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Документ Methods of hiding data in computer networks: from classics to IoT and Ai(Хмельницький національний університет, 2025) Shelest, Mykhailo; Pidlisnyi, Yurii; Kapustian, MariiaThe article presents an overview of key methods in network steganography, including the classification of data hiding techniques in network protocols and discussion of promising directions for further research. Special attention is paid to the use of steganography in modern network environments such as IoT, as well as the application of artificial intelligence to traffic masking. The paper also outlines current approaches to hidden channel detection and threat modeling in digital communication systems.Документ Approach to a decentralized, physician-oriented ehr architecture with cryptographic protection(Хмельницький національний університет, 2025) Kysil, Volodymyr; Kysil, TetianaIn modern Electronic Health Record (EHR) systems, electronic records play a primary role in storing examination data; however, classic centralized storage systems face challenges regarding security, privacy, and data availability. Centralized databases are vulnerable to cyberattacks, data leaks, and interoperability issues with other systems, threatening patient confidentiality as well as the efficiency and transparency of medical institutions. In the specific case of a distributed network architecture where servers may operate autonomously without constant internet connectivity, decentralized solutions are required that combine cryptographic protection with ease of use for medical personnel. Physician-centric systems allow for workflow optimization in institutions where doctors collaborate within a trusted environment, while adhering to ethical standards of transparency for patients. The objective is to create a flexible, autonomous platform that ensures cryptographic data protection through envelope encryption with combined Data Encryption Keys (DEK), server key rotation, and hash chains for modification detection. The system supports profile migration between nodes, the exchange of signed data between physicians, and resource optimization by offloading completed records to a registry node. The primary focus is on patient transparency: any data decryption must be accompanied by a notification to the patient via an active notification system; if the notification system is unavailable, the decryption operation is not performed. Consequently, data decryption is logged (identifying the entity performing the decryption) for future reference. The system workflow begins with the creation of a patient profile on the physician’s local node, where data is encrypted using a combined DEK (static user key + daily node key). Hash chains ensure file integrity, preventing undetected changes, while two differently encrypted copies of the key allow for access recovery by an administrator. This makes the system resilient to failures, with minimal requirements for users, maintaining a balance between efficiency for medical personnel and patient rights regarding access information, data access, and data portability. Performance evaluation of the proposed architecture demonstrates that the storage overhead for cryptographic metadata is approximately 248 bytes per user profile (server-encrypted key, user-encrypted key, key-encrypted identifier, creation time; the size of encrypted data depends on the algorithm) and 40 bytes (SHA-256 standard + timestamp) per record to ensure cryptographic linkage. This is a negligible amount (<0.001%) compared to the volume of medical data (up to 32 MB per record with images). The absence of a need for global consensus (unlike blockchain solutions) ensures simple O(1) write operations, guaranteeing high performance even on resource-constrained hardware. Efficiency assessment indicates that the architecture adds minimal overhead: only a slight increase in size and access time within the constraints. Thanks to the local consensus model, the system does not require network synchronization, ensuring O(1) write complexity and high speed even on personal devices without dedicated server hardware.Документ Decision support system for project resource planning based on the Random Forest method(Хмельницький національний університет, 2025) Hnatchuk, Yelyzaveta; Lebedovska, MariiaThe study develops and justifies the structure of a decision support system (DSS) designed to automate project resource planning processes using the Random Forest method. The relevance of the research is driven by the necessity to transition from subjective estimates to analytical tools for forecasting project costs and duration. The proposed system architecture covers the full data processing cycle: from automated input data collection from corporate databases (such as Jira or MS Project) to the generation of visual reports for management. Implementing the Random Forest algorithm within the DSS framework enables the identification of critical project parameters, specifically technical complexity and external risks, directly at the initiation and planning stages. Special emphasis is placed on the development and implementation of a feature importance visualization mechanism, which transforms the forecasting model into a transparent analytical tool. This allows managers to not only obtain predicted values but also understand the underlying structure of the factors influencing them. It was established that the feature hierarchy, where technical complexity plays a leading role (0.793), enables the project manager to focus on the most critical planning nodes. Such an approach significantly enhances the transparency of decision-making and fosters increased stakeholder trust in the system's recommendations. The practical significance of the results lies in the possibility of implementing predictive management methods. The system identifies potential project bottlenecks before actual difficulties arise, providing the manager with a basis for timely reviews of team composition, budget limit adjustments, or schedule modifications. Thus, the proposed DSS serves as an effective tool for active management, providing decision support to prevent cost overruns and project schedule delays in dynamic environments.Документ Method for synthesis of a scalable architecture of a distributed computer systems, resistant to social engineering attacks(Хмельницький національний університет, 2025) Bokhonko, Oleksandr; Atamaniuk, OlhaSocial engineering continues to be one of the most dangerous classes of threats for modern distributed IT systems, where event processing, resource access, and protection mechanisms are performed on a large number of heterogeneous nodes. The growth of the scale of architectures, the emergence of multi-channel interaction scenarios, remote users, and a high level of dynamism create challenges for the synthesis of systems that are able to maintain resistance to social engineering attacks. The study proposes methods and tools for the synthesis of distributed systems focused on ensuring structural, behavioral, and functional resistance to such attacks. The basis of the approach is the use of a population multi-agent mean-field model, which allows considering a large number of nodes as a coordinated system of local detectors interacting through an aggregated state space. This makes it possible to describe the impact of attacks not on individual components, but on the entire distributed system as a whole, and to evaluate its response through integrated risk and resilience indicators. The study forms a generalized model of a distributed system, defines the roles of different types of nodes, protections and interaction channels, and also describes the methodology for architecture synthesis, which includes the classification of local actions, coordination mechanisms and evaluation criteria. Special attention is paid to the integration of protective measures - deception components, multifactor authentication, filtering and segmentation mechanisms - into the structure of a distributed system. Methods for optimizing the distribution of these measures at different levels of the architecture are proposed in accordance with the dynamics of the mean field and target requirements for stability. An iterative approach to architecture synthesis is developed, which combines the adaptation of local node strategies with the tuning of global system parameters. The results demonstrate that the use of the mean field concept allows to ensure scalability of solutions, consistency of node behavior, and also to increase the ability of a distributed system to counteract social engineering attacks in conditions of uncertainty and high variability of scenarios. The methodology can be used for the design, improvement and engineering synthesis of real distributed IT architectures operating in critical environments.Документ Метод виявлення зловмисних дроперів на основі графової уваги та моделей викликів API(Хмельницький національний університет, 2025) Лигун, Олексій; Lyhun, OleksiiУ роботі представлено вдосконалений метод виявлення зловмисних дроперів у комп’ютерних системах, який ґрунтується на побудові орієнтованих графів викликів API та їх подальшому аналізі за допомогою Graph Attention Networks (GAT). Запропонований підхід орієнтований на сучасні типи дроперів, що використовують поліморфізм, метаморфізм, різноманітні техніки обфускації, динамічне завантаження компонентів та умовне виконання коду, які ускладнюють їх виявлення традиційними засобами. На відміну від сигнатурних методів, що залежать від наявності відомих зразків шкідливих програм, та евристичних підходів, які часто страждають від високої кількості хибних спрацювань, модель на основі GAT дозволяє аналізувати структурні та контекстні залежності між викликами API. Завдяки механізмам уваги мережа здатна визначати найбільш інформативні вершини і ребра в графі, що відображають приховані патерни поведінки дроперів, незалежно від модифікацій їхнього машинного коду. У роботі виконано детальний експериментальний аналіз із використанням корпусу Windows PE-файлів, що включає як легітимні, так і шкідливі зразки різних сімейств. Метод порівняно з базовими моделями машинного навчання, такими як Random Forest, SVM та градієнтний бустинг. Отримані результати демонструють суттєві переваги підходу на основі GAT як у точності класифікації, так і у стійкості до складних технік обфускації, що підтверджує його ефективність для практичного застосування в системах захисту.Документ Legal and ethical bases for creating representative datasets to detecting manifestations of cyberbullying in text content(Хмельницький національний університет, 2025) Sobko, Olena; Chochia, ArchilThe article is devoted to developing the method for creating of representative text data datasets for detecting manifestations of cyberbullying in text content, considering ethical and legal principles. The primary focus is ensuring fair and equal representation of different demographic groups in text samples, which is critical for creating non-discriminatory and socially responsible artificial intelligence models. Emphasis is placed on compliance with key ethical principles – preventing harm, avoiding bias, and ensuring representativeness – and provisions of international law, particularly the General Data Protection Regulation. Proposed method for creating of representative text data datasets for detecting manifestations of cyberbullying in text content, taking into account ethical principles, which includes the following stages: preliminary processing of text data, analysis of distributions according to ethical aspects (age, gender, religion etc.), and representative adjustment through multi-criteria optimization. Machine learning models are trained on prepared balanced samples using appropriate reference datasets to classify text samples according to ethical criteria. The comparison is based on official demographic data for Ukraine, which ensures the reliability of the assessment of deviations. As a result of applying the developed method, a representative sample was created with a deviation of the proportions of ethical groups from the target values within 0.00-0.04%. The statistical metrics obtained confirmed the effectiveness of the selected models and demonstrated a high degree of compliance with the ethical responsibility requirements of the results. The analysis showed that the initial datasets contained imbalances, which were successfully eliminated through multi-criteria optimization and data augmentation. The developed approach can be integrated into preparing training samples for ethically oriented artificial intelligence systems that perform automated detection of cyberbullying manifestations in text content, reducing the risks of reproducing social biases and increasing trust in algorithmic decisions.Документ System for cybersecurity evaluation of corporate networks(Хмельницький національний університет, 2025) Ramskyi, Ihor; Drozd, Andriy; Lyhun, Oleksii; Ponochovna, OlenaIn the context of rapidly increasing cyber threats and the growing complexity of corporate IT infrastructure, ensuring a reliable and proactive approach to cybersecurity is becoming critically important for organizations of all sizes. Traditional cybersecurity assessment methods often fail to keep up with the dynamic nature of emerging threats – necessitating the development of more adaptive and intelligent evaluation systems. This article presents a comprehensive modular system for assessing the cybersecurity level of corporate networks – offering a holistic view of the security landscape by integrating both technical and organizational indicators. The proposed system utilizes self-organizing analytical methods to dynamically process large volumes of data related to vulnerabilities, configuration states, and network behavior patterns. Through intelligent algorithms and adaptive learning, the system is capable of autonomously detecting anomalies, evaluating potential attack vectors, and correlating threats with the network’s weak points. Additionally, the inclusion of organizational factors – such as policy compliance, user behavior, and access structures – enables a more contextual and in-depth risk assessment. A key advantage of the system is its ability to perform real-time monitoring and dynamic risk evaluation – empowering decision-makers to take informed actions in response to incidents. The system's architecture supports scalability and compatibility with existing security tools and network management platforms. To validate its effectiveness, the system was implemented and tested in a simulated corporate environment reflecting modern structural and operational challenges. The experimental results confirmed its capability to identify vulnerabilities, prioritize responses, and enhance overall cyber resilience. This research contributes to the advancement of next-generation cybersecurity assessment tools – ensuring the continuous improvement of corporate defense mechanisms in an ever-changing cyber landscape.Документ Cyber-physical system for determining soil parameters(Хмельницький національний університет, 2025) Voichur, Yurii; Payonk, IllyaThe relevance of a cyber-physical system for determining soil parameters in Ukraine is determined by several important factors, including climate change, declining soil fertility, and the need to implement efficient technologies to ensure sustainable agriculture. In Ukraine, where a large part of the economy depends on the agricultural sector, accurate soil monitoring is a key aspect to increase the efficiency of agricultural production, optimize the use of water and land resources, and reduce the cost of fertilizers and pesticides. Cyber-physical systems can provide timely data collection on soil moisture, temperature, pH, and other critical soil parameters, allowing farmers to respond quickly to changes in environmental conditions. Such systems can reduce the negative impact of excessive irrigation and optimize the use of water resources, which is especially important in the face of drought, which is increasingly common in Ukraine due to climate change. These systems also allow for accurate forecasts of yields and soil conditions, as well as the development of individualized recommendations for each field or plot. Since Ukraine has a wide variety of climatic conditions and soil types, cyber-physical systems are able to adapt to different agricultural needs, making them extremely useful for the development of precision agriculture. The introduction of such technologies helps not only to preserve natural resources but also to improve the economic efficiency of agriculture. Therefore, the development and implementation of cyber-physical systems for soil monitoring is an extremely important step for the sustainable development of Ukraine's agricultural sector. Therefore, our research is devoted to the development of a method and a cyber-physical system for determining soil parameters. The cyber-physical system for determining soil parameters consists of three levels: the level of sensors, the level of the controller to which the sensors are connected, and the system for collecting, monitoring, and managing data in real time. To build a cyber-physical system for determining soil parameters, we selected sensors, selected a controller, selected a data transmission standard, and developed a method for collecting, monitoring, and controlling data. The proposed method of data acquisition, monitoring and control for the upper level of the cyber-physical soil parameterization system allows for efficient data acquisition, monitoring and control in a cyber-physical system with various parameters stored in real time.Документ Reinforcement learning method for autonomous flight path planning of multiple UAVs(Хмельницький національний університет, 2025) Velychko, Maksym; Kysil, TetianaThis study aims to develop a reinforcement learning method for autonomous flight path planning of multiple UAVs under real-world conditions with limited observations and multiple conflicting optimization objectives. The research proposes a multi-agent reinforcement learning approach based on Proximal Policy Optimization (PPO) combined with centralized training and decentralized execution (CTDE). Additionally, a recurrent neural network (RNN) layer is integrated into the critic and actor networks to address partial observability. The reward function is designed to balance time efficiency, safety, and area coverage. Experimental results demonstrate that the proposed method significantly outperforms independent learning approaches in terms of reward accumulation, convergence speed, and decision stability. The CTDE architecture with RNN-enhanced critics proved effective in handling the challenges of multi-agent coordination and partial observability. The trained model enables real-time trajectory planning in three-dimensional environments, surpassing traditional optimization methods. The novelty lies in the application of a multi-agent PPO architecture enhanced by RNNs under CTDE for solving real-time multi-objective optimization problems in UAV path planning. A customized reward structure was developed to simultaneously optimize safety, time, and coverage objectives without retraining. The developed method enables efficient and reliable online trajectory planning for UAV groups, making it applicable in surveillance, search and rescue, and exploration missions where rapid and adaptive decision-making is essential.Документ Monitoring system for critical infrastructure objects based on digital twins(Хмельницький національний університет, 2025) Andrieiev, Dmytro; Lyhun, Oleksii; Drozd, Andriy; Ponochovna, OlenaCritical infrastructures are fundamental to the seamless operation of modern societies, encompassing sectors such as energy, healthcare, transportation, and communications. Ensuring their reliability, performance, continuous operation, safety, maintenance, and protection is a national priority for countries worldwide. The digital twins play a crucial role in critical infrastructure, as they enhance security, resilience, reliability, maintenance, continuity, and operational efficiency across all sectors. Among the benefits offered by digital twins are intelligent and autonomous decision-making, process optimization, improved traceability, interactive visualization, and real-time monitoring, analysis, and prediction. Furthermore, the study revealed that digital twins have the capability to bridge the gap between physical and virtual environments, can be used in combination with other technologies, and can be integrated into various contexts and industries. The use of digital twins was explored as the foundation for developing a modern monitoring system for critical infrastructure facilities enables multi-level assessment of asset conditions in real time, ensuring precise threat detection, anomaly identification, and timely decision-making. Integration with artificial intelligence and big data technologies allows not only the collection and analysis of large volumes of information but also the creation of adaptive behavioral models for systems in emergency situations. Special attention was given to the method of optimizing critical IT infrastructure using digital twins, which combines virtual modeling, predictive algorithms, and automated management. The proposed approach enhances the reliability of digital systems, minimizes downtime, optimizes maintenance costs, and strengthens cybersecurity. This system is especially relevant in the context of growing risks and increasing demands for the stability of strategically important infrastructure assets. The application of digital twins for monitoring and optimizing critical infrastructure demonstrates considerable potential for improving its resilience, safety, and operational efficiency. The approaches discussed in the study confirm the relevance of implementing digital models as tools for timely risk identification, failure prediction, and informed decision-making. By integrating such technologies, organizations can reduce operational costs, minimize downtime, and improve the overall stability of infrastructure operations. Therefore, digital twins represent a vital step toward the digital transformation and modernization of mission-critical systems across various sectors.Документ Artificial intelligence approach to identifying propaganda techniques and objects, taking into account ethical and legal aspects(Хмельницький національний університет, 2025) Molchanova, Maryna; Dutt, Pawan KumarThe article explores the ethical and legal aspects of applying artificial intelligence (AI) technologies to detect propaganda techniques in textual content. The study presents a multi-level approach to identifying signs of propaganda in textual data, recognizing common rhetorical strategies of influence, and establishing semantic links between the detected techniques and their respective targets. The consistent use of neural network models is justified, as it ensures both classification accuracy and transparency of the obtained results through the application of local interpretability methods. The paper presents experimental results based on a corpus of Ukrainian-language news texts and informational messages from social media platforms. The proposed approach demonstrated alignment between the model's predictions and independent expert assessments, confirming its potential applicability in conditions with limited human oversight. Special attention is given to the compliance of the proposed system with existing regulatory frameworks, including constraints on automated decision-making, the user's right to explanation, and the prevention of discriminatory effects resulting from biased training data. The study addresses risks associated with misclassification, potential impacts on freedom of expression, and the accountability of developers in cases where the system is applied in automated content moderation scenarios. The integration of interpretability tools into neural network analysis is proposed as a core design principle to ensure adherence to ethical AI standards. Based on the obtained findings, the study concludes that the development of such systems requires the simultaneous consideration of technical effectiveness, legal compliance, and social responsibility, which are essential conditions for their safe implementation in the practice of analyzing public communications.Документ Analysis of biometric access control systems(Хмельницький національний університет, 2025) Bouhissi, Houda El; Yurko, PavloThe paper presents a method and a software-hardware tool for an access control system based on biometric data. The method involves the collection, processing, and verification of biometric features such as fingerprints, facial recognition, or iris scans to authenticate individuals. The system ensures secure access while minimizing the risks associated with traditional password-based security systems. The software-hardware tool integrates biometric sensors, data storage, and authentication algorithms to provide an efficient and secure means of controlling access to protected areas or resources. This approach aims to enhance security, streamline user access, and reduce the likelihood of unauthorized access or identity theft.Документ Method for obtaining rotation-invariant image representation by removing orientation features from autoencoder latent space(Хмельницький національний університет, 2025) Bedratiuk, AnnaIn many computer vision tasks, accurate object recognition is complicated by arbitrary object orientations. Ensuring rotation invariance is critical for improving classification accuracy and reducing errors related to the varying placement of objects. This issue is particularly important in real-world environments, where object orientation is rarely controlled. The goal of this study is to develop a method that allows separating rotational features from the semantic essence of an object, while preserving high classification accuracy after removing orientation-related components. This approach enables the construction of models that remain effective under a wide range of input perspectives, thus improving robustness in practical applications. The proposed method is based on using a convolutional variational autoencoder trained on a dataset of images subjected to various rotation angles. Linear regression is then used to identify those latent components that correlate most strongly with the rotation parameter. These components are removed, and the remaining features are used for classification. Additionally, image reconstruction is performed from the reduced latent vector to visually validate rotation invariance and evaluate the preservation of object shape. Experiments on a synthetically rotated binarized digit dataset (modified MNIST) demonstrated that removing rotationsensitive components led to a classification accuracy decrease of no more than 25–30% across latent space dimensions 3–10 (e.g., normalized accuracy dropped from 1.000 to 0.704 at d = 7). Reconstruction experiments showed that the semantic shape of digits was preserved, while specific orientation information was suppressed. The scientific novelty of this work lies in introducing a simple and reproducible method for removing orientation-related features from the latent space of an autoencoder without modifying the model architecture or introducing specialized regularizers. The practical significance of the method is in reducing the influence of arbitrary object orientation on recognition accuracy, thereby increasing the universality and reliability of vision systems in uncontrolled settings. The proposed approach may be useful for building classifiers capable of handling images with varying or unknown orientations during data collection.Документ Heating optimization system in a smart home based on fuzzy logic and integration with cloud services(Хмельницький національний університет, 2025) Lytvinchuk, Ihor; Savenko, Bohdan; Danchuk, SerhiiSmart home technologies are increasingly being used to automate various aspects of everyday life, and one of the main problems these systems solve is energy optimization. Heating is one of the largest energy consumers in a home, so its efficient management plays a key role in reducing energy costs and increasing the comfort level of residents. A fuzzy logic-based system for optimizing the use of heating in a smart home is an important step towards energy efficiency and comfort in modern residential buildings. The relevance of this work lies in the fact that existing heating systems in Smart Homes are often not fully optimized, especially in terms of fuel management and reducing the frequency of temperature fluctuations. Many current systems do not fully take into account variable conditions such as outdoor temperature, time of day, humidity levels, or individual user needs. This results in inefficient operation: fuel consumption can be excessive and room temperatures fluctuate frequently, creating discomfort for occupants. Frequent changes in temperature can also negatively affect human health, and excessive fuel consumption leads to economic losses and increased environmental impact. Optimization of these processes through the use of fuzzy logic can achieve a more stable and energy-efficient heating system, which is essential for improving comfort and reducing costs. This paper proposes a fuzzy logic-based system for optimizing the use of heating in a Smart home. According to the results obtained, the use of fuzzy logic significantly improves the stability of the temperature in the house, which is important for the comfort of the residents. For the experiments, two models were compared: a basic heating model and a model based on fuzzy logic. The basic system, which does not take into account variable factors with this level of flexibility, leads to large and sharp temperature fluctuations, which can create discomfort and increase energy consumption. Instead, the fuzzy logic model demonstrates smoother and more stable temperature control, which can significantly reduce energy costs.Документ Mobile-oriented cyber-physical system for food allergen detection based on machine learning and image analysis(Хмельницький національний університет, 2025) Talapchuk, Valentyn; Zaitseva, ElenaThe prevalence of food allergies necessitates the development of effective methods for the timely detection of allergenic components in food products to prevent dangerous medical reactions. In this work, a mobile-oriented cyber-physical system is proposed, leveraging state-of-the-art machine learning techniques and image analysis for the automated detection of food allergens. The developed system integrates the capabilities of mobile devices equipped with high-quality cameras and efficient computational resources, enabling accurate processing and classification of food product images either locally or via cloud-based inference. This approach ensures flexibility in deployment while maintaining high detection accuracy across diverse environments. This study examines both the theoretical and practical aspects of applying deep neural networks to object recognition tasks. Particular emphasis is placed on the EfficientDet model, which, due to its optimal balance between detection accuracy and computational cost, represents a promising solution for mobile applications. To enhance recognition performance, image preprocessing methods—including normalization, scaling, and data augmentation—are employed to increase the model’s resilience to variations in imaging conditions. The methodology for data collection and image annotation is described in detail, including the pre-processing procedures that ensure improved model robustness under diverse external conditions. Experimental investigations conducted on a large annotated dataset demonstrate the high accuracy and effectiveness of the system in detecting the presence of food allergens, thereby enabling the prompt identification of potentially hazardous components. The results of the work highlight the practical applicability of the proposed system in mobile applications for monitoring food quality and preventing allergic reactions. The conclusions outline prospects for further research, focusing on expanding the platform’s functional capabilities through the integration of additional sensor technologies and the refinement of data processing algorithms.Документ Method of FPV drone stabilization on an automatically determined target and its further observation(Хмельницький національний університет, 2025) Halytskyi, Oleksandr; Denysiuk, Dmytro; Kozhemiako, Yaroslava; Kvassay, MiroslavIn recent years, the use of FPV (First Person View) drones has gained significant traction across various fields, including recreational activities, infrastructure inspections, search-and-rescue missions, and military operations. The paper considers the problem of the lack of stabilization in FPV drones, which significantly limits their functionality for applications that require precise tracking of a specific target. Such drones, although characterized by high maneuverability and affordable cost, are inferior to commercial quadcopters such as the DJI Mavic, which are equipped with effective stabilization systems, but are significantly more expensive due to the use of proprietary technologies. The paper proposes a new approach to stabilizing FPV drones, based on the use of computer vision algorithms for automatic target detection and tracking. The main concept includes target detection based on image analysis from the drone camera, further determination of its trajectory and transmission of appropriate control commands to the flight controller using the MAVLink protocol. This approach allows to significantly increase the accuracy and stability of FPV drones when performing tasks that require focusing on an object, such as infrastructure inspection, search and rescue operations, or video shooting. The proposed solution is based on accessible and open technologies, which ensures its adaptability and low implementation cost. The paper describes in detail the developed system architecture, which includes a computer vision module for video stream analysis, algorithms for data processing and filtering, as well as integration mechanisms with existing flight controllers. A series of experiments were conducted to evaluate the effectiveness of the proposed approach. The results demonstrate that the proposed system is able to ensure stable drone tracking on a specified target even in difficult flight conditions.Документ Method of operation of the cyber-physical water resources monitoring system(Хмельницький національний університет, 2025) Voichur, Yurii; Balan, AndriiThe relevance of designing and developing a cyber-physical water monitoring system for Ukraine is driven by the need for effective water management in the face of climate change, water pollution, and growing water supply needs. Modern challenges, such as the lack of clean drinking water, irrational use of resources, emergency condition of water supply networks and environmental threats, require the introduction of innovative technologies. The use of sensor networks, artificial intelligence, and cloud computing allows us to quickly obtain information about water quality and quantity, predict changes, and prevent emergencies. The introduction of cyber-physical systems in the field of water resources monitoring will help to increase the efficiency of water management, reduce losses, improve the ecological condition of water bodies and provide the population with quality water. For Ukraine, where water security is a strategic issue, such solutions will be an important step towards sustainable development and environmental balance. The use of Internet of Things (IoT), Big Data, and artificial intelligence technologies can automate the processes of data collection, analysis, and forecasting, which will help optimize water use, prevent pollution, and increase the efficiency of water infrastructures. Thus, the task of designing and developing a cyber-physical water resources monitoring system is currently relevant for Ukraine. The article develops a method for the operation of a cyber-physical water resources monitoring system that provides cyber-physical integration (a combination of physical (sensors, objects) and cybernetic (analytics, control) components), autonomy (the ability to function without constant human intervention), scalability (the ability to expand the geography of monitoring), and monitoring continuity (round-the-clock real-time monitoring).Документ Decision-making method in interdependent computing systems(Хмельницький національний університет, 2025) Kryzhanyvskyi, Dmytro; Drozd, Andriy; Besedovskyi, OleksiiThe relevance of this paper lies in the fact that modern interdependent computing systems are being actively implemented in critical areas ranging from smart energy grids and transportation systems to autonomous robotic platforms and distributed cloud services. These systems are characterized by a complex structure, a large number of interacting agents, and high requirements for real-time decision-making. Despite significant scientific and technological progress, a number of challenges remain unresolved to ensure the sustainability, adaptability, and coherence of all system components. One of the key challenges is the need to ensure rational decision-making in a decentralized environment where each agent has limited information about the state of the system as a whole and operates under conditions of uncertainty and potential distrust of other agents. Classical centralized methods are often ineffective or inapplicable in such cases due to excessive complexity or delays in data exchange. The issue of developing methods that ensure not only the correctness of decisions but also compliance with time constraints is particularly relevant. In interdependent computing environments, where the decision of one agent affects the outcome of the work of others, any delay or error in the strategy can lead to degradation of the performance of the entire system. In such environments, it is crucial to use adaptive, game-based, and reputation-based approaches that allow for dynamic consistency and stability of the system. In this paper, we develop a decision-making method for interdependent computing systems that combines Bayesian reputation updating, log-linear strategy learning, and reinforcement learning mechanisms. The peculiarity of the proposed method is its ability to adapt to changes in the environment and effectively detect unscrupulous agents by dynamically adjusting reputations. The algorithmic implementation of the model allows achieving the Bayesian-Nash equilibrium, which indicates the stability of the system even in complex interaction scenarios. The results of experimental modeling have demonstrated that the proposed method strikes a balance between adaptability, reliability, and efficiency of interactions. The system demonstrates the ability to self-organize, stabilizes in fewer iterations compared to classical approaches, and effectively prevents the influence of sabotaging behavior of individual agents. The prospect of further research is to adapt the model to different types of computing environments, including MEC infrastructures, edge systems, and IoT platforms. Special attention is planned to be paid to the development of new objective functions that would take into account not only the stability and speed of convergence, but also energy consumption, network bandwidth, and quality of service (QoS).Документ Task optimisation in multiprocessor embedded systems(Хмельницький національний університет, 2025) Martiniuk, Dmytro; Lyhun, Oleksii; Drozd , Andriy; Besedovskyi, OleksiiThe relevance of this work lies in the fact that the existing task distribution in multiprocessor embedded systems plays a key role in the development of devices used in various industries. Despite the progress made, there are still many research challenges that require in-depth analysis and implementation of effective solutions. One of the main challenges is to ensure the reliability of embedded systems, especially in environments where safety is critical. Although the functionality of such systems is usually defined at the design stage, ensuring their stable operation in real time remains a challenge. It is necessary not only to guarantee the correctness of calculations, but also to adhere to time constraints, which requires new approaches to managing the resources of multiprocessor systems. Another important problem is the need to meet stringent real-time requirements. This is a characteristic feature of embedded systems, which differ from general-purpose systems that have more flexibility in functionality but do not guarantee such predictability and reliability. Therefore, optimization of task scheduling that takes into account the specifics of embedded systems requires further research. It is also important to take into account the variety of embedded systems, which are divided into control systems and streaming systems that have different data processing requirements. Control systems must respond quickly to environmental events while minimizing delays, while streaming systems process continuous data streams, requiring high throughput and efficiency. The development of universal solutions that can optimize the performance of both types of systems is an urgent task for scientists and engineers. Therefore, task optimization in multiprocessor embedded systems has significant potential for development and is relevant for reliability, real-time guarantees, and efficient resource management, which will contribute to the creation of more secure and productive systems. In this paper, we develop a method for optimizing task execution using replication in a multiprocessor system, which allows to effectively minimize the total execution time, ensure load balance, and minimize communication delays. The peculiarity of the method is the implementation of task migration according to replication using the optimization objective function. An experiment with the system demonstrated that the chosen optimization method effectively balances the load, but additional objective functions are needed to optimize energy consumption. The simulation results show that an increase in the number of processors leads to a decrease in the maximum load and the number of migrations, an increase in the number of tasks increases the system load and the number of migrations at the initial stages, and the migration mechanism effectively balances the load, especially at the initial stages of execution. The areas of further research are the detailing of embedded devices and their classification. For each class of embedded devices, it will be necessary to adapt the algorithms and method of task optimization, as well as to develop the target optimization function.Документ Method and cyber-physical system for forecasting and optimizing electricity consumption in residential districts(Хмельницький національний університет, 2025) Pysmeniuk, Volodymyr; Levashenko, VitalyThe development of cyber-physical systems combined with machine learning algorithms opens new opportunities for forecasting and optimizing electricity consumption in residential districts. This study examined existing technologies and solutions for energy consumption management, identifying their advantages and disadvantages. The analysis showed that modern commercial systems are primarily designed either for industrial use or individual consumption, lacking a comprehensive approach for residential districts. The proposed forecasting and optimization method is based on hybrid machine learning algorithms. For energy consumption forecasting, a combination of recurrent neural networks (RNN) and XGBoost was used, allowing for the consideration of both temporal dependencies and nonlinear factors. For energy consumption optimization, a combination of genetic algorithms (GA) and particle swarm optimization (PSO) was implemented, ensuring efficiency in finding optimal solutions. The developed cyber-physical system includes sensors for data collection, microcontrollers (Raspberry Pi) for data processing, and intelligent systems for controlling electrical appliances. This enables real-time energy consumption analysis and management, improving the energy efficiency of residential districts. Experimental results confirmed the effectiveness of the proposed approach, demonstrating high accuracy in energy consumption forecasting and the potential for reducing electricity costs through optimized usage. The proposed method has significant potential for scaling and implementation in large residential complexes, contributing to sustainable development and reducing the load on energy grids. Thus, the results of this study can be used for further improvement of energy management systems, promoting efficient electricity use, reducing consumer costs, and minimizing the environmental impact of energy systems.Документ Use of smart contracts on the ton blockchain for innovative educational solutions development ed on machine learning algorithms(Хмельницький національний університет, 2025) Askerov, Viacheslav; Tomchyshen, Bohdan; Bouhissi, Houda ElIn the modern world, blockchain technologies are gaining popularity due to their ability to ensure security, transparency and decentralization of data. One of the most promising platforms is The Open Network (TON), which provides unique opportunities for the development of smart contracts. This article discusses the main features of the TON blockchain and its advantages in the context of educational process automation. Smart contracts implemented on the TON platform can serve as a tool for optimizing educational systems. They allow to automate processes related to knowledge validation, grade management, and even finance in educational institutions. For example, smart contracts can provide automatic scholarships based on students' grades, as well as control over the implementation of curricula. The paper also analyzes the benefits of using smart contracts in the educational process, such as reducing administrative costs, increasing transparency, and reducing fraud risks. In addition, blockchain technologies provide an opportunity to create decentralized platforms for storing and sharing knowledge, which makes learning more accessible and effective. Particular attention is paid to the mathematical aspects that ensure the functioning of TON, as well as sharing mechanisms that allow the platform to process thousands of transactions per second. These technologies can be used to create educational applications requiring high bandwidth and data processing speed. The paper contains formulas that illustrate the technical characteristics of the TON blockchain and provides a detailed analysis of its architecture. The study shows that smart contracts on the TON platform have the potential to revolutionize educational processes by providing new tools for data management and security.