System research and information technologies

Automated semantic ontology construction for foresight studies using large language models

Serhii Lupenko, Mykhailo Stoliar, Oleksandr Terentiev, Volodymyr Savastiyanov — Tue, 30 Jun 2026 00:00:00 +0300

Recent advances in large language models (LLMs) enable the automated discovery of semantic structures and emerging signals within text streams, offering an opportunity to redesign foresight workflows into continuous, data-driven systems. This study aims to develop and validate an automated framework for extracting, structuring, and comparing semantic ontologies using LLMs. The paralyzed approach was used for data mining from social media platforms and filtering non-domain data. The key semantic elements, goals and hypernyms corresponded, were extracted using multiple LLM configurations, with a consensus mechanism to provide semantic reliability and minimize hallucination. The extracted elements were embedded in a high-dimensional vector space, clustered iteratively using cosine similarity, and merged hierarchically. Convergence process and structural stability were analyzed using the elbow criterion and similarity metrics. The Proposed approach provides a cost-efficient alternative to traditional expert-based foresight analysis. By integrating LLM-driven semantic extraction with quantitative clustering, it enables the identification of emerging trends, weak signals, and long-term thematic structures. The results highlight the potential of LLM-based semantic modeling as a foundation for automated foresight systems.

Scalable text clustering based on word embeddings and noise analysis

Dmytro Shutiak, Gleb Podkolzin, Oleksandr Pokhylenko — Tue, 30 Jun 2026 00:00:00 +0300

Text data clustering is a key component of unstructured text message analysis. To utilize these methods, text data must be converted into vector representations, i.e., word embeddings must be performed. This paper presents a modification of the HDBSCAN* clustering algorithm using custom distance metrics from the Minkowski family (L1, L2, L∞) and parameters specifically tailored for clustering unstructured text data. A major contribution is a novel evaluation metric based on the relative point density of identified clusters and surrounding noise formations (“clouds”). Beyond assessing overall clustering quality, this metric highlights problematic dense accumulations within the noise that require additional manual analysis. Experimental evaluation on the “20 Newsgroups” dataset demonstrated that clustering quality is independent of the α parameter but highly sensitive to the distance metric, with L∞ yielding the best results. The nomic-embedding-v1 model significantly outperformed gte-v1.5 in both the silhouette score and the proposed relative density metric.

Training artificial neural networks for the implementation of the Petri nets synthesis process

Alexander Gurskiy, Andrey Denisenko, Alexander Goncharenko — Tue, 30 Jun 2026 00:00:00 +0300

The important task has been solved in this scientific research related to specific development and verification of the fundamental suitability of the method for automatic synthesis of Petri nets based on the functioning of an artificial neural network. This allows to automate the generating process of logical control algorithms. It has been proposed to divide the artificial neural network into separate local networks for the subsequent stepwise training. Such stepwise training is necessary to form the logical control algorithm and to synthesize the appropriate Petri net. The proposed principles can be applied in the early stages of training for an artificial neural network while synthesizing a Petri net. It has been conducted the experiments linked to the training of local neural networks and to implement the formation of an algorithm for logical control of the laboratory machine for processing chemical solutions. As a result of the experiment, it has been determined the fundamental suitability for the stepwise training method of neural networks for generating Petri nets and the automated formation of logical control algorithms.

Estimating biological age using Kolmogorov–Arnold networks on small data

Volodymyr Slipchenko, Liubov Poliahushko, Volodymyr Rudyk, Vladyslav Shatylo — Tue, 30 Jun 2026 00:00:00 +0300

This article explores the issue of the application of Kolmogorov–Arnold Networks (KAN) for biological age estimation using a dataset of 344 male patients. The dataset includes biomarkers related to bone health and body composition. To enhance model performance, data preprocessing techniques such as polynomial interpolation for missing values and standardization were applied. Pearson and Spearman correlation analyses identified the most relevant biomarkers. Machine learning models were evaluated, along with neural networks and KANs. Experimental results demonstrate that KANs outperform traditional machine learning models and classical neural networks on small datasets. The optimal KAN architecture achieved a correlation coefficient of 0.93, a mean squared error of 18.81, and a mean absolute error of 2.8, surpassing the best-performing conventional models. These findings highlight the potential of KANs as a robust alternative for biological age estimation in resource-limited settings.

Regression analysis of LIGO-Virgo observations WG200115 using curvature tensors from Einstein’s equations and Dirac’s gravitational waves

Yoshio Matsuki — Tue, 30 Jun 2026 00:00:00 +0300

This research presents the mathematical derivation of curvature tensors from the Schwarzschild solution of the gravitational field, along with the derivation of gravitational waves as the second derivative of the field. The elimination of the event horizon is the core contribution of this paper, building on Paul Dirac’s approach in his 1975 book, General Theory of Relativity. This paper introduceslinear and non-linear models for the spacetime distortion function. The fit of the tensor equation for gravitational waves is tested using geometrical information from the LIGO-Virgo observation of WG200115, employing econometric techniques with indicators such as R-squared for the robustness of the given data and the fit of the model to the data distribution, as well as the Durbin–Watson statistic for time-series predictability. The results show a good fit of the derived mathematical model to the observed geometry. Different models for simulating spacetime distortion exhibit varying degrees of fit to the observed geometry, with a less distorted model providing a better explanation for the observed gravitational waves. The model’s fit decreases when considering the rotation of the source object, whether it be a neutron star or a black hole. Further modeling effort is needed to accurately represent the NSBH merger process and its role in the formation of gravitational waves.

Model of factor influence on the operation of the information and measuring system

Ihor Hryhorenko, Svitlana Hryhorenko, Iurii Khoroshailo, Pavlo Biletskyy — Tue, 30 Jun 2026 00:00:00 +0300

The solution of the scientific and practical problem of determining the effect of factor influence on the result of the work of the information measuring system for the technological process of manufacturing processed cheese is considered through the use of a factor influence model that takes into account the simultaneous effect of five factors and their cross-interactions on the control indicator. The task of the study is to implement for use a simplified cross-classification model that makes it possible to estimate the amount of expected information about the levels of the control parameter when taking into account the levels of both influencing factors and their mutual interactions. An electrical schematic diagram of the control system has been developed and its practical implementation has been carried out, thanks to which statistical data on the main parameters of the technological process have been obtained. Conclusions have been drawn about the possibility of further use of the proposed cross-classification model for various information measuring systems regardless of their purpose.

Design and evaluation of a quality assurance method for commit messages in version control systems using Word2Vec, FastText, and GloVe embeddings

Bohdan Semonov, Sergiy Pogorilyy — Tue, 30 Jun 2026 00:00:00 +0300

This paper substantiates the relevance of addressing the problem of ensuring the quality of change descriptions in source code files within version control systems. To filter commit messages, machine learning methods are employed, including neural networks of various architectures. The use of neural networks is justified by the need to identify descriptions that accurately reflect the intent of the changes. A comparative analysis of word embedding methods (Word2Vec, FastText, and GloVe) was conducted, along with their application in binary classifiers such as MLP and RNN for filtering code changes. The models were trained on a dataset of change descriptions collected via the GitHub REST API. Model performance was evaluated using Accuracy and F1-score metrics. The effectiveness of the Google Colab environment for prototyping machine learning models was also confirmed.

Research and processing of ECG signals using discrete and continuous wavelet analysis

Yurii Taranenko, Olha Oliynyk, Borys Moroz, Dmytro Moroz, Valerii Lopatin — Tue, 30 Jun 2026 00:00:00 +0300

The publication is devoted to analyzing electrocardiogram signals of artificial origin of realistic form with the possibility of controlling the duration, sampling frequency, noise level and pulse rate using Ingrid Daubechies wavelets. The synthesized signals were investigated using discrete wavelet analysis to study the influence of these parameters on the approximation and detail coefficients. The priority influence of noise on the detail coefficients and the dependence of the number of signal peaks on the given parameters were established. The article uses for the first time the method of packet discrete wavelet filtering of detail coefficients and approximation coefficients. This allowed to provide a high degree of signal restoration to the original form. Similar studies were conducted for continuous wavelet transformation with the generation of wavelet scalogram images, which provide additional diagnostically significant information. The results obtained in the form of an algorithm are promising for use in analyzing signals from radar systems. The developed model for generating realistic-shaped signals is more efficient and exceeds the average accuracy (96.2 %) compared to analogues (88.03 %). The effectiveness of the developed method is fully confirmed by the correlation matrix of functions of discrete spectra of arti-ficial ECG signals.

Software quality function deployment technology in support quality improving for IT-services digital accessibility standards

Inna Moshchenko, Yevhen Zabolotnyi, Oleg Zaporozhets — Tue, 30 Jun 2026 00:00:00 +0300

Improving the quality of IT services that support accessibility standards is a way to increase the inclusiveness level of digital content. Taking into account the individual requests of users with disabilities in the technical characteristics of software products ensures their digital accessibility. The goal of the article is to develop a comprehensive approach to the application of Software Quality Function Deployment (SQFD) technology to improve the quality of support for IT service accessibility standards. The SQFD method was implemented by using the developed ArtiHoQ software solution, which made it possible to establish connections between consumer quality indicators and technical characteristics of the service, as well as to determine the target values of technical characteristics. The proposed measures to improve technical characteristics were implemented on the example of the uRemediate IT service to ensure accessibility for the users with disabilities, which made it possible to come close to the target values for all key metrics, and significantly increase the comprehensive quality indicator.

The dynamic programming method application to the solution of one fuzzy salesman problem

Eugene Ivokhin, Konstantin Yushtin, Larisa Adzhubey — Tue, 30 Jun 2026 00:00:00 +0300

A method for solving the traveling salesman problem is proposed, utilizing dynamic programming to determine the shortest duration route, considering the fuzzy representation of travel time between individual points. Approaches for the approximation of fuzzy values, arithmetic operations, and methods for ordering fuzzy numbers are presented. The problem formulation with trapezoidal fuzzy numbers is considered. A representation form of such fuzzy numbers based on a Gaussian-like approach is proposed. Methods of the state space tree and dynamic programming are employed. The proposed technique is illustrated with an example.

Hybrid computing intelligent system for assessing the stability of the water distribution system and determining the optimum locations of pressure sensors

Yuriy Zaychenko, Tetiana Starovoit — Tue, 30 Jun 2026 00:00:00 +0300

This study introduces a method for determining the best locations for pressure sensors in water supply networks and for assessing network conditions using artificial intelligence techniques. The goal is to identify the network nodes that would provide the most important information for detecting water leaks and evaluating the overall network status. The selection of sensor locations was based on data sets of pressure changes caused by various leak scenarios generated by EPANET simulations. Genetic algorithms were used to rank candidate nodes and determine the optimal number of sensor locations. The next step involved assessing the network state using the ANFIS neuro-fuzzy network and the Mamdani neuro-fuzzy logical inference algorithm. These algorithms were implemented in the Google Colab environment and tested on a section of the water supply network in Kyiv, Ukraine.

Development of a mathematical model of wave processes in multilayered structures with an adaptive algorithm and hybrid calculations

Yurii Bilak — Tue, 30 Jun 2026 00:00:00 +0300

The paper investigates the numerical simulation of wave processes in multilayer thin films, which is relevant for understanding their physical properties and optimization for various applications. An integrated mathematical model has been developed that combines Maxwell’s equations, mechanical vibrations and thermal conductivity, taking into account the interaction of physical fields in structures with defects. Adaptive algorithms have been proposed for automatic mesh refinement depending on local gradients of physical parameters, which allows to increase the accuracy of modeling in critical zones. A hybrid approach to calculations using CPU and GPU has been implemented, which ensures efficient use of resources for large-scale problems. Software with a modular architecture has been developed that allows integrating numerical methods, optimization and visualization of results in real time. Experimental validation has confirmed the high accuracy and reliability of the model. The results obtained contribute to a deeper understanding of physical processes in thin films and are the basis for the creation of highly efficient multilayer structures in industrial and scientific applications.