Herald of Advanced Information Technology
type: channel
publisher: Publ. Nauka i Tekhnika
language: en
publication name: Herald of Advanced Information Technology
items: Computer simulation of an electric bicycle wheel drive with real load emulation
items: Method for video data transmission at the application layer with adaptive control in an unmanned aerial vehicles network
items: Model and method for evolutionary control of the decision-making structure in intelligent systems
items: Implicit vs explicit reasoning in strategic narrative detection
items: Cross-modal representation learning for accurate harmonized system code classification in е-commerce systems
items: Controlled spectral parameter synthesis based on an adaptive informative subset
items: Convolved numbers of k-section of the Fibonacci sequence: properties, consequences
items: Comparative evaluation of deep neural networks for brain tumor classification from magnetic resonance imaging
items: Optimizing the control of a transactive energy system with periodically changing architecture based on Internet of energy
items: Anomaly detection on time series data for autonomous robot operation
items: Non-numerical information processing in biotechnical systems with biofeedback
items: A modified image encryption algorithm based on the chaotic Tent map
items: Methodology for land cover change detection in aerial images using a deep convolutional neural network and gradient boosting
items: A method for detecting financial phishing in instant messengers using an ensemble of dialogical intelligent assistants based on large language models
items: Hybrid neural network-heuristic model for forecasting the energy demand of livestock farms
items: IMECA method of risk-based assessment and ensuring cybersecurity of Large Language Models
items: A method for constructing non-basic GL-models by combining models with arbitrary graph structures
items: Game-theoretic method of decentralized load balancing in microservice architectures
items: Unsupervised Re-identification architecture based on segmented tracklets for animal behavior analysis
items: Comparative analysis of signal processing techniques for multimode polymer fiber interferometers
items: Estimation of task-specific manipulability scores for a robotic manipulator in vacuuming scenarios
items: Development of a product pricing algorithm using RFM strategy for user cohorts using machine learning methods
items: Comparative analysis of chaotic and deterministic methods to territory coverage by drone swarms
items: Road traffic accident classification using a sparse videotransformer and adaptive fragmentation
items: Development of a geoinformation method for assessing the accessibility of medical services in community infrastructure development projects
items: Analysis of methods for supporting personalization in IT education
items: Information model of remote sensing of soil
items: A model for keyword spotting in voice signal for specialized computer systems
items: Development and optimization of distributed high-performance systems with real-time data consistency
items: AI-Generated video evaluation by fragment processing

Computer simulation of an electric bicycle wheel drive with real load emulation
type: item
description: Topicality. Micro-mobile electric vehicles, whose drives are based on brushless DC motors, have become widespread in the last decade. The features of the electric drive systems of these vehicles and their on-board power supply systems are the subject of scientific research aimed at solving such key problems as: increasing the driving range, reducing electromagnetic torque ripple, improving the efficiency of regenerative braking, etc. A review of the literature shows that solving each individual problem involves two different approaches: computer simulation and physical experimentation. Purpose and objectives. To combine both approaches of testing: conducting experimental research with real vehicle systems and simulating the effect of road load and real dynamic load on the drive in transient modes using a corresponding load emulator. The latter consists of hardware that generates mechanical load on the drive and software that allows simulating various specified and repeated vehicle motion modes in experiments. Methods. In this work, the Hardware-In-The-Loop approach is applied to an electric bicycle test bench, in which a real wheel with in-wheel motor is connected via a friction transmission to a DC loading machine controlled by a two-quadrant DC-DC converter. Results. In this work, to control the torque of the loading machine, an emulator control system structure was developed, in which the conditions for the movement of the electric bicycle are set and tasks related to these conditions are formed for static and dynamic loads. A computer model of a test bench for studying the electric bicycle drive, developed in the Matlab/Simulink environment, made it possible to identify the features of the hardware part of the in-wheel motor load emulator. In particular, during the emulation of a dynamic load associated with the high moment of inertia of the drive, when the electric bicycle starts and initially accelerates at low speeds, the two-quadrant DC-DC converter is unabl…
subject: Information technologies in energy systems engineering and manufacturing
  topic: section
subject: Information technology in energy engineering and manufacturing
  topic: category
subject: Electric bicycle
  topic: keywords
subject: test bench
  topic: keywords
subject: physical model of an electric drive
  topic: keywords
subject: brushless DC motor
  topic: keywords
subject: real load emulator
  topic: keywords
subject: computer model
  topic: keywords
creator: Ihor Z. Shchur
creator: Ihor Y. Lutchyn
creator: Mariusz Korkosz
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-04-01
publication date: 2026-04-01
starting page: 247
ending page: 260
doi: 10.15276/hait.09.2026.17

Method for video data transmission at the application layer with adaptive control in an unmanned aerial vehicles network
type: item
description: Video streams transmitted from unmanned aerial vehicles are a critical source of situational information for monitoring, search-and-rescue, emergency response, and security applications. In such time-critical scenarios, the practical value of a video frame rapidly decreases after a deadline, while unmanned aerial vehicle networks are characterized by stochastic variability of delay, jitter, losses, and dynamic topology. Therefore, the problem of ensuring timely delivery of video frames through adaptive, application-layer control becomes relevant. The object of research is the process of video frame transmission in unmanned aerial vehicle networks under stochastic channel and routing conditions. The subject of research is methods and models of adaptive control of video frame delivery at the application layer, including strategy selection under deadline constraints. The purpose of this paper is to develop an application-layer method for adaptive transmission of video frames that increases the probability of delivery before a deadline and reduces the fraction of late-or-lost frames in unmanned aerial vehicle networks. A conceptual framework for adaptive control of video frame transmission is proposed, including a decision model for selecting transmission strategies based on observed network conditions and deadline requirements. An adaptive algorithm for per-frame strategy selection is developed, incorporating rules for retransmission limitation, buffering management, and robustness mechanisms (e.g., redundancy-based delivery). A set of evaluation metrics is defined to characterize timeliness and reliability, including the probability of on-time delivery, average delay of delivered frames, loss probability, and the late-or-lost fraction. Simulation-based assessment demonstrates that adaptive control improves deadline reliability compared to fixed transmission strategies across representative unmanned aerial vehicle network regimes.
subject: Information technologies and computer systems
  topic: section
subject: Information technologyand computer systems
  topic: category
subject: Unmanned aerial vehicle networks
  topic: keywords
subject: video streaming
  topic: keywords
subject: application-layer control
  topic: keywords
subject: adaptive transmission
  topic: keywords
subject: deadline reliability
  topic: keywords
subject: stochastic delay
  topic: keywords
subject: jitter
  topic: keywords
subject: packet loss
  topic: keywords
creator: Serhii G. Semenov
creator: Yu Jian
creator: He Jiang
creator: Olena P. Chernykh
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-04-01
publication date: 2026-04-01
starting page: 212
ending page: 229
doi: 10.15276/hait.09.2026.15

Model and method for evolutionary control of the decision-making structure in intelligent systems
type: item
description: The relevance of the study is обусловлена the fact that modern intelligent software systems operate under conditions of incomplete, conflicting, and dynamically changing data, as well as limited computational resources, which necessitates improving the adaptability, robustness, and correctness of decision-making processes compared to traditional approaches focused on tuning individual algorithms. Furthermore, the increasing complexity of information environments and the need to process heterogeneous data sources intensify the requirements for consistency and reliability of analysis results. Under such conditions, the transition to controlling the structure of the decision-making process as a higher-level object becomes particularly important. The aim of the study is to improve the adaptability, robustness, and correctness of decisions by shifting from the tuning of individual algorithms to the controlled modification of the structure of the analysis process, including the composition of active software components, the order of their interaction, and the rules governing transitions between processing modes. The methods of the study are based on the approach of evolutionary control of the decision-making structure, which involves controlled transformations of admissible configurations, integration of fuzzy interpretation and evidence aggregation, assessment of the reliability and consistency of information sources, as well as formal proof of correctness, finite convergence, and local optimality properties. The proposed approach enables adaptive selection of the analysis process configuration depending on the level of uncertainty and available resources. In addition, the method ensures a balance between decision-making accuracy and computational resource costs under dynamic operating conditions. The results of the study consist in the development of a model and a method for evolutionary control of the decision-making structure, as well as their practical implementation…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Adaptive systems
  topic: keywords
subject: intelligent software systems
  topic: keywords
subject: evolutionary control
  topic: keywords
subject: decision-making structure
  topic: keywords
subject: uncertainty
  topic: keywords
subject: fuzzy interpretation
  topic: keywords
subject: evidence aggregation
  topic: keywords
subject: source reliability
  topic: keywords
subject: adaptive architecture
  topic: keywords
subject: life cycle
  topic: keywords
creator: Nataliia O. Komleva
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-04-01
publication date: 2026-04-01
starting page: 181
ending page: 198
doi: 10.15276/hait.09.2026.13

Implicit vs explicit reasoning in strategic narrative detection
type: item
description: Relevance of the research: The proliferation of state-sponsored propaganda across the global digital ecosystem constitutes a severe threat to international security and democratic stability. As cognitive warfare evolves, adversaries rely on subtle, culturally nuanced strategic narratives to polarize public opinion and alter collective behavior. Defending the information space requires advanced natural language processing systems capable of deep, context-aware reasoning to identify and deconstruct manipulative narratives in real time, moving beyond traditional manual analysis. Aim and objectives: This study evaluates the efficacy of advanced artificial intelligence Reasoning Models in classifying strategic narratives. The research investigates the interplay between internal computational reasoning budgets and the enforcement of explicit step-by-step reasoning instructions, seeking to uncover how modern architectures process geopolitical nuances and identify potential interference effects caused by traditional instruction techniques. Methods used: To achieve this, the investigation conducted extensive empirical evaluations across a diverse spectrum of large language models utilizing the DIPROMATS 2024 dataset, analyzing social media posts published by diplomatic authorities. The experimental framework isolated variables by varying internal reasoning resources and prompt output instructions. The methodology bypassed complex modular architectures in favor of highly optimized, singular instruction configurations embedded with contextual examples. The assessment rigorously measured exact classification precision, operational latency, and economic efficiency. Results: The findings reveal a significant shift in optimal processing strategies. The research identified a critical reasoning paradox, demonstrating that enforcing explicit step-by-step reasoning actively degrades the classification accuracy of some highly capable models by disrupting their optimized latent analytic…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Рropaganda 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: cognitive warfare
  topic: keywords
creator: Oleg A. Boiko
creator: Valeriy Ya. Danylov
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-04-01
publication date: 2026-04-01
starting page: 168
ending page: 180
doi: 10.15276/hait.09.2026.12

Cross-modal representation learning for accurate harmonized system code classification in е-commerce systems
type: item
description: Accurate classification of goods according to the Harmonized System remains a critical challenge in international trade and e-commerce due to the complexity of product descriptions, ambiguity of textual data, and variability in product representation. The novelty of this study lies in the development of a cross-modal representation learning approach for automated Harmonized System code classification that integrates both textual and visual product information within a unified framework. By leveraging multimodal data, including product descriptions and images, the proposed system improves classification accuracy and robustness compared to traditional approaches that rely solely on textual information. In addition, the proposed framework enables more reliable identification of product characteristics by aligning semantic and visual representations in a shared feature space, which enhances the model’s ability to handle incomplete or ambiguous product descriptions commonly encountered in e-commerce environments. The methodology is based on contrastive learning techniques that align semantic representations across modalities, enabling the model to capture deeper relationships between product attributes and Harmonized System codes. Transformer-based encoders are employed for textual feature extraction, while convolutional or vision transformer architectures are used for image representation. A joint embedding space is constructed to facilitate effective cross-modal interaction and classification. Experimental evaluation is conducted on a real-world e-commerce dataset, demonstrating that the proposed approach significantly outperforms baseline models in terms of accuracy, precision, and recall. The results highlight the effectiveness of multimodal learning in handling noisy, incomplete, and heterogeneous product data commonly encountered in customs and trade environments. The proposed framework contributes to the advancement of intelligent customs classification systems by…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Harmonized System classification
  topic: keywords
subject: cross-modal learning
  topic: keywords
subject: multimodal machine learning
  topic: keywords
subject: contrastive learning
  topic: keywords
subject: е-commerce
  topic: keywords
subject: product classification
  topic: keywords
subject: transformer models
  topic: keywords
subject: computer vision
  topic: keywords
subject: customs automation
  topic: keywords
subject: trade compliance
  topic: keywords
creator: Stepan M. Krupa
creator: Yurii P. Kryvenchuk
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-04-01
publication date: 2026-04-01
starting page: 158
ending page: 167
doi: 10.15276/hait.09.2026.11

Controlled spectral parameter synthesis based on an adaptive informative subset
type: item
description: The relevance of the study is due to the increasing complexity of inverse spectroscopy problems arising in materials science, optoelectronics and related fields, as well as the limitations of classical methods of spectral synthesis of parameters, which are based on the use of the full spectrum. This leads to high computational costs, reduced numerical stability and deterioration of the identifiability of parameters in the presence of noise. In this regard, it is relevant to develop methods that provide a controlled reduction in the dimensionality of spectral data without losing the physical correctness and accuracy of parameter synthesis. The aim of the work is to develop a method of controlled spectral synthesis of parameters, in which the inverse spectral problem is solved on an adaptively formed informative subset of the spectrum. To achieve the set goal, the following tasks were solved: the concept of spectral informativeness was formalized based on the sensitivity analysis of parameters; a mechanism for adaptive formation of an informative spectral subset was developed; A controlled computational cycle of parameter synthesis using a physically based model was constructed; criteria for stability and stopping the synthesis process were determined. The work applied methods of mathematical modeling of spectral characteristics, variational optimization methods, sensitivity analysis, regularization of inverse problems, and principles of controlled computational processes. In the developed method, spectral synthesis of parameters is considered not as a one-time optimization procedure, but as a closed controlled cycle with dynamic adaptation of the spectral region of analysis. The results obtained demonstrate that the use of an adaptive informative subset of the spectrum allows to significantly reduce the amount of spectral data while maintaining an admissibly small relative error in parameter estimation. It is shown that in the process of iterative controlled synthesi…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Сontrolled synthesis
  topic: keywords
subject: spectral analysis
  topic: keywords
subject: inverse problems
  topic: keywords
subject: controlled method
  topic: keywords
subject: adaptive subset
  topic: keywords
subject: parameter sensitivity
  topic: keywords
creator: Yurii Yu. Bilak
creator: Antonina M. Reblian
creator: Oksana Yu. Mulesa
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-04-01
publication date: 2026-04-01
starting page: 140
ending page: 157
doi: 10.15276/hait.09.2026.10

Convolved numbers of k-section of the Fibonacci sequence: properties, consequences
type: item
description: Chebyshev polynomials are used to solve numerous applied problems in computer science involving interpolation theory, approximation theory, numerical analysis, dynamical systems theory, and number theory. However, when forming pseudo-random recurrent sequences, the use of derivatives of Chebyshev polynomials, especially of higher orders, is much less common in the literature, although the issue is quite actual. This article somewhat fills this gap. It is shown that among recurrent sequences used for information analysis and to improve its cryptographic protection, the Fibonacci sequence and its generalizations are the most popular. This article considers a further generalization of Fibonacci numbers, namely, folded numbers k-sections of the Fibonacci sequence. The objective of the research is to further generalize the Fibonacci numbers, namely, the collapsed numbers of the k-intersection of the Fibonacci sequence. The research used modern methods of number theory. The properties of the obtained sequences are determined, and new connections between their elements are found. A further development of Fibonacci-type sequences is proposed, based on the relationship between the derivatives of Chebyshev polynomials of the second kind and Chebyshev polynomials themselves, as well as on the relationship between the folded numbers  the cross-sections of k the Fibonacci sequence and the derivatives of Chebyshev polynomials of the second kind through Lucas numbers. A number of identities are obtained linking Fibonacci numbers and Lucas numbers. It is shown that higher-order derivatives of Chebyshev polynomials prove to be an effective basis for solving certain problems in number theory, namely, the construction of new sequences. Thus, the study resulted in a family of generalized sequences with higher order growth and more complex coupling coefficients than the known Fibonacci sequences. This makes the resulting generalized sequences ideal for sparse data compression and for s…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Information systems
  topic: keywords
subject: data encryption
  topic: keywords
subject: linear shift algorithm
  topic: keywords
subject: Fibonacci numbers
  topic: keywords
subject: Lucas numbers
  topic: keywords
subject: folded Fibonacci numbers
  topic: keywords
subject: k-sections of the Fibonacci sequence
  topic: keywords
subject: derivatives of Chebyshev polynomials of the second kind
  topic: keywords
creator: Vitaly M. Khamitov
creator: Dmitro V. Dmitrishin
creator: Alexander M. Stokolos
creator: Daniel A. Gray
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-04-01
publication date: 2026-04-01
starting page: 129
ending page: 139
doi: 10.15276/hait.09.2026.09

Comparative evaluation of deep neural networks for brain tumor classification from magnetic resonance imaging
type: item
description: The growing prevalence of brain tumors and the complexity of magnetic resonance image interpretation create a need for automated decision support tools that can improve diagnostic accuracy and reduce the workload on medical specialists. Modern deep learning approaches demonstrate significant potential in medical image analysis, but the selection of appropriate model architectures requires systematic comparative evaluation considering accuracy, robustness, and computational efficiency. The aim of this study is to evaluate the performance of modern deep learning architectures for multiclass brain tumor classification based on magnetic resonance images and to determine their suitability for practical implementation under different computational conditions. The research is based on the application of transfer learning using several convolutional neural network architectures. The dataset was prepared through cleaning, normalization, and augmentation procedures to improve model generalization and robustness. The models were trained and evaluated using multiple quality criteria, including classification performance, stability under data distortions, and interpretability analysis based on visualization of decision regions. Computational complexity and inference efficiency were also analyzed to assess deployment feasibility. The comparative evaluation demonstrated that deep neural networks are capable of reliably distinguishing between different tumor types and normal brain conditions. One architecture showed the highest overall classification performance and robustness to noisy input data, while architecture provided a more balanced trade off between computational efficiency and prediction quality, making it suitable for resource constrained environments. Visualization analysis confirmed that the models focus on diagnostically relevant regions of the images, supporting the validity of their predictions. The study confirms the effectiveness of deep learning models for automa…
subject: Information technologies in socio-economic, organisational and technical systems
  topic: section
subject: Information technology in socio-economic,organisational and technical systems
  topic: category
subject: Deep learning
  topic: keywords
subject: magnetic resonance imaging
  topic: keywords
subject: brain tumor classification
  topic: keywords
subject: medical image analysis
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: decision support systems
  topic: keywords
creator: Dmytro I. Uhryn
creator: Yurii O. Ushenko
creator: Artem O. Karachevtsev
creator: Yurii O. Halin
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 100
ending page: 113
doi: 10.15276/hait.09.2026.08

Optimizing the control of a transactive energy system with periodically changing architecture based on Internet of energy
type: item
description: The article proposes a solution to the problem of improving the quality and efficiency of control systems for transactive energy systems, which are characterized by repeated periodic changes to their architecture. Information technologies are expanding the capabilities of microgrids, one such technology being the Internet of Energy (IoE). IoE is a global interconnected network consisting of smart grids interacting with each other via information technologies. IoE facilitates the convergence of cyber-physical and economic indicators of network operation, providing the ability to improve control quality by predicting expected changes in dynamics. The proposed solution for IoE control optimization is based on load change forecasting technology, ensuring efficient power distribution between generators and an optimal generator on/off schedule. The optimization criterion and constraints are formulated mathematically. The proposed load forecasting technology is based on the use of the eigenvalues of the state matrices of the transactive energy system. The results of calculating the eigenvalues calculated by the methods of the first group (the Power method and the Khilenko method) and the second group (the Krylov method and others) are presented. The article considers a transactive system with a variable structure based on the Internet of Energy, which can consist of a different set of generation sources, solving an optimization problem based on technical and economic criteria. The solution of the optimization problem of minimizing the cost of primary fuel for diesel generators to reduce costs and reduce CO2 emissions using power distribution programs for forecasted load schedules and forecasted RES schedules is demonstrated using examples of two transactive systems.
subject: Information technologies in energy systems engineering and manufacturing
  topic: section
subject: Information technology in energy engineering and manufacturing
  topic: category
subject: transactive energy system
  topic: keywords
subject: Internet of Energy
  topic: keywords
subject: microgrid
  topic: keywords
subject: control optimization
  topic: keywords
subject: Krylov method
  topic: keywords
subject: Khilenko method
  topic: keywords
creator: Volodymyr V. Khilenko
creator: Serhii P. Denysiuk
creator: Halyna S. Bielokha
creator: Oleksii V. Stepanov
creator: Li-Wei Kang
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-02-18
publication date: 2026-02-18
starting page: 85
ending page: 99
doi: 10.15276/hait.09.2026.07

Anomaly detection on time series data for autonomous robot operation
type: item
description: Autonomous robots deployed in safety-critical applications require real-time monitoring systems to detect both environmental and mechanical anomalies. While anomalies in visual sensor data (changing lighting, unexpected obstacles) are often obvious, mechanical failures – such as motor malfunctions or wheel slippage – may not be directly observable in images, making detection challenging. Additionally, real-world operational data is severely imbalanced: normal behavior is well-represented, but anomalous events are rare. This imbalance renders traditional supervised learning approaches ineffective. To address these challenges, this paper presents a staged multimodal autoencoder architecture – a neural network system that processes both visual information (RGB camera images) and motion sensor data simultaneously. Unlike conventional multimodal systems that train all components jointly and suffer from modality competition, proposed architecture employs a three-stage training curriculum that trains visual and motion encoders independently before joint optimization, preventing gradient imbalance and ensuring robust representations. The system performs anomaly detection through reconstruction error analysis: lower errors indicate normal operation patterns, while deviations signal potential anomalies. The method requires only normal operational data for training – no labeled anomaly samples are necessary. Experimental validation demonstrates that the architecture detects visual anomalies (color distortions, unexpected objects) and motion anomalies (sudden stops, jerks, velocity changes) in real-time. The proposed method is needed for safety-critical applications like autonomous robot navigation and warehouse automation, where detecting mechanical and environmental anomalies is essential for operational safety.
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Anomaly detection
  topic: keywords
subject: staged training
  topic: keywords
subject: robot operation
  topic: keywords
subject: multimodal fusion
  topic: keywords
subject: imbalanced data
  topic: keywords
creator: Maksym A. Shavarskyi
creator: Yurii P. Kryvechuk
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-02-18
publication date: 2026-02-18
starting page: 46
ending page: 59
doi: 10.15276/hait.09.2026.04

Non-numerical information processing in biotechnical systems with biofeedback
type: item
description: The paper considers issues of coordinating functional and cybernetic models when creating biotechnical systems with biological feedback. It is noted that the functional model of a biological object, built on the imperative principle of achieving a useful result, has a number of features, the most important of which is the ambiguity of the response to the input impact. The principle of the constancy of the output response of a technical object to the same input stimulus is fundamental for the construction and testing of hardware and software. The reasons for the emergence of uncertainty in the output feature space of a biological object as a response to an input stimulus are analyzed. The necessity of processing the output feature information of a biological object in real time by means of non-numerical transformation of a sequence of samples with the determination of the Kameni median and decision-making based on the sign criterion is substantiated. The effectiveness of using a non-numerical approach for statistical processing of data with a limited sample size and the presence of anomalous outliers is demonstrated. As an application, the developed method and an infrared peloidotherapy chamber with radiation intensity control based on the reaction of a person in a biological feedback loop are used. The output feature space of a biological object is analyzed, and the feasibility of using skin resistance indicators is justified and modeled. The results of experimental studies of the relationship between skin resistance and heart rate indicators and the intensity of infrared radiation are presented. The possibility of controlling the permeability of human skin using a technical component of the system is demonstrated.
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Biotechnical system
  topic: keywords
subject: model matching
  topic: keywords
subject: feature space
  topic: keywords
subject: ambiguity of response
  topic: keywords
subject: non-numerical processing
  topic: keywords
creator: Vladimir I. Mescheryakov
creator: Dmitry V. Mescheryakov
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 20
ending page: 29
doi: 10.15276/hait.09.2026.02

A modified image encryption algorithm based on the chaotic Tent map
type: item
description: In a number of professional image exchange industries over open communication channels, ensuring the confidentiality and security of images is a key requirement. Furthermore, image exchange must be carried out quickly. These industries include telemedicine, forensic and forensic examinations, high-resolution satellite imagery transmission, and the Internet of Things. In this context, developing image encryption algorithms that meet these requirements is highly relevant. Encryption systems based on chaotic maps offer promising solutions for such problems. Chaotic map-based encryption is a data security algorithm (most often, images) that exploits the properties of deterministic chaos to generate pseudorandom sequences. The key advantages of these algorithms are sensitivity to initial conditions; speed—the algorithms are mathematically simple and faster than many classical ciphers; and security—they provide protection against statistical attacks and brute-force attacks thanks to a huge key space. These algorithms are used to encrypt multimedia (particularly images) in industries where high-speed processing of large volumes of data while maintaining a high level of confidentiality is essential. A modified image encryption algorithm based on the Tent chaotic map has been developed. This algorithm generates a long pseudorandom sequence based on a short key (seed). This sequence is then applied to image encryption using the Vernam algorithm. The sequence can be long enough to encrypt a large image. An integrated encryption quality criterion has been developed for comparative evaluation of encryption quality. The integral criterion combines encryption quality metrics (correlation, entropy, and resistance metrics) into a convolution constructed using the ideal point method. To evaluate the quality of the developed encryption algorithm, a preliminary computer experiment was conducted. In the experiment, the developed algorithm was compared with well-known stream and block en…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: image encryption
  topic: keywords
subject: deterministic chaos
  topic: keywords
subject: chaotic Tent map
  topic: keywords
subject: Vernam cipher
  topic: keywords
subject: integrated quality indicator
  topic: keywords
creator: Dmitro V. Dmitrishin
creator: Vitaly M. Khamitov
creator: Svitlana G. Antoshchuk
creator: Viktor O. Boltenkov
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 9
ending page: 19
doi: 10.15276/hait.09.2026.01

Methodology for land cover change detection in aerial images using a deep convolutional neural network and gradient boosting
type: item
description: Tracking surface land cover changes using high-resolution aerial imagery is complicated by the varying spatial resolutions of images, spectral variability, and the presence of optically comparable anthropogenic features. Identifying amber extraction sites is especially difficult because of their fragmented morphology, numerous localized impacts of diverse geometries, and substantial internal heterogeneity. Such factors drastically reduce the precision of traditional pixel-based or threshold-driven methods. The work aims to develop a methodology for automated detection of land cover changes using high-resolution aerial photographs, utilizing a deep convolutional neural network to extract high-level spatial texture features and gradient-boosting algorithms to subsequently categorize the observed changes. To achieve this objective, a multi-stage processing pipeline is implemented, including aerial image preprocessing and manual annotation, formation of training and testing datasets, patch-level representation of high-resolution scenes, and spatial aggregation of classification results into continuous thematic maps. The methodology explicitly accounts for the spatial context and textural organization of the surface, enabling reliable discrimination between amber mining areas and other visually similar anthropogenic changes. It was implemented in Python, leveraging TensorFlow and Keras for deep learning, along with a gradient boosting framework for final classification. The outputs include a thematic map with three semantic classes: “no change,” “amber mining zones,” and “other changes”, and vectorized contours of disturbed areas, facilitating a spatially explicit representation of changes. Quantitative evaluation using the harmonic mean of precision and recall, the intersection over union coefficient, the root mean square error, the mean absolute error, and the zero-mean normalized cross-correlation demonstrated higher performance for amber mining detection, outperformi…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Aerial imagery
  topic: keywords
subject: land cover change
  topic: keywords
subject: deep convolutional neural network
  topic: keywords
subject: gradient boosting
  topic: keywords
subject: machine learning
  topic: keywords
creator: Vita Yu. Kashtan
creator: Volodymyr V. Hnatushenko
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-04-01
publication date: 2026-04-01
starting page: 199
ending page: 211
doi: 10.15276/hait.09.2026.14

A method for detecting financial phishing in instant messengers using an ensemble of dialogical intelligent assistants based on large language models
type: item
description: In the context of the rapid digitalization of financial services, instant messengers have become the dominant communication channel, which has led to an increase in the activity of cybercriminals in this segment. Financial phishing in instant messengers takes the form of complex sociotechnical attacks, the recognition of which using traditional signature methods and even classical neural network tools is complicated, since such attacks are based on psychological manipulations and contextual mimicry, which requires the use of large language models for deep semantic analysis of content. At the same time, the practical application of individual large language models is limited by their tendency to generate false facts and uneven sensitivity to different threat vectors, which makes the use of ensemble approaches relevant, which potentially provide increased recognition efficiency. The aim of the work is to increase the efficiency of detecting financial phishing in instant messengers by developing and experimentally testing a method for detecting financial phishing in instant messengers using an ensemble of dialogical intelligent assistants based on large language models. The original feature of the developed method is the use of an approach to the aggregation of recognition results, which is based on the mechanism of weighted linear convolution of responses of the ensemble of dialogical intelligent assistants taking into account the adaptive coefficients of their competence. To ensure the adaptability of the method and determine the weight coefficients of the competence of the models, an automated calibration procedure was developed using an iterative cross-validation algorithm. Also, within the framework of the proposed method, a classification of financial phishing features was carried out, which allowed identifying six dominant attack vectors, in particular: imitation of official institutions, creation of artificial urgency, technical masking of links, incitement to …
subject: Information technologies and computer systems
  topic: section
subject: Information technologyand computer systems
  topic: category
subject: Financial phishing
  topic: keywords
subject: instant messengers
  topic: keywords
subject: large language models
  topic: keywords
subject: dialogical intelligent assistants
  topic: keywords
subject: social engineering
  topic: keywords
subject: cybersecurity
  topic: keywords
creator: Oleksandr G. Korchenko
creator: Ihor A. Tereikovskyi
creator: Oleksandr Y. Korystin
creator: Liudmyla O. Tereikovska
creator: Oleh I. Tereikovskyi
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-02-19
publication date: 2026-02-19
starting page: 71
ending page: 84
doi: 10.15276/hait.09.2026.06

Hybrid neural network-heuristic model for forecasting the energy demand of livestock farms
type: item
description: Relevance. The article is devoted to the development and investigation of neural network and hybrid neural network–heuristic models for forecasting the energy demand of livestock farms. Purpose and objectives. The study aims to develop a hybrid neural network–heuristic model for forecasting the energy demand of livestock farms based on the integration of recurrent neural networks and heuristic optimization algorithms, enabling improved short-term forecasting accuracy under variable production and external conditions. The study develops a neural network model for forecasting the energy demand of a livestock farm based on recurrent architectures of long short-term memory and gated recurrent units, capable of processing multifactor hourly time series that characterize the operating modes of farm equipment. Methods. The baseline forecasting model is based on recurrent neural network architectures, including long short-term memory and gated recurrent units, while hyperparameter adjustment is performed using an evolutionary genetic algorithm. Model implementation and computational experiments were carried out using the Python programming language in a cloud computing environment. Results. The baseline neural network model demonstrated an acceptable level of forecasting accuracy, confirming its ability to reproduce the overall dynamics of energy consumption and seasonal variations. A hybrid neural network–heuristic model for forecasting the energy demand of livestock farms is proposed and formalized, combining a recurrent neural network forecasting module with a heuristic hyperparameter optimization algorithm. The use of an evolutionary genetic algorithm for automated tuning of the neural network architecture and training parameters is substantiated. This approach increased the model’s adaptability to changes in livestock farm operating regimes and reduced forecasting errors without a significant increase in computational complexity. As a result of the optimization procedu…
subject: Information technologies in energy systems engineering and manufacturing
  topic: section
subject: Information technology in energy engineering and manufacturing
  topic: category
subject: Energy demand
  topic: keywords
subject: livestock farms
  topic: keywords
subject: neural networks
  topic: keywords
subject: hybrid model
  topic: keywords
subject: genetic algorithm
  topic: keywords
subject: heuristic optimization
  topic: keywords
creator: Inna L. Тryhuba
creator: Nazarii Ya. Koval
creator: Taras D. Hutsol
creator: Marian P. Kotsylovskyi
creator: Roman I. Oliinyk
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-04-01
publication date: 2026-04-01
starting page: 230
ending page: 246
doi: 10.15276/hait.09.2026.16

IMECA method of risk-based assessment and ensuring cybersecurity of Large Language Models
type: item
description: Large Language Models (LLMs) help perform complex tasks that previously relied only on humans. Many different areas of human activity already use this technology or are actively exploring its capabilities with a view to future integration into work processes. In addition to the positive effects of their use, there are problems of uncertain and unexpected behavior, in particular, the generation of forbidden content. Given the expanding use of these models and their behavior, it is necessary to assess the level of security and further ensure the cybersecurity of this technology. The object of the study is the processes of ensuring cybersecurity for large language models. The article proposes a methodology for countering this threat by assessing the risks of such behavior and ensuring an acceptable level of cybersecurity for LLMs using the IMECA (Intrusion Modes Effects Criticality Analysis) technique of risk-based assessment. A set of countermeasures has been developed to increase the security level of LLMs, and procedures for their selection based on the criteria of maximum productivity and best rating using a countermeasure rating matrix are defined. An example of testing and ensuring the cybersecurity of a test language model is provided, the results of which show that the criticality level of cyber risks before the use of countermeasures is significantly decreased after using the most productive and highest-rated countermeasures, but threats with a high level of cyber risk criticality still exist. Directions for future research are proposed to deepen the procedure for evaluating and ensuring the security of LLMs, given the continuous development of these models and their protection mechanisms. The main result of this work is the combination of a model for ensuring the cybersecurity of LLMs and an improved method for analyzing the criticality of their vulnerabilities for further adaptation of the IMECA method of quantitative risk-based assessment and cybersecurity …
subject: Information technologies and computer systems
  topic: section
subject: Information technologyand computer systems
  topic: category
subject: Risk-based assessment
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Intrusion Modes Effects Criticality Analysis
  topic: keywords
subject: countermeasures
  topic: keywords
subject: threat
  topic: keywords
subject: vulnerability
  topic: keywords
subject: attack
  topic: keywords
creator: Oleksii S. Neretin
creator: Vyacheslav S. Kharchenko
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-02-18
publication date: 2026-02-18
starting page: 60
ending page: 70
doi: 10.15276/hait.09.2026.05

A method for constructing non-basic GL-models by combining models with arbitrary graph structures
type: item
description: The paper proposes a method for constructing GL-models of failure behavior for a special class of non-basic fault-tolerant multiprocessor systems. The considered class includes systems whose failure behavior is determined by the fulfillment of one of several conditions that depend on combinations of processor states and are specified by the corresponding Boolean expressions. When any of these conditions is satisfied, the system failure behavior can be described by an individual auxiliary model constructed using known methods. The objective of this study is to develop a generalized method for constructing models of such non-basic systems by combining multiple auxiliary models of arbitrary types into a single model that correctly represents the system failure behavior. The proposed method is based on modifying the edge functions of the auxiliary models using the Boolean expressions of the corresponding conditions, after which the modified models are combined by merging arbitrarily selected vertices of their graphs. It is shown that the resulting model reproduces the behavior of the corresponding auxiliary model under each condition and thus correctly describes the failure behavior of the original system. The scientific novelty of this work lies in the proposed approach to combining auxiliary models of a general form, which imposes no constraints on the structure of their graphs and, unlike existing approaches, does not require these models to be based on cycle graphs. This makes it possible to construct models for a wide class of non-basic fault-tolerant multiprocessor systems with complex operability conditions. A comparative analysis of the complexity of edge-function expressions in models constructed using the proposed method and those obtained by existing approaches is carried out on a set of representative examples, revealing a significant reduction in the complexity of such expressions. The case of parallel evaluation of edge-function values is considered separa…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  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 Romankevich
creator: Kostiantyn Morozov
creator: Alexei Romankevich
creator: Daniil Halytsky
creator: Lefteris Zacharioudakis
rights: Copyright (c) 2026 Herald of Advanced Information Technology
date: 2026-02-18
publication date: 2026-02-18
starting page: 30
ending page: 45
doi: 10.15276/hait.09.2026.03

Game-theoretic method of decentralized load balancing in microservice architectures
type: item
description: The paper presents a game-theoretic method of decentralized load balancing in microservice architectures, aimed at improving the efficiency of request distribution among service instances without using centralized controllers. The main idea is to represent the balancing process as a non-cooperative potential game in which each microservice is considered an autonomous agent seeking to minimize its own cost function. Unlike traditional algorithms such as Round Robin and Least Connections, the proposed approach is based on adaptive adjustment of agent strategies depending on the current state of the system, which ensures the achievement of Nash equilibrium and a stable load distribution. The mathematical model of game-theoretic method of decentralized load balancing takes into account the intensity of the request flow, the throughput of each node, and the quadratic component of the cost associated with resource overload. To optimize the decision-making process, a stochastic Softmax-update dynamics are used, which approximate the gradient descent of the potential function. This allows the system to gradually balance the load even in the presence of asynchronous updates and communication delays between nodes. It has been proven that the process converges to a stationary state in polynomial time, ensuring scalability and predictable behavior in large distributed environments. An experimental study conducted in the SimPy simulation environment demonstrated that the proposed method significantly outperforms classical algorithms in key metrics. Under peak load, the Game-Theoretic Load Balancer algorithm reduced the average system response time compared to the Round Robin and Least Connections algorithms. The standard deviation of processor utilization decreased, indicating a more balanced workload distribution and the absence of overloaded computing nodes. The obtained results confirm the analytical stability, convergence, and practical effectiveness of the game-theoretic ap…
subject: Information technologies and computer systems
  topic: section
subject: Information technologyand computer systems
  topic: category
subject: балансування навантаження
  topic: keywords
subject: мікросервісна архітектура
  topic: keywords
subject: теорія ігор
  topic: keywords
subject: рівновага Неша
  topic: keywords
subject: розподілені системи
  topic: keywords
subject: програмна інженерія
  topic: keywords
subject: оптимізація ресурсів
  topic: keywords
creator: Andrii I. Hryshchenko
creator: Nataliia O. Komleva
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-12-03
publication date: 2025-12-03
starting page: 488
ending page: 496
doi: 10.15276/hait.08.2025.31

Unsupervised Re-identification architecture based on segmented tracklets for animal behavior analysis
type: item
description: In this paper, we present Mask-TAUDL, an advanced unsupervised re-identification architecture that combines instance segmentation, unsupervised deep learning, and tracklet association for detailed analysis of object behavior in long-term recordings. It combines the Mask R-CNN dual-stream detector/segmenter with dual ResNet-18 backbones and the unsupervised deep learning module based on tracklet association (TAUDL). Mask R-CNN provides accurate object localization and binary masks from which we construct tracklets with improved segmentation. The two ResNet-18 streams use these masks to extract appearance and motion-sensitive features at the tracklet level, which are combined into a common feature descriptor. The TAUDL module operates directly on the masked tracklet features and co-trains discriminative embeddings and cross-session associations without manual labeling. The proposed Mask-TAUDL architecture trains a model so that features of a single individual remain close in embedding space over time, while providing a clear separation of features between different individuals. Integrating pure masked regions with temporally aggregated features helps suppress spurious variations caused by shadows, reflections, or overlapping objects. Long-term animal re-identification is challenging due to frequent overlaps, appearance drift, and subtle visual differences between individuals, and most existing solutions rely on large annotated datasets, which limits their applicability in real-world laboratory settings. The Mask-TAUDL architecture overcomes these limitations by explicitly modeling temporally consistent, mask-refined tracks and training embeddings that preserve identity in a fully unsupervised manner. Mask-TAUDL is designed for animal behavior studies, namely small laboratory species such as mice and fish observed in closed or semi-structured arenas, where reliable long-term identity tracking is essential for quantitative behavioral analysis, longitudinal experiments, …
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Identification
  topic: keywords
subject: segmentation
  topic: keywords
subject: image processing
  topic: keywords
subject: unsupervised re-identification
  topic: keywords
subject: deep learning
  topic: keywords
subject: TAUDL
  topic: keywords
subject: Mask R-CNN
  topic: keywords
subject: tracklets
  topic: keywords
subject: architecture
  topic: keywords
subject: animal behavior tracking
  topic: keywords
creator: Natalia P. Volkova
creator: Maksym A. Shvandt
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-12-05
publication date: 2025-12-05
starting page: 476
ending page: 487
doi: 10.15276/hait.08.2025.30

Comparative analysis of signal processing techniques for multimode polymer fiber interferometers
type: item
description: This study explores speckle pattern matching methods for deformation sensing using multimode polymer fiber interferometers. The research highlights the benefits of multimode polymer optical fibers, including their flexibility, cost-efficiency, and compatibility with complex structures, making them ideal for strain sensing applications. The primary focus is to evaluate and compare advanced image matching techniques, specifically the sum of squared differences, zero mean normalized cross-correlation, and phase-only correlation. These methods are assessed based on essential metrics such as linearity, precision, and signal-to-noise ratio. Experimental results indicate that phase-only correlation outperforms the other techniques in overall performance, offering combination of high linearity, moderate precision, and strong noise rejection. The normalized cross-correlation also demonstrates competitive results, while the sum of squared differences, despite its strengths in linearity, shows lower precision and weaker resistance to noise. The findings provide a practical framework for selecting suitable signal processing techniques to optimize multimode polymer fiber interferometers for various sensing demands. This work offers novel contributions to the field, highlighting the unique strengths and limitations of each method. These insights are expected to guide the future design and development of advanced fiber-based sensing systems.
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Fiber optic interferometer
  topic: keywords
subject: multimode interference
  topic: keywords
subject: speckle pattern matching
  topic: keywords
subject: image processing techniques
  topic: keywords
creator: Vasyl I. Varyshchuk
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-12-06
publication date: 2025-12-06
starting page: 423
ending page: 433
doi: 10.15276/hait.08.2025.26

Estimation of task-specific manipulability scores for a robotic manipulator in vacuuming scenarios
type: item
description: Reliable vacuum-cleaning trajectories require selecting joint configurations from which a robotic arm can continue motion in task-relevant directions without encountering self-collision, joint-limit violations, or singularities. Classical manipulability measures describe dexterity using the Jacobian but do not account for the geometric constraints and directional motion patterns typical in floor-cleaning tasks. This work presents a data-driven method for estimating a task-specific manipulability score that reflects how easily a seven degrees-of-freedom robotic arm equipped with a floor-contact vacuum tool can continue motion in six primitive directions (forward, backward, left, right, clockwise rotation, counterclockwise rotation). A dataset of one hundred and seventy-six thousand and twenty valid joint configurations was generated by sweeping a four-dimensional grid of feasible end-effector poses, computing up to three inverse-kinematics solutions per pose, and simulating incremental movements with collision checking. Each configuration was assigned a directional score based on inverse-kinematics reachability, collision outcomes, and distance-dependent penalties. A fully connected neural network was trained to regress six scores from the seven joint angles. The model achieved a denormalized Mean Absolute Error of approximately two point zero for translational directions and one point fifty-five to one point sixty for rotational directions (approximately eight percent of the full score range), while enabling extremely fast inference–around ninety-five thousand eight hundred and eighty evaluations per second on a consumer GPU. By shifting the computationally expensive stage of collision checking and inverse-kinematics sampling to offline preprocessing, the method provides a lightweight surrogate for online motion planning. The learned score can help planners avoid unfavorable configurations and maintain consistent vacuuming trajectories. Limitations include the absen…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Manipulability
  topic: keywords
subject: robotic arm
  topic: keywords
subject: vacuuming robot
  topic: keywords
subject: neural networks
  topic: keywords
subject: collision avoidance
  topic: keywords
subject: trajectory planning
  topic: keywords
subject: collision prediction
  topic: keywords
creator: Andrii Y. Medvid
creator: Vitaliy S. Yakovyna
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-11-28
publication date: 2025-11-28
starting page: 434
ending page: 446
doi: 10.15276/hait.08.2025.27

Development of a product pricing algorithm using RFM strategy for user cohorts using machine learning methods
type: item
description: This paper presents a comprehensive approach to developing a dynamic product pricing algorithm that integrates RFM (Recency, Frequency, Monetary) customer segmentation strategy with machine learning methods. The research addresses the critical challenge of personalizing pricing strategies for different user cohorts in competitive retail environments, particularly for supermarkets and online marketplaces. The study extends the classical RFM model by incorporating price elasticity of demand coefficients to create a cohort-based pricing framework. Using the K-Nearest Neighbors (KNN) algorithm, the authors developed an automated classification system that accurately segments customers based on their behavioral characteristics, achieving 100% classification accuracy for repeat purchasers. The methodology comprises five key stages: data collection and preprocessing, RFM score calculation using a quartile-based ranking, customer segmentation into five distinct groups (Champions, Loyal Users, Potential Loyalists, At-Risk, and Lost), training a machine learning model for segment prediction, and cohort formation based on the timing of the first purchase. The research implements a pricing algorithm that selectively applies discounts to target segments (“Potential Loyalists” and “At Risk” customers) who demonstrate inelastic demand and have maintained activity for at least six months. Experimental results demonstrate that strategic discount application not only reduces customer churn but also increases overall revenue through enhanced purchase volume. The proposed framework, implemented in the R programming language within the RStudio environment, provides businesses with a data-driven decision support tool for optimizing personalized pricing strategies while maintaining profitability. This approach enables companies to balance customer retention efforts with revenue maximization by utilizing sophisticated behavioral analytics and predictive modeling.
subject: Information technologies in socio-economic, organisational and technical systems
  topic: section
subject: Information technology in socio-economic,organisational and technical systems
  topic: category
subject: RFM analysis
  topic: keywords
subject: customer segmentation
  topic: keywords
subject: machine learning
  topic: keywords
subject: dynamic pricing algorithm
  topic: keywords
subject: cohort analysis
  topic: keywords
creator: Vitaliy M. Kobets
creator: Ivan D. Shlyakhtenko
creator: Sergiy М. Zinchenko
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-12-04
publication date: 2025-12-04
starting page: 497
ending page: 509
doi: 10.15276/hait.08.2025.32

Comparative analysis of chaotic and deterministic methods to territory coverage by drone swarms
type: item
description: This study proposes a hybrid chaotic-deterministic method for controlling a swarm of unmanned aerial vehicles, integrating social interactions such as separation, cohesion, alignment, and binding with an attraction-driven search strategy. The two-level anti-stagnation system operates at the level of individual agents (‘micro’) and the collective swarm (‘macro’) to prevent local minima and maintain controlled trajectory smoothing. The method uses double course conditioning to balance smooth trajectories with stochastic exploration. Meanwhile, an attractiveness function evaluates potential movement directions quantitatively based on territory novelty, distance factors and course stability. Social interaction forces – repulsion, cohesion, alignment and centroid binding – ensure swarm stability and collision avoidance throughout mission execution. Comparative experimental validation was conducted through multiple simulation launches for each method in irregular polygonal territories. Both the chaotic and deterministic waypoint-based approaches demonstrated exceptional mission reliability, achieving a target coverage threshold in all trials, thereby confirming complete success rates. In terms of coverage efficiency, the chaotic method achieved superior average territory completeness compared to the deterministic approach, representing a measurable improvement. However, this enhanced coverage precision comes at a significant computational time cost: the chaotic method required substantially longer average mission duration compared to the optimized waypoint method. The chaotic approach also exhibited notably higher variability in results, reflecting the inherently stochastic nature of exploration-based methods. Thus, while the chaotic method demonstrates superior coverage efficiency, it exhibits inferior time efficiency compared to the deterministic baseline. These findings quantify the fundamental trade-off between thorough exploration and time efficiency in unmanned aeri…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Information technology
  topic: keywords
subject: swarm intelligence
  topic: keywords
subject: chaotic algorithm
  topic: keywords
subject: multi-agent systems
  topic: keywords
subject: scan optimization
  topic: keywords
subject: swarm social interactions
  topic: keywords
subject: anti-stagnation method
  topic: keywords
subject: swarm trajectory optimization
  topic: keywords
creator: Mykyta S. Kozlov
creator: Eugene V. Malakhov
creator: Yuliia O. Shvets
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-12-04
publication date: 2025-12-04
starting page: 447‒463
ending page: 447‒463
doi: 10.15276/hait.08.2025.28

Road traffic accident classification using a sparse videotransformer and adaptive fragmentation
type: item
description: In this work, we propose a simple yet effective approach for classifying short road traffic video clips into car accident and normal scenes. From each clip 8 frames are uniformly sampled across the sequence to ensure that key events are preserved even in longer videos. Based on the Farnebäck optical flow map, an adaptive fragment selection is performed, where the patch size (eight/sixteen/thirty-two pixels) is determined for each region of the base grid. Smaller patches are used in areas with intensive motion (to capture finer details), while larger patches are used in static regions (to reduce computations). The selected fragments are non-overlapping, resized to a uniform scale, and converted into feature vectors. The architecture operates in two stages. First, a spatial transformer processes each frame independently; attending only to the selected fragments this drastically reduces the number of feature tokens. Second, a temporal transformer processes the sequence of classify tokens (compact per-frame representations), aggregating temporal dynamics across frames. This space-to-time factorization significantly lowers computational cost and memory consumption while maintaining high informativeness in motion-intensive regions. To address class imbalance, we employ a weighted cross-entropy loss (or focal loss emphasizing hard examples) and weighted random sampling during training. Optical flow maps and fragment lists are precomputed and cached on disk, which accelerates training epochs even on CPUs without specialized hardware. Evaluation was conducted on the Car Crash Dataset (one thousand and five hundred accident and three thousand normal videos) using an eighty to twenty percent train-test split with preserved class proportions. The proposed method achieved Accuracy = 0.864 and Macro-F1 = 0.851. Preliminary comparisons show that our approach outperforms both the baseline uniform-patch Vision Transformers and traditional temporal aggregation schemes. The key advant…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: video classification
  topic: keywords
subject: neural networks
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: object classification
  topic: keywords
subject: video stream analysis
  topic: keywords
subject: data classification
  topic: keywords
subject: image fragment processing
  topic: keywords
creator: Tetiana V. Normatova
creator: Sergii V. Mashtalir
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-12-05
publication date: 2025-12-05
starting page: 464
ending page: 475
doi: 10.15276/hait.08.2025.29

Development of a geoinformation method for assessing the accessibility of medical services in community infrastructure development projects
type: item
description: The article develops a geoinformation method that provides a solution to the scientific and applied problem of assessing the accessibility of medical services in community infrastructure development projects. Solving this problem is the basis for planning community infrastructure development during their post-war reconstruction. The relevance of the study is determined by the need to improve the effectiveness of management decisions regarding the assessment of medical service accessibility to community populations in conditions of spatial unevenness in the distribution of medical infrastructure facilities and limited resources. The work aims to develop a geoinformation method for assessing the accessibility of medical services for the population of communities, based on the use of open geospatial data from the OpenStreetMap framework, which takes into account the topology of the road network, the spatial distribution of medical facilities, the density and distribution of the population in the communities, and the time parameters of access to medical services. The proposed method is implemented on the basis of open data using OpenStreetMap tools, OSMnx and NetworkX libraries, which ensure flexibility, scalability and transparency of calculations. A distinctive feature of the method is the automated determination of areas of community access to medical services based on the construction of isochrones around medical infrastructure facilities, taking into account the type of facility and the acceptable time of arrival of community residents to them. The developed geoinformation method was tested for adequacy. It was found that deviations are within 1 minute or 0.3 km. The results of the experimental application of the method on the example of a typical territorial community showed its ability to identify ‘grey areas’, i.e. settlements that are not sufficiently covered by the network of medical institutions, and to assess the impact of changes in the structure of the med…
subject: Information technologies in energy systems engineering and manufacturing
  topic: section
subject: Information technology in energy engineering and manufacturing
  topic: category
subject: Management
  topic: keywords
subject: project
  topic: keywords
subject: method
  topic: keywords
subject: territorial community
  topic: keywords
subject: medical services
  topic: keywords
subject: geodata
  topic: keywords
subject: infrastructure development
  topic: keywords
subject: management decision-making
  topic: keywords
creator: Roksolana Ya. Sholudko
creator: Oksana M. Malanchuk
creator: Аnatoliy M. Тryhuba
creator: Inna L. Тryhuba
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-09-26
publication date: 2025-09-26
starting page: 382
ending page: 404
doi: 10.15276/hait.08.2025.25

Analysis of methods for supporting personalization in IT education
type: item
description: The increasing complexity of digital learning environments and the growing demand for individualized educational experiences underscore the importance of developing effective methods for personalizing practical tasks. The purpose of this study is to analyze existing approaches to the personalization of practical tasks in IT education and to identify their advantages, limitations, and potential for integration into digital learning ecosystems. The main objectives include the identification of technological solutions that enable automated generation and assessment of personalized practical tasks, and their effectiveness for supporting adaptive and scalable learning processes. The research methodology is based on a comparative analysis of scholarly sources, existing educational platforms, and practical implementations of personalized tasks. Special attention is given to the development of conceptual frameworks that enable personalization not only at the level of task complexity but also in terms of content variability, learning pathways, and assessment mechanisms. The review identifies the most effective strategies for achieving scalable personalization in digital education. The analysis also reveals the limitations of current solutions. The critical limitations are: insufficient support for comprehensive practical task parametrization, high technical barriers for instructors, incomplete learning management system integration, security vulnerabilities in local deployment scenarios, and limited automation in assessment processes. These constraints highlight the need for more sophisticated, user-friendly solutions. The findings provide a foundation for developing integrated information technologies that combine personalization methods with adaptive learning and artificial intelligence tools, supporting the evolution of digital learning ecosystems toward more effective, scalable, and integrity-focused educational approaches. The scientific novelty of the research lies in …
subject: Information technologies in energy systems engineering and manufacturing
  topic: section
subject: Information technology in energy engineering and manufacturing
  topic: category
subject: methods
  topic: keywords
subject: analysis
  topic: keywords
subject: personalized assignments
  topic: keywords
subject: automated generation
  topic: keywords
subject: automated assessment
  topic: keywords
subject: academic integrity
  topic: keywords
subject: parameterized task
  topic: keywords
creator: Andrii V. Khyzhniak
creator: Volodymyr V. Kazymyr
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-09-25
publication date: 2025-09-25
starting page: 366
ending page: 381
doi: 10.15276/hait.08.2025.24

Information model of remote sensing of soil
type: item
description: This paper presents a comprehensive information model and open architecture for unmanned aerial vehicle (UAV)-based remote sensing of soil. The development of remote sensing technologies using unmanned aerial vehicles (UAVs) and sensors of various measurement methods makes it possible to contactless detect various anomalies. The reliability of information after its processing is achieved by a combination of measurement methods, the correct choice of contactless measurement tools, and the use of machine learning methods for data processing and training on known anomalies. The modular UAV platform supports adaptive sensor integration and real-time data fusion for precision agriculture, environmental monitoring, and post-conflict applications. The information model defines the structure and flow of data between UAV sensors, onboard processors, and the ground control station, incorporating a digital twin for dynamic environmental modeling. A minimum set of necessary tools has been selected, based on the measurements and processing of which there will be enough information to identify anomalies in the soil with a high degree of reliability. The scope of application of the obtained results is quite extensive: from precision farming, including monitoring crop diseases and chemical non-contact soil analysis, to identifying foreign inclusions and anomalies in areas where military actions took place.
subject: Information technologies and computer systems
  topic: section
subject: Information technologyand computer systems
  topic: category
subject: Unmanned aerial vehicle
  topic: keywords
subject: information model
  topic: keywords
subject: remote sensing
  topic: keywords
subject: soil monitoring
  topic: keywords
subject: digital twin
  topic: keywords
subject: multisensor integration
  topic: keywords
subject: geophysical survey
  topic: keywords
subject: multimodal data fusion
  topic: keywords
subject: UAV aerial image
  topic: keywords
subject: cyberphysical systems
  topic: keywords
subject: geographic information system
  topic: keywords
creator: Oleksandr Y. Shunin
creator: Oleh V. Maslov
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-09-26
publication date: 2025-09-26
starting page: 352
ending page: 365
doi: 10.15276/hait.08.2025.23

A model for keyword spotting in voice signal for specialized computer systems
type: item
description: Keyword spotting in voice signal is a crucial task for specialized, low-resource computer systems, such as ground drones, particularly when operating under challenging conditions with limited computational power and without reliable cloud access. This paper presents a novel, modular model for efficient keyword spotting that does not rely on deep neural networks. The model's core principle is the differential weighting of Mel-Frequency Cepstral Coefficients , prioritizing those coefficients most discriminative for phonetic content. The architecture incorporates robust signal conditioning, dynamic feature extraction (including delta and delta-delta derivatives), the transformation of acoustic features into compact string-based "fingerprints", and final classification using the Levenshtein distance. Experimental validation, conducted on a Ukrainian-language corpus of drone commands with lexicons of up to 200 words, demonstrated the model's high performance and scalability. The system achieved an F1-score of 0.92 under ideal conditions and showed significant resilience in noisy environments, maintaining an F1-score of 0.78 at a 5dB signal-to-noise ratio. Furthermore, the proposed system significantly outperformed a baseline version (using only basic Mel-Frequency Cepstral Coefficients without derivatives or normalization) by up to 33 percentage points in F1-score under challenging conditions. The study validates that this optimized classical Keyword Spotting approach provides an effective and fully autonomous solution for edge computing applications where resource efficiency and independence from cloud infrastructure are paramount, especially in critical scenarios like military operations.
subject: Information technologies and computer systems
  topic: section
subject: Information technologyand computer systems
  topic: category
subject: Keyword spotting
  topic: keywords
subject: voice signal processing
  topic: keywords
subject: low-resource systems
  topic: keywords
subject: mel-frequency cepstral coefficients
  topic: keywords
subject: hidden markov models
  topic: keywords
subject: dynamic time warping
  topic: keywords
subject: edge computing
  topic: keywords
subject: Ukrainian language
  topic: keywords
subject: speech
  topic: keywords
creator: Ihor A. Tereikovskyi
creator: Andrii V. Didus
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-09-26
publication date: 2025-09-26
starting page: 341
ending page: 351
doi: 10.15276/hait.08.2025.22

Development and optimization of distributed high-performance systems with real-time data consistency
type: item
description: This article examines the theoretical and applied aspects of building distributed high-performance systems capable of processing large volumes of data with minimal latency while maintaining real-time consistency guarantees. The relevance of the research is determined by the rapid growth of data-intensive domains such as high-frequency trading, telemedicine, smart cities, the Internet of Things, and autonomous transport, where even millisecond delays can have critical consequences. The study focuses on the analysis of modern architectural models, including microservices, CQRS, Lambda and Kappa architectures, and event-driven systems with Apache Kafka. Methods of performance optimization are explored in detail, covering asynchronous request handling, caching, replication, and load balancing, with special attention to edge computing and 5G-based infrastructures. Particular emphasis is placed on theoretical and practical aspects of data consistency, including CAP-theorem (Consistency Availability Partition tolerance) trade-offs, consensus algorithms (Paxos, Raft), and CRDT (Conflict-free Replicated Data Type) structures. The results demonstrate that no universal architectural solution exists, but hybrid approaches combining strong and eventual consistency models can ensure reliability in mission-critical domains. The proposed analytical model for evaluating performance under different workload profiles enables the selection of optimal system configurations, balancing throughput, latency, and consistency requirements. The scientific novelty of the research lies in the integrated framework that unites architectural patterns, optimization techniques, and consensus mechanisms, as well as in the development of an analytical evaluation model that had not been sufficiently presented in previous studies. The practical significance is manifested in the formulation of recommendations for the design of fault-tolerant, scalable infrastructures for finance, telecommunications, healt…
subject: Information technologies and computer systems
  topic: section
subject: Information technologyand computer systems
  topic: category
subject: Distributed systems
  topic: keywords
subject: high throughput
  topic: keywords
subject: system architecture
  topic: keywords
subject: data consistency
  topic: keywords
subject: real-time synchronization
  topic: keywords
subject: performance optimization
  topic: keywords
subject: CAP theorem
  topic: keywords
subject: horizontal scaling
  topic: keywords
subject: fault tolerance
  topic: keywords
subject: consensus algorithms
  topic: keywords
creator: Andrii O. Humeniuk
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-09-26
publication date: 2025-09-26
starting page: 326
ending page: 340
doi: 10.15276/hait.08.2025.21

AI-Generated video evaluation by fragment processing
type: item
description: Recent advances in generative AI have led to the development of techniques to generate visually realistic synthetic video. As a result, there is a growing demand for detectors capable of distinguishing between AI-generated videos. In this paper we propose a compact, fragment-based representation of video frames that enables robust spatial-temporal analysis and new approach to discrimination between real and synthetically generated footage. To achieve this goal, each frame is divided into fragments and a square matrix of size BxB is formed. Next, we compute the dominant singular value for every fragment, yielding a square S-map. This construction preserves local structure while normalizing geometry, so standard 2-D operators can be applied uniformly. We analyze spatial organization via 2-D Discrete Cosine Transform (DCT) energies and temporal change with robust thresholding to form a binary change mask. Then we apply Connected Component Labeling (CCL) on the binary change mask (4- or 8-connectivity) and compute the area of the Largest Connected Component (LCC). We derive an LCC time series that measures the spatial concentration of change. Empirically, synthetic videos exhibit higher rates of near-binary LCC toggling, longer plateaus, increased mass at rational steps, and fewer unique levels than real videos - signatures consistent with temporal quantization and procedural dynamics. The pipeline is lightweight (fragment-wise rank-1 SVD + CCL on a small grid), auditably interpretable, and suitable for batch screening and edge devices. It attains ROC-AUC ≈ 0.86 and TNR ≈ 0.94 on mixed-resolution datasets with further gains from per-granularity calibration.
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: AI-generated video evaluation
  topic: keywords
subject: largest connected component
  topic: keywords
subject: video processing
  topic: keywords
subject: computer vision
  topic: keywords
subject: machine learning
  topic: keywords
subject: combined models
  topic: keywords
subject: intelligent analysis system
  topic: keywords
subject: time series analysis
  topic: keywords
creator: Sergii V. Mashtalir
creator: Dmytro P. Lendel
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-09-18
publication date: 2025-09-18
starting page: 316
ending page: 325
doi: 10.15276/hait.08.2025.20