Control, Navigation and Communication Systems. Academic Journal
type: channel
description: Journal represent the research results on the development and improvement of control, navigation and communication systems in various areas
publisher: Національний університет «Полтавська політехніка імені Юрія Кондратюка»
language: en
publication name: Control, Navigation and Communication Systems. Academic Journal
issn: 2073-7394
items: MULTI-CRITERIA EVALUATION OF SHIP MODERNIZATION STRATEGIES FOR CO2 EMISSION REDUCTION BASED ON THE TOPSIS METHOD
items: DATA PROCESSING METHODS IN A CORPORATE NETWORK
items: SYSTEMATIC ANALYSIS OF THE CAUSES OF NAVIGATIONAL INCIDENTS DURING SHIP MOORING AND WAYS TO IMPROVE THE INTERNATIONAL REGULATORY FRAMEWORK
items: RESEARCH OF FLIGHT PLANNING METHODS OF UNMANNED AERIAL VEHICLES IN COMPLEX FLIGHT CONDITIONS FOR MONITORING THE STATE OF CRITICAL INFRASTRUCTURE ELEMENTS
items: ENHANCING TRUSTWORTHINESS OF IOT-ENABLED AUTOMATED VEHICLE LOCALIZATION SYSTEMS
items: RESEARCH OF FACTORS OF INTERANNUAL AND INTRA-SEASONAL VARIABILITY OF THE MONSOON DEPRESSION IN THE INDIAN OCEAN
items: STUDY OF THE PARAMETERS OF THE TOTAL HARMONIC DISTORTION OF A CONTROL STATION WITH AN INPUT PHASE-SHIFT AUTOTRANSFORMER
items: DETERMINATION OF OPTIMAL DIESEL TRAIN MOTION ON A SECTION OF A ROUTE WITH A KNOWN PROFILE
items: PARALLEL IMPLEMENTATION OF VOICE SIGNAL PROCESSING METHODS ON MULTICORE CPU AND GPU
items: BLOCK CIPHER MODE BASED ON HUFFMAN CODING FOR NIBBLES
items: MODELS AND METHODS FOR BUILDING A DECENTRALIZED IOT SYSTEM – ESP32-BASED SENSORS USING THE MQTT PROTOCOL
items: METHODS OF DATA MINING USING MACHINE LEARNING
items: COMPARATIVE ANALYSIS OF REAL-TIME GESTURE RECOGNITION METHODS BASED ON MEDIAPIPE, OPENCV, AND YOLOV8
items: PRACTICAL APPLICATION AND VULNERABILITIES OF HILL CIPHER IN A MODERN CONTEXT
items: IMPROVING THE AI STOCK MARKET FORECASTING WITH CANDLESTICK PATTERNS
items: SOLVING PRODUCTIVITY PROBLEMS USING ASYNCHRONOUS METHODS IN JAVASCRIPT
items: ESTIMATIONS OF THE ASYMPTOTIC COMPLEXITY OF APPLIED MULTIPLE TRANSPORT ALGORITHMS WHEN DIVISIONING THEM INTO CLUSTERS
items: ADAPTIVE METHOD FOR DYNAMIC RESOURCE CONTROL OF THE INDUSTRIAL INTERNET OF THINGS BORDER LAYER
items: ANALYTICAL MODEL OF A DISTRIBUTED REGISTRY BASED ON THE CONCEPT OF CROWDING
items: RESEARCH OF DISTRIBUTED DATA EXCHANGE TECHNOLOGIES IN THE CONTEXT OF INTELLIGENT TRANSPORT SYSTEMS
items: STRUCTURAL AND FUNCTIONAL MODEL OF A CONVOLUTIONAL NEURAL NETWORK FOR PROCESSING, ANALYSING AND CLASSIFYING IMAGES OF VARYING COMPLEXITY
items: SHALLOW ANN MODELS TO CLASSIFY UKRAINIAN AI-GENERATED TEXT
items: TINYML NETWORK PROFILER IN BROWSER
items: INTELLIGENT APPROACH TO PLANNING TAKING INTO ACCOUNT THE CONCEPT OF ACCEPTABLE WORK BALANCE
items: STRATEGIC PLANNING IN THE CONTEXT OF COMBINED SOFTWARE TESTING
items: MODELS AND METHODS OF ARTIFICIAL INTELLIGENCE FOR DATA PROCESSING IN COMPUTER NETWORKS
items: MICROCONTROLLER-BASED INTELLIGENT LIGHTING CONTROL SYSTEM WITH ADAPTATION TO ENVIRONMENTAL CONDITIONS
items: USING CISCO SECUREX FOR SOC AUTOMATION
items: FUZZY ENSEMBLE OF DECISION TREES FOR THE COMPUTER SYSTEMS STATE IDENTIFICATION
items: USE OF VIRTUAL MEASURING DEVICES IN METROLOGY, ELECTRONICS AND ELECTRICAL MACHINES FOR THE TRAINING OF ELECTRICAL ENGINEERING SPECIALISTS

MULTI-CRITERIA EVALUATION OF SHIP MODERNIZATION STRATEGIES FOR CO2 EMISSION REDUCTION BASED ON THE TOPSIS METHOD
type: item
description: Relevance. In the context of implementing a decarbonization strategy for maritime transport, the task of selecting the optimal ship modernization strategy becomes particularly relevant. Such a strategy must reduce CO₂ emissions while maintaining economic viability and technical feasibility. Given the diversity of available alternatives—differing in cost, payback period, environmental impact, and risk—a comprehensive decision-making method is required. Object of research: a multicriteria model for evaluating ship modernization strategies. Purpose: to apply the TOPSIS method to determine the most effective ship modernization alternative, taking into account economic, environmental, and engineering factors. Results. The study analyzes five ship modernization strategies. An integrated efficiency indicator was calculated using five evaluation criteria. The analysis results show that the slow steaming strategy presents the most balanced profile. Conclusions. The TOPSIS model proved effective for ranking modernization alternatives based on multiple criteria and can be used to support informed decision-making in sustainable maritime transport project management.
subject: Road, river, sea and air transport
  topic: section
subject: maritime transport
  topic: keywords
subject: ship modernization
  topic: keywords
subject: CO₂ emissions
  topic: keywords
subject: slow steaming strategy
  topic: keywords
subject: multicriteria assessment
  topic: keywords
subject: energy efficiency
  topic: keywords
subject: environmental protection
  topic: keywords
subject: sustainable development
  topic: keywords
subject: environmental safety
  topic: keywords
subject: project management
  topic: keywords
creator: Mykola Bulgakov
creator: Oleh Lohinov
creator: Oleksii Melnyk
rights: Copyright (c) 2025 Mykola Bulgakov, Oleh Lohinov, Oleksii Melnyk http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 5
ending page: 9
doi: 10.26906/SUNZ.2025.3.005

DATA PROCESSING METHODS IN A CORPORATE NETWORK
type: item
description: Relevance. In the context of modern digital business transformation, corporate networks have become critically important components of enterprise information infrastructure, integrating numerous systems, devices, and services into a unified information environment. Every day, vast volumes of data are generated, transmitted, stored, and processed within these networks, encompassing all aspects of organizational activity – from financial operations and logistics to customer communications and internal management processes. The successful functioning of a corporate network is impossible without effective data processing, which enables the extraction of valuable insights from raw data, supports strategic decision-making, ensures information security, and optimizes resources. Amid intense competition, the rapid growth of information flows, and increasing demands for swift decision-making, data processing methods play a crucial role in enabling the analytical capabilities of enterprises. Data processing methods in corporate environments find applications across diverse business sectors. In the financial domain, they are used for transaction analysis, risk management, market fluctuation forecasting, and fraud detection. In logistics, they support route optimization, warehouse stock control, and real-time supply coordination. In marketing, they facilitate customer behavior analysis, audience segmentation, and personalized communication. In HR departments, they assist in employee performance evaluation and candidate selection. In healthcare, they support patient data analysis, service process optimization, and clinical decision-making. Furthermore, data processing forms the foundation for the development of artificial intelligence systems, digital twins, cybersecurity solutions, and automated enterprise management. Traditional approaches to data processing are gradually being replaced by advanced technologies based on Big Data, real-time stream processing, cloud computing, i…
subject: Information Technology
  topic: section
subject: corporate network
  topic: keywords
subject: data processing
  topic: keywords
subject: architecture
  topic: keywords
subject: transaction
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: Big Data
  topic: keywords
creator: Dmytro Diachenko
creator: Matvii Korobeinikov
creator: Oleksandr Korobeinikov
creator: Andriy Kovalenko
creator: Pavlo Kravchenko
rights: Copyright (c) 2025 Dmytro Diachenko, Matvii Korobeinikov, Oleksandr Korobeinikov, Andriy Kovalenko, Pavlo Kravchenko http://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 81
ending page: 86
doi: 10.26906/SUNZ.2025.3.081

SYSTEMATIC ANALYSIS OF THE CAUSES OF NAVIGATIONAL INCIDENTS DURING SHIP MOORING AND WAYS TO IMPROVE THE INTERNATIONAL REGULATORY FRAMEWORK
type: item
description: Relevance. Due to the increasing size of merchant ships and the load on port infrastructure, the issue of ensuring safety during the mooring process is becoming strategically important. Frequent incidents in ports around the world indicate the imperfection of existing practices and the fragmentation of the implementation of modern risk management methods, in particular Bridge Resource Management (BRM). The object of study is navigation incidents during mooring of large-tonnage vessels. Purpose - to systematically analyze the factors that cause mooring incidents and to formulate recommendations for improving the international regulatory framework and risk management procedures in ports. Methodology. The study uses comparative analysis of incidents, expert risk assessment, classification of causes based on a factor model, and analysis of international IMO regulations (SOLAS, STCW, MSC.255(84), etc.). Results. The study confirmed that the key causes are excessive approach speed, inconsistency of towing support actions, human factor, and lack of standardized BRM procedures on the approach to the berth. Comparison of practices in different countries has shown uneven implementation of simulator training and intelligent decision support systems. Conclusions. The paper provides recommendations for the harmonization of regional practices that promote the development of decision support systems and the introduction of simulator training for ship crews. The expediency of integrating BRM in the port and developing adaptive risk management protocols, taking into account the real state of infrastructure provision, is proved.
subject: Road, river, sea and air transport
  topic: section
subject: maritime transport
  topic: keywords
subject: mooring of ships
  topic: keywords
subject: ship control
  topic: keywords
subject: maneuvering
  topic: keywords
subject: incidents
  topic: keywords
subject: navigation safety
  topic: keywords
subject: international conventions
  topic: keywords
subject: human factor
  topic: keywords
subject: port infrastructure
  topic: keywords
subject: risk management
  topic: keywords
subject: bridge resource management (BRM)
  topic: keywords
creator: Sergiy Voliansky
creator: Yuriy Buchkovsky
creator: Oleksiy Melnyk
creator: Andriy Voloshyn
rights: Copyright (c) 2025 Sergiy Voliansky, Yuriy Buchkovsky, Oleksiy Melnyk, Andriy Voloshyn http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 5
ending page: 11
doi: 10.26906/SUNZ.2025.4.005

RESEARCH OF FLIGHT PLANNING METHODS OF UNMANNED AERIAL VEHICLES IN COMPLEX FLIGHT CONDITIONS FOR MONITORING THE STATE OF CRITICAL INFRASTRUCTURE ELEMENTS
type: item
description: Relevance. The vast majority of modern UAV flight path planning algorithms designed for real-time onboard application do not take into account UAV dynamics, which negatively affects the accuracy and optimality of route planning, especially when tracking moving objects. Object of research: processes of monitoring critical infrastructure elements with UAVs. Purpose of the article. evaluation of methods for flight planners of unmanned aerial vehicles in difficult flight conditions for monitoring the condition of critical infrastructure elements. Research results. The article presents a comparative analysis of existing methods for constructing a UAV flight route during the observation of objects - elements of critical infrastructure objects, namely the exhaustive search method and the greedy algorithm with the improved algorithm proposed by the author. When modeling the emulation of the UAV flight using the MATLAB package, twenty scenarios were developed. For each scenario, a list of five objects to be monitored was selected. The objects were selected as dynamic and stationary. Based on the analysis of the considered scenarios, conclusions were formulated about the effectiveness of the improved method. Conclusions. The routes constructed using the improved method completely coincided with the routes constructed using the exhaustive search method. At the same time, the calculation time is significantly lower than the existing methods, which allows using the improved algorithm as part of the on-board software complex and quickly constructing and changing the route depending on the situation with respect to the monitored objects. Using an identifier in the control system circuit to compensate for the influence of the wind allows reducing the flight time along the route by 15%.
subject: Road, river, sea and air transport
  topic: section
subject: unmanned aerial vehicle
  topic: keywords
subject: routing
  topic: keywords
subject: monitoring
  topic: keywords
subject: critical infrastructure objects
  topic: keywords
subject: route planning
  topic: keywords
creator: Ruslan Kulish
rights: Copyright (c) 2025 Ruslan Kulish http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 12
ending page: 16
doi: 10.26906/SUNZ.2025.4.012

ENHANCING TRUSTWORTHINESS OF IOT-ENABLED AUTOMATED VEHICLE LOCALIZATION SYSTEMS
type: item
description: Relevance. Autonomous vehicles rely on multi-sensor localization systems operating within IoT infrastructures, creating interconnected vulnerabilities from sensor anomalies, network failures, and cybersecurity threats that require comprehensive solutions addressing both vehicle-level and infrastructure-level reliability challenges. The object of research is IoT-enabled automated vehicle localization systems requiring trustworthy operation under adverse conditions, including sensor malfunctions, GPS spoofing attacks, and infrastructure failures. Purpose of the article is to develop and validate a unified resilience framework that integrates transformer-based anomaly detection for in-vehicle sensor streams with federated learning agents deployed across IoT edge gateways, ensuring sub-second recovery from infrastructure failures while maintaining localization accuracy. Research results. The proposed framework achieves 9498% anomaly detection accuracy while maintaining localization errors below 0.5 meters during fault conditions. The federated learning component demonstrates 40% reduced communication overhead compared to centralized approaches, with sub-second failover capabilities during infrastructure failures. Explainable ML integration provides interpretable alerts through transformer attention mechanisms, enabling real-time system diagnostics. Conclusions. The unified framework successfully addresses critical challenges in autonomous vehicle deployment by combining multi-layer anomaly detection, coherent reliability broadcasting, and explainable AI techniques, providing a comprehensive foundation for trustworthy autonomous vehicle operation in IoT-enabled smart city environments.
subject: Road, river, sea and air transport
  topic: section
subject: autonomous vehicles
  topic: keywords
subject: IoT reliability
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: federated learning
  topic: keywords
subject: sensor fusion
  topic: keywords
subject: cybersecurity
  topic: keywords
creator: Olena Sevostianova
creator: Nataliia Kosenko
creator: Vladlen Filippov
creator: Maksym Diachenko
creator: Ivan Kharakhaichuk
rights: Copyright (c) 2025 Olena Sevostianova , Nataliia Kosenko , Vladlen Filippov , Maksym Diachenko , Ivan Kharakhaichuk http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 17
ending page: 21
doi: 10.26906/SUNZ.2025.4.017

RESEARCH OF FACTORS OF INTERANNUAL AND INTRA-SEASONAL VARIABILITY OF THE MONSOON DEPRESSION IN THE INDIAN OCEAN
type: item
description: Monsoon depressions (MDs) – areas of low pressure with cyclonic circulation that form over the Bay of Bengal - have a significant impact on weather conditions in the northern Indian Ocean during the summer monsoon. They are characterised by gale-force winds, high waves, intense cloud cover and heavy rainfall, posing a serious threat to maritime navigation and coastal infrastructure in the region. The recurrence and intensity of MDs are subject to significant fluctuations in time over a wide range of scales, from intra-seasonal to inter-decadal. Understanding the factors that control the variability of the MD regime is essential to reduce risks and losses in the maritime transport sector, which plays a key role in the global economy. In this paper, based on a comprehensive analysis of reanalysis data, observations, and numerical experiments, we investigate the large-scale dynamic and thermodynamic factors that determine the MD regime on interannual and intra-seasonal time scales. It is shown that changes in atmospheric circulation and ocean surface temperature in recent decades have contributed to a decrease in the number of MDs over the Bay of Bengal. At the same time, the results of regional model experiments indicate a possible increase in the frequency of the MDs in the context of the ongoing warming of the World Ocean. The analysis of intra-seasonal variations revealed statistically significant links between the MD activity and fluctuations in the intensity and position of the Intratropical Convergence Zone (ICZ). The quantitative estimates obtained in this work provide a basis for improving the quality of weather forecasts on sea lanes in the northern Indian Ocean and optimising strategies to minimise weather risks for the maritime industry.
subject: Road, river, sea and air transport
  topic: section
subject: monsoon depression
  topic: keywords
subject: monsoon circulation
  topic: keywords
subject: cyclogenesis
  topic: keywords
subject: interannual variability
  topic: keywords
subject: intra-seasonal variations
  topic: keywords
subject: weather risks
  topic: keywords
subject: intratropical convergence zone
  topic: keywords
subject: remote sensing
  topic: keywords
creator: Georgiy Tomchakovsky
rights: Copyright (c) 2025 Georgiy Tomchakovsky http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 22
ending page: 26
doi: 10.26906/SUNZ.2025.4.022

STUDY OF THE PARAMETERS OF THE TOTAL HARMONIC DISTORTION OF A CONTROL STATION WITH AN INPUT PHASE-SHIFT AUTOTRANSFORMER
type: item
description: When developing minerals in Ukraine, such as oil, mechanized extraction methods are used, using submersible electric motors. Such extraction methods provide high productivity and the ability to adjust to the current parameters of the well, which in turn provides maximum flow rate. To ensure the optimal operation of the pump, frequency-controlled submersible electric motor control stations are used, which consume non-sinusoidal current from the power supply network during operation. Also, to reduce distortions of the consumed current, various methods of reducing the total harmonic distortion (THDi) of input currents are used. To solve this problem, a control station with a multi-pulse rectifier powered by a phaseshifting autotransformer was developed, which allows you to reduce the THDi of input currents. This solution allows you to improve the overall performance of the equipment and its cost by reducing the power of the phase-shifting transformer. The functional capabilities of the electric drive control station are considered based on experimental data of measuring THDi of input currents of the network +30% in the frequency range of 47.5-52.5Hz. This frequency range is due to the fact that in many cases, in the fields, work is carried out with power supply from diesel generators, in which the fluctuations of frequency and supply voltage are in a significant range. Additionally, the possibility of drive operation at input supply voltages of 460-480V and 430460V is investigated. The measurement results are analyzed, and recommendations are proposed for increasing the energy efficiency of the station.
subject: Management in Complex Systems
  topic: section
subject: frequency converter
  topic: keywords
subject: control station
  topic: keywords
subject: energy efficiency
  topic: keywords
subject: THDi
  topic: keywords
subject: multi-pulse rectifier
  topic: keywords
subject: phase-shifting transformer
  topic: keywords
creator: Dmytro Gapon
creator: Petro Kachanov
creator: Andrey Olshevskiy
creator: Evgeniy Petrik
rights: Copyright (c) 2025 Dmytro Gapon, Petro Kachanov, Andrey Olshevskiy, Evgeniy Petrik http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 27
ending page: 31
doi: 10.26906/SUNZ.2025.4.027

DETERMINATION OF OPTIMAL DIESEL TRAIN MOTION ON A SECTION OF A ROUTE WITH A KNOWN PROFILE
type: item
description: Relevance. Railway transport is one of the main consumers of energy resources, therefore, the development and implementation of energy-saving technologies is a relevant task, the solution of which has recently become one of the priority areas of activity for specialists in both fundamental and applied science in this field. Object of research: the processes of controlling the traction, dynamic and energy characteristics of multiple-unit rolling stock. Purpose of the article. To solve the problem of optimal control of a diesel train’s movement along a section of the track with a known profile, taking into account optimal fuel consumption and compliance with the traffic schedule. Research results. The article formulates and solves the problem of synthesizing a control system for the optimal movement of a diesel train with a known track profile using the terminal control method. The following were applied: the theory of train traction and traction electric drive, methods of system analysis and optimal control theory, mathematical modeling and computational experiments. Conclusions. An algorithm for optimal control of a diesel train has been developed, with the help of which program laws have been determined that establish the dependence of traction on the train’s mass, track parameters, and technical and economic conditions (speed, acceleration, traffic schedule) taking fuel consumption into account.
subject: Management in Complex Systems
  topic: section
subject: multiple unit rolling stock
  topic: keywords
subject: optimal train movement
  topic: keywords
subject: terminal controls
  topic: keywords
subject: control system synthesis
  topic: keywords
subject: mathematical modeling
  topic: keywords
subject: railway track profile
  topic: keywords
creator: Valentin Noskov
creator: Maksym Lipchanskyi
creator: Volodymyr Panchenko
creator: Hennadii Heiko
rights: Copyright (c) 2025 Valentin Noskov, Maksym Lipchanskyi, Volodymyr Panchenko, Hennadii Heiko http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 32
ending page: 38
doi: 10.26906/SUNZ.2025.4.032

PARALLEL IMPLEMENTATION OF VOICE SIGNAL PROCESSING METHODS ON MULTICORE CPU AND GPU
type: item
description: Relevance. Systems such as voice assistants and speaker identification tools, which operate based on voice signal processing, have become increasingly widespread. The performance of such systems depends on the volume of data and operating conditions. Processing large collections of voice signals or ensuring real-time operation requires high-performance computing. Such performance can be achieved with massively parallel systems, including multiprocessor clusters or discrete GPUs. Object of research is the organisation of parallel computing processes in voice signal processing tasks using the capabilities of modern processor architectures. Purpose of the article is to develop a parallel voice signal processing system with adapted algorithms for a multi-core CPU and an integrated and discrete GPU. Research results. Comparative analysis revealed that for small loads (such as voice assistants and personal applications), CPU usage is sufficient to ensure efficient computation with low latency. However, when processing large datasets and performing streaming analytics, the proposed parallel approach, implemented on both the CPU and GPU, reduces execution time by 25-30% compared to a sequential implementation on the CPU. Conclusions. Research has shown that parallelism on the CPU is suitable for stages of voice signal processing that require a small amount of computation. At the same time, discrete GPUs can be utilised in stages with intensive computational tasks such as MFCC calculation, spectral subtraction, and wavelet filtering.
subject: Information Technology
  topic: section
subject: voice identification
  topic: keywords
subject: signal processing
  topic: keywords
subject: feature extraction
  topic: keywords
subject: normalisation
  topic: keywords
subject: MFCC
  topic: keywords
subject: spectral subtraction and wavelet filtering
  topic: keywords
subject: CPU
  topic: keywords
subject: GPU
  topic: keywords
subject: iGPU
  topic: keywords
creator: Maksym Bondarenko
creator: Heorhii Ivashchenko
rights: Copyright (c) 2025 Maksym Bondarenko, Heorhii Ivashchenko http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 39
ending page: 44
doi: 10.26906/SUNZ.2025.4.039

BLOCK CIPHER MODE BASED ON HUFFMAN CODING FOR NIBBLES
type: item
description: Relevance. The relevance of this work is driven by the need for cryptographic solutions that combine high security, efficient compression, and low computational overhead, which is particularly important for IoT, mobile, and embedded systems. The purpose of the article is to research the development and analysis of a block cipher mode based on Huffman coding for nibbles, with integrated hash protection mechanisms and support for both global and per-block processing. Object of research: the use of Huffman transformation of half-bytes (nibbles) to create a block cipher mode. Research results. Experiments have shown that for data with low nibble entropy (H < 3.5 bits/nibble), the proposed mode reduces the size of encrypted data by 10– 20% without significant performance loss, ensuring error resistance and integrity preservation. Conclusion: the integration of statistical coding and symmetric encryption with adaptive parameter selection provides a balanced solution for secure and efficient information transmission under resource constraints.
subject: Information Technology
  topic: section
subject: block cipher
  topic: keywords
subject: Huffman coding
  topic: keywords
subject: data compression
  topic: keywords
subject: cryptography
  topic: keywords
subject: symmetric algorithms
  topic: keywords
creator: Maksym Glavchev
creator: Yuliia Hlavcheva
creator: Heorhii Molchanov
rights: Copyright (c) 2025 Maksym Glavchev, Yuliia Hlavcheva, Heorhii Molchanov http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 45
ending page: 50
doi: 10.26906/SUNZ.2025.4.045

MODELS AND METHODS FOR BUILDING A DECENTRALIZED IOT SYSTEM – ESP32-BASED SENSORS USING THE MQTT PROTOCOL
type: item
description: Relevance. The relevance is due to the growing interest in ESP32-based IoT systems, the limitations of centralized systems, and the potential of the MQTT protocol. Article subject. Methods for implementing decentralized WSNs and their components. Purpose. The article's main purpose is to propose a decentralized architecture for Wireless Sensor Networks (WSNs). It aims to shift from the traditional centralized model, where all sensor nodes depend on a single hub, to a more resilient system where each node possesses direct internet access. The goal of this proposed design is to significantly boost the reliability and overall productivity of the network by eliminating the single point of failure inherent in a centralized setup. Article results. The article culminates in a specific and cost-effective recommendation of components to build a decentralized sensor node. These include: the ESP32 module, communication modules, a development environment, a data transfer protocol, and brokers to ensure its operation. Conclusions. Based on an analysis of available hardware and software, the article concludes that building a reliable and cost-effective decentralized Wireless Sensor Network (WSN) is highly feasible.
subject: Information Technology
  topic: section
subject: Decentralization
  topic: keywords
subject: WSN
  topic: keywords
subject: ESP32
  topic: keywords
subject: MQTT
  topic: keywords
subject: Wi-Fi
  topic: keywords
subject: 2G
  topic: keywords
subject: LTE
  topic: keywords
subject: Ethernet
  topic: keywords
creator: Oleh Drozd
creator: Oleksii Sytnyk
creator: Maksym Nesterenko
rights: Copyright (c) 2025 Oleh Drozd, Oleksii Sytnyk, Maksym Nesterenko http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 51
ending page: 55
doi: 10.26906/SUNZ.2025.4.051

METHODS OF DATA MINING USING MACHINE LEARNING
type: item
description: Relevance . In the context of the continuous growth of information volumes across various fields of the economy, science, and technology, the problem of effective processing and analysis of large-scale data has become increasingly urgent. Traditional analytical methods are no longer capable of providing fast and accurate extraction of useful knowledge and patterns from massive information flows. The response to this challenge lies in the methods of data mining, which are based on modern machine learning technologies. These methods enable the automatic discovery of hidden patterns, the generation of accurate predictions, and the support of data-driven decision-making in real time. Given the rapid development of digitalization, artificial intelligence, and the need for prompt decision-making in a competitive environment, the relevance of developing and improving data mining methods is growing steadily. The object of research is process of data mining using machine learning methods, namely the set of algorithms, models, tools, and approaches that ensure the detection of hidden patterns, anomalies, and structures in large volumes of heterogeneous information. Purpose of the article. This study explores contemporary approaches to intelligent data analysis based on machine learning techniques and assesses their effectiveness across a range of application domains. The article aims to provide a structured overview of state-of-the-art algorithms and to evaluate their respective advantages and limitations in processing large-scale and highdimensional datasets. Research results. A systematic analysis of key data mining methods based on machine learning algorithms was carried out. It was found that the most effective approaches for processing large and heterogeneous datasets include classification, clustering, regression analysis, and dimensionality reduction techniques. Deep neural networks demonstrated effectiveness when applied to unstructured data such as text, images, and …
subject: Information Technology
  topic: section
subject: intelligent data analysis
  topic: keywords
subject: machine learning
  topic: keywords
subject: classification
  topic: keywords
subject: clustering
  topic: keywords
subject: regression
  topic: keywords
subject: deep learning
  topic: keywords
subject: neural networks
  topic: keywords
subject: analytical tools
  topic: keywords
subject: Data Mining
  topic: keywords
subject: CRISP-DM
  topic: keywords
subject: AutoML
  topic: keywords
subject: Big Data
  topic: keywords
subject: Python
  topic: keywords
creator: Dmytro Diachenko
creator: Mykhailo Prokopchyk
creator: Vladyslav Rovenchak
creator: Andriy Frolov
rights: Copyright (c) 2025 Dmytro Diachenko, Mykhailo Prokopchyk, Vladyslav Rovenchak, Andriy Frolov http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 56
ending page: 61
doi: 10.26906/SUNZ.2025.4.056

COMPARATIVE ANALYSIS OF REAL-TIME GESTURE RECOGNITION METHODS BASED ON MEDIAPIPE, OPENCV, AND YOLOV8
type: item
description: The subject of the article is real-time gesture recognition methods based on computer vision, designed for integration into human-computer interaction (HCI) systems, particularly for device control via a webcam. The purpose of the work is a comparative analysis of the efficiency of three modern methods MediaPipe, OpenCV, and YOLOv8 by evaluating their performance using key metrics (FPS and Detection Rate) for detecting basic gestures. The article solves the following tasks: identifying the impact of external factors (lighting, background) on gesture recognition, implementing algorithms for three basic gestures (open palm, index finger up and down), conducting experimental testing and modeling in the Python environment. The following methods are used: computer vision and image processing (in particular, segmentation, keypoint tracking, and object detection); machine learning based on CNN (LeNet, YOLO); analysis of datasets and performance metrics (precision, recall, mAP); simulation modeling in real conditions using the libraries mediapipe, opencv-python, and ultralytics. The following results were obtained: a comparative analysis of the methods was conducted, where MediaPipe provided the highest accuracy (95% Detection Rate), OpenCV—the maximum speed (50.7 FPS), and YOLOv8—a balance for limited resources (73% Detection Rate); recommendations for hybrid approaches to optimization were proposed. Conclusions: The developed comparative analysis of real-time gesture recognition methods demonstrates that MediaPipe effectively eliminates interference from variable lighting and background, achieving a stable accuracy of 95%, while OpenCV optimizes processing speed to 50.7 FPS. Modeling was performed in Python with visualization of the results.
subject: Information Technology
  topic: section
subject: gesture recognition
  topic: keywords
subject: computer vision
  topic: keywords
subject: MediaPipe
  topic: keywords
subject: OpenCV
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: FPS
  topic: keywords
subject: Detection Rate
  topic: keywords
creator: Olha Yeroshenko
creator: Vadym Tsipkovskyi
rights: Copyright (c) 2025 Olha Yeroshenko, Vadym Tsipkovskyi http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 62
ending page: 65
doi: 10.26906/SUNZ.2025.4.062

PRACTICAL APPLICATION AND VULNERABILITIES OF HILL CIPHER IN A MODERN CONTEXT
type: item
description: The article examines the Hill cipher as a classical example of applying linear algebra and modular arithmetic in cryptography. It elucidates the mathematical foundations of the algorithm, including the formation of the key matrix over finite fields, the conditions for its invertibility, and the implementation of encryption as the multiplication of plaintext vectors by the key matrix. This approach is presented as a distinctive “bridge between theory and practice,” combining formal mathematical constructions with practical data protection mechanisms. Particular attention is given to the vulnerabilities of the Hill cipher. It is shown that the algorithm is insecure in modern contexts due to the linearity of its transformations and the absence of nonlinear components. Examples of cryptanalytic attacks are provided, demonstrating that the cipher can be “broken apart” even with a minimal number of plaintext-ciphertext pairs. This makes it evident that, in the context of 21st-century cybersecurity, the Hill cipher is merely a “shadow of its era” and cannot serve as a fully reliable tool for data protection. At the same time, the educational and methodological value of the cipher is emphasized. It allows one to “see the mechanism from the inside,” clearly illustrating the transition from monoalphabetic ciphers to block transformations. The article highlights that the Hill cipher played the role of a “first step” in the development of structures that later evolved into DES and AES, where the use of block structures and matrix operations became a fundamental element of modern cryptography.
subject: Information Technology
  topic: section
subject: cyber security
  topic: keywords
subject: cyber threats
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: cryptography
  topic: keywords
subject: cryptanalysis
  topic: keywords
subject: cipher
  topic: keywords
creator: Yevhen Zhyvylo
creator: Yurii Kuchma
rights: Copyright (c) 2025 Yevhen Zhyvylo, Yurii Kuchma http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 66
ending page: 73
doi: 10.26906/SUNZ.2025.4.066

IMPROVING THE AI STOCK MARKET FORECASTING WITH CANDLESTICK PATTERNS
type: item
description: In the rapidly evolving digital economy, the application of Artificial Intelligence (AI) in financial forecasting has gained significant traction. This study investigates the effect of various candlestick patterns on the performance of Long Short-Term Memory (LSTM) models in predicting stock market movements. Experiments conducted on the stock price history data demonstrate that supplementing traditional input parameters (e.g., open price) with a range of candlestick patterns enhances the predictive accuracy of LSTM models. Although the initial model architecture lacked hyperparameter optimization for solving this kind of task, our findings suggest notable improvement in prediction performance when candlestick pattern flags are incorporated. Future work will focus on incorporating additional financial indicators into the model's training data and fine-tuning it through optimization algorithms to achieve greater robustness and accuracy.
subject: Information Technology
  topic: section
subject: artificial intelligence
  topic: keywords
subject: LSTM model efficiency
  topic: keywords
subject: stock market prediction
  topic: keywords
subject: candlestick patterns
  topic: keywords
subject: improving AI model accuracy
  topic: keywords
creator: Oleksandr Zakovorotnyi
creator: Nataliia Ausheva
creator: Larysa Levchenko
rights: Copyright (c) 2025 Oleksandr Zakovorotnyi, Nataliia Ausheva, Larysa Levchenko http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 74
ending page: 77
doi: 10.26906/SUNZ.2025.4.074

SOLVING PRODUCTIVITY PROBLEMS USING ASYNCHRONOUS METHODS IN JAVASCRIPT
type: item
description: The Data-Driven approach, in which the interface is defined as a function of the current state (UI = f(state)), has become the dominant practice in front-end development. Despite the convenience of the declarative approach, the complexity of systems has led to the identification of a number of architectural shortcomings. The main ones are the loss of transparency of the sources of changes, asynchronous conflicts when updating the state, and the lack of semantic context of events. The article compares the Data-Driven and Event-Driven approaches, explores the key problems of the former, and offers practical solutions that can improve control over application logic. The purpose of this work is to identify typical architectural shortcomings inherent in the Data-Driven model in client-side development, analyze the causes of their occurrence, and develop conceptual and technical ways to minimize the corresponding risks. Special attention is paid to the problems of implicit state mutation, loss of change context, query synchronization, and dependence on the component life cycle in framework-oriented systems. The following results were obtained: As a result of the study, it was found that the Data-Driven approach in complex applications does not provide sufficient control over the sources of changes. It was also proven that even when using tools such as Redux DevTools or React Developer Tools, the developer often does not have a complete picture of state changes, since they occur at different points in the system without a single control path. It was found that the most effective compensatory approaches are the creation of a semantic event layer, centralization of mutations, and the combination of reactive modeling with declarative representation. Conclusions. Data-Driven architecture significantly simplifies UI construction in the conditions of simple or medium-complexity projects. However, with an increase in the number of state sources, the complexity of the relationship…
subject: Information Technology
  topic: section
subject: Data-Driven
  topic: keywords
subject: Event-Driven
  topic: keywords
subject: frontend
  topic: keywords
subject: state
  topic: keywords
subject: mutation
  topic: keywords
subject: asynchrony
  topic: keywords
subject: reactivity
  topic: keywords
creator: Oleksandr Zakovorotnyi
creator: Oleskandr Sapalskyi
rights: Copyright (c) 2025 Oleksandr Zakovorotnyi, Oleskandr Sapalskyi http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 78
ending page: 81
doi: 10.26906/SUNZ.2025.4.078

ESTIMATIONS OF THE ASYMPTOTIC COMPLEXITY OF APPLIED MULTIPLE TRANSPORT ALGORITHMS WHEN DIVISIONING THEM INTO CLUSTERS
type: item
description: Applied multiple transport algorithms are usually heuristic and overly complex. Depending on the chosen heuristic of the algorithm, the real time of the software implementation depends on the programming language, the structure of the input data representation and the algorithmic complexity of the solution. The research is based on the use of the provisions of discrete mathematics and graph theory. It is shown that by analyzing asymptotic complexity it is possible to determine effective heuristics. Refinement of applied problem models also contributes to reducing complexity. Since the multiplicity of transport tasks is contained in a discrete coverage or in the functionality of multiple vehicles, it is possible to simplify the algorithm by decomposing them into clusters for which the conditions and constraints of the standard transport task apply. The features of geometric and combinatorial decomposition into clusters are presented. The formalization of cluster construction algorithms for multiple transport problems is provided. A comparative analysis of optimal and heuristic algorithms for solving multiple transport problems is carried out. The hierarchy of asymptotic complexity of heuristic algorithms is determined and the possibilities of their decomposition into clusters are analyzed, which simplify the solution of multiple applied transport problems. The notation of the complexity of the solution and the possibility of their evaluation by the objective function of the selected algorithm or by its approximation are provided. An analysis of the asymptotic complexity of heuristic algorithms for Big-O notation has been carried out. It has been shown that algorithms with a complexity not exceeding polynomial are suitable for implementation, and the use of algorithms with an exponential complexity estimate and higher is undesirable. It is noted that asymptotic estimates of algorithm complexity are suitable for large-dimensional tasks, and for tasks of small dimension…
subject: Information Technology
  topic: section
subject: asymptotic complexity
  topic: keywords
subject: heuristics
  topic: keywords
subject: clusters
  topic: keywords
subject: logistics
  topic: keywords
subject: multiple transportation tasks
  topic: keywords
subject: notations
  topic: keywords
subject: emergency care system
  topic: keywords
subject: vehicles
  topic: keywords
subject: objective function
  topic: keywords
creator: Volodymyr Karlov
creator: Oleksii Kolomiitsev
creator: Oleksandr Kuznietsov
creator: Oksana Biesova
creator: Anna Biesova
rights: Copyright (c) 2025 Volodymyr Karlov, Oleksii Kolomiitsev, Oleksandr Kuznietsov, Oksana Biesova, Anna Biesova http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 82
ending page: 87
doi: 10.26906/SUNZ.2025.4.082

ADAPTIVE METHOD FOR DYNAMIC RESOURCE CONTROL OF THE INDUSTRIAL INTERNET OF THINGS BORDER LAYER
type: item
description: In the process of functioning of the Industrial Internet of Things, the shortage of resources of the boundary layer manifests itself as an increase in processing delays, queues, data loss or service degradation. The purpose of this work is to develop an adaptive method for dynamic resource management of the IIoT boundary layer, which will allow for the effective use of computing resources.. The following results were obtained: A conceptual adaptive method for dynamic resource management of the boundary layer in IIoT using a multi-agent approach and horizontal and vertical scaling is proposed. Monitoring, evaluation, decision-making and learning methods are integrated into a single architecture. The developed mathematical model allows formalizing the balance of queues, resources, and task transfer between nodes. The method provides flexible adaptation to variable load and minimizes losses, delays, and also effectively uses limited resources. Conclusions. Adaptive management of computing resources of the edge layer of the IoT allows to increase the efficiency of the system and reduce the impact of resource shortages.
subject: Information Technology
  topic: section
subject: Industrial Internet of Things
  topic: keywords
subject: edge layer
  topic: keywords
subject: computing resources
  topic: keywords
subject: adaptive control
  topic: keywords
creator: Sergii Klivets
creator: Alexander Kuleshov
creator: Tetiana Kulieshova
rights: Copyright (c) 2025 Sergii Klivets, Alexander Kuleshov, Tetiana Kulieshova http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 88
ending page: 91
doi: 10.26906/SUNZ.2025.4.088

ANALYTICAL MODEL OF A DISTRIBUTED REGISTRY BASED ON THE CONCEPT OF CROWDING
type: item
description: The main problem in the formation of a registry in distributed systems is to determine the dependence between the parameters of the request flow, the speed of transaction service and the general characteristics of the system performance. The purpose of the study: to build an analytical model of a distributed registry, which allows describing the transaction processing process in terms of the theory of queueing and determining the key indicators of the system's efficiency. Results. The article proposes an analytical model of a distributed registry, built on the basis of the theory of queueing. The model describes the processes of recording, verification and confirmation of transactions in a decentralized environment, taking into account the limitations of the node's bandwidth and the intensity of incoming requests. The use of a queuing apparatus allows us to formalize the time characteristics of the system, estimate the average waiting time for transaction processing, the load factor of nodes, and the probability of delay. An analytical study of the influence of network parameters on the productivity of the registry has been conducted and the conditions for system stability have been determined. The proposed approach can be used to optimize the architecture of distributed systems with increased requirements for speed and reliability of data processing.
subject: Information Technology
  topic: section
subject: distributed registry
  topic: keywords
subject: queuing
  topic: keywords
subject: analytical model
  topic: keywords
subject: transaction
  topic: keywords
subject: system productivity
  topic: keywords
subject: stability
  topic: keywords
creator: Andriy Kovalenko
creator: Ivan Zamriy
creator: Volodymyr Popov
creator: Dmytro Zharikov
rights: Copyright (c) 2025 Andriy Kovalenko, Ivan Zamriy, Volodymyr Popov, Dmytro Zharikov http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 92
ending page: 97
doi: 10.26906/SUNZ.2025.4.092

RESEARCH OF DISTRIBUTED DATA EXCHANGE TECHNOLOGIES IN THE CONTEXT OF INTELLIGENT TRANSPORT SYSTEMS
type: item
description: The article presents the results of a study of distributed data exchange technologies aimed at ensuring effective interaction between components of intelligent transport systems (ITS). Modern approaches to the organization of decentralized information transmission networks are considered, in particular, the use of the concepts of distributed registries, peer-to-peer protocols, and service-oriented architectures. An analysis of the requirements for reliability, latency, and bandwidth of communication channels, which are critical for data exchange scenarios between vehicles and infrastructure, is conducted. Based on a comparative analysis, the advantages and limitations of existing technological solutions in the context of ensuring security, scalability, and fault tolerance are determined. The results of the study can be used to design adaptive traffic management models and develop intelligent interaction modules in new generation transport networks.
subject: Information Technology
  topic: section
subject: intelligent transportation systems
  topic: keywords
subject: distributed data exchange
  topic: keywords
subject: decentralized networks
  topic: keywords
subject: distributed ledger
  topic: keywords
subject: scalability
  topic: keywords
subject: data transmission reliability
  topic: keywords
creator: Nina Kuchuk
creator: Maksym Tregubenko
creator: Danylo Kovalenko
creator: Dmytro Lysytsia
creator: Oleksandra Bellorin-Herrera
rights: Copyright (c) 2025 Nina Kuchuk , Maksym Tregubenko , Danylo Kovalenko , Dmytro Lysytsia , Oleksandra Bellorin-Herrera http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 98
ending page: 102
doi: 10.26906/SUNZ.2025.4.098

STRUCTURAL AND FUNCTIONAL MODEL OF A CONVOLUTIONAL NEURAL NETWORK FOR PROCESSING, ANALYSING AND CLASSIFYING IMAGES OF VARYING COMPLEXITY
type: item
description: The article presents presents a structural and functional model of a convolutional neural network (CNN) designed for processing, analysing and classifying images of varying complexity. The model is based on a multi-level architecture using convolutional, residual and parallel computing blocks, which ensure high adaptability to different types of input data. The input tensor is normalised by the mean and standard deviation of the sample, which reduces data variability and stabilises the learning process. The first convolutional layer performs the initial extraction of image features with ELU activation, which ensures continuity of gradients and eliminates the problem of ‘dead neurons.’ The implementation of skip connections ensures the consistency of information flows and increases the stability of training. The results demonstrate increased classification accuracy and noise resistance compared to basic CNN architectures.
subject: Information Technology
  topic: section
subject: convolutional neural network
  topic: keywords
subject: image processing
  topic: keywords
subject: classification
  topic: keywords
subject: feature analysis
  topic: keywords
subject: structural-functional model
  topic: keywords
subject: deep learning
  topic: keywords
creator: Oleksandr Mozhaiev
creator: Heorhii Kuchuk
creator: Renat Safarov
creator: Maksym Lavrovskyi
creator: Kostiantyn Moroz
rights: Copyright (c) 2025 Oleksandr Mozhaiev , Heorhii Kuchuk , Renat Safarov , Maksym Lavrovskyi , Kostiantyn Moroz http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 103
ending page: 107
doi: 10.26906/SUNZ.2025.4.103

SHALLOW ANN MODELS TO CLASSIFY UKRAINIAN AI-GENERATED TEXT
type: item
description: In this study, we address the task of detecting AI-generated fragments within Ukrainian-language texts. The objective is to develop a tool capable of identifying content produced with the assistance of artificial intelligence, particularly in PDF documents related to the IT domain. The research explores and analyzes existing solutions and approaches currently available in this area. Several commercial AI-content detectors were evaluated using our custom datasets. The dataset was constructed by segmenting bachelor's theses from IT-related fields into fragments of approximately 1,000 characters each. Five artificial neural network models were tested using the custom dataset combined with a traditional NLP pipeline, achieving an accuracy of 87–88%. Given the complexity of the problem and the ethical considerations within the educational context, the classification results should be further validated by human experts. The current implementation can serve as a foundation for future improvements.
subject: Information Technology
  topic: section
subject: artificial neural networks (ANN)
  topic: keywords
subject: shallow ANN models
  topic: keywords
subject: AI-generated content (AIGC)
  topic: keywords
subject: LLM
  topic: keywords
subject: AI detector
  topic: keywords
creator: Olena Peredrii
rights: Copyright (c) 2025 Olena Peredrii http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 108
ending page: 113
doi: 10.26906/SUNZ.2025.4.108

TINYML NETWORK PROFILER IN BROWSER
type: item
description: This research presents a client-side TinyML profiler of network in the web browser, designed for operation on resource-constrained devices and under unstable or low-bandwidth network conditions. The solution combines a threshold-based decision rule with a lightweight logistic (softmax) model executed locally to classify the quality of the network connection. Relevance. The rising share of mobile traffic, the heterogeneity of network environments, and the limited fidelity of existing browser indicators complicate accurate client-side decision-making. Object of study: client-side methods for profiling and classifying the quality of a browser-based network connection using standard Web Performance APIs and ML models. Aim: to analyze, design, and implement a profiler capable of classifying connection types and producing interpretable features without server support. Methods. Navigation/Resource Timing and PerformanceObserver are used to collect raw signals. A 14-dimensional feature vector is formed (medians/quantiles of RTT and throughput, variability measures, a loss-likelihood heuristic, and protocol/Service Worker indicators). The proposed a threshold rule with hysteresis and decision confidence, together with a softmax model. Results. The developed profiler requires no special permissions or third-party services. In controlled network-emulation scenarios it improves classification accuracy over a pure threshold baseline, while maintaining low overhead and decision explainability. Conclusions. The proposed client-side approach provides an effective, rapid, and interpretable assessment of network conditions in the browser and is suitable for resource-constrained settings. The results can be leveraged to further enhance adaptive content loading, expand the feature space, and deploy more compact ML models in web applications.
subject: Information Technology
  topic: section
subject: client-side network profiling
  topic: keywords
subject: TinyML
  topic: keywords
subject: Web Performance API
  topic: keywords
subject: softmax classification
  topic: keywords
subject: RUM measurements
  topic: keywords
subject: adaptive content loading
  topic: keywords
subject: QoE
  topic: keywords
creator: Artem Prylipa
creator: Anna Filatova
rights: Copyright (c) 2025 Artem Prylipa, Anna Filatova http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 114
ending page: 120
doi: 10.26906/SUNZ.2025.4.114

INTELLIGENT APPROACH TO PLANNING TAKING INTO ACCOUNT THE CONCEPT OF ACCEPTABLE WORK BALANCE
type: item
description: The article presents an intelligent approach to the problem of scheduling computing tasks in a distributed environment taking into account the concept of admissible load balance. The proposed model combines the principles of multi-criteria optimization and machine learning for adaptive resource allocation between system nodes. The introduction of an admissibility coefficient allows you to dynamically limit the set of machines available for performing a specific task, which increases the efficiency of capacity use and reduces task waiting time. The developed approach takes into account various efficiency criteria — productivity, load balance, and scheduling stability — and can be implemented in grid and cloud infrastructures. Experimental results demonstrate that the use of intelligent algorithms allows you to achieve an optimal compromise between system performance and uniform loading of computing resources.
subject: Information Technology
  topic: section
subject: planning
  topic: keywords
subject: distributed systems
  topic: keywords
subject: grid
  topic: keywords
subject: resources
  topic: keywords
subject: heterogeneous systems
  topic: keywords
creator: Viacheslav Radchenko
creator: Yuliia Andrusenko
rights: Copyright (c) 2025 Viacheslav Radchenko, Yuliia Andrusenko http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 121
ending page: 125
doi: 10.26906/SUNZ.2025.4.121

STRATEGIC PLANNING IN THE CONTEXT OF COMBINED SOFTWARE TESTING
type: item
description: Relevance. Given the rapid development of software engineering practices and the need for cost-effective quality assurance in competitive environments, the relevance of developing strategic planning approaches for combined testing is growing steadily. The object of research is the strategic planning process for combined software testing that integrates multiple testing methodologies through systematic framework implementation, risk assessment, and resource optimization algorithms. Purpose of the article. This study explores strategic planning approaches for combined software testing and assesses their effectiveness across various application domains. The article aims to provide a structured framework for testing strategy integration and evaluate optimization mechanisms for resource allocation in complex testing environments. Research results. A comprehensive Strategic Planning Framework for Combined Software Testing (SPF-CST) was developed, consisting of six interconnected components: context analysis, multi-dimensional risk assessment, AI-driven prioritization, strategy selection, resource optimization, and monitoring systems. Empirical validation across eight industry case studies demonstrates a 35% reduction in defect leakage rates, 28% improvement in testing efficiency, and 45% decrease in regression testing costs. The study revealed that strategic planning significantly enhances testing effectiveness through systematic methodology integration and adaptive resource management. Conclusions. The study demonstrates the effectiveness of risk-based prioritization and mathematical optimization in testing strategy selection. The proposed framework provides practical tools for organizations to implement comprehensive testing strategies while managing resource constraints and project timelines.
subject: Information Technology
  topic: section
subject: strategic planning
  topic: keywords
subject: combined testing
  topic: keywords
subject: software quality assurance
  topic: keywords
subject: test optimization
  topic: keywords
subject: resource allocation
  topic: keywords
subject: testing integration
  topic: keywords
subject: framework development
  topic: keywords
creator: Dmytro Rosinskiy
creator: Vitalii Sitnikov
creator: Daria Pyvovarova
creator: Dmytro Vasylenko
rights: Copyright (c) 2025 Dmytro Rosinskiy, Vitalii Sitnikov, Daria Pyvovarova, Dmytro Vasylenko http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 126
ending page: 129
doi: 10.26906/SUNZ.2025.4.126

MODELS AND METHODS OF ARTIFICIAL INTELLIGENCE FOR DATA PROCESSING IN COMPUTER NETWORKS
type: item
description: The article discusses modern models and methods of artificial intelligence for effective data processing in computer networks. It analyzes the main approaches to the application of machine learning, deep learning, and neural networks for optimizing network traffic, detecting anomalies, and improving the security of network systems. It investigates algorithms for classifying network traffic, methods for predicting load, and AI-based intrusion detection systems. The goal of this work is to develop and study smart ways to handle data in computer networks that are scalable, adaptable, and energy efficient. To do this, we plan to create traffic classification models, load balancing algorithms, and cyber threat detection systems based on machine learning and deep learning technologies. Results: The paper proposes a hybrid model for network traffic classification, an adaptive load balancing algorithm based on reinforcement learning, and a real-time cyber threat detection system. Experimental studies have confirmed the effectiveness of the methods: classification accuracy exceeds 94%, and network performance has increased by more than 20%. Conclusions: The use of machine learning and deep learning methods significantly improves the efficiency of computer network management. The results obtained are of practical importance for building scalable, energyefficient, and secure next-generation network systems.
subject: Information Technology
  topic: section
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: network traffic classification
  topic: keywords
subject: adaptive load balancing
  topic: keywords
subject: cyber threat detection
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: intelligent networks
  topic: keywords
creator: Oleg Slobodianyk
creator: Igor Zykov
creator: Denys Grynov
rights: Copyright (c) 2025 Oleg Slobodianyk, Igor Zykov, Denys Grynov http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 130
ending page: 133
doi: 10.26906/SUNZ.2025.4.130

MICROCONTROLLER-BASED INTELLIGENT LIGHTING CONTROL SYSTEM WITH ADAPTATION TO ENVIRONMENTAL CONDITIONS
type: item
description: Relevance. The growing need for energy-efficient lighting solutions and the development of smart home technologies create the need to develop intelligent lighting control systems. Modern systems must adapt to changing environmental conditions, ensure user comfort and significantly reduce energy consumption. Research object: intelligent lighting control systems based on microcontrollers with functions of adaptation to environmental conditions. Purpose of the article: conducting an analytical review of existing intelligent lighting control solutions, determining optimal technical solutions and identifying promising development directions. Research results: A comprehensive analysis of modern intelligent lighting control systems was conducted, key technologies of sensors, microcontrollers and adaptation algorithms were considered. The advantages and disadvantages of the main commercial solutions were identified, and optimal hardware and software platforms were identified. Conclusions. Intelligent lighting control systems demonstrate significant potential for energy savings (up to 60%) and increased user comfort. The most promising solutions are based on 32-bit ARM Cortex-M microcontrollers with integration of multiple sensors and machine learning algorithms.
subject: Information Technology
  topic: section
subject: intelligent lighting
  topic: keywords
subject: microcontroller
  topic: keywords
subject: adaptive systems
  topic: keywords
subject: energy efficiency
  topic: keywords
subject: IoT
  topic: keywords
subject: smart home
  topic: keywords
subject: light sensors
  topic: keywords
creator: Anton Sorokin
creator: Mykola Chaikin
rights: Copyright (c) 2025 Anton Sorokin, Mykola Chaikin http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 134
ending page: 137
doi: 10.26906/SUNZ.2025.4.134

USING CISCO SECUREX FOR SOC AUTOMATION
type: item
description: Relevance. The article is devoted to the analysis of the capabilities of the Cisco SecureX platform in the context of automating processes within Security Operations Centers (SOC). The study emphasizes the relevance of this research, which is driven by the growing number of cyber threats, the increasing complexity of attacks, and the shortage of highly qualified professionals in the field of cybersecurity. It is underlined that traditional SOC operation methods are insufficiently effective under conditions of multivector attacks, which objectively necessitates the implementation of orchestration and automation technologies. The research systematizes the core functional features of Cisco SecureX, including: integration with multi-component cybersecurity infrastructures (SIEM systems, EDR platforms, IDS/IPS solutions), which ensures the creation of a unified information space; orchestration of SOC processes through the use of response playbooks, enabling the automation of routine operations, reducing incident handling time, and minimizing the human factor; enhanced analytical capabilities, based on the use of machine learning mechanisms and event correlation from heterogeneous data sources; improved accuracy of threat detection through multi-layered data analysis, including the examination of user behavioral patterns and network activity. Particular attention is paid to the research aspect of SecureX’s impact on SOC efficiency. The study substantiates that the use of this platform makes it possible to reduce the Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR), which are critical indicators for evaluating SOC performance. It is demonstrated that integrating SecureX with Threat Intelligence systems provides a more comprehensive contextual understanding of attacks, increases the level of proactive defense, and contributes to the development of an adaptive security architecture. From the standpoint of scientific novelty, the article presents a systematization o…
subject: Information Technology
  topic: section
subject: information security
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: cyber threats
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: architecture
  topic: keywords
subject: scalability
  topic: keywords
subject: SOC
  topic: keywords
creator: Tatiana Fesenko
creator: Yuliia Kalashnikova
rights: Copyright (c) 2025 Tatiana Fesenko, Yuliia Kalashnikova http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 138
ending page: 143
doi: 10.26906/SUNZ.2025.4.138

FUZZY ENSEMBLE OF DECISION TREES FOR THE COMPUTER SYSTEMS STATE IDENTIFICATION
type: item
description: The object of the research is the process of identifying the state of computer systems. The subject of the research is the methods of fuzzy ensemble decision trees with multidimensional decision nodes for identifying the state of computer systems. The goal of the research is to develop and evaluate the effectiveness of a fuzzy ensemble of decision trees to improve the accuracy of identifying computer system states under conditions of uncertainty, noise, and incomplete data. Methods used: machine learning methods, data preprocessing techniques, ensemble classifiers, stacking approaches, methods for feature selection and combination of computer system attributes. Results obtained: the effectiveness of both classical and newly developed methods for identifying the state of computer systems under complex conditions, including data imbalance and the presence of anomalous states, was investigated. A comprehensive approach using Fuzzy Stacking with MDT was proposed, providing high accuracy and stability of classification. The best results were achieved with the stacking approach, which combines base classifiers and fuzzy decision trees, minimizing both type I and type II errors and achieving high generalization ability (MCC, F1-score, TS, LN(DOR)). Conclusions. Based on the results of the study, an improved approach for identifying the state of computer systems is proposed, which combines the stacking method with Fuzzy MDT and feature selection optimization. The integrated use of these methods significantly enhances classification accuracy, result stability, and model robustness to data imbalance, while ensuring high-quality classification even in the presence of new anomalous states.
subject: Information Technology
  topic: section
subject: state identification
  topic: keywords
subject: computer systems
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subject: machine learning
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subject: ensemble
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subject: stacking
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subject: decision trees
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subject: fuzzy logic
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creator: Viktor Chelak
creator: Oleksii Hornostal
rights: Copyright (c) 2025 Viktor Chelak, Oleksii Hornostal http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 144
ending page: 150
doi: 10.26906/SUNZ.2025.4.144

USE OF VIRTUAL MEASURING DEVICES IN METROLOGY, ELECTRONICS AND ELECTRICAL MACHINES FOR THE TRAINING OF ELECTRICAL ENGINEERING SPECIALISTS
type: item
description: The work shows that a promising direction for the modernization of the educational laboratory base is the use of open systems methods together with the introduction of technologies for the use of virtual devices, based on the use of computer measurement methods. The purpose of the work is to analyze the possibilities of creating virtual laboratory work in various disciplines and their use in professional training of specialists in electrical engineering in higher educational institutions. As a result of the analysis of the most well-known application computer packages intended for the design of electronic units, the authors found that the ability to model both the simplest electrical circuits and power electronics circuits and rather complex control circuits for them, as well as various electrical machines is the main advantage of the Simulink package of the MATLAB environment in comparison with other software tools. Examples of constructing circuits for virtual laboratory work in the metrology course are given. It is shown that virtual laboratory work increases students' interest in the learning process, as well as the level of their skills and abilities.
subject: Information Technology
  topic: section
subject: metrology
  topic: keywords
subject: electronics
  topic: keywords
subject: electrical machines
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subject: remote access
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subject: laboratory course
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subject: virtual measuring device
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creator: Оleksandr Shefer
creator: Nataiia Yermilova
creator: Оleksandr Dryuchko
creator: Mariia Stepanko
creator: Sergii Pasichko
rights: Copyright (c) 2025 Оleksandr Shefer, Nataiia Yermilova, Оleksandr Dryuchko, Mariia Stepanko, Sergii Pasichko http://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 151
ending page: 154
doi: 10.26906/SUNZ.2025.4.151