The subject matter of this article is the evaluation of alternative solutions for ensuring cyber security of critical information infrastructures and the selection of a more effective solution. The goal of the study is to create a cybersecurity system with an effective structure for critical information infrastructures. The tasks to be solved include determining the methods, tools, and measures to be included in the system. For this purpose, the hierarchical analysis method was used, and first of all, the decomposition of the problem was given and the corresponding hierarchical structure was compiled. On the basis of expert evaluations for each level of the hierarchical structure, pairwise comparison matrices of alternatives and priorities were constructed and their priority vectors were calculated sequentially. Taking into account the main priorities vector (Confidentiality, Integrity, Availability, Manageability), the degrees of importance of information protection measures (methods and means) were calculated and sorted according to the pairwise preference relations of alternative solutions obtained from the synthesis of the intermediate priorities vector (Physical Security, Network Security, Data Security, Application Security, Access Security). As a result of such a ranking, it is possible to determine which security measures should be given more importance to ensure the cyber security of critical information infrastructures. Conclusion. Thus, based on the hierarchical analysis method, it is possible to quantitatively evaluate alternative solutions for ensuring cyber security of critical information infrastructures, which allows for easy ranking of these solutions by degree of importance. As a result, an effective decision can be made about which methods are more important to include in the cyber security system, and which are relatively less important. The corresponding calculations and analyses were performed on the example of special purpose organizations based on a generalized hierarchical scheme of the cyber security system of critical infrastructures. Thus, the information infrastructure of one of the organizations producing special equipment was taken as the object of the study. According to the obtained results, it was determined that among the methods, means and measures of security, cryptographic and steganographic methods of data protection for this type of organization have higher degrees of importance than others.
The paper develops an integrated theoretical and mathematical framework for information processing on resource-limited Industrial Internet of Things (IIoT) end devices operating within cloud–fog–edge architectures. The study is motivated by heterogeneous, nonstationary event streams whose direct transmission to upper tiers is often infeasible due to bandwidth scarcity, strict latency targets, and the energy and computational limitations of end devices. Consequently, the end device must execute sensing-driven preprocessing, manage finite-buffer queues, and regulate outgoing traffic while preserving the informativeness required for monitoring, control, and analytics. The proposed formalization treats the end device as an active decision node that shapes system dynamics by controlling local transformations and offloading decisions under time-varying resource conditions. A class- and priority-aware stream model captures heterogeneity in criticality and service requirements, while finite-buffer queueing dynamics represent delay and loss under bursty arrivals and constrained service capacity. The device state is described by a resource vector reflecting available CPU capacity, memory and buffer occupancy, channel quality and transmission rate, and energy-related limitations, enabling state-dependent admissibility conditions for local computation and communication. An operator-level processing chain systematizes the end-device reduction pipeline, including preprocessing, informativeness assessment, adaptive filtering, temporal and semantic aggregation, controlled compression, and compact feature formation. The chain produces structured, semantically annotated packets supporting lightweight local decision-making and selective offloading to fog or cloud tiers. A multi-criteria efficiency structure is specified to jointly account for latency, packet loss, energy expenditure, communication load, and informativeness preservation, thereby enabling Pareto-oriented synthesis of admissible adaptive policies. The research objective is to establish unified decision variables, constraints, and stability and feasibility conditions coupling queue behavior with resource limitations, providing an analytically traceable basis for subsequent method construction, parameter tuning, and scenario-driven validation in realistic industrial environments. Unlike purely empirical benchmarking, the contribution is intentionally analytical: it consolidates fragmented models of local reduction and offloading, and exposes explicit operator definitions for reproducible analysis.
The work is devoted to the development of a context-adaptive method for object detection in video streams that dynamically responds to environmental conditions. The relevance of the topic is explained by the need to increase the reliability of assistive systems for visually impaired people and other real-world applications, where variable weather and lighting conditions significantly reduce detection accuracy. The subject of the article is the study of multimodal fusion of acoustic, video, and LiDAR data for object recognition tasks. The goal of this paper is to propose and experimentally validate a method of adaptive preprocessing activation triggered by acoustic artifact classification. The task of this work is to analyze state-of-the-art preprocessing approaches (derain, defog, low-light enhancement), select appropriate acoustic classification models (e.g., PANNs, YAMNet), integrate LiDAR for spatial complementarity, and evaluate the impact of different preprocessing chains on detection metrics. Methods such as comparative analysis, experimental benchmarking of YOLO and DETR models, acoustic signal classification, and multimodal data fusion were applied. The results of the work include a confirmed increase in accuracy (mAP, Precision, Recall, IoU) and stability of detection under adverse conditions when using adaptive preprocessing pipelines, with YOLOv9m and YOLOv10m models showing the most balanced performance. Further research will focus on extending the model with full LiDAR integration, optimizing computational efficiency for mobile/embedded platforms, and scaling the approach for broader classes of environmental challenges such as fog, snow, and urban noise.
This paper presents a detailed analysis of the problem of constructing encoders for linear block codes, with a special emphasis on low-density parity-check (LDPC) codes. The aim of this paper is to provide a comprehensive description of the mathematical methods for the transition from the verification matrix to the efficient coding process. Both classical linear algebra approaches and specialized methods for sparse matrices. Results. The fundamental algebraic constructions underlying the duality between the generating and checking matrices over Galois fields, in particular GF (2), are considered. The classical Gaussian exclusion method for systematic coding is analyzed in detail and its shortcomings in the context of LDPC codes, related to the fill-in phenomenon and loss of sparsity, are revealed. The central place in the study is occupied by the Approximate Lower Triangulation method proposed by Richardson and Urbanky, which allows achieving linear coding complexity. The article contains a detailed description of matrix preprocessing algorithms, mathematical derivation of formulas for calculating parity bits, as well as an analysis of quasicyclic constructions used in modern telecommunications standards (5G, Wi-Fi). Full numerical examples of transformations for low-dimensional codes and a detailed analysis of the LDPC encoder architecture are given. Conclusion. The solution was to abandon the explicit use of the generating matrix in favor of approximate triangulation methods of the check matrix and the use of quasicyclic structures, which has become standard in 5G and Wi-Fi. The integration of algebraic-geometric methods opens up new prospects for creating codes with specified properties.
Long-Term Evolution (LTE) technology is largely used in distributed computing and IoT in providing low latency, reliable and high bandwidth transmission. This paper discusses the LTE adoption in distributed computing environment and the IoT solution space with a special consideration to its performance in the areas of latency, scalability and energy consumption. The study assesses the efficacy of LTE for IoT under a range of network conditions using a mixed method approach of theorization, simulations, and case studies on IoT applications in smart cities, manufacturing, and health. At the same time, it analytically proves that LTE decreases latency times by 40% to the first legacy systems, increases the reliability of data transmission and allows for the construction of horizontally scalable IoT networks. In addition, owing to its adaptive modulation, the energy efficiency in dense IoT environment is improved by 25%. This article provides a detailed description of LTE’s application in improving IoT systems and further recommends the study of coexistence of LTE and 5G to enhance the functionality of the system.
Intrusion Detection Systems (IDS) remain a critical component of cybersecurity. They are rapidly evolving to counter increasingly complex threats across various environments, such as the Internet of Things (IoT), the Industrial Internet of Things (IIoT), vehicular networks, and critical infrastructure. The objective of this work is a comprehensive analysis of the evolution of Intrusion Detection Systems (IDS) from 2020 to 2025. Grounded in contemporary research, it examines the integration of Machine Learning (ML), Deep Learning (DL), Federated Learning (FL), and novel hybrid techniques into IDS, summarizing advancements in their operational capabilities. Key trends include a significant shift toward deep learning architectures - specifically Transformers and Vision Transformers (ViT) - for enhanced pattern recognition. Additionally, the adoption of Federated Learning and fog computing-based systems is observed, aiming to preserve privacy and address challenges related to data decentralization and non-independent and identically distributed (Non-IID) data. Furthermore, there is growing emphasis on Explainable AI (XAI), attack lifecycle-based datasets, and model robustness against adversarial attacks. The results obtained. The review proposes a comprehensive multi-criteria classification of systems, enabling a thorough description and comparison of various solutions. The paper critically evaluates contemporary input datasets and conducts a comparative efficiency analysis of different intrusion detection methodologies. Analysis indicates that although algorithms achieve accuracy exceeding 98% on benchmark datasets, several critical challenges remain unresolved. These include class imbalance, the capability to detect novel and unknown threats, scalability in real-world operational environments, and ethical privacy concerns. Conclusions. This study addresses gaps in previous reviews by highlighting the lack of unified datasets, the need for model validation in real-world environments, and adaptive protection against zero-day attacks and encrypted traffic. It proposes a roadmap for the development of more robust, decentralized, and interpretable IDS.
Relevance. Currently, the volume of transmitted information and the quality requirements for its transmission are increasing. Recently, Software-Defined Networking (SDN) technology has been gaining popularity; however, challenges arise related to the uncertainty of the SDN network state and its elements, as well as the integration of various data streams that have different quality delivery requirements. Therefore, the task of utilizing an intelligent multi-agent system (MAS) for managing SDN networks becomes relevant. The object of research is the process of managing SDN networks. The subject of the research is models and methods for managing SDN networks. The purpose of this paper is to develop a model for the interaction of intelligent agents to ensure the effective functioning of multi-agent systems (MAS) in dynamic management of SDN. Research results. Using the analytical framework of probabilistic temporal graphs, mathematical models have been developed for two options for coordinating agents in the dynamic management of SDN networks: a system with a coordinating agent and a self-regulating MAS. Based on the analysis of the obtained probabilistic temporal characteristics of various coordination options, it has been established that self-regulating MAS are advisable in situations where agents have sufficient knowledge to solve the overwhelming majority of emerging tasks, where these solutions are highly likely to be correct, the number of agents in the system is small, and there is a high probability of effective control over the decisions made.
Effective formation and management of human potential in a multi-project environment contributes to ensuring the sustainable development of recovery programs, increasing the resilience and resilience of project teams. The object of the study is the processes of human potential management in a multi-project environment to ensure the sustainable development of programs. The subject of the study is the models, methods and processes of formation and management of human potential in a multi-project environment to ensure the sustainable development of programs. The purpose of the study is to develop a conceptual model of formation and management of human potential in a multi-project environment to ensure the sustainable development of programs. The research methods are based on the use of a data-driven approach for project-oriented, stakeholder-oriented, donor-acceptor management, systems analysis methods, project, portfolio and program management methodology. The results of the work are the development of a conceptual model of formation and management of human potential in a multi-project environment to ensure the sustainable development of programs, the development of a model of formation and management of human potential in a multi-project environment to ensure sustainable development. It is proposed to use metrics that describe resource potential to assess the obtained options for distributing resource provision between program projects: performer's resource potential, project resource potential, program resource potential. The process of forming and managing human potential in a multi-project environment to ensure sustainable program development was modeled. The scientific novelty of the proposed results lies in the development of a conceptual model of forming and managing human potential in a multi-project environment to ensure sustainable program development, designed for the systematic formation of human capital for recovery programs; effective distribution of personnel between portfolio projects and recovery programs; coordination of HR solutions with sustainable development goals; reduction of personnel risks in a multi-project environment; reduction of cyber risks associated with human resource management processes. Conclusions: the developed set of models of forming and managing human potential in a multi-project environment to ensure sustainable development allow for the formalization of management processes and contribute to ensuring sustainability. The proposed model involves the integration of human potential, digital technologies and sustainable development principles using a data-driven approach and analytics to support management decisions in managing recovery programs. Recommendations for the implementation of models are provided. The application of models is considered on the example, which allowed generating solutions to the problem of resource provision of program projects (reducing the cost of attracting applicants by 25%), an assessment of the resource potential of the performer, projects and programs was carried out and risks were identified and recommendations were proposed.
The subject matter of the article is a method for predicting the flight path of long-range unmanned aerial systems based on the elite ants algorithm. The goal is to develop a method for predicting the flight path of long-range unmanned aerial systems. The tasks are: analysis of existing methods for laying flight paths, development of a method for predicting the flight path of long-range unmanned aerial systems based on the elite ant algorithm, practical verification of the operation of the developed method, conducting experimental studies on predicting the flight path of movement using the method based on a simple ant algorithm and based on the elite ant algorithm, conducting a comparative analysis of the obtained experimental results. The methods used are: graph modeling, multi-criteria optimization, simple ant algorithm, ant algorithm based on elite ants, computer modeling, and comparative analysis of results. The following results are obtained. The methods of laying flight paths are analyzed depending on the approach to optimization, taking into account the specified flight restrictions. They are divided into four main groups, and their main advantages and disadvantages are determined. We will give a formal description of the problem of predicting the path of long-range unmanned aerial systems based on the ant algorithm. A simple ant algorithm and an elite ant algorithm are considered. A method of predicting the path of long-range unmanned aerial systems based on the elite ant algorithm is developed. Experimental studies are conducted on the operation of the method of predicting the path of long-range unmanned aerial systems. A comparative assessment of the efficiency of the simple ant algorithm and the ant algorithm based on elite ants in solving the problem of predicting the optimal path of long-range unmanned aerial systems is carried out. Conclusions. Analysis of experimental studies showed that the use of the elite ant algorithm is more appropriate for the task of predicting the flight path of long-range unmanned aerial systems. The direction of further research is to optimize the input parameters of the elite ant’s algorithm to solve the problem of predicting the flight path of long-range UASs in order to increase its accuracy and stability.
The paper considers the problem of designing a multi-tiered control structure for optimizing virtual machine migration processes in virtualized data centers. The relevance of the study is due to the growth of computing workloads, resource heterogeneity, and the need to ensure high performance and energy efficiency of the infrastructure while maintaining QoS (quality of service). Inefficient VM migration can lead to node overload, increased delays, and additional resource costs. The purpose of the paper is to develop a multi-tiered virtual machine migration management model that provides adaptive resource allocation, reduced downtime, and minimized costs for moving virtual machines. The object of the study is the processes of functioning of a virtualized data center, and the subject is methods and models for optimizing VM migration in a multi-tiered control architecture. The proposed structure provides for strategic, tactical and operational levels of management, which allows combining long-term resource planning with operational response to load changes. The work takes into account the criteria of load balancing, energy efficiency, network traffic minimization and SLA (Service Level Agreement) provision. The results of the study can be used in the design of cloud and grid infrastructures to increase the efficiency of computing resources and ensure stable operation of services under dynamic load conditions. The areas of further research are the implementation of intelligent decision-making algorithms and the use of simulation modeling to assess the effectiveness of the proposed structure.
Although, the quadcopter drone systems have significantly impacted the drone industry, they are considered to be complicated due to the nature of cooperation in accomplishing specific missions. The complications come from the way of movements and arranges in flying tasks which need to be guided in a certain way and have the skill of obstacle dodging. In this research, a developed proposal of a hybrid robot biological swarming algorithm introduced a significant enhancement in swarming rules and blended between the leadership and members' movement control. This enhancement comes from combining two major abilities from observing the selected biological swarms. From the bird flocks the quadcopter drone will have the capability of formations, obstacle avoidance, and safe distance keeping while flying while preserving the ability to alter directions and speed. However, due to the lack of ability to guide the quadcopter drones into specified stored locations which limits the potential applications, the use of ant colony swarm inspiration has solved this issue. The developed algorithm is suitable for a wide range of real-time applications such as firefighting in open lands, rescue missions, delivery, and scanning in time of disasters, and agricultural field like air scanning, health status, and irrigation condition.
The reliability of modern deep learning models in the medical domain is frequently questioned due to their black-box nature. Post-hoc explainability techniques from the field of explainable artificial intelligence (XAI) offer a means to improve transparency and assess the reliability of predictions produced by convolutional neural networks. The research aims to investigate how XAI methods, specifically Gradient-weighted Class Activation Mapping (Grad-CAM), can provide reliable explanations for medical image classification. For this purpose, MRI images of brain were used to train a convolutional neural network to categorize the four stages of dementia in Alzheimer's disease. To make each prediction transparent, the areas of the brain which the trained network used to make the categorization on were highlighted using Grad-CAM. The resulting relevance maps, heatmaps, were evaluated using two approaches: spatial comparison with anatomically defined brain regions associated with Alzheimer’s disease using atlas overlay, and quantitative faithfulness assessment using a deletion-based metric, where highly influential regions identified by Grad-CAM were progressively removed and the impact on classification confidence was measured.
Context. With the development of digital communication systems, the need for efficient data protection methods increases, especially in environments with limited computational resources. Cellular automata, due to their simplicity, reversibility, and ability to support parallel processing, are gaining attention as a promising foundation for next-generation cryptographic algorithms. Objective. This work aims to develop and experimentally validate a block cipher with a reversible structure based on a three-dimensional cellular automaton, offering a high level of diffusion, avalanche effect, and cryptographic resistance without relying on classical cryptographic primitives. Method. The proposed cipher employs a modified Margolus neighborhood adapted for a 3D structure with 64-byte blocks. Within each round, bitwise transformations are performed using a combination of XOR, inversion, and cyclic shift operations. The key schedule process is based on rule 30 of elementary cellular automata, generating a sequence of round keys. The algorithm is implemented in CBC mode to ensure additional encryption robustness. Results. The algorithm was tested using the Dieharder and NIST STS statistical test suites. Results confirmed the statistical randomness of the ciphertext, the presence of the avalanche effect, and resistance to linear and differential cryptanalysis. A comparison with AES-256 in CBC mode demonstrated a comparable level of cryptographic strength. Conclusions. The proposed algorithm is suitable for use in cryptographic systems, particularly in resource-constrained devices. Its advantages include implementation simplicity, reversibility without external lookup tables, and flexibility in topology configuration. This work opens prospects for further research on formalizing the security of cellular automata-based ciphers and their adaptation to other operational modes.
The object of the study is the process of 1-D signal processing by means of DCT-based filter. The subject of the study is the method for prediction of filtering efficiency in terms of signal-to-noise ratio improvement. The goal of the study is to identify which parameters can be used for prediction, evaluate the potential accuracy of the predictions, and assess whether the proposed approach is sufficiently generalizable. Methods used: numerical simulation, verification for a set of test 1-D signals of different origins. Results obtained: (1) accurate prediction is feasible, with a high level of accuracy achieved; (2) prediction accuracy depends on an input parameter that can be computed relatively easily; and (3) the proposed approach is sufficiently general to be applicable to both speech and medical signals affected by additive white Gaussian noise. Conclusions: (1) If the input SNR is below 30 dB, DCT-based filtering with appropriately chosen parameters can enhance it; (2) the extent of this improvement varies significantly but is predictable; and (3) this predictability enables informed decisions about whether filtering is beneficial and how to optimally configure its parameters.
Relevance. High-density IoT environments are characterized by a large concentration of sensors and devices that exchange data intensively within a limited space. Under such conditions, edge-layer intelligent gateways become particularly important. These gateways can locally process information, optimize traffic, and ensure consistent interaction among heterogeneous devices. The development of a test pool for an edge-layer intelligent gateway in high-density IoT is relevant due to the rapid growth in the number of connected devices and the increase in their spatial density. In such conditions, the gateway must maintain stable operation despite high levels of radio interference and competition for network resources. An additional challenge is the heterogeneity of the IoT environment, as devices use different protocols, have different data formats, and exhibit diverse load profiles. Without a specially constructed test pool, it is impossible to reliably evaluate the behavior of the gateway under a realistic mix of technologies and topologies. However, due to substantial heterogeneity, the space of possible test-pool configurations has very high dimensionality. Moreover, there are significant time and resource constraints associated with operating the test pool. The subject of this study is the methods for constructing test pools. The purpose of the article is to develop a method for synthesizing a test pool for an edge-layer intelligent gateway in high-density IoT. The following results were obtained. A five-layer architecture of an edge-layer intelligent gateway for high-density IoT is proposed. The operational specifics of the gateway and the particular aspects of its testing are identified. The task of synthesizing the test pool is reduced to a combinatorial problem of selecting an optimal configuration within an extremely large state space. To solve it, the use of a classical genetic algorithm is proposed. The proposed algorithm made it possible, within an acceptable time, to obtain a test pool with nearly minimal execution time, a minimal number of tests, and maximal coverage of the gateway components. Conclusion. The proposed method enables the construction of a test pool for an intelligent gateway within a high-dimensional state space while meeting the specified requirements. Future research concerns the development of a method for reducing the dimensionality of the state space of individual tests for gateway components.
In the context of the rapid development of information technologies, software quality is becoming critical for the successful operation of organizations in various industries. The growing complexity of modern software solutions requires the involvement of highly qualified specialists in software testing and quality assessment, capable of effectively identifying shortcomings and ensuring that the product meets established standards. At the same time, assessing the level of competence of such experts remains a difficult task, which is often based on subjective criteria and methods. The relevance of the study is due to the acute need of the modern IT market for objective tools for assessing the professional level of specialists, especially in the field of software quality assurance. Traditional approaches to qualification assessment, such as interviews, test tasks or resume analysis, often do not provide a complete and objective picture of the expert's competence. This problem becomes especially acute in the conditions of the global labor market, when companies are forced to evaluate specialists remotely, relying only on a limited set of data on their experience and skills. Today, software has become an integral part of many areas of our everyday life - from automation and optimization of production processes to creating comfort for an individual. The object of the study is the process of determining the level of competence of experts in software quality assessment. The subject of the study is a mathematical model for calculating the level of competence of an expert. The practical value of the results of the work is determined by the possibility of using the developed system by HR managers for effective selection of specialists, by heads of QA departments for the formation of balanced testing teams, by certification centers for objective assessment of competence, as well as by the experts themselves for planning their own professional development. Conclusion the developed mathematical model for calculating the level of competence of an expert allows you to reduce the time for assessing the competence of specialists, minimize the influence of subjective factors when making personnel decisions, and optimize the distribution of human resources in software development and testing projects.
The object of this study is a stationary stochastic input flow of material arriving at the input of an industrial conveyor transport system. The goal of this research is to develop a universal, statistically mathematical model of the input flow of materials, fully identifiable from a single long-term experimental implementation, as well as to create a multi-level system of dimensionless stochastic similarity criteria, enabling the objective classification and comparison of heterogeneous flows with similar structural properties. The results obtained. A simplified canonical decomposition of a stationary ergodic process with a minimum number of random coefficients is proposed, reproducing the specified mathematical expectation, variance, correlation function, and one-dimensional probability density of flow values. Analytical expressions are derived for approximating the distribution density of random coefficients with guaranteed fulfillment of the conditions of centering, normalization, and non-negativity. A multilevel system of stochastic similarity criteria is developed, including aggregated dimensionless criteria, a functional similarity criterion based on a normalized autocorrelation function, and a functional criterion based on quantile-quantile diagrams. A dimensionless flow normalization method is proposed, ensuring model transferability between conveyor systems differing by orders of magnitude in throughput and time scales. Using six independent long-term implementations of real conveyor systems in the mining and processing industries, the accuracy of the developed stochastic input flow generator using an analytical approximation of random coefficients is demonstrated. Conclusion. The developed methodology enables the classification and comparison of material input flows in transport systems and serves as the basis for a universal approach to constructing mathematical models and flow control algorithms under stochastic uncertainty.
Relevance. Shielding is the most effective means of improving electromagnetic safety for people and electromagnetic compatibility for electronic equipment. Only composite materials can control the protective properties (ratio of reflection, absorption and transmission coefficients) of electromagnetic waves. However, designing materials with the required protective properties is complex and requires large amounts of calculations. This makes it expedient to automate these processes by creating application software. The aim of this work is to automate the design processes of composite materials with controllable protective properties. Research results. Mathematical functions are provided to determine the effectiveness of electromagnetic radiation shielding by reflection and absorption of electromagnetic waves. It is shown that it is advisable to use fundamental relations of electrodynamics of continuous media to automate the design processes of composite protective materials. A list of theoretical and experimental data necessary for the design of protective materials is determined. Applied software has been developed that allows obtaining data on the effectiveness of electromagnetic radiation shielding by reflection and absorption of electromagnetic waves depending on the electrophysical parameters of the composite components and the volume content of the electrically conductive filler in the dielectric matrix. Using the example of a silicate material with a granulated copper filler, dependencies of the effectiveness of shielding ultra-high frequency electromagnetic radiation on the filler content were obtained. A comparison of the obtained data with the experiment shows their acceptable convergence. To accelerate the design of protective materials, a generalised function of the dependence of the electrical conductivity of the composite on the filler content was obtained. This allows reducing the amount of experimental work to obtain the initial data. Conclusions. The creation of application software for automating the design processes of composite materials allows optimising the effectiveness of protective materials by selecting the most acceptable components and the content of electrically conductive material in the dielectric matrix.
The presented research relates to the field solving the problem of increasing the efficiency transmission and noise immunity reception discrete messages used for the exchange traffic flows between communication systems and radio engineering complexes of entities. The object of the study is hardware and software systems and radio channels multiservice communication networks using multi-antenna technologies. Multi-antenna systems in multiservice communication networks allow increasing the capacity radio channels by transmitting a signal using several antennas on the transmitter side and several antennas on the receiver side. It is worth noting that the capacity of the radio channel is still limited due to the use of a power distribution algorithm. The efficiency and noise immunity indicators of the functioning of communication systems in the presence of interference sources are analyzed based on the architectural concept of the following and future public communication networks. The subject area is the problems applying a new approach to multiservice communication networks for optimal use resources end-to-end digital technology and modern wireless cellular communication technologies. The purpose of the study is to develop a new approach to constructing a method for calculating the evaluation of the characteristics of transmission efficiency and noise immunity when receiving traffic flow messages in a complex signal-noise environment. Based on the methods for calculating the evaluation of the performance indicators of multiservice communication networks, important analytical expressions for further research were obtained. As a result of the study, the main conclusions of the study were obtained, which can be implemented and used in multiservice stationary and wireless cellular networks to calculate the transmission efficiency and reception noise immunity indicators. The technical and economic effect for multiservice networks and radio engineering complexes consists in increasing their throughput by attracting funds and resources of modern cellular mobile network technologies. The substantiation proposed main stages of the study is provided, the results of the analytical study and simulation modeling are presented, confirming the validity of the theoretical conclusions made.
Relevance. The full-scale military invasion of the Russian Federation has caused unprecedented distortions in the labour market of Ukraine. These deformations are characterized by deep sectoral and territorial disproportions, which are caused by mass migration, mobilization, destruction of production, and changes in the structure of labor supply and demand. This causes an urgent need to develop tools to quantify and predict said deformations, which is essential for making informed decisions. The purpose of this research is to develop and test a complex technique based on neural network modelling (Long Short-Term. Memory – LSTM). This methodology aims to identify, assess, and forecast labour market deformations and imbalances in Ukraine, and includes the development of a system of criteria for their evaluation. The research methodology is based on an integrated approach that incorporates time series analysis, neural network forecasting (LSTM), methods for detecting structural shifts and anomalies (Isolation Forest), cluster analysis (K-Means), and determination of influencing factors (Random Forest). The research presents a developed system of criteria for assessing war-induced deformations, conducts a quantitative evaluation of sectoral disruptions resulting from the conflict, provides a forecast of imbalance dynamics, and identifies the most vulnerable sectors of the economy. The conclusions emphasise the scientific and practical significance of the developed methodology for monitoring the labour market, as well as for developing adaptive employment policies and programs to support the post-war recovery of the Ukrainian economy. They also demonstrate the potential of neural network models for analysing labour markets under extreme conditions нof uncertainty.
