CSIT - 2025 рік

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https://elar.khmnu.edu.ua/handle/123456789/18099

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Approach to a decentralized, physician-oriented ehr architecture with cryptographic protection
(Хмельницький національний університет, 2025) Kysil, Volodymyr; Kysil, Tetiana
In 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.
Cyber-physical system for determining soil parameters
(Хмельницький національний університет, 2025) Voichur, Yurii; Payonk, Illya
The 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.
Method and cyber-physical system for forecasting and optimizing electricity consumption in residential districts
(Хмельницький національний університет, 2025) Pysmeniuk, Volodymyr; Levashenko, Vitaly
The 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.
Method for obtaining rotation-invariant image representation by removing orientation features from autoencoder latent space
(Хмельницький національний університет, 2025) Bedratiuk, Anna
In 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.
Method of operation of the cyber-physical water resources monitoring system
(Хмельницький національний університет, 2025) Voichur, Yurii; Balan, Andrii
The 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).
Mobile-oriented cyber-physical system for food allergen detection based on machine learning and image analysis
(Хмельницький національний університет, 2025) Talapchuk, Valentyn; Zaitseva, Elena
The 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.

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