Applied Aspects of Information Technology
type: channel
description: 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.
publisher: Odessa Polytechnic National University
language: en
publication name: Applied Aspects of Information Technology
issn: 2617-4316
items: A system internals modeling and annotation language for large language model-driven software engineering
items: Assessment of digital image quality for visual objects in Gamified Systems
items: Models of semantic analysis of product descriptions for automatic determination of customs codes
items: An empirical evaluation of reasoning models for classifying information manipulation techniques
items: A fragment-based spatial-temporal video analysis method for detecting anomalous violent events
items: Improving the quality of mine detection by a mine-detecting drone by selecting an architecture for classifying objects in visible-light camera images
items: Specialized recurrent U-Net architecture for immunohistochemistry image segmentation
items: Estimating the number of lines of code of the latest releases of open-source applications using nonlinear regression models
items: A machine vision system for real-time grain quality classification using machine learning
items: Analysis of the total random error in simulation systems
items: A method for constructing GL-models of behavior under failure flow for complex non-basic fault-tolerant multiprocessor systems
items: Structure of a model for scientific research of complex electrical objects
items: Vector logic structures and functions of computing
items: A method for synthesizing index policies to ensure the survivability of a mobile platform-based information system
items: Vision-based GUI testing: a systematic review of computer vision advances in software quality assurance
items: Research on the impact of optimization algorithms on the accuracy of YOLOv11 neural networks
items: Online anomaly detection in correlated data streams using robust Kalman filtering
items: Parametric approach to improving significant indicators of thermoelectric systems for maintaining thermal conditions
items: Method for automated control of rotor engine blade synchronization
items: Technological platforms, tools, and standardization in the unified information space for automated unmanned aerial vehicle design
items: Research on improving a graph neural network model forcomputer network simulation
items: Performance evaluation of photon mapping methods
items: Hybrid graphs for code smells: a multi-level model for anti-pattern detection in software components
items: Methods for addressing labeled data scarcity in applied computer vision systems
items: Post-clustering interpretation of gene expression data using functional enrichment and network analysis
items: Generalization of the method for constructing GL-models of complex fault-tolerant multiprocessor systems with additional failure conditions
items: Risk assessment and network description modules in a multi-level wireless sensor network risk assessment ontology
items: A hybrid method for detecting anomalous traffic in computer networks
items: A systematic approach to selecting architectural patterns for IoT development
items: Method for incremental control of consistency between structural and behavioral views of software architecture

A system internals modeling and annotation language for large language model-driven software engineering
type: item
description: Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: System modeling, software architecture, language models, token efficiency, code summarization, software engineering Rapid scaling of large language models has significantly disrupted traditional software engineering paradigms with unprecedented capabilities for code understanding, generation, and automated review. However, practical repository-level deployment is constrained by context limits and token cost. While retrieval-augmented generation is widely used to address this limit, it often splits a codebase into disconnected semantic chunks, omitting high-level structural dependencies. In contrast, alternative approaches that attempt to feed the entire repository structure into the context window typically rely on generic formats such as JSON. While widely recognized, these formats introduce redundant syntactic units that significantly influence the token budget, effectively reintroducing the bottleneck. This work introduces SiMAL (System internals Modeling and Annotation Language), a domain-specific language designed specifically for language-model-driven software engineering workflows, with the primary objective of providing a compact, human-readable, error-tolerant, yet highly structured representation of a software system, optimized for iterative interaction with language models. It combines both static and dynamic aspects of a software system, unifying architectural views, component and endpoint definitions, runtime deployment metadata, and other development artifacts into a single textual schema that can be converted to and from a normalized machine representation. This effort includes the definition of the language’s syntax and grammar, an open-source parser, and a visualizer that renders schemas into nested system diagrams. The proposed language is validated through a comprehensive protocol that assesses token efficiency alongside schema validi…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: System modeling
  topic: keywords
subject: software architecture
  topic: keywords
subject: language models
  topic: keywords
subject: token efficiency
  topic: keywords
subject: code summarization
  topic: keywords
subject: software engineering
  topic: keywords
creator: Mykyta V. Syromiatnikov
creator: Victoria M. Ruvinskaya
rights: Copyright (c) 2026 Applied Aspects of Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 103
ending page: 121
doi: 10.15276/aait.09.2026.08

Assessment of digital image quality for visual objects in Gamified Systems
type: item
description: Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: visual perception, data informativeness, color difference, fractal dimension, integral quality indicator This paper devoted to the development and justification of a comprehensive approach to assessing the quality of digital images in gamification environments, taking into account the peculiarities of the perception of visual objects by users. The relevance of the study is due to the growing role of gamified visual interfaces in digital applications and the need for objective quality control of visual content, which directly affects the level of user engagement and motivation. The paper analyzes modern methods for assessing the informativeness and quality of digital images, in particular, approaches based on texture analysis, spatial structure, machine learning, and deep neural networks. A hybrid method is proposed that combines the evaluation of color differences according to the CIEDE2000 standard with fractal analysis of structural characteristics of images, which allows taking into account both color and geometric-textural properties of visual objects. The research methodology is based on the formulation and testing of hypotheses regarding the influence of image processing quality on the perception of objects in gamified environments, mathematical modeling of the integral quality indicator using weighting factors, as well as software implementation of the proposed approach using Python. Practical testing was carried out on a set of digital images of different levels of graphic complexity (simple, medium complexity, and complex), for which the CIEDE2000 indicators, fractal dimension, integral quality index, and MSE and SSIM metrics were calculated. The results obtained confirm that the combination of color and structural analysis provides a more complete and objective assessment of the quality of gamified images compared to the use of separate method…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: visual perception
  topic: keywords
subject: data informativeness
  topic: keywords
subject: color difference
  topic: keywords
subject: fractal dimension
  topic: keywords
subject: integral quality indicator
  topic: keywords
creator: Denys S. Pastushenko
creator: Olena B. Vovk
rights: Copyright (c) 2026 Applied Aspects of Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 86
ending page: 102
doi: 10.15276/aait.09.2026.07

Models of semantic analysis of product descriptions for automatic determination of customs codes
type: item
description: Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Customs classification, semantic analysis, machine learning, text processing, automation, harmonized system In today's global trade environment, accurate and fast classification of goods by Harmonized System (CUSTOMS/CUSTOMS) codes is crucial for the efficient functioning of customs processes. The increase in data volumes and the complexity of product descriptions create a need to implement intelligent methods for analyzing text information. The use of Natural Language Processing (NLP) technologies in combination with machine learning opens up new opportunities for automating the process of determining customs codes and reducing the human factor in customs classification. The aim of the study is to increase the accuracy of automated determination of Harmonized System codes by applying modern models of semantic analysis of text descriptions of goods. To achieve this goal, the following tasks were set: to analyze existing natural language processing models, to investigate their effectiveness for customs classification tasks, to form the optimal model architecture and to evaluate its advantages compared to traditional algorithms. The work uses semantic modeling and machine learning methods. An experimental approach was used to combine these models with classification algorithms, in particular, logistic regression, decision trees, and neural networks. The evaluation was carried out using accuracy, completeness, and measure indicators. The results of the study showed that the use of contextual embeddings, in particular the BERT model, provides a significant improvement in the accuracy of automated classification of goods compared to traditional statistical methods. The proposed generalized model, combining semantic analysis with machine learning, allows to increase the level of correct assignment of customs codes based on text descriptions, even in cases of …
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Customs classification
  topic: keywords
subject: semantic analysis
  topic: keywords
subject: machine learning
  topic: keywords
subject: text processing
  topic: keywords
subject: automation
  topic: keywords
subject: harmonized system
  topic: keywords
creator: Stepan M. Krupa
creator: Yurii P. Kryvenchuk
rights: Copyright (c) 2026 Applied Aspects of Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 76
ending page: 85
doi: 10.15276/aait.09.2026.06

An empirical evaluation of reasoning models for classifying information manipulation techniques
type: item
description: Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Propaganda detection, artificial intelligence, reasoning models, large language models, information security, information space The rapid evolution of modern geopolitical conflicts has transformed information manipulation and propaganda techniques from a tool of persuasion into a sophisticated weapon of mass influence. As these operations become increasingly complex, relying on subtle psychological tactics rather than overt falsehoods, the development of advanced, automated detection mechanisms is a critical security challenge. This study aims to bridge the gap between theoretical capabilities and practical applications by empirically evaluating the performance of emerging reasoning models in the specific task of classifying information manipulation techniques. The primary objective is to benchmark these generative architectures against historical supervised baselines to determine if their internal chain-of-thought capabilities offer a tangible advantage over traditional pattern-matching approaches in identifying complex rhetorical strategies. The research methodology utilizes a standardized international benchmark dataset for propaganda detection (SemEval-2020 Task 11) to conduct a comparative analysis of frontier models without task-specific fine-tuning. The study employs an inference-only strategy, integrating role-playing, definition embedding, and structured reasoning instructions to simulate expert analysis. A key methodological contribution involves the systematic variation of the reasoning budget allocation during inference to measure the correlation between computational deliberation and classification accuracy. The investigation reveals a distinct semantic advantage where reasoning models significantly outperform previous supervised systems in detecting nuanced techniques that rely on cultural context, emotional weight, and indirect logic. How…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Propaganda detection
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: reasoning models
  topic: keywords
subject: large language models
  topic: keywords
subject: information security
  topic: keywords
subject: information space
  topic: keywords
creator: Oleg A. Boiko
creator: Valeriy Ya. Danylov
rights: Copyright (c) 2026 Applied Aspects of Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 62
ending page: 75
doi: 10.15276/aait.09.2026.05

A fragment-based spatial-temporal video analysis method for detecting anomalous violent events
type: item
description: Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: video analysis, spatio-temporal video processing, event detection, anomalous behavior detection, violent event detection, fragment-based representation, adaptive fragmentation, motion-based analysis, video anomaly detection In this work, we address the problem of violent event detection and classification in video streams under realistic computational constraints. Many safety-critical events, such as violent interactions or abnormal behavior, are characterized by short-term and spatially localized motion patterns, while the majority of video content remains static or irrelevant. Conventional deep learning approaches typically process full video frames or dense spatio-temporal representations, which leads to high computational cost and inefficient use of computational resources. We propose a fragment-based spatio-temporal video analysis method inspired by principles of video coding. Each video frame is divided into fragments, and motion activity is estimated using dense optical flow between consecutive frames. Only fragments exhibiting significant temporal changes are selected for further processing, while static regions are suppressed at an early stage. The fragment size is adaptively adjusted according to local motion intensity, allowing finer spatial resolution in dynamic regions and coarser representation in static areas. The selected fragments form a compact representation that is subsequently used for event classification via lightweight temporal aggregation. By reducing spatio-temporal redundancy prior to feature extraction, the proposed method significantly lowers computational complexity while preserving discriminative motion cues. The proposed method is evaluated on the UBI-Fights dataset, with additional training data augmentation using the Video Fight Detection (VFD2000) dataset. Experimental results demonstrate that the method achieves compe…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: video analysis
  topic: keywords
subject: spatio-temporal video processing
  topic: keywords
subject: event detection
  topic: keywords
subject: anomalous behavior detection
  topic: keywords
subject: violent event detection
  topic: keywords
subject: fragment-based representation
  topic: keywords
subject: adaptive fragmentation
  topic: keywords
subject: motion-based analysis
  topic: keywords
subject: video anomaly detection
  topic: keywords
creator: Tetiana V. Normatova
creator: Sergii V. Mashtalir
rights: Copyright (c) 2026 Applied Aspects of Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 37
ending page: 50
doi: 10.15276/aait.09.2026.03

Improving the quality of mine detection by a mine-detecting drone by selecting an architecture for classifying objects in visible-light camera images
type: item
description: Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Convolutional neural network, transformer, attention, weights, classification quality, tokenization The use of unmanned aerial vehicles (drones) is increasingly being used in humanitarian demining. Multiple sensors operating on different physical principles are used simultaneously to search for mines. This increases the probability of mine detection. To expedite the survey of the area, the initial processing of sensor signals is performed onboard the drone. Therefore, the requirement for sensor signal processing algorithms not only increases the probability of object detection but also improves the ratio of classification accuracy to the required computational effort. One of the sensors used is visible-light cameras. A large number of neural networks have been developed for classifying objects in images. However, a specific feature of their use for humanitarian demining is the lack of large datasets for training and testing. Therefore, the challenge arises of finding neural networks that offer a high ratio of classification accuracy to the required computational effort, while also being able to train on very small data sets. This paper compares convolutional neural networks and networks based on transformers. The networks were trained on a small dataset containing images of anti-tank mines, rocks, and various backgrounds. The study showed that convolutional neural networks train faster and are more resilient to image quality degradation. However, their potential is limited; increasing the number of layers does not significantly improve mine classification quality. Transformer-based neural networks offer greater flexibility and a wealth of options for architectural configuration, resulting in superior performance compared to convolutional networks. Although they are slower to train and potentially require an expanded training dataset, they are the prefer…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Convolutional neural network
  topic: keywords
subject: transformer
  topic: keywords
subject: attention
  topic: keywords
subject: weights
  topic: keywords
subject: classification quality
  topic: keywords
subject: tokenization
  topic: keywords
creator: Oleg M. Galchonkov
creator: Alexey M. Baranov
creator: Illia O. Baskov
rights: Copyright (c) 2026 Applied Aspects of Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 9
ending page: 24
doi: 10.15276/aait.09.2026.01

Specialized recurrent U-Net architecture for immunohistochemistry image segmentation
type: item
description: Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: U-net, deep learning, segmentation, immunohistochemistry, software architecture, biomedical images Microobject segmentation in biomedical images is a complex and time-consuming process, and current efforts are aimed at qualitatively separating the studied objects from the background in the image. Immunohistochemical images are used for breast cancer analysis. One of the types of images is estrogen samples. The specificity of immunohistochemical images requires a more thorough approach to segmentation, because standard algorithms or classical neural network models do not allow achieving the required accuracy. This article presents a new convolutional network architecture based on a recurrent U-network for segmentation of immunohistochemical images. The proposed architecture is specifically designed to process the unique features of immunohistochemical images. The principles of recurrent convolutional neural networks were used to develop the model and train it using open datasets. The architecture proposed in this work is based on the configuration of neural network layers, which allows to obtain the best result for a given type of image. In addition, a pipeline of continuous integration of the program code and the model was implemented to ensure continuous retraining in a cloud environment, which significantly simplified the workflow for researchers and engineers. Additionally, a mechanism for processing medical images and providing automation of the big data processing process using computer vision and deep learning was proposed. The proposed method achieved higher segmentation accuracy. The architecture allows for more accurate cell segmentation on immunohistochemical images, which is a crucial step in the diagnostic pipeline. High-quality cell segmentation increases the reliability of subsequent analyses and parameter extraction, thereby contributing …
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: U-net
  topic: keywords
subject: deep learning
  topic: keywords
subject: segmentation
  topic: keywords
subject: immunohistochemistry
  topic: keywords
subject: software architecture
  topic: keywords
subject: biomedical images
  topic: keywords
creator: Oleh Y. Pitsun
creator: Oleh M. Berezsky
rights: Copyright (c) 2026 Applied Aspects of Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 51
ending page: 61
doi: 10.15276/aait.09.2026.04

Estimating the number of lines of code of the latest releases of open-source applications using nonlinear regression models
type: item
description: Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: regression model, estimation, lines of code, open-source application, normalizing transformation, Mardia criterion, outlier, Mahalanobis distance, test statistic, support vector regression, machine learning The problem of early estimating the number of lines of code of the latest releases of software, including open-source applications, is important because it directly affects the effort prediction for their development and subsequent improvement. The research aim is to build several regression models for early estimating the number of lines of code of the latest releases of open-source applications. The research object is the process of early estimating the number of lines of code of the latest releases of open-source applications. The research subject is the regression models for early estimating the number of lines of code of the latest releases of open-source applications. For early estimating the number of lines of code of the latest releases of open-source applications, the models, confidence, and prediction intervals of two nonlinear regressions with two predictors were constructed using the Box-Cox three-variate normalizing transformation and specialized techniques. These techniques, relying on multiple nonlinear regression analyses incorporating the use of multivariate normalizing transformations, account for the presence of outliers in multidimensional non-Gaussian data. In the paper, we built two nonlinear regression models for early estimating the number of lines of code of the latest releases of open-source applications that depend on two predictors: both the number of classes of their latest and first releases. The first model has good quality, but the second one can only be used for estimating the conditional mean, and it has poor quality for predicting the response as the dependent random variable. We have compared the quality of two con…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: regression model
  topic: keywords
subject: estimation
  topic: keywords
subject: lines of code
  topic: keywords
subject: open-source application
  topic: keywords
subject: normalizing transformation
  topic: keywords
subject: Mardia criterion
  topic: keywords
subject: outlier
  topic: keywords
subject: Mahalanobis distance
  topic: keywords
subject: test statistic
  topic: keywords
subject: support vector regression
  topic: keywords
subject: machine learning
  topic: keywords
creator: Yurii O. Gunchenko
creator: Volodymyr I. Meshcheryakov
creator: Sergiy B. Prykhodko
creator: Alla L. Rachinska
rights: Copyright (c) 2026 Applied Aspects of Information Technology
date: 2026-02-18
publication date: 2026-02-18
starting page: 122
ending page: 133
doi: 10.15276/aait.09.2026.09

A machine vision system for real-time grain quality classification using machine learning
type: item
description: Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Machine vision, grain classification, machine learning, support vector machine, image processing, grain sorting Grain quality control directly affects the efficiency of grain processing, the stability of product parameters, and the economic outcomes of storage and cleaning operations. In industrial practice, visual inspection and manual sampling remain widespread, yet these approaches are time-consuming and sensitive to human subjectivity, especially when large grain volumes must be assessed continuously. This study addresses the need for an automated, objective, and scalable solution for grain quality classification that can operate in real time under conveyor-based conditions. The purpose of the research is to develop and experimentally validate a machine vision system that classifies grain by quality and supports operational decisions for fractional separation and cleaning. An experimental test stand was built to simulate conveyor transportation of grain, enabling controlled variation of belt speed and illumination conditions. A dataset of wheat, corn, and barley was formed using laboratory image capture and manual labeling into three quality classes based on visible defects and damage severity. Image preprocessing and augmentation were applied to increase variability and improve robustness. Two supervised approaches were implemented for comparative evaluation: a deep learning image classifier and a kernel-based classifier using handcrafted visual descriptors. The experimental results demonstrate that the deep learning approach achieves higher classification accuracy, while the kernel-based classifier provides faster inference with a moderate reduction in accuracy. The most frequent misclassifications occur between adjacent quality categories, indicating the importance of borderline-class coverage and labeling consistency. Processing time measurement…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Machine vision
  topic: keywords
subject: grain classification
  topic: keywords
subject: machine learning
  topic: keywords
subject: support vector machine
  topic: keywords
subject: image processing
  topic: keywords
subject: grain sorting
  topic: keywords
creator: Dennis I. Kuznetsov
creator: Ivan O. Muzyka
creator: Oleksiy R. Ivashchenko
rights: Copyright (c) 2026 Applied Aspects of Information Technology
date: 2026-02-18
publication date: 2026-02-18
starting page: 25
ending page: 36
doi: 10.15276/aait.09.2026.02

Analysis of the total random error in simulation systems
type: item
description: 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 o…
subject: Applied information technologies in energy engineering and automation
  topic: section
subject: Applied information technology in energyengineering and automation
  topic: category
subject: Simulation modelling
  topic: keywords
subject: forecasting
  topic: keywords
subject: machine learning
  topic: keywords
subject: information technologies
  topic: keywords
subject: high-precision calculations
  topic: keywords
subject: floating point representation
  topic: keywords
subject: random error summation
  topic: keywords
subject: simulation errors
  topic: keywords
subject: improving accuracy of simulation systems
  topic: keywords
subject: unequal-sized data
  topic: keywords
subject: simulation system
  topic: keywords
subject: representation as arrayslist skills
  topic: keywords
subject: labour market analysis
  topic: keywords
subject: unsupervised learning
  topic: keywords
subject: clustering
  topic: keywords
subject: academic program
  topic: keywords
subject: specific subject competences
  topic: keywords
creator: Andrii А. Levchenko
creator: Ilnara V. Sharipova
rights: Copyright (c) 2025 Applied Aspects of Information Technology
date: 2025-12-04
publication date: 2025-12-04
starting page: 481
ending page: 494
doi: 10.15276/aait.08.2025.31

A method for constructing GL-models of behavior under failure flow for complex non-basic fault-tolerant multiprocessor systems
type: item
description: 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, a…
subject: Applied information technologies in energy engineering and automation
  topic: section
subject: Applied information technology in energyengineering and automation
  topic: category
subject: Fault-tolerant multiprocessor systems
  topic: keywords
subject: GL-models
  topic: keywords
subject: non-basic systems
  topic: keywords
subject: control systems
  topic: keywords
subject: reliability evaluation
  topic: keywords
subject: statistical experiments
  topic: keywords
creator: Vitaliy A. Romankevich
creator: Kostiantyn V. Morozov
creator: Daniil V. Halytsky
creator: Ihor A. Yermolenko
creator: Lefteris Zacharioudakis
rights: Copyright (c) 2025 Applied Aspects of Information Technology
date: 2025-12-05
publication date: 2025-12-05
starting page: 465
ending page: 480
doi: 10.15276/aait.08.2025.30

Structure of a model for scientific research of complex electrical objects
type: item
description: 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…
subject: Systems analysis, applied information systems and technologies
  topic: section
subject: Systems analysis, applied information systems and technology
  topic: category
subject: System analysis
  topic: keywords
subject: system approach
  topic: keywords
subject: methodology
  topic: keywords
subject: principles
  topic: keywords
subject: graph-analytical methods
  topic: keywords
subject: graph
  topic: keywords
subject: modeling
  topic: keywords
subject: management
  topic: keywords
subject: modern power supply
  topic: keywords
creator: Oleh M. Sinchuk
creator: Tetiana M. Beridze
creator: Ihor O. Sinchuk
creator: Oleh V. Dozorenko
creator: Mila L. Baranovska
creator: Oleksandr О. Yalovyi
rights: Copyright (c) 2025 Applied Aspects of Information Technology
date: 2025-12-04
publication date: 2025-12-04
starting page: 453
ending page: 464
doi: 10.15276/aait.08.2025.29

Vector logic structures and functions of computing
type: item
description: 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.
subject: Computer engineering and cybersecurity
  topic: section
subject: Computer engineering and cybersecurity
  topic: category
subject: Element structure
  topic: keywords
subject: vector logic functionality
  topic: keywords
subject: vector logics
  topic: keywords
subject: logic vector
  topic: keywords
subject: Cartesian logic
  topic: keywords
subject: intelligent computing
  topic: keywords
subject: test map
  topic: keywords
subject: fault simulation
  topic: keywords
subject: good-value simulation
  topic: keywords
subject: in-memory modeling for simulation
  topic: keywords
subject: prompt engineering
  topic: keywords
creator: Vladimir I. Hahanov
creator: Svetlana V. Chumachenko
creator: Eugenia I. Litvinova
creator: Hanna V. Hahanova
creator: Volodymyr I. Obrizan
creator: Nataliya H. Maksymova
rights: Copyright (c) 2025 Applied Aspects of Information Technology
date: 2025-12-03
publication date: 2025-12-03
starting page: 442
ending page: 452
doi: 10.15276/aait.08.2025.28

A method for synthesizing index policies to ensure the survivability of a mobile platform-based information system
type: item
description: 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 compo…
subject: Computer engineering and cybersecurity
  topic: section
subject: Computer engineering and cybersecurity
  topic: category
subject: Information system
  topic: keywords
subject: mobile platform
  topic: keywords
subject: survivability
  topic: keywords
subject: index policy
  topic: keywords
subject: dual-loop adaptation
  topic: keywords
subject: method
  topic: keywords
creator: Ihor V. Ruban
creator: Vitalii M. Tkachov
rights: Copyright (c) 2025 Applied Aspects of Information Technology
date: 2025-12-05
publication date: 2025-12-05
starting page: 424
ending page: 441
doi: 10.15276/aait.08.2025.27

Vision-based GUI testing: a systematic review of computer vision advances in software quality assurance
type: item
description: 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 ta…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Computer vision
  topic: keywords
subject: GUI testing
  topic: keywords
subject: multimodal artificial intelligence
  topic: keywords
subject: software quality assurance
  topic: keywords
subject: deep learning
  topic: keywords
subject: large language models
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: automation
  topic: keywords
creator: Maksym A. Chaikovskyi
creator: Eugene V. Malakhov
rights: Copyright (c) 2025 Applied Aspects of Information Technology
date: 2025-12-04
publication date: 2025-12-04
starting page: 397
ending page: 423
doi: 10.15276/aait.08.2025.26

Research on the impact of optimization algorithms on the accuracy of YOLOv11 neural networks
type: item
description: 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.
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Artificial intelligence
  topic: keywords
subject: computer vision
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: COCO dataset
  topic: keywords
subject: hyperparameter optimization
  topic: keywords
subject: SGD
  topic: keywords
subject: Adam
  topic: keywords
subject: AdamW
  topic: keywords
subject: YOLOv11
  topic: keywords
subject: mAP
  topic: keywords
subject: IoU
  topic: keywords
subject: precision
  topic: keywords
subject: recall
  topic: keywords
creator: Serhii M. Kovbasa
creator: Anton O. Holosha
rights: Copyright (c) 2025 Applied Aspects of Information Technology
date: 2025-12-04
publication date: 2025-12-04
starting page: 386
ending page: 396
doi: 10.15276/aait.08.2025.25

Online anomaly detection in correlated data streams using robust Kalman filtering
type: item
description: 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 predi…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Streaming data
  topic: keywords
subject: inertial navigation
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: Kalman filter
  topic: keywords
subject: iForest
  topic: keywords
creator: Taras I. Zavaliy
creator: Nataliya B. Shakhovska
rights: Copyright (c) 2025 Applied Aspects of Information Technology
date: 2025-12-04
publication date: 2025-12-04
starting page: 377
ending page: 385
doi: 10.15276/aait.08.2025.24

Parametric approach to improving significant indicators of thermoelectric systems for maintaining thermal conditions
type: item
description: 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 …
subject: Applied information technologies in energy engineering and automation
  topic: section
subject: Applied information technology in energyengineering and automation
  topic: category
subject: reliability indicators
  topic: keywords
subject: dynamic characteristics
  topic: keywords
subject: thermoelectric material
  topic: keywords
subject: efficiency
  topic: keywords
subject: current mode
  topic: keywords
creator: Vladimir P. Zaykov
creator: Vladimir I. Mescheryakov
creator: Andrii S. Ustenko
rights: Copyright (c) 2025 ААІТ
date: 2025-09-22
publication date: 2025-09-22
starting page: 348
ending page: 358
doi: 10.15276/aait.08.2025.23

Method for automated control of rotor engine blade synchronization
type: item
description: 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 lo…
subject: Applied information technologies in energy engineering and automation
  topic: section
subject: Applied information technology in energyengineering and automation
  topic: category
subject: automated control
  topic: keywords
subject: computer modeling rotary-blade internal combustion engine
  topic: keywords
subject: computer simulation
  topic: keywords
subject: electrical synchronisation
  topic: keywords
subject: torque control
  topic: keywords
creator: Yurii O. Ushenko
creator: Dmytro I. Uhryn
creator: Artem O. Karachevtsev
creator: Yurii O. Halin
creator: Viktor A. Ilin
rights: Copyright (c) 2025 ААІТ
date: 2025-09-23
publication date: 2025-09-23
starting page: 334
ending page: 347
doi: 10.15276/aait.08.2025.22

Technological platforms, tools, and standardization in the unified information space for automated unmanned aerial vehicle design
type: item
description: 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 pr…
subject: Systems analysis, applied information systems and technologies
  topic: section
subject: Systems analysis, applied information systems and technology
  topic: category
subject: model-based engineering
  topic: keywords
subject: product lifecycle
  topic: keywords
subject: digital thread
  topic: keywords
subject: tool interoperability
  topic: keywords
subject: unmanned aircraft
  topic: keywords
subject: systems integration
  topic: keywords
creator: Maksym D. Myrnneko
creator: Vladimir V. Shevel
rights: Copyright (c) 2025 ААІТ
date: 2025-09-26
publication date: 2025-09-26
starting page: 316
ending page: 333
doi: 10.15276/aait.08.2025.21

Research on improving a graph neural network model forcomputer network simulation
type: item
description: 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 nov…
subject: Computer engineering and cybersecurity
  topic: section
subject: Computer engineering and cybersecurity
  topic: category
subject: Сomputer network modeling
  topic: keywords
subject: machine learning
  topic: keywords
subject: graph neural network
  topic: keywords
subject: network parameters
  topic: keywords
subject: node characteristics
  topic: keywords
subject: optimization
  topic: keywords
subject: network topology
  topic: keywords
creator: Viktor S. Buiukli
creator: Petr M. Tishin
creator: Roman I. Naumenko
creator: Oleksandr N. Martynyuk
rights: Copyright (c) 2025 ААІТ
date: 2025-09-04
publication date: 2025-09-04
starting page: 306
ending page: 315
doi: 10.15276/aait.08.2025.20

Performance evaluation of photon mapping methods
type: item
description: 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 …
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Computer graphics
  topic: keywords
subject: photon mapping
  topic: keywords
subject: rendering
  topic: keywords
subject: machine learning
  topic: keywords
creator: Natalia G. Aksak
creator: Dmytro I. Mohylevskyi
rights: Copyright (c) 2025 ААІТ
date: 2025-09-26
publication date: 2025-09-26
starting page: 286
ending page: 305
doi: 10.15276/aait.08.2025.19

Hybrid graphs for code smells: a multi-level model for anti-pattern detection in software components
type: item
description: 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 la…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Machine learning
  topic: keywords
subject: software engineering
  topic: keywords
subject: program analysis
  topic: keywords
subject: graph representation learning
  topic: keywords
subject: static analysis
  topic: keywords
subject: uncertainty estimation
  topic: keywords
subject: transfer learning
  topic: keywords
subject: empirical evaluation
  topic: keywords
creator: Dmytro D. Kurinko
rights: Copyright (c) 2025 ААІТ
date: 2025-09-26
publication date: 2025-09-26
starting page: 274
ending page: 285
doi: 10.15276/aait.08.2025.18

Methods for addressing labeled data scarcity in applied computer vision systems
type: item
description: 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 c…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Machine learning
  topic: keywords
subject: augmentation
  topic: keywords
subject: convolution neural networks
  topic: keywords
subject: labeled images
  topic: keywords
subject: data generation
  topic: keywords
subject: pseudolabeling
  topic: keywords
creator: Anatolii O. Nikolenko
creator: Kyrylo B. Mikhalev
rights: Copyright (c) 2025 ААІТ
date: 2025-09-23
publication date: 2025-09-23
starting page: 263
ending page: 273
doi: 10.15276/aait.08.2025.17

Post-clustering interpretation of gene expression data using functional enrichment and network analysis
type: item
description: 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 pathw…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Computational biology data analysis
  topic: keywords
subject: bioinformatics
  topic: keywords
subject: integrative analysis
  topic: keywords
subject: gene expression data
  topic: keywords
subject: post-clustering interpretation
  topic: keywords
subject: functional enrichment
  topic: keywords
subject: gene ontology
  topic: keywords
subject: kyoto encyclopedia of genes and genomes
  topic: keywords
subject: reactome
  topic: keywords
subject: cytoscape
  topic: keywords
subject: network-based analysis
  topic: keywords
creator: Oleg R. Yarema
creator: Denys O. Senchyshen
creator: Sergii A. Babichev
rights: Copyright (c) 2025 ААІТ
date: 2025-09-25
publication date: 2025-09-25
starting page: 249
ending page: 262
doi: 10.15276/aait.08.2025.16

Generalization of the method for constructing GL-models of complex fault-tolerant multiprocessor systems with additional failure conditions
type: item
description: 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 consec…
subject: Computer engineering and cybersecurity
  topic: section
subject: Computer engineering and cybersecurity
  topic: category
subject: GL-models
  topic: keywords
subject: non-basic fault-tolerant multiprocessor systems
  topic: keywords
subject: consecutive two-dimensional system
  topic: keywords
subject: mixed-type systems
  topic: keywords
subject: reliability assessment
  topic: keywords
creator: Vitaliy A. Romankevich
creator: Ihor A. Yermolenko
creator: Kostiantyn V. Morozov
creator: Alexei M. Romankevich
creator: Oleksandr O. Melnyk
rights: Copyright (c) 2025 ААІТ
date: 2025-06-27
publication date: 2025-06-27
starting page: 216
ending page: 230
doi: 10.15276/aait.08.2025.15

Risk assessment and network description modules in a multi-level wireless sensor network risk assessment ontology
type: item
description: 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 demon…
subject: Computer engineering and cybersecurity
  topic: section
subject: Computer engineering and cybersecurity
  topic: category
subject: Wireless sensor networks
  topic: keywords
subject: multi-level ontology
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subject: risk analysis
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subject: sensor node
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subject: threat intensity
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subject: threat probability
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subject: Supervisory Control and Data Acquisition
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creator: Pavlo R. Shtilman
creator: Petr M. Tishin
creator: Yevhen V. Shendryk
creator: Vitaliy O. Elkin
rights: Copyright (c) 2025 ААІТ
date: 2025-06-27
publication date: 2025-06-27
starting page: 202
ending page: 215
doi: 10.15276/aait.08.2025.14

A hybrid method for detecting anomalous traffic in computer networks
type: item
description: 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.
subject: Computer engineering and cybersecurity
  topic: section
subject: Computer engineering and cybersecurity
  topic: category
subject: Anomaly detection
  topic: keywords
subject: hybrid method
  topic: keywords
subject: network traffic
  topic: keywords
subject: packet signature
  topic: keywords
subject: fuzzy logic
  topic: keywords
subject: self-similarity
  topic: keywords
subject: behavioral model
  topic: keywords
subject: outbound traffic
  topic: keywords
subject: intrusion detection
  topic: keywords
subject: cybersecurity
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creator: Yurii P. Klots
creator: Nataliia S. Petliak
creator: Vira Y. Titova
rights: Copyright (c) 2025 ААІТ
date: 2025-06-27
publication date: 2025-06-27
starting page: 191
ending page: 201
doi: 10.15276/aait.08.2025.13

A systematic approach to selecting architectural patterns for IoT development
type: item
description: 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 significantl…
subject: Computer engineering and cybersecurity
  topic: section
subject: Computer engineering and cybersecurity
  topic: category
subject: Internet of Things
  topic: keywords
subject: architectural design patterns
  topic: keywords
subject: pattern selection methodology
  topic: keywords
subject: quality evaluation model
  topic: keywords
subject: system architecture
  topic: keywords
creator: Danylo K. Chumachenko
creator: Vira V. Liubchenko
rights: Copyright (c) 2025 ААІТ
date: 2025-06-27
publication date: 2025-06-27
starting page: 178
ending page: 190
doi: 10.15276/aait.08.2025.12

Method for incremental control of consistency between structural and behavioral views of software architecture
type: item
description: 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 examp…
subject: Computer science and software engineering
  topic: section
subject: Computer science and software engineering
  topic: category
subject: Model consistency
  topic: keywords
subject: incremental verification
  topic: keywords
subject: model synchronization
  topic: keywords
subject: metamodel
  topic: keywords
subject: ontological constraints
  topic: keywords
subject: reliability
  topic: keywords
creator: Nataliia O. Komleva
creator: Maksym I. Nikitchenko
rights: Copyright (c) 2025 ААІТ
date: 2025-06-27
publication date: 2025-06-27
starting page: 162
ending page: 177
doi: 10.15276/aait.08.2025.11