as the technological foundation for implementing this approach. The study describes the main types of information systems that support personalization, including learning management systems (LMS), adaptive learning platforms, learning analytics services, artificial intelligence tools, as well as mobile and cloud-based solutions. The article outlines the principles of designing individual learning trajectories, the use of adaptive content, automated feedback, analytical dashboards, and recommendation engines. The paper discusses both the benefits and challenges of implementing personalized learning. Among the advantages are increased learner motivation, more effective knowledge acquisition, and the development of autonomy in learning. Challenges include technical limitations of educational institutions, the need to improve the digital competence of educators, ethical concerns related to the use of personal data, and the risk of digital inequality. The article provides examples of personalized educational solutions from both international experience (Khan Academy, Coursera, EdX, Open Learning Initiative) and Ukrainian practice (Human platform, expanded functionality of Moodle). The prospects for the further development of personalization are examined in relation to the integration of intelligent technologies, augmented and virtual reality, microlearning, and the construction of interoperable educational ecosystems. The article is of a scientific and methodological nature and is intended for researchers, educators, and developers of digital learning environments.
CREATION OF SAFETY ENCLAVES FOR THE INDUSTRIAL NETWORK OF THE STEAM-HYDROGEN METHANE CONVERSION PROCESS
In the article, creation of safety enclaves for the industrial network of the steam-hydrogen metan conversion process, authored by Sytnikov O. V., Kvitka O. O., Zhlobinska K. U. and Onyshchenko D. S., the task of developing a network structure for protecting enclave perimeters in the context of their connection and determining criticality is presented.The network structure is presented, consisting of three main levels, each of which allows performing specific tasks: corporate, operator, production.A general description of the process of steam-hydrogen conversion of methane is given. It is indicated which circuit will be considered in the studies – the natural gas compressor network, since it is from the stage of supplying natural gas compressed in the compressor to the required pressure to the next heater device that the production process itself begins.The network is divided into functional groups, which will be based on circuits, corresponding connection rules and according to network levels. The studies were guided by the principle that functional groups correspond to the specified network levels, and therefore devices belong to a specific group if they are located in a common subnet defined for them.It is stated that from a statistical point of view, accidents in production most often occur due to deviations of technological parameters from standard values. Since these types of accidents lead not only to the production of defective products, but also to production accidents with consequences for employees, a higher level of criticality should be set for the technological installations themselves, similar to the highest level.The principle of determining perimeters is presented, which corresponds to the level of the scheme – corporate, operator, production network, compressor control subsystem. As a result of the research, a scheme for protecting the perimeters of enclaves in the context of communication between them is presented and programmed, indicating the appropriate means of monitoring data flows. Based on the results of the research, a request for the selection of technical and software cybersecurity tools is created.
METHODS FOR OPTIMIZING CONTAINER PLACEMENT IN DISTRIBUTED SYSTEMS
Containerization is one of the key solutions in distributed service management due to its scalability and efficient use of resources. However, inefficient container placement in distributed computing environments can cause load imbalances between nodes, reduce overall system performance, and increase response times. This study aims to develop a method for dynamic container placement in distributed computing environments that does not require predictive models and improves the stability of web portals. To achieve this goal, existing approaches to containerization were analyzed, and an algorithm based on real-time resource monitoring, task prioritization, and availability constraints was developed. Research methods included test environment modeling, service deployment in Docker containers, Kubernetes orchestration, Prometheus monitoring, and performance metric analysis. The paper proposes an improved method for optimizing container placement based on real-time analysis of the current state of resources, considering CPU usage, memory, network traffic, and the importance of the tasks being performed. Unlike traditional approaches that use fixed rules or predictive models, the developed method uses adaptive responses to changing environmental conditions without prior estimates. The methodology ensures balanced load distribution and increases system stability. The results demonstrate that the proposed methodology reduced the average response time by 34 %, reduced the number of failures by 75 %, and increased resource utilization efficiency by 37 % compared to baseline algorithms (Round-Robin, static scheduling, predictive models). Testing results in a simulated load environment showed a 20–35 % reduction in task execution time and increased the efficiency of computing resource utilization. The practical significance of the work lies in the possibility of integrating the methodology into DevOps CI/CD cycles, where flexibility, scalability, and reliability are important. The approach is suitable for dynamic cloud environments, microservice architecture systems, automated deployment processes (CI/CD), and high-load web platforms. The methodology can serve as a basis for further research in intelligent service placement and self-learning resource management systems.
CROSS-PLATFORM VIRTUAL TWO-COORDINATE RECORDER BASED ON UT61E MULTIMETERS
Scientific and technological progress is closely related to the development of means for measuring physical quantities (PQ). When conducting natural science research, digital multimeters and two-coordinate recorders (TCR) are often used.Digital multimeters have fairly high accuracy, low cost, and allow measuring various physical quantities in a wide range of their variation.Two-coordinate recorders record functional dependencies between PQ during various processes.Currently, two-coordinate recorders are created in the form of virtual devices that have hardware based on a data acquisition system and a computer program that controls the operation of the device and displays the measurement results.When operating virtual devices, it is important to ensure their functioning on different computer platforms.Development of cross-platform virtual TCR with the ability to measure various electrical quantities in a wide range of their variation is an urgent task. This problem can be solved by using digital multimeters as data collection systems, which have a COM or USB port for outputting measurement results to a connected computer.Cross-platform TCR on these devices can significantly expand the range of physical quantities that are measured, relative to existing two-coordinate recorders, without significantly deteriorating the accuracy of such measurements.Virtual cross-platform two-coordinate recorders based on multimeters have not been developed.Using the Java programming language, a cross-platform virtual two-coordinate recorder was created based on UT61E multimeters from Uni-Trend.The computer program of the device has a graphical user interface and allows the recorder to record, for each of the two coordinates (X and Y), a constant voltage within 0 ± 1000 V and a constant current within 0 ± 220 mA.In addition, it has the ability to save measurement results and access them if necessary.The functioning of the virtual recorder was tested when obtaining the volt-ampere characteristic of a zener diode, the transfer characteristic of a transistor by current in a circuit with a common emitter, and the dependence of the voltage on the transistor emitter on the voltage on the base in an emitter follower circuit.
ANALYSIS OF THE EFFECTIVENESS OF MACHINE LEARNING ALGORITHMS IN THE CLASSIFICATION OF MILITARY EQUIPMENT IMAGES
The article analyses the effectiveness of machine learning algorithms in classifying images of military equipment.It examines linear regression, decision trees, support vector methods, and deep neural networks according to criteria of training speed, prediction accuracy, scalability, and computational requirements. It has been established that neural networks demonstrate the highest accuracy (96 %), but require significant resources and training time (600 seconds).The support vector method provides an accuracy of 88 % with moderate resource consumption, while random forests show an optimal balance between accuracy (85 %) and training speed (80 seconds). Key efficiency factors have been identified: data size, model complexity, computational resources, and input data quality. Experiments conducted demonstrate that the training time for neural networks and the support vector method increases linearly in proportion to the increase in the number of images. At the same time, random forests exhibit a nonlinear increase in training time, indicating their ability to handle large sets of pictures of military equipment more efficiently.Optimisation strategies have been proposed using distributed computing and parallel processing to enhance the performance of the algorithms. For deep neural networks, using multiple GPUs is recommended, which can significantly reduce the training time of the model on large image datasets. Prospects for creating hybrid models that combine various algorithmic approaches to improve efficiency and accuracy in classifying images of military equipment have been outlined. Data cleaning and normalisation procedures are also essential to ensure high-quality input information.
METHOD FOR IMPROVING THE EFFICIENCY OF FUNCTIONING UNIX-LIKE SYSTEMS
The article proposes a method for improving the efficiency of the functioning of UNIX-shaped operating systems, aimed at ensuring stable and reliable operation of the server infrastructure of the corporate network in conditions of high load and limited hardware resources. This approach involves the use of automation mechanisms for the main system resources that contribute to the continuous operation of server infrastructure, as well as the implementation of optimization measures based on methods of monitoring and diagnostics using built -in system utilities. The proposed method allows you to detect and eliminate potential failures in a timely manner, prevent excessive use of resources and ensure stable and continuous operation of the system in high load conditions. The work provides recommendations on the automation of the processes of monitoring and management of resources, as well as the possibilities of integration of the proposed method with centralised control systems that use early detection tools in real time. The use of intellectual approaches to administration helps to increase the overall productivity, stability and energy efficiency of server infrastructure. Particular attention is paid to optimization measures that reduce the load on hardware resources, reduce the delays in large data processing and improve the energy efficiency of server infrastructure. The efficiency of implementation of the proposed method has been verified by modelling the overload of a server system of the local computer network of the Department of Electronic Computing Machines of Kharkiv National University of Radio Electronics. The results have confirmed the expediency and efficiency of applying the proposed approach to ensure stable servers in modern corporate networks.
FEATURES AND DIFFERENCES OF USING GRAPH THEORY FOR ANALYSIS AND VISUALIZATION OF CONNECTIONS IN SOCIAL NETWORKS
This article explores the potential for adapting classical graph theory to the analysis of social networks through key criteria such as node types, edge types and weights, graph directionality, temporal variability, centrality metrics, community structures, analytical algorithms, big data processing, visualization, prediction capabilities, and the role of content. The aim of the research is to enhance the effectiveness of analyzing the structure and dynamics of social networks by applying graph theory methods and visualization tools to identify interaction patterns between users.The paper provides a comparative analysis of how graph theory is applied to study the structure and user interactions in four of the most popular social networks: Facebook, Instagram, TikTok, and LinkedIn. It discusses differences in graph structure (nodes, edges, edge weights, directionality), connection types and their evolution over time, the nature of user interactions, analytical approaches (methods and forecasting), big data handling, and visualization techniques.The key analytical methods used to examine connections within each of the selected networks are outlined, and visualizations of graphs representing these connections are presented. The study demonstrates that the defined graph- theoretical features for analyzing the structure of leading social networks can significantly improve the use of these tools in the areas of: ensuring cybersecurity, supporting political interest analysis through public opinion monitoring, enhancing customer relationship management, and optimizing social media algorithms.Data sources for this type of analysis may include: direct platform data; data from electronic communication systems (e.g., email, chats, forums); internal organizational records; results of surveys conducted to map social ties and user interactions; and publicly available datasets.The scientific novelty of the study lies in the development of a methodology for selecting appropriate types of graph models and visualization strategies for analyzing the structure and dynamics of multilevel social connections with maximum efficiency. The results obtained may be applied by government institutions, business entities, and civil society organizations to gain deeper insights into digital social interactions – not only at the level of basic connections (“friendship”, “subscription”), but also in more complex informational and behavioral contexts.
SCENARIO-PRECEDENT APPROACHES TO THE COORDINATION OF HETEROGENEOUS UNMANNED AERIAL VEHICLE ENSEMBLES
The article addresses the pressing issue of coordinating heterogeneous ensembles of unmanned aerial vehicles (UAVs) under conditions of high dynamism and uncertainty. A coordination approach based on a scenario-precedent model is proposed, which integrates elements of centralized, decentralized, and hybrid control. Particular attention is paid to the effective distribution of tasks among different types of UAVs, considering their unique capabilities and limitations.The objective of the study is to develop a flexible and adaptive approach capable of ensuring effective interaction among agents with diverse characteristics.The research envisions the creation of a precedent database that will enable the use of accumulated experience to select relevant mission execution scenarios. An adaptive coordination algorithm is proposed, based on a combination of machine learning methods (neural networks) and reinforcement learning, to implement real-time dynamic adaptation.The proposed architecture also includes mechanisms for communication and information exchange between individual UAVs to ensure coherence of actions. The influence of the quality and volume of the precedent database on coordination efficiency is separately analyzed. The algorithm also considers the possibility of unforeseen situations and the need for rapid replanning of actions. The planned simulation allows for a comparative analysis of the effectiveness of the developed approach against existing methods, particularly in terms of decision-making speed and fault tolerance.The results of the study demonstrate that the use of the scenario-precedent approach contributes to reducing decision- making time, increasing system flexibility, and enhancing resilience in a dynamic environment. The findings confirm the relevance of further development of the proposed model, particularly by expanding the precedent database and improving the adaptation algorithms.
THE WORLDVIEW-FORMING POTENTIAL OF PHILOSOPHICAL KNOWLEDGE IN THE PROFESSIONAL TRAINING SYSTEM OF FUTURE SPECIALISTS IN THE FIELD OF SOCIAL WORK
The article discusses the importance of integrating philosophical ideas with purposeful educational practice for the effective training of social workers who are able to adapt to various social challenges and contribute to the harmonization of social processes. It also outlines the personality of a social worker and the ways of forming his social and communicative skills, including structured training, self-study and practical experience. The personality of a social worker is outlined and his main components of social and communicative skills are identified: the ability to listen and understand the client’s problem; awareness, education, empathy, emotional intelligence; the ability to simply explain complex things; dispute resolution skills; cultural competence and tolerance; reflexivity and the ability to self- criticism. It also suggests ways of forming the social and communicative skills of a social worker: structured training and education, self-study and self-development, practical activity. The text emphasizes the importance of continuous learning and self-improvement to maintain a high level of professional competence. Furthermore, the article emphasizes the value of specialized educational activities, such as curricula and courses on intercultural communication and ethics, in the training of social workers. The conclusion indicates the need for a holistic approach that combines philosophical and educational principles with practical methods to create an environment that promotes personal development based on values and ethical considerations. It is also emphasized that the philosophical and educational approach to the formation of social and communicative skills opens up wide opportunities for personal and professional growth for the social worker, as it helps to master the methods of effective communication, develop inner sensitivity, empathy and critical thinking, which is the key to high-quality social work.
STRATEGIES FOR ENSURING ECONOMIC SECURITY IN THE CONTEXT OF INSTABILITY IN THE CONTEXT OF STIMULATING ENTREPRENEURSHIP DEVELOPMENT
This paper examines the conceptual framework for ensuring and improving the level of economic security in the context of stimulating entrepreneurship. In the context of global instability, economic security is the foundation for stable functioning and an extremely important component for stimulating entrepreneurship. A significant number of internal and external threats and dangers that affect the efficiency of doing business in conditions of risk and instability require a systematic and comprehensive approach to assessing, ensuring and improving the level of economic security of entrepreneurship. In accordance with the above, the purpose of this study is to substantiate the types of strategies for ensuring and improving the level of economic security in conditions of instability in the context of stimulating entrepreneurship development.On the basis of generalization of domestic and foreign directions of theoretical research on the essence of economic security, it is established that this definition is a multicomponent system consisting of numerous indicators with different measurement bases. The set of indicators that affect the level of economic security of entrepreneurship systematically characterizes the level of financial stability of a business entity, the efficiency of resource management and business processes, profitability, economic resilience to external and internal threats, as well as the ability of a business entity to neutralize the risks associated with dynamic changes and uncertainty in an unstable environment.The results of the survey on the situation of Ukrainian business in the conditions of war after the full-scale invasion of the russian federation into the territory of Ukraine are systematized. It has been established that even during the war, Ukrainian business is gradually adapting to new realities. Domestic entrepreneurs are transforming, modernizing, and looking for new opportunities under martial law. In Ukraine, small and medium-sized businesses are returning to work, but recovering more slowly than large businesses.Based on the results of the carried out theoretical and methodological research, it is proposed to distinguish seven strategies for ensuring economic security of entrepreneurship, depending on five possible levels of economic security.The results of the study can be used by Ukrainian entrepreneurs in the process of forming and choosing a strategy for ensuring and improving the level of economic security.