Herald of Advanced Information Technology
type: channel
publisher: Publ. Nauka i Tekhnika
language: en
publication name: Herald of Advanced Information Technology
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: A method for detecting financial phishing in instant messengers using an ensemble of dialogical intelligent assistants based on large language models
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
items: Uncertainty-aware multi-objective refactoring for code duplication
items: Method for building ensemble classifiers of structured and unstructured data based on a unified approach
items: Improving the performance of сlustering with wavelet function by using inequality constraints
items: On the computational complexity of cascade GL-models for fault-tolerant multiprocessor systems
items: Internet of Energy based cellular structures for distributed Microgrids
items: A multi-criteria scoring metric for evaluating deep learning models in bitcoin price forecasting
items: Fuzzy models of wireless components sensor networks
items: A model for constructing neural network systems forrecognizing emotions of text fragments
items: Analysis of existing approaches to automated refactoring of object-oriented software systems

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

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

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

Uncertainty-aware multi-objective refactoring for code duplication
type: item
description: Code clones are recurring code fragments that may hinder software maintainability if not properly managed. While many clone detection tools exist, they often stop at identification and provide no clear guidance on whether a detected clone group should be refactored, how to do so, or in what order. This paper presents a machine learning–based method for recommending clone refactorings with prioritization and confidence estimation. The proposed approach represents code fragments using abstract syntax trees, program dependency graphs, and semantic embeddings from a pre-trained CodeBERT model. In addition, version control data is used to extract evolutionary features such as churn, age, and co-change patterns. A multi-class classifier predicts refactoring types, while open-set recognition techniques identify uncertain cases and flag them as unknown. Effort and benefit estimation models help prioritize suggestions based on a cost-effectiveness ratio. We evaluated the method on four open-source Java projects using a manually labeled dataset of 600 clone groups. The system achieves a macro-F1 score of zero point seven six on known refactoring types and an AUROC of zero point nine one for unknown detection. Prioritized recommendation quality reaches NDCG@3 of zero point eight nine, showing strong alignment with expert assessments. The results indicate that clone refactoring can be effectively supported through integrated code representation, uncertainty modeling, and prioritization. The approach transforms clone analysis from a passive task into an actionable process.
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Clone refactoring
  topic: keywords
subject: artificial intelligence in software engineering
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: clone classification
  topic: keywords
subject: open-set recognition
  topic: keywords
subject: uncertainty estimation
  topic: keywords
creator: Dmytro D. Kurinko
creator: Viktoriia I. Kryvda
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-09-26
publication date: 2025-09-26
starting page: 301
ending page: 315
doi: 10.15276/hait.08.2025.19

Method for building ensemble classifiers of structured and unstructured data based on a unified approach
type: item
description: Effectively classifying heterogeneous data, including structured and unstructured data, is essential in diverse fields such as healthcare, finance, information security, and audio content analysis. This study aims to develop a unified approach for constructing ensemble classifiers capable of handling diverse data formats within a single framework, enhancing classification accuracy and robustness. The methodology integrates feature extraction and data preprocessing techniques, transforming heterogeneous datasets to a standardized numerical format suitable for ensemble learning. Eight base classifiers including K-nearest neighbors, support vector machines, random forest, extreme gradient boosting, logistic regression, multilayer perception, convolution neural networks and long short-term memory networks–were trained with optimized hyperparameters. The ensemble classification uses stacking with various aggregation types such as hard voting, soft voting, and soft voting using Gompertz fuzzy ranking to effectively combine model predictions while accounting for uncertainty and noise. Experimental evaluation across five datasets, covering medical diagnosis, credit risk, emotion recognition, music genres and deepfake detection–demonstrates consistent improvement in accuracy and F1-score metrics, with gains up to 8 percent compared to the best individual classifiers. The approach proves particularly effective for unstructured audio data, where temporal and spectral dependencies pose significant challenges. The results underscore the versatility the proposed unified ensemble methodology in addressing class imbalance and noise offering a scalable solution adaptable to various domains. This work contributes a comprehensive framework facilitating the development of robust classifiers for complex real-world data and paves the way for future research integrating heterogeneous data sources within cohesive predictive models.
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Ensemble classifiers
  topic: keywords
subject: machine learning
  topic: keywords
subject: hard voting
  topic: keywords
subject: soft voting
  topic: keywords
subject: Gompertz function
  topic: keywords
creator: Olena O. Arsirii
creator: Oleksandr K. Andronati
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-09-23
publication date: 2025-09-23
starting page: 288
ending page: 300
doi: 10.15276/hait.08.2025.18

Improving the performance of сlustering with wavelet function by using inequality constraints
type: item
description: Clustering methods based on gradient estimation are common in automated control and diagnostic systems, where reliable data processing is needed under noise and multimodality. Their use, however, is constrained by low robustness and high computational costs. Wavelet-based approaches are relevant because they enhance noise immunity and improve efficiency. The purpose of this work is to develop and study a clustering method that employs wavelet functions to introduce constraints and ensure stable performance under noisy conditions. The research included analysis of existing approaches, development of a wavelet-based method with inequality-type second-order constraints, creation of an algorithm for its implementation, and experimental evaluation. The proposed method relies on wavelet transforms with hyperbolic functions, which reduce the number of oracle calls, decrease computational stages, and accelerate convergence in classification and clustering problems. Experiments show that the method shortens the search time for the optimum by about one and a half to seven times at different signal-to-noise ratios, with a moderate increase in error of roughly five to fifteen percent for the De Jong test function. On synthetic datasets, the gain in computation time exceeded one point one compared with the baseline method. In a practical case of reliability assessment for resistors used in critical equipment, efficiency improved by nearly eight percent. Finally, the novelty lies in the clustering method with constraints-inequalities defined by wavelet processing. This can increase the computational speed in conditions of high noise levels, asymmetric objective functions, and small data samples.
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Clustering
  topic: keywords
subject: wavelet transform
  topic: keywords
subject: second-order constraints
  topic: keywords
subject: optimization
  topic: keywords
subject: oracle calls
  topic: keywords
subject: noise immunity
  topic: keywords
creator: Galina Y. Shcherbakova
creator: Daria V. Koshutina
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-09-18
publication date: 2025-09-18
starting page: 277
ending page: 287
doi: 10.15276/hait.08.2025.17

On the computational complexity of cascade GL-models for fault-tolerant multiprocessor systems
type: item
description: The article addresses the problem of evaluating the computational complexity of basic cascade GL-models used for modeling the behavior of fault-tolerant multiprocessor systems in the flow of failures. The purpose of this work is to reduce the complexity of such models by optimal selection of their parameters. It has been demonstrated that a single system usually corresponds to an entire family of cascade GL-models, differing in cascade depth and parameters, with each having its own computational complexity. To simplify the process of modeling the system behavior under a flow of failures, it is advisable to choose the cascade GL-model configuration with the lowest complexity. However, additional constraints on the model, such as cascade depth limitations, must also be considered. This work applies an empirical-analytical research method. An analysis of computational complexity for cascade GL-models was conducted using specially developed software, which automated model construction for various combinations of parameters. Subsequently, a comparative analysis of the complexity of their edge function expressions was performed to identify dependencies on parameter values. Experimental studies were carried out for fault-tolerant multiprocessor systems with varying numbers of processors and different maximum allowable failure multiplicities (but not exceeding half of the total number of processors in the system). It was shown that cascade GL-models typically have significantly lower computational complexity compared to standard basic GL-models, especially for systems with a small maximum number of allowed failures. However, in cases where the allowed number of failures equals or exceeds half of the processor count, standard models may become less complex. Based on the conducted analysis, practical recommendations for selecting the parameters of cascade GL-models were formulated for the first time. In particular, the lowest complexity is achieved when the fault tolerance co…
subject: Information technologies in energy systems engineering and manufacturing
  topic: section
subject: Information technology in energy engineering and manufacturing
  topic: category
subject: Cascade GL-models
  topic: keywords
subject: fault-tolerant multiprocessor systems
  topic: keywords
subject: computational complexity
  topic: keywords
subject: basic systems
  topic: keywords
subject: parameters adjustment
  topic: keywords
subject: reliability assessment
  topic: keywords
creator: Vitaliy A. Romankevich
creator: Kostiantyn V. Morozov
creator: Alexei M. Romankevich
creator: Yehor O. Nikishyn
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-06-27
publication date: 2025-06-27
starting page: 245‒258
ending page: 245‒258
doi: 10.15276/hait.08.2025.16

Internet of Energy based cellular structures for distributed Microgrids
type: item
description: In the face of evolving global energy challenges and the urgent need for resilient, decentralized, and digitized power systems, the concept of the Internet of Energy (IoE) emerges as a transformative framework for modern energy infrastructure. This paper explores the development and implementation of IoE-based cellular structures within distributed Microgrids, with a focus on Ukraine’s critical need for energy security and system restoration. Embracing the “3D” paradigm – Decarbonization, Decentralization, and Digitalization –the proposed architecture organizes energy systems into interconnected energy cells. These cells leverage distributed energy resources, demand-side management, and Energy Cloud 4.0 technologies to enable real-time energy balancing, market participation, and transactive energy operations. The paper presents a comprehensive structure for Microgrids based on IoE principles, detailing energy cell typologies, control algorithms for cost optimization, and strategies for grid flexibility and reliability. It highlights how cellular architectures can ensure autonomous operation, enhance resilience to outages, and foster integration with the European energy market. Through modeling and simulation, the paper validates the feasibility of this approach, demonstrating significant improvements in energy cost efficiency, stability, and sustainability. To organize the work and interaction of cells, 5 types of energy cells (generators, prosumers, passive consumers, electric vehicles, utility) have been identified that interact and have impacts on all participants of the Microgrid: economic impacts (changes in consumer electricity bills and revenues from energy sales), technical impacts (changes in the network scheme, approaches to network planning and operation) and social impacts (support, reliability of consumption, reduction of CO2 emissions). The technical, economic, and social benefits of transitioning to IoE-based cellular Microgrids are a foundational ele…
subject: Information technologies in energy systems engineering and manufacturing
  topic: section
subject: Information technology in energy engineering and manufacturing
  topic: category
subject: Internet of Energy
  topic: keywords
subject: Microgrid
  topic: keywords
subject: energy cell
  topic: keywords
subject: transactive energy
  topic: keywords
subject: energy market
  topic: keywords
subject: distributed energy resources
  topic: keywords
creator: Halyna S. Bielokha
creator: Serhii P. Denysiuk
creator: Denys G. Derevianko
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-06-27
publication date: 2025-06-27
starting page: 233
ending page: 244
doi: 10.15276/hait.08.2025.15

A multi-criteria scoring metric for evaluating deep learning models in bitcoin price forecasting
type: item
description: The increasing computational demands of deep learning have raised concerns about the environmental sustainability of artificial intelligence applications, particularly in high-frequency domains such as financial forecasting. This paper addresses the need for more holistic evaluation criteria by proposing a multi-criteria scoring metric for deep learning models used in Bitcoin price forecasting. The purpose of the study is to develop a performance metric that balances predictive accuracy with computational efficiency and environmental impact. The method involves combining traditional accuracy measures with training time, energy consumption, and carbon emissions into a unified performance score, calculated using a logistic scoring function. The metric was validated by applying it to forty-two configurations of Long Short-Term Memory models trained on historical Bitcoin price data. Each configuration was assessed for its forecasting accuracy, energy use and emissions (measured using a carbon-tracking tool). The results show that simpler Long Short-Term Memory models can offer competitive accuracy while significantly reducing training time and emissions. The highest-performing model achieved a balance of all criteria, while deeper architectures with marginal accuracy gains incurred disproportionate environmental costs. The study concludes that the proposed scoring metric offers a practical and scalable solution for selecting deep learning models under sustainability constraints, supporting more responsible Artificial Intelligence deployment in real-world settings.
subject: Information technologies and computer systems
  topic: section
subject: Information technologyand computer systems
  topic: category
subject: Deep learning
  topic: keywords
subject: time series forecasting
  topic: keywords
subject: Long Short-Term Memory
  topic: keywords
subject: Performance Metric
  topic: keywords
subject: Sustainability
  topic: keywords
creator: Mykola M. Zlobin
creator: Volodymyr M. Bazylevych
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-06-27
publication date: 2025-06-27
starting page: 221
ending page: 232
doi: 10.15276/hait.08.2025.14

Fuzzy models of wireless components sensor networks
type: item
description: The article addresses the problem of modeling wireless sensor network components operating under uncertainty and the influence of external factors. The relevance of the research stems from the growing need for reliable and autonomous monitoring and control systems in industrial environments, especially under conditions where wired communication is not feasible or consistent. Classical models with strictly defined input parameters fail to adequately reflect the dynamics and unpredictability of sensor node behavior and communication links, which limits the accuracy of performance assessment and complicates the design of robust systems. The aim of this study is to develop a formalized approach for describing wireless sensor network behavior under uncertainty using fuzzy logic and semantic representation. The proposed solution is based on a generalized modeling framework that utilizes the theory of fuzzy sets and supports both crisp and fuzzy input/output variables. Two universal formulations are introduced: one that models relationships solely between fuzzy variables, and another that links crisp parameters with fuzzy outputs. These models serve as a flexible basis for representing typical processes in wireless sensor networks, where parameters are often only partially known, qualitatively estimated, or described in linguistic terms. Variables are mapped into an orthogonal semantic space, allowing the use of membership functions for further reasoning and integration into ontological structures. Although detailed structural models of sensor nodes and communication channels are not included in this version and will be presented in a separate follow-up study, the current work lays the methodological groundwork for their development. The scientific novelty lies in the creation of a unified fuzzy modeling framework that supports both abstract and data-driven representations of uncertainty in wireless sensor networks. The practical significance is seen in the applicability o…
subject: Information technologies and computer systems
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subject: Information technologyand computer systems
  topic: category
subject: Wireless sensor networks
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subject: fuzzy models
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subject: sensor node
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subject: communication channel
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subject: linguistic variables
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subject: orthogonal semantic space
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subject: reliability
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subject: fault tolerance
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subject: fuzzy logic
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subject: industrial systems
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creator: Sergiy A. Nesterenko
creator: Petr M. Tishin
creator: Pavlo R. Shtilman
creator: Oleksandr N. Martynyuk
creator: Igor G. Mileiko
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-06-27
publication date: 2025-06-27
starting page: 209
ending page: 220
doi: 10.15276/hait.08.2025.13

A model for constructing neural network systems forrecognizing emotions of text fragments
type: item
description: Emotion Recognition in text is a crucial task in Natural Language Processing, particularly relevant given the exponential growth of textual data from social media and voice interfaces. However, developing effective emotion recognition systems for low-resource languages, such as Ukrainian, faces significant challenges, including linguistic informality, dialectal variations, and cultural specificities. This paper introduces a modular model (framework) for developing neural network-based tools for recognizing emotions in Ukrainian text fragments. The model encompasses a comprehensive data preprocessing pipeline, flexible architectural choices (including approaches based on Word to Vector, Long Short-Term Memory, and Transformers), and rigorous validation using standard metrics and interpretability methods. As part of an experimental study, two prototypes were implemented and compared: a lightweight classifier based on FastText and a more powerful classifier based on pretrained RoBERTa-base, both trained to recognize seven basic emotions. The results demonstrate that RoBERTa-base achieves high accuracy, significantly outperforming FastText and a baseline translation-based approach, yet it demands substantially more computational resources for inference. The study underscores the importance of creating Ukrainian-language corpora to enhance recognition capabilities and highlights the critical trade-off between accuracy and efficiency. It provides practical recommendations for model selection based on resource constraints and performance requirements for emotion analysis tasks in the Ukrainian language.
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Emotion recognition
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subject: natural language processing
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subject: neural networks based tools
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subject: modular model
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subject: low-resource languages
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creator: Ihor A. Tereikovskyi
creator: Oleksandr S. Korovii
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-06-27
publication date: 2025-06-27
starting page: 197
ending page: 208
doi: 10.15276/hait.08.2025.12

Analysis of existing approaches to automated refactoring of object-oriented software systems
type: item
description: Automated refactoring plays a crucial role in the maintenance and evolution of object-oriented software systems, where improving internal code structure directly impacts maintainability, scalability, and technical debt reduction. This paper presents an extended review of current approaches to automated refactoring, emphasizing methodological foundations, automation levels, the application of artificial intelligence, and practical integration into CI/CD workflows. We examine rule-based, graph-based, machine learning–based (CNNs, GNNs, LLMs), and history-aware (MSR) techniques, along with hybrid systems incorporating human-in-the-loop feedback. The taxonomy of refactoring types is aligned with established terminology – particularly Fowler’s classification – distinguishing structural, semantic (architectural), and behavioral transformations, all grounded in the principle of behavior preservation. Formal models are introduced to describe refactorings as graph transformations governed by preconditions and postconditions that ensure semantic equivalence between program versions. The paper provides a concrete example of a transformation generated by the DeepSmells tool, demonstrating the «before/after» change and explaining the rationale behind the AI-driven recommendation. The study also addresses the challenges of explainability and semantic drift, proposing mitigation strategies such as SHAP-based analysis, attention visualization in transformer architectures, integration with formal verification tools (e.g., SMT solvers, symbolic execution), and explainable AI recommendations. Special attention is given to the limitations of automated refactoring in dynamically typed languages (e.g., Python, JavaScript), where the lack of static type information reduces the effectiveness of traditional techniques. Generalization to multilingual systems is supported through models like CodeBERT, CodeT5, and PLBART, which operate over token-level, syntactic, and graph-based representatio…
subject: Theoretical aspects of computer science, programming and data analysis
  topic: section
subject: Theoretical aspects of computer science, programming anddata analysis
  topic: category
subject: Automated refactoring
  topic: keywords
subject: object-oriented programming
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subject: deep learning
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subject: software maintenance
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subject: code smells
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subject: graph neural networks
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subject: software engineering
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subject: code transformation
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subject: source code analysis
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subject: semantic code modeling
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subject: software metrics
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creator: Mykola A. Hodovychenko Mykola A. Hodovychenko
creator: Dmytro D. Kurinko Dmytro D. Kurinko
rights: Copyright (c) 2025 Herald of Advanced Information Technology
date: 2025-06-27
publication date: 2025-06-27
starting page: 179
ending page: 196
doi: 10.15276/hait.08.2025.11