Applied Aspects of Information Technology
The publication focuses on the practical implementation of the latest information technologies in industry, energy, financial technologies, healthcare, sociology, education, project management, ecology, and human safety.
Analysis of the total random error in simulation systems
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: Simulation modelling, forecasting, machine learning, information technologies, high-precision calculations, floating point representation, random error summation, simulation errors, improving accuracy of simulation systems, unequal-sized data, simulation system, representation as arrayslist skills, labour market analysis, unsupervised learning, clustering, academic program, specific subject competences The article presents an analytical assessment of error in computer simulation systems for cases of an unknown arbitrary type of symmetric distributions of the total random error of calculations. The study is based on the use of asymptotic properties of a sequence of random variables. The work includes the development of a mathematical framework for predicting a confidence estimate of random error. An innovative approach to selecting the initial approximation is proposed. The proposed approach increases the efficiency of the iterative error calculation process. It has been established that, for any type of symmetric distribution laws, the series contains only integer powers of n. Using the established pattern ensures better convergence and optimizes the calculation of the numerical value of the modeling error. Simplified formulas have been created for calculating confidence intervals at high probability levels. The applicability conditions of the methodology have been determined. It has been experimentally confirmed that for a uniform distribution, acceptable accuracy is achieved with at least two components. At the same time, for a sinusoidal distribution, at least three components are needed to achieve an error below the acceptable limit. For high levels of modeling reliability, the required accuracy is achieved with two components without the need to introduce additional adjustments. The reliability of the obtained results was verified for two cases of input data. In the first case, the results of studies on modelling accuracy using the representation of numbers in the binary-hexadecimal system as arrays were used. In the second case, input data from error calculations of solving linear GPU NVIDIA tasks with floating-point representation were used. The methodology is universal and can be applied to both random and systematic errors that occur in simulation systems.
Applied information technologies in energy engineering and automation
Applied information technology in energyengineering and automation
improving accuracy of simulation systems
Copyright (c) 2025 Applied Aspects of Information Technology
A method for constructing GL-models of behavior under failure flow for complex non-basic fault-tolerant multiprocessor systems
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: Fault-tolerant multiprocessor systems, GL-models, non-basic systems, control systems, reliability evaluation, statistical experiments The paper proposes a method for constructing GL-models of the behavior of complex non-basic fault-tolerant multiprocessor systems under a failure flow. The aim of the study is to develop a universal approach that enables the formation of an integrated GL-model for systems characterized by multiple independent or weakly coupled operability conditions. Such models can be used, in particular, to evaluate the reliability parameters of the systems under consideration using statistical simulation methods. The study focuses on systems whose operability is determined by the simultaneous fulfillment of several relatively simple conditions, for each of which established methods for constructing GL-models are available (for example, a condition limiting the number of failures within a certain subset of processors). These include, in particular, hierarchical systems composed of multiple subsystems with their own levels of fault tolerance, as well as systems containing specialized processors of different types. The proposed method involves the preliminary construction of auxiliary GL-models for each operability condition; followed by their integration into a unified model through the sequential merging of their graphs via selected vertices (the merged vertices form a single vertex, while the remaining vertices and edges are copied). The order of model merging and the choice of corresponding vertices can be defined arbitrarily, providing flexibility in the structure of the resulting GL-model. Examples of the method’s application are presented, illustrating various options for determining the sequence of merging auxiliary models and selecting the connecting vertices of their graphs, as well as the use of different methods for constructing these models. The scientific novelty of the work lies in the generalization and formalization of the sequential GL-model merging procedure, which makes it possible to combine models of arbitrary structure and type into a unified model of a complex system without compromising the correctness of its behavior. Experimental results confirm that, despite structural differences in the graphs of the obtained models, their behavior on identical input vectors coincides completely and accurately reflects the operation of the fault-tolerant multiprocessor system under a failure flow. It is also shown that the method imposes no restrictions on the construction techniques of GL-models for individual conditions: models of different types can be combined, the conditions do not necessarily correspond to basic systems, and the model graphs may be other than a cycle graph. Furthermore, the paper provides complexity estimates for GL-models constructed by the proposed method, including the number of vertices and edges in their graphs and the overall complexity of edge functions, depending on the characteristics of the corresponding auxiliary models. The practical value of the method is that it enables automated construction of comprehensive models for systems with complex operability conditions and supports efficient reliability evaluation of real multiprocessor systems.
Structure of a model for scientific research of complex electrical objects
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: System analysis, system approach, methodology, principles, graph-analytical methods, graph, modeling, management, modern power supply One of the main directions of systems research is systems analysis and systems approach. Systems theory, as a subdivision, is one of its main concepts and, along with model theory, control theory, and information theory, belongs to the methodological base in scientific research. It is essential to examine the laws and principles that facilitate effective research into the structural transformation of research organization. In practice, a systems approach involves systems coverage, systems representation, and systems orientation in research. System coverage requires considering the problem from different aspects and perspectives. Systems representation is achieved by building a single model that can replace a real object and provide relevant information about the object being modeled. Using systems analysis, an assessment of the network topology is carried out - the number of substations, line lengths, voltage levels. The formation of systemology is based on a systems approach and is used in the construction of more intelligent control systems. Ensuring the organization of research is of no small importance, which means identifying the problem; internal analysis of the problem; external analysis of the problem; system identification: goals, constraints, trends, factors, properties; model synthesis; model behavior analysis – system operation modeling; system optimization. Planning an optimization experiment, experimental model identification. Analysis of the results of the experiment with the model. Interpretation of the results in relation to the system solving the problem P. Solution implementation. Within the framework of the system approach, the tasks of analysis and synthesis are interconnected, alternating with a given regularity and characterizing two sides of a single cycle in the research process. The described cycle of problem solving applies to both the quantitative and qualitative system approaches. In this case, identification and imitation are implemented at an intuitive, heuristic level. The main disadvantage of the qualitative approach is that counterintuitive behavior that manifests itself in complex socio-economic systems is overlooked and not analyzed. The advantage of the qualitative approach over the quantitative one is a significant saving of time and money for conducting research. The set of methodological tools used to prepare and justify decisions regarding complex, scientific, and technical issues includes system analysis. System analysis is defined as a set of methods for studying complex objects by presenting them in the form of systems and further analyzing such systems. The order of stages of system analysis includes: problem formulation (choosing the object of study, determining the goal and criteria for its study); isolation and structuring (or decomposition) of the system under study, i.e., dividing it into subsystems that are relatively clearly described; creation of a mathematical model of the system. The article presents graphical and analytical stages of making evaluative management decisions based on network modeling. The results obtained proved the feasibility of using a structured system-parametric approach in the study of power supply systems. This is the difference between system analysis and the local approach, which consists of studying the structure and functional features of autonomous, separately taken elements of the system. The inability to comprehensively cover all parties, connections and «mediation» allows, during the system analysis, on the one hand, to strive for maximum completeness of the description, and on the other hand, to carry out a reasonable simplification of the object. This is especially relevant and appropriate in the field of research on processes in power supply systems.
Systems analysis, applied information systems and technologies
Systems analysis, applied information systems and technology
Vector logic structures and functions of computing
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Element structure, vector logic functionality, vector logics, logic vector, Cartesian logic, intelligent computing, test map, fault simulation, good-value simulation, in-memory modeling for simulation, prompt engineering Vector logic computing is a cost-effective mechanism for intelligent in-memory computing, utilizing read-write transactions to address practical problems in the analysis and management of physical, social, and business processes based on monitoring. The concept of a mechanism is introduced as a harmonious relationship between the model and the algorithm for computing. The investigation aims to significantly reduce time and energy costs associated with simulation processes in the physical, social, and digital worlds. The means to achieve this goal is using vector-logical in-memory computing mechanisms, which significantly simplify algorithms due to the exponential redundancy of data structures. The mechanisms for reducing the algorithm's computational complexity, which is typically exponential due to the use of data structures based on vector logic, are considered. An analysis of functions and structures is conducted from the perspective of algorithmic simplicity for modeling and simulation purposes. Several mechanisms based on Cartesian logic are proposed for modeling a logical vector from the structure of a digital circuit. A Boolean vector is used to create a test map in three matrix operations. A cloud-based MOSI service is offered that simulates the behavior and faults of digital circuits and their functionalities using truth table addresses.
A method for synthesizing index policies to ensure the survivability of a mobile platform-based information system
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Information system, mobile platform, survivability, index policy, dual-loop adaptation, method This paper proposes a method for synthesizing index policies to ensure the survivability of a mobile platform-based information system operating under intermittent connectivity and incomplete observability. The method aims to maintain the probability of violations within specified budgets by combining event-based index ranking with calibrated risk management and dual-loop parameter tuning. The fast loop adaptively adjusts the weight of the risk component of the index based on the magnitude of recorded excesses, while the slow loop periodically recalibrates feature normalization, base weights, and relevance factors, thereby enhancing robustness to non-stationarity and small sample sizes. Probabilistic constraints are interpreted through upper confidence bounds on a short sliding window, enabling controlled risk without rigid assumptions regarding disturbance distributions and without reliance on continuous telemetry or frequent global coordination. To maintain decision consistency, a computationally lightweight acceptability check and event logging are employed, facilitating independent reproducibility. The study presents the index rule formulation—including feature normalization, weight structure, and risk testing—along with a risk correction protocol for sampling per decision step and a procedure for detecting mode changes and safe reconfiguration. The computational complexity of the method is amortized linear-logarithmic with respect to the number of active objects and step capacity, ensuring suitability for real-time operation. Verification was performed on event logs featuring intermittent connectivity and surge load scenarios. The results demonstrate the convergence of the violation frequency upper bound to specified budgets, the stabilization of the risk component weight without fluctuations, and predictable service metric dynamics. A step-by-step ablation study highlighted the contribution of each component: disabling the fast loop delays the achievement of target risk levels, while the absence of the slow loop locks the risk component at an excessive weight, degrading the operational balance. Compared to heuristic rules lacking explicit risk control and model-predictive approaches sensitive to telemetry quality, the proposed method ensures the survivability of the information system through controllable probabilistic guarantees, event scalability, and reproducible verification procedures.
Vision-based GUI testing: a systematic review of computer vision advances in software quality assurance
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Computer vision, GUI testing, multimodal artificial intelligence, software quality assurance, deep learning, large language models, reinforcement learning, automation Ensuring the reliability and adaptability of graphical user interfaces (GUIs) has become a central challenge in contemporary software quality assurance, as modern applications increasingly rely on visually dense, dynamic, and cross-platform interaction patterns. Traditional script-based and DOM-dependent testing approaches are brittle and expensive to maintain, since even minor layout shifts can invalidate large portions of test logic. These limitations have accelerated interest in computer vision and artificial intelligence techniques that evaluate GUIs directly from rendered visuals, enabling more robust, flexible, and human-like validation. This study aims to systematize current research on vision-based GUI testing and to identify how emerging multimodal and language-guided methods reshape quality-assurance practices. Conducted as a structured review of academic and industrial work published between 2010 and 2025, the analysis synthesizes findings across five methodological families: classical image-processing and template-matching approaches, deep neural detectors, generative models for synthetic augmentation, reinforcement-learning agents for autonomous exploration, and transformer-based multimodal systems integrating large language models. The review incorporates an additional empirical-synthesis chapter that consolidates reported evaluation results for vision transformer-based perception, large language models -guided test reasoning, and multimodal vision–language pipelines. The results reveal a consistent technological trajectory: from early perceptual matching to high-fidelity object detection, interactive workflow exploration, and, most recently, semantic interpretation of GUI tasks through natural-language grounding. Experimental evidence shows that visual transformers improve structural perception, reinforcement learning agents enhance interaction coverage, and multimodal systems introduce reasoning capabilities that approach human-like test generation. At the same time, the review identifies persistent challenges, including dataset scarcity – particularly of multimodal screenshot-instruction-execution corpora – fragmented evaluation practices, cross-platform variability, and the computational overhead of multimodal inference. The study concludes that vision-based GUI testing is evolving into a cognitively informed quality-assurance paradigm that integrates perception, behavior, and semantic reasoning. Its novelty lies in providing the unified taxonomy of methods, a consolidated synthesis of empirical verification results, and a set of actionable future research directions. These contributions offer practical value for advancing reproducibility, robustness, and methodological coherence in next-generation GUI-testing systems.
Computer science and software engineering
Research on the impact of optimization algorithms on the accuracy of YOLOv11 neural networks
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Artificial intelligence, computer vision, convolutional neural networks, COCO dataset, hyperparameter optimization, SGD, Adam, AdamW, YOLOv11, mAP, IoU, precision, recall Visual inspection and positioning based on image detection results is a rapidly growing component of automation systems. Machine vision is increasingly used in production lines for various technological purposes, as well as in special equipment. Improving recognition accuracy in such applications can be a difficult task, especially in conditions of possible limitations, one of which may be size and weight restrictions, which in turn limit the power of computer devices that implement image detection and recognition. A possible solution to this problem is to improve recognition accuracy by automatically tuning the hyperparameters of detection models using various optimization methods. This article presents the results of a study of the effectiveness of algorithms for automatically tuning the hyperparameters of the YOLO (You Only Look Once) image detection model, built on the basis of the SGD (stochastic gradient descent), Adam (adaptive learning rate estimation), and AdamW (improved version of Adam) optimization methods. The models were trained on the COCO 2017 dataset, limited to eight classes and balanced in terms of the number of images. For each optimization algorithm, 10 iterations of automatic selection were used, followed by training for 30 epochs. The test results showed that in tasks where detection accuracy is important and there are sufficient computational resources for hyperparameter selection during model training, it is advisable to use the stochastic gradient descent optimization algorithm, since the detection model configured with its use provides a higher probability of successful image recognition in real time under conditions of limited computational resources.
Online anomaly detection in correlated data streams using robust Kalman filtering
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Streaming data, inertial navigation, anomaly detection, Kalman filter, iForest The demand for online data analysis opens new challenges and research opportunities. The growing wave of IoT devices, low-cost sensors, and robotic systems generates vast amounts of high-frequency streaming data. Efficient online analysis of such data requires algorithms that operate under memory and latency constraints, often within a sliding-window framework. However, the reliability of these data streams critically affects the accuracy of the inference results.This study considers one of the tasks in streaming data analysis – anomaly detection in the smartphone sensors data streams. Our goal was to improve the quality of the geolocation by filtering out anomalies in the signal and then measure the accuracy of trajectory estimation for pedestrian navigation. Pedestrian navigation in urban environment is non-trivial because of global navigation sattelite system signal distortions. These distortions can be caused by various factors such as multipath effects, signal blockage from tall buildings, and interference, which are common in dense urban areas. The full data pipeline requires robust techniques for smartphone sensor data processing wich include low-pass or high-pass filtering of acceleration signal, synchronizing several streams by the timestamps, converting measurements from the device frame of reference to the global coordinate system, feature enrichments etc. When multiple data streams from device sensors are available, their fusion can be used to mitigate the limitations of individual sources. One of the adopted methods for this is the so-called robust Kalman filter. We compared this method with an ensemble anomaly detection method (iForest) applied to the geolocation data stream in the pedestrian navigation set up. We used orthogonal distance metric to compare predicted trajectories with groud truth coordinates and showed that robust Kalman filter achieves superior performance in the streaming setting. A mean deviation from the ground truth trajectory of one metre and eighty-three centimetres was achieved on the test dataset, with the total route length measuring one hundred eighty-four metres.
Parametric approach to improving significant indicators of thermoelectric systems for maintaining thermal conditions
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: reliability indicators, dynamic characteristics, thermoelectric material, efficiency, current mode The problem of improving the reliability of complex systems is fundamental, since it is impossible to achieve complete failure-free operation in real systems that interact with the external environment. In modern information systems, increasing the probability of failure-free operation by stabilizing the temperature of critical heat-loaded elements using a thermal control system remains a pressing issue. To solve this problem, a mathematical model of a thermoelectric cooler of a given design has been developed. The model is intended for comparative analysis and assessment of the influence of various combinations of parameters of the initial thermoelectric material of equal efficiency on the main performance indicators of the product under variable current load. The results of calculations of reliability indicators, dynamic and energy characteristics of single-stage thermoelectric coolers, and the geometry of thermoelectric element chains are presented for various combinations of parameters of the initial thermoelectric materials. It is shown that with a rational design of a single-stage thermoelectric cooler, it is possible to select a combination of parameters of the initial material with the same efficiency to reduce the operating current and increase the supply voltage at different thermal loads. Additional means of controlling the parameters of a thermoelectric cooler are the geometry of the thermocouple branches, the physical parameters of the initial thermocouple materials, the operating current, the supply voltage, and the failure rate. Analysis of the research results showed that it is possible to select a combination of raw material parameters that reduces the operating current by an average of 20% and reduces the relative failure rate by up to 40% under otherwise equal conditions.
Method for automated control of rotor engine blade synchronization
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: automated control, computer modeling rotary-blade internal combustion engine, computer simulation, electrical synchronisation, torque control This study addresses the critical issue of synchronization accuracy in rotary-blade internal combustion engines, which directly influences their efficiency and operational reliability. Traditional mechanical synchronization methods suffer from significant drawbacks, including rapid wear, high maintenance costs, and limited modernization potential. To overcome these limitations, we propose an innovative electrical synchronization approach based on adaptive torque control of electric machines. The developed algorithm dynamically adjusts torque in real time according to the displacement of the bisector of the angle between the blades forming the combustion chamber, ensuring precise synchronization during each engine cycle. A mathematical model was formulated to describe the relationship between torque, bisector displacement, and engine dynamics. This model was implemented in a custom simulation environment developed in JavaScript with interactive visualization tools. Computer simulations were conducted under various operating conditions to evaluate the algorithm’s performance. Results demonstrate a substantial reduction in bisector displacement - from several degrees to thousandths of a degree and stabilization of cycle duration after only a few iterations. These findings confirm the algorithm’s ability to maintain synchronization accuracy and improve engine stability. The proposed approach offers practical benefits for the design of advanced rotary-blade internal combustion engines, reducing mechanical wear and enhancing energy efficiency. Future research will focus on optimizing the correction factor, adapting the algorithm for engines operating under variable loads and temperatures, and integrating PID-based control strategies for improved responsiveness.
computer modeling rotary-blade internal combustion engine
Technological platforms, tools, and standardization in the unified information space for automated unmanned aerial vehicle design
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: model-based engineering, product lifecycle, digital thread, tool interoperability, unmanned aircraft, systems integration Unmanned aerial vehicle development entails highly interdisciplinary processes, often hampered by fragmented digital toolchains and siloed data. This paper addresses the critical need for an integrated unified information space across the unmanned aerial vehicle lifecycle. We present a review of modern platforms and standards that enable end-to-end data continuity – the “digital thread” – from initial requirements through design, manufacturing, and operations. The aim is to synthesize current approaches to achieving interoperability and an authoritative source of truth for unmanned aerial vehicle projects. Key objectives include mapping prevalent Model-Based Systems Engineering and Product Lifecycle Management platforms, comparing lifecycle standards (e.g., systems modeling language, Standard for the Exchange of Product Data, Open Services for Lifecycle Collaboration, Open platform communications unified architecture) across development phases, proposing an integration framework to connect heterogeneous tools, and identifying gaps in adoption. In terms of methods, a structured literature review was conducted using IEEE Xplore, Scopus, and engineering databases. Search strings targeted unmanned aerial vehicle tool integration, Model-Based Systems engineering and Product Lifecycle Management interoperability, and digital thread case studies. Inclusion criteria focused on publications and standards addressing cross-domain data integration; industry white papers and standards documentation were also analyzed. The results reveal a rich landscape of standards for requirements management, system architecture, product data exchange, simulation interoperability, and IoT-based operational feedback. We provide a comparative analysis (including tables) of these standards versus lifecycle stages, and of integration approaches (Application Programming Interfaces, service buses, Open Services for Lifecycle Collaboration links, industrial Internet of Things protocols) against factors like latency, scalability, and security. A conceptual integration blueprint is outlined, leveraging open standards to connect systems modeling language models, Computer-Aided Design/Product Lifecycle Management data, and real-time sensor information into a cohesive environment. Discussion highlights trade-offs such as proprietary Product Lifecycle Management suites versus openness, the maturity and portability of Model-Based Systems Engineering models (systems modeling language version 1.0 limitations and systems modeling language version 2.0 prospects), and cloud vs. on-premises deployment given aerospace security International Traffic in Arms Regulations constraints. Organizational readiness (business process reengineering, stakeholder buy-in) emerges as a key success factor. In conclusion, unifying the information space can dramatically improve unmanned aerial vehicle development efficiency, traceability, and innovation. However, realizing this vision requires not only technical solutions but also adoption of standards and cultural change in engineering practices. The paper’s synthesis provides practical insights and a roadmap for both researchers and practitioners aiming to implement a consistent digital thread for complex aerospace systems.
Research on improving a graph neural network model forcomputer network simulation
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Сomputer network modeling, machine learning, graph neural network, network parameters, node characteristics, optimization, network topology Modern computer networks face increasing challenges due to the growing complexity of their structure, dynamic traffic fluctuations, and the need to maintain high performance. Traditional approaches to network modeling often fail to accurately predict parameters such as latency or packet loss, as they have limited capacity to capture the specific characteristics of individual network elements. This highlights the relevance of developing novel methods that can adapt to real operating conditions and ensure efficient resource management. The aim of this study is to enhance network modeling methods by developing a model that incorporates the individual properties of network elements to improve the accuracy of parameter prediction and to optimize routing processes. The research objectives include the analysis of current modeling approaches, the design of an improved model based on machine learning techniques, the refinement of training algorithms, and the execution of experiments to evaluate the model’s effectiveness. Machine learning methods were applied in the implementation, with particular emphasis on a graph neural network, which enables the modeling of complex interdependencies among network elements. The proposed model integrates node-specific characteristics into the data processing pipeline, thereby ensuring adaptability to heterogeneous conditions. Experiments were conducted on multiple datasets representing real-world network topologies; with prediction accuracy assessed using several evaluation metrics. The results demonstrate that the proposed model provides higher accuracy in predicting network parameters compared to baseline approaches, exhibiting the ability to generalize to unseen topologies. The scientific novelty of the work lies in the incorporation of element-level characteristics into the modeling process, allowing for a more precise reflection of real-world conditions. The practical significance is manifested in the potential application of the model in network management systems for routing optimization and infrastructure cost reduction. The findings open new prospects for further development of modeling and management methods in modern networked systems.
Performance evaluation of photon mapping methods
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Computer graphics, photon mapping, rendering, machine learning This research presents a comprehensive performance evaluation of photon mapping techniques for global illumination, with a focus on real-time rendering and large-scale visual simulations. The study introduces a custom implementation of a KD-tree-based spatial indexing structure for photon retrieval, providing an empirically validated alternative to linear search in high-density photon environments. The integration of this method with controlled benchmarking constitutes the core novelty of the work. Five modern photon mapping strategies are investigated collectively without emphasizing individual advantages, including traditional formulations, progressive and stochastic progressive variants, Monte Carlo-based hybridization, and machine-learning-augmented methods. Experiments are conducted on diverse test scenes with varying geometric and lighting complexity, using both Central processing unit (CPU) and Graphics processing unit (GPU) platforms to measure scalability and efficiency under distinct resource constraints. Evaluation is based on rendering time, memory usage, and image quality indicators such as mean squared error and peak signal-to-noise ratio. A uniform testing pipeline ensures methodological consistency across hardware setups and photon emission scales. The KD-tree approach demonstrates improved query performance as photon count increases, reducing lookup overhead and enabling more efficient scene processing in dense illumination contexts. The findings are applicable to next-generation physically based rendering engines, interactive graphics applications, and high-fidelity simulation systems where computational cost and responsiveness are critical. The implementation insights, experimental results, and derived recommendations serve as a foundation for the selection and deployment of photon mapping algorithms in scenarios requiring accurate global illumination and scalable real-time visualization. The work supports design decisions in rendering architecture by quantifying trade-offs in algorithmic structure, hardware acceleration, and memory behavior. The novelty of this work lies in the implementation and evaluation of a custom KD-tree algorithm for photon retrieval, which has not been previously benchmarked in the context of large-scale photon mapping. Unlike purely theoretical analyses, this study provides empirical validation of KD-tree efficiency compared to linear search, offering practical insights into scalability trade-offs for real-time and high-fidelity rendering applications.
Hybrid graphs for code smells: a multi-level model for anti-pattern detection in software components
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Machine learning, software engineering, program analysis, graph representation learning, static analysis, uncertainty estimation, transfer learning, empirical evaluation The paper proposes a hybrid, multi-level method for detecting code smells and anti-patterns in software components, where structure, semantics, metrics, and evolution are treated as first-class signals. A heterogeneous Code Property Graph (Abstract Syntax Tree + Control-flow Graph + Program Dependence Graph) is constructed and enriched with textual embeddings from a pretrained code language model, classical quality metrics (Chidamber–Kemerer, Halstead), and version-control history (churn, co-change, recency). Local idioms are summarized via a sequence–graph encoder at the method/block level, component structure is aggregated by a relation-aware Graph Neural Network at the class/module level, and project context is propagated over a component-interaction graph. To support deployment in evolving codebases, an open-set head is introduced: energy, entropy, and stochastic variance are combined to enable calibrated abstention on unfamiliar patterns. The approach is evaluated on polyglot Java Virtual Machine corpora using time-aware, cross-project splits with multi-label targets (Long Method, God Class, Feature Envy, Data Class, Shotgun-Surgery–like, No-smell). Improvements in macro Area Under the Precision–Recall Curve and F1 overrule/metric baselines, Abstract Syntax Tree-only, and text-only models are observed, while FPR@95TPR is maintained or reduced. Withheld-class experiments show that open-set gating increases Area Under ROC for Open-Set Recognition and TNR@TPR and lowers calibration error, yielding probabilities suitable for thresholded automation and human triage. Cross-language transfer (train Java → test Kotlin/Scala) is shown to be stronger than with single-view models, aided by language-agnostic typing and per-project normalization. Incremental graph maintenance confines computation to changed regions, aligning inference time with CI/CD budgets. By exposing hierarchical attention and channel gates, explanations are produced that align with practitioner reasoning. It is concluded that hybrid graphs with hierarchical reasoning and selective prediction deliver detectors that are more accurate, transferable, and operationally safer for evolving software systems.
Methods for addressing labeled data scarcity in applied computer vision systems
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Machine learning, augmentation, convolution neural networks, labeled images, data generation, pseudolabeling This article systematizes approaches to addressing the shortage of labeled images in datasets for deep learning models, which constitute the core of modern computer vision applications. It covers methods such as transfer learning using pre-trained networks, weakly supervised and semi-automatic labeling techniques, and various sample expansion strategies through data augmentation. Particular attention is given to data augmentation as a widely applicable and technologically accessible approach. The article presents an experimental analysis of the impact of different augmentation methods, including classical transformations such as rotation, reflection, noise addition, and modern techniques like MixUp, CutMix, and AugMix, on the performance of convolutional neural networks in image classification tasks. Results demonstrate that even basic augmentation methods can substantially improve model generalization when training data are limited, and combining multiple strategies can approach the accuracy achievable with fully labeled datasets. In addition, the study explores the application of pseudo-labeling, a semi-automatic labeling method, with a focus on the RAF-DB dataset for facial expression recognition. By generating confident pseudo-labels for unlabeled samples, the model’s performance improved by up to two percent in overall accuracy and up to ten percent in individual class recognition. This highlights the potential of combining augmentation with pseudo-labeling to enhance model robustness in scenarios with scarce labeled data. An additional experiment using a basic CNN on the CIFAR-10 dataset confirmed the positive effect of augmentation on classification accuracy. The employed transformations included horizontal flipping, random cropping, and color jittering, resulting in a аfive percent increase in overall accuracy compared to models trained without augmentation. The findings indicate that integrating classical and advanced augmentation techniques with semi-automatic pseudo-labeling provides a practical and effective strategy for improving deep learning performance on limited datasets. This combined approach is particularly valuable for real-world applications where labeled data are scarce or costly to obtain, demonstrating a promising direction for further research and deployment in domains such as facial emotion recognition and general object classification.
Post-clustering interpretation of gene expression data using functional enrichment and network analysis
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Computational biology data analysis, bioinformatics, integrative analysis, gene expression data, post-clustering interpretation, functional enrichment, gene ontology, kyoto encyclopedia of genes and genomes, reactome, cytoscape, network-based analysis Clustering of gene expression profiles is a core technique used to reveal hidden biological structures and differentiate disease subtypes in high-dimensional biomedical datasets. Nevertheless, translating cluster structures into biologically meaningful insights requires integrative analytical strategies that go beyond unsupervised learning. In this work, we introduce a novel integrative computational approach that emphasizes post-clustering interpretation by combining statistical functional enrichment with network-based modeling. Clusters of gene expression profiles, previously identified in patients with distinct cancer types, were subjected to enrichment analysis using Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, and Reactome databases. The enrichment was performed with the g:Profiler tool, allowing the detection of significantly overrepresented biological processes, molecular functions, cellular components, and signaling pathways within each cluster. To visualize and further interpret the enriched functional categories, Cytoscape software was employed. Functional interaction networks were constructed using two key modules: ClueGO, which integrates Gene Ontology and pathway annotation into a functionally grouped network, and CluePedia, which expands these networks by showing relationships between genes and enriched terms. This network-based visualization enabled deeper biological interpretation and facilitated the identification of core functional themes. The analysis revealed that each gene cluster is associated with distinct biological processes, such as immune signaling, metabolic pathways, DNA repair, or cell cycle regulation. The novelty of the proposed approach lies in its systematic integration of enrichment statistics with graph-based visualization, ensuring both computational rigor and biological interpretability. These findings confirm that the method can extract biologically consistent knowledge from complex gene expression data. In summary, the study presents an innovative post-clustering interpretation strategy that bridges unsupervised machine learning and functional genomics. This approach advances the explainability of computational analysis and supports its application in disease subtyping, biomarker discovery, and personalized medicine research.
Generalization of the method for constructing GL-models of complex fault-tolerant multiprocessor systems with additional failure conditions
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: GL-models, non-basic fault-tolerant multiprocessor systems, consecutive two-dimensional system, mixed-type systems, reliability assessment The article is devoted to methods for constructing GL-models of fault-tolerant multiprocessor systems. GL-models can be used as behavioral models of such systems under failure flows to evaluate their reliability metrics through statistical experiments. The study considers two types of systems: consecutive two-dimensional systems and mixed-type systems. A consecutive two-dimensional system is defined as one in which components are arranged in the form of a rectangular matrix, and the system fails when a rectangular block of a certain size appears, consisting entirely of failed components. A mixed-type system fails if at least one of the following conditions is met: a specified number of arbitrary components have failed; a specified number of consecutive components have failed; or a rectangular block of a certain size, consisting entirely of failed components, appears within the component matrix. Currently, there are no formalized methods for constructing GL-models for the aforementioned types of systems. The objective of this work is to develop a universal method for constructing GL-models for both consecutive two-dimensional systems and mixed-type systems. It is shown that, to construct a GL-model for such a system, it is sufficient to determine the maximum number of failed components under which the system remains operational. Based on this threshold, a basic system model is constructed without considering additional failure conditions. Then, all combinations of component failures that lead to system failure are identified. The basic model is subsequently weakened at the vectors corresponding to these critical failure combinations. This paper presents, for the first time, an algorithm for constructing GL-models for consecutive two-dimensional systems and mixed-type systems. In addition, it introduces methods for calculating the maximum allowable number of component failures under which the system remains functional, as well as estimating the total number of failure combinations that result in system failure. Experimental results confirm that the proposed models adequately represent the real behavior of such systems under failure flows. Examples are provided to illustrate the GL-model construction process for both of the aforementioned system types.
non-basic fault-tolerant multiprocessor systems
Risk assessment and network description modules in a multi-level wireless sensor network risk assessment ontology
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Wireless sensor networks, multi-level ontology, risk analysis, sensor node, threat intensity, threat probability, Supervisory Control and Data Acquisition Wireless sensor networks have become widely used in industrial automation systems due to their ease of deployment, flexibility, and cost-effectiveness. They provide real-time monitoring, diagnostics, and control of technological processes without the need for complex cabling infrastructure. However, the operation of wireless sensor networks in production environments is accompanied by risks associated with limited energy resources of nodes, the impact of electromagnetic interference, data transmission delays, the inability to guarantee communication stability, and vulnerability to cyberattacks. These threats are exacerbated in complex environments where equipment is located in hard-to-reach places, and the network has limited bandwidth and variable topology. Such conditions require the construction of multi-level structures for network health analysis, risk assessment, and adaptive management. The aim of the research is to develop two functional modules of a multi-level ontology: a risk assessment module and a network description module, that allow for an integrated analysis of threats, vulnerabilities, and wireless sensor network parameters, taking into account their changing state. The research methodology is based on the construction of formalized mathematical models: the network description is implemented through a graph representation with the parameters of nodes, connections, and their dynamics, while the risk assessment is performed using a multifactor model that takes into account the probability of threats, the level of their impact, the criticality of network components, and external risks. The article presents examples of the application of modules based on typical production scenarios that demonstrate the relationship between the structural description of the network and the dynamic assessment of threats. Information from the network description module is used to calculate risks in real time, while the results of the risk analysis affect the dynamic adjustment of network operation parameters. The scientific novelty of the research lies in the integration of two formalized models within a single ontological system of risk-oriented wireless sensor network management. The practical significance lies in the possibility of implementing the proposed approaches in real industrial conditions to increase the reliability, flexibility, adaptability and information security of sensor networks. The proposed system is the basis for building self-adjusting smart networks capable of independently responding to changes in technical condition, threats and environmental parameters.
Supervisory Control and Data Acquisition
A hybrid method for detecting anomalous traffic in computer networks
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Anomaly detection, hybrid method, network traffic, packet signature, fuzzy logic, self-similarity, behavioral model, outbound traffic, intrusion detection, cybersecurity This study addresses the increasing difficulty of detecting anomalies in network traffic caused by growing threats to information and communication systems. Traditional intrusion detection systems often fail to adapt to new threats, particularly when analyzing outbound traffic, which may signal internal compromise. To overcome these limitations, the study proposes a hybrid detection method aimed at improving anomaly identification accuracy. The method integrates three components. First, traffic is classified using a signature-based approach with predefined sets of allowed and prohibited signatures. Second, self-similarity analysis with the Hurst coefficient detects long-term traffic patterns. Third, fuzzy logic is applied to interpret uncertain traffic characteristics, such as port numbers, protocols, intensity, and packet sizes, using linguistic variables and fuzzy rules. The research presents formalized models of both legitimate and malicious user behavior and a composite packet signature model for comprehensive traffic analysis. This approach enhances adaptability and reduces the proportion of unclassified traffic. Experimental validation using real and synthetic data confirms improved detection accuracy and a lower false positive rate compared to conventional methods. The scientific novelty lies in combining deterministic classification with fuzzy logic within a single detection pipeline, with a special emphasis on outbound traffic monitoring. The practical value of the proposed system is its suitability for integration into existing cybersecurity frameworks, contributing to more effective threat detection and reduced operational risks in evolving network environments.
A systematic approach to selecting architectural patterns for IoT development
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Internet of Things, architectural design patterns, pattern selection methodology, quality evaluation model, system architecture The increasing complexity and scale of Internet of Things (IoT) systems, especially within industrial environments, pose significant challenges in system design, including issues of security, interoperability, scalability, and efficient resource utilization. With a wide array of architectural patterns available to address these challenges, developers often struggle to select the most suitable solutions. This paper presents a systematic methodology for evaluating and choosing the best combinations of architectural design patterns tailored for various IoT deployment scenarios. The approach begins by analyzing existing IoT design patterns and modeling their key operational characteristics. A structured template is used to describe each pattern, facilitating consistency and comparability. These descriptions are evaluated using a quality model comprising criteria such as reliability, safety, usability, responsiveness, adaptability, durability, interoperability, and security. A weighted-sum model, with adjustable criterion weights, transforms qualitative assessments into quantitative aggregated scores. This enables objective ranking of patterns and supports defensible architectural decision-making. The methodology is validated through multiple case studies, including general-purpose IoT systems (e.g., smart homes) and Industry 4.0 environments. In each case, patterns are selected based on system-specific priorities. Notably, high-performing patterns such as Cloud-on-the-Loop, Closed-Loop Control, and Role-Based Access Control align well with known best practices and demonstrate the method’s practical applicability. Sensitivity analysis further confirms the approach's adaptability, illustrating how changes in evaluation weights significantly influence the resulting pattern rankings. This systematic methodology improves the reproducibility, transparency, and flexibility of IoT architecture design processes. It empowers developers to tailor architectural solutions to specific domain needs while maintaining alignment with industry standards. Future research will explore extending the methodology to emerging IoT sectors, constructing specialized pattern catalogs, and integrating the selection framework into automated design tools to further streamline the development of IoT systems.
Method for incremental control of consistency between structural and behavioral views of software architecture
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Model consistency, incremental verification, model synchronization, metamodel, ontological constraints, reliability The development of software engineering poses a challenge for researchers to maintain the integrity of models stored simultaneously in a lightweight text format and in a formally rich metadata view. The existence of two views ensures broad compatibility with developer tools and accurate reproduction of semantics, but creates the risk of discrepancies between structural and behavioral descriptions. The relevance of the research is determined by the need for methods that prevent the accumulation of contradictions without significantly affecting the speed of design iterations. The aim of this work is to provide a theoretical justification for an incremental approach that can guarantee the consistency of a metamodel with two views during any successive changes. To achieve this goal, a generalized metamodel has been formed that distinguishes between a structural view for static entities and a behavioral view for dynamic aspects. A correspondence relationship has been introduced between views, which describes pairs of equivalent elements and sets rules for their mutual consistency. The set of rules is formalized in the language of object invariants. Incrementalism is ensured by localizing changes: after editing, only those fragments that are directly involved in the modification are checked, so that the time spent remains proportional to the volume of the updated part. The result of applying the method is to prove the correctness of the proposed restrictions, which excludes the possibility of inconsistent model states. An analytical assessment of the complexity of the procedure confirms a linear dependence on the number of changed elements, which indicates the suitability of the approach for industrial-scale models. A demonstration control example, built on a representative domain, showed that the method detects inconsistency immediately after a single edit and proposes a sequence of actions sufficient to eliminate it without involving outside expertise. As a result, the work proposes a new formal methodology for maintaining consistency between views of a single model, which comprehensively combines localized verification with a declarative description of dependencies. The practical significance is manifested in the reduction of error correction costs, increased reliability of documentation, and the ability to integrate the method into modern modeling and continuous development environments, making it a promising tool for the development and maintenance of large corporate systems.
Heterogeneous swarm complex multilevel reconfiguration based on Swarm Chemistry
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Information technology, swarm intelligence, genetic algorithm, multi-agent systems, scan optimisation, swarm chemistry The need for the rapid and safe analysis of areas affected by emergencies is driving the search for innovative approaches in the field of autonomous systems. One such solution is the use of swarms of unmanned aerial vehicles to scan territories. This study presents a hybrid approach to the dynamic control of swarms of drones, combining self-organisation mechanisms based on ‘swarm chemistry’ with the optimisation of global parameters using an evolutionary algorithm. The proposed multi-level swarm reconfiguration module allows the behaviour of individual agents and the entire formation to adapt in response to environmental changes. A clustering module has been implemented to divide the scanning area into subregions, as well as a route planning system that considers the scanning width of each individual drone. To effectively distribute zones between agents, a proportional algorithm is employed that considers the functional capabilities of each device. Simulation results showed a reduction in mission execution time of more than twofold and an increase in the stability of swarm behaviour, even under conditions of heterogeneous composition and partial agent loss. This approach has significant application potential in emergency monitoring, victim search and damage assessment. This study formulates the architecture of a dynamic swarm reconfiguration system that can adapt to environmental changes in real time. This approach ensures the system’s flexibility and stability when performing tasks in difficult conditions. Further development of the model involves integrating machine learning methods to enhance adaptability and expanding to three-dimensional space to improve the accuracy and realism of simulations.
GL-models for analyzing multiprocessor systems tolerant to one and two failures
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: fault-tolerance, multiprocessor systems, reliability estimation, GL-models, non-basic systems, cyclic graph, edge function The work is devoted to the problem of constructing GL-models of behavior in the fault flow of non-basic fault-tolerant multiprocessor systems. We consider systems that are fault-tolerant to any one failure, and under a certain condition – to any two failures of their processors. It is assumed that this condition is satisfied by a Boolean expression that depends on the states of the system's processors. To build models of such systems, we use the previously proposed method based on combining the expressions of the edge functions of auxiliary basic GL-models corresponding to 1- and 2-fault-tolerant systems. Two alternative ways of constructing auxiliary K(2, n) models are considered. It is shown that the obtained GL-models are based on the same cyclic graphs and differ only in the expressions of their edge functions. The complexity of the expressions of the edge functions of the obtained models is analyzed for the cases of using auxiliary GL-models of each type. The formulas for estimating the complexity of these expressions (the number of elementary logical operations) depending on the number of processors in the system, in particular, for cases when this number is a power of two, are obtained. Also, for the case of using one of the auxiliary models, the possibility of further simplification of the expressions of some of the edge functions is revealed. It is determined in which cases, namely, for what number of system processors, it is advisable to use auxiliary models of one or another type. Experiments confirm that the GL-models constructed in the above way adequately reflect the behavior of the system. Examples are given that demonstrate the application of the considered method of constructing non-basic GL-models and confirm the correctness of estimation the complexity of expressions of their edge functions when using auxiliary models of various types. It has also been shown that further simplification of the expressions for the edge functions in GL-models under construction is possible, particularly by reordering the edges in auxiliary models. However, this possibility has not been investigated in the general case within this article.
Investigation of the efficiency of neural network models for developing a classifier of ophthalmic pathologies
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: machine learning, ophthalmic disease classification, fundus images, deep learning, VGG16, VGG19, EfficientNet, medical image analysis This study presents the development and evaluation of a machine learning-based system for the classification of ophthalmic diseases using fundus images. The dataset consists of images categorized into four main classes: cataract, diabetic retinopathy, glaucoma, and healthy eye. To ensure the accuracy and reliability of the models, the data underwent preprocessing steps, including outlier detection, normalization, balancing, and splitting into training and testing sets. Three deep learning models - VGG16, VGG19, and EfficientNet were utilized for disease classification. The experimental results demonstrated high prediction accuracy across different disease categories, with EfficientNet achieving the highest performance (up to 96.94% for diabetic retinopathy). The system allows users to upload eye images, select a model, and obtain diagnostic predictions with specified accuracy levels. The models were rigorously tested using the Python unittest framework, confirming their stability and reliability. The findings highlight the potential of machine learning in improving ophthalmic disease diagnosis, reducing diagnostic time, and enhancing medical decision-making. The integration of these models into medical practice can significantly improve the quality of healthcare services and assist doctors in providing more efficient and accurate diagnoses.
Using large language models for video processing in the agricultural industry
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: artificial intelligence, large language models, multimodal models, computer vision, video analysis, poultry monitoring, behavior recognition, precision agriculture, OpenCV, streamlit application Modern artificial intelligence technologies, particularly large language models, are increasingly being applied in agriculture to enhance automation, decision-making, and sustainability. This study presents a comprehensive analysis of large language models and their integration with computer vision and video processing for real-time livestock monitoring. A software system was developed that utilizes multimodal large language models to analyze poultry behavior from video streams, enabling the detection of anomalies, prediction of potential health issues, and automatic generation of recommendations for farmers. The system is based on a modular architecture and combines technologies such as OpenCV, FastAPI, and Streamlit. Comparative evaluation of models including GPT-4o, Claude 3.7, and LLaVA demonstrates their suitability for different agricultural tasks. The results confirm the effectiveness of large language model-based solutions in improving operational efficiency, reducing human intervention, and supporting precision agriculture. Despite high computational demands, the proposed approach significantly simplifies the deployment of intelligent monitoring systems and opens new opportunities for smart farming innovations.
Integration of physiological factors into a mathematical model of the human eye condition
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: human eye, mathematical modeling, integral index, nonlinear dependencies, optimization methods, adaptability, monitoring, prediction Mathematical models of the human eye serve as adaptive tools for analyzing and predicting ophthalmological parameters, considering their interrelations and individual patient characteristics. Their application in ophthalmology enhances the quality of diagnostics, monitoring, and treatment, ultimately improving patients' quality of life. The developed eye condition model is based on a mathematical function that integrates physiological eye parameters, each assigned a weight coefficient to determine its impact on the overall condition index. The model accounts for complex nonlinear interactions between parameters, allowing for a more accurate representation of physiological processes. To optimize the weight coefficients, the L-BFGS-B method is employed—an iterative algorithm that efficiently minimizes the loss function and ensures high accuracy in adapting the model to individual patient data.This model offers several advantages: it enables early diagnosis of diseases such as glaucoma, cataracts, and macular degeneration, tailors treatment plans based on individual patient parameters, and facilitates disease monitoring and progression prediction for timely therapy adjustments. Furthermore, the model can be integrated with modern technologies, including virtual and augmented reality systems, as well as artificial intelligence for automated diagnostics. Thus, the proposed mathematical model serves as a universal tool for analyzing eye conditions and developing innovative diagnostic and therapeutic technologies. By incorporating parameter interdependencies and their effects on the physiological state of the eye, it provides ophthalmologists with a powerful instrument for enhancing diagnostics, prediction, and disease monitoring in vision healthcare.
Thermal control of a complex of thermoelectric cooling devices
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: reliability indicators, dynamic characteristics, mass and dimensions, temperature difference, current mode The paper presents the results of research into the possibility of controlling a complex of thermoelectric cooling devices with parallel electrical connection in a non-uniform temperature field. Such formulation of the problem of creating systems for ensuring thermal conditions of radio-electronic equipment is relevant for on-board information systems, which are subject to stringent requirements for reliability and mass-size characteristics. Research are carried out for the most used temperature range from 295K to 250K in the range of power dissipation from 0.5 W to 15 W for different values of supply voltage. The model of the executive body of the system of thermal modes provision based on the complex of thermoelectric coolers with parallel electrical connection is developed. The model takes into account different dissipation power, non-uniform temperature field, different supply voltages and geometry of thermoelectric cell branches. The comparative analysis of basic parameters, reliability indices and dynamic functioning of the complex of thermoelectric coolers for different supply voltages, different temperature levels of cooling with the corresponding thermal load is carried out. The possibility of selecting the optimal supply voltage taking into account the limitations on the operating current, mass, energy, dynamic and reliability characteristics of the thermoelectric cooler complex is shown. The analysis of the research results has shown the possibility of choosing the nominal of supply voltages taking into account the limitations on mass-size, energy, dynamic and reliability characteristics for different geometry of thermoelements branches.
Hybrid detection of fuzzy duplicate texts: сosine similarity and transformers
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: hybrid methods, fuzzy duplicates, cosine similarity, transformer models, ukrainian language texts, content moderation systems This paper addresses the challenge of detecting texts that share the same meaning but differ in wording and structure. Such “fuzzy duplicates” are increasingly prevalent in user-generated content, media articles, and academic materials. Traditional TF-IDF-based methods with cosine similarity process data swiftly but often overlook deeper semantic nuances, especially in languages with free word order and complex morphology (for example, Slavic languages such as Ukrainian or Bulgarian, and agglutinative languages like Hungarian). Fully neural solutions (e.g., transformers) typically offer higher accuracy yet can be slow and computationally demanding. To tackle these issues, we propose a hybrid approach that integrates a simplified neural component with classical cosine similarity. The workflow normalizes text variants (correcting spelling and inflectional forms), converts them into semantic vectors using a lightweight transformer model, and then applies a dynamic threshold mechanism tuned to text genre (e.g., news vs. social media). Experiments on Ukrainian-language datasets suggest that this method balances accuracy and speed more effectively than a fully neural pipeline. The approach is novel in combining domain-specific preprocessing and lightweight neural embeddings for fuzzy duplicate detection in text, achieving approximately ten to twelve percent higher detection accuracy than known solutions while maintaining faster runtime than a full BERT model. Preliminary tests in editorial and plagiarism-checking scenarios indicate that the system more reliably identifies paraphrased content than purely statistical methods, thereby reducing the burden of manual verification. Overall, the hybrid design offers a practical compromise between detection performance and computational requirements, which is especially beneficial for resource-constrained applications in morphologically rich languages like Ukrainian or other Slavic languages. Future efforts will focus on extending morphological coverage to further improve reliability.
Using complex-valued neural networks for aircraft identification
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: complex-valued networks, radar signals, aircraft recognition, phase analysis, aviation safety, object identification This paper presents an approach to aircraft recognition using complex-valued neural networks. The objective of the article is to study the effectiveness of complex-valued neural networks for aircraft identification tasks based on radar data, the efficiency evaluated based on criteria such as classification accuracy, robustness to noise interference, the ability to maintain high accuracy with limited training data, and an optimal trade-off between accuracy and computational complexity. The study focuses on aircraft identification using phase and amplitude characteristics of radar signals, which are essential for aviation security and airspace monitoring. The research method includes theoretical analysis, modeling, and experimental testing. The paper discusses the architectural features of artificial neural networks that utilize complex numbers for signal processing. This approach enables the incorporation of phase information, which significantly improves the accuracy of radar data analysis. The results confirm that complex-valued neural networks surpass traditional models in recognition accuracy. Specifically, the inclusion of the phase component provides an increase in accuracy by up to eight and a half percent. Additionally, complex-valued neural networks demonstrate high resistance to noise interference, maintaining classification accuracy of up to ninety-two and three-tenths percent even at a noise level of thirty percent. Despite these advantages, the primary limitation of complex-valued neural networks is their higher computational complexity compared to real-valued models. This requires significant resources for training and implementation, which can be a critical factor for applications where real-time signal processing speed is essential. The study also explores optimization possibilities for artificial neural networks by developing hybrid approaches that combine the strengths of different network types and by simplifying architectures without compromising accuracy. The findings indicate that artificial neural networks are an effective tool for aircraft classification, particularly in complex signal environments and conditions with noise interference. These networks have significant potential for widespread use in both military and civilian airspace monitoring systems, providing enhanced accuracy and reliability in recognition tasks. The results obtained in this study open new opportunities for advancing aviation security technologies and automating aircraft recognition systems.
Development of a combined model for analyzing gas mixtures using machine learning methods
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: spectral analysis, gas mixtures, complex models, neural networks, optimization methods, machine learning Analysis of gas mixtures is an important task in spectroscopy, environmental monitoring, industrial control and scientific research. Accurate determination of component concentrations in complex gas environments requires advanced approaches that combine physical modeling and artificial intelligence methods. The use of neural networks in spectral analysis allows increasing the accuracy and stability of calculations under variable experimental conditions, which indicates the relevance of the work. The aim of the research is to develop a combined model for spectral light flux analysis that combines physical modeling of spectral absorption of gases with machine learning methods. This provides increased accuracy in determining the concentration of components in multicomponent gas mixtures and allows adaptive adjustment of analysis parameters depending on the measurement conditions. An integrated methodology is proposed, which includes modeling of spectral light flux based on Gaussian and Lorentzian absorption profiles, the use of the Bouguer-Lambert-Beer equations to determine gas concentrations, and training a neural network to predict the light flux. To assess the performance of the developed model, a series of numerical experiments were conducted with varying network parameters and optimizing the configuration. The results obtained confirmed the high efficiency of the model, which is reflected in the high value of the coefficient of determination and low values of the mean square error. The model was tested when changing gas concentrations and the length of the optical path, which confirmed its stability and adaptability. The study showed that the optimal configuration of the neural network includes three hidden layers with an optimal number of neurons, which provides a balance between accuracy and efficiency. A rectified linear activation function was used for stable convergence, and for weight optimization - an adaptive stochastic gradient descent method, which improves performance. The proposed method of combining physical modeling and machine learning provides high accuracy of gas mixture analysis and resistance to variations in external conditions. The scientific novelty of the study lies in the use of a combined approach, which allows adapting the model to a wide range of spectral characteristics. The practical significance of the work lies in the possibility of applying the developed methodology for industrial control, environmental monitoring, and laboratory research, providing a reliable tool for the analysis of complex gas mixtures.