Alina Yanko

@nupp.edu.ua

PhD, Associate Professor Department of Computer and Information Technologies and Systems/Educational and Research Institute of Information Technologies and Robotics
National University "Yuri Kondratyuk Poltava Polytechnic"

Alina Yanko


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https://researchid.co/yankoas

RESEARCH, TEACHING, or OTHER INTERESTS

Computational Theory and Mathematics, Computer Engineering, Hardware and Architecture, Information Systems
44

Scopus Publications

429

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • AN ADAPTIVE MODEL FOR SOFTWARE CODE QUALITYASSESSMENT IN REFACTORING TASKS BASED ON FUZZY LOGIC
    Sergii Liubarskyi, Alina Yanko, Yurii Zdorenko, Bakhtiyar Khudayarov
    Advanced Information Systems, 2026
    The article's objective is to develop a hybrid adaptive model for assessing software code quality based on code smell characteristics by combining fuzzy logic and machine learning methods to enhance the objectivity and efficiency of refactoring. The methodology underlying this research is aimed at developing a hybrid adaptive model for software code quality assessment. It combines fuzzy logic and artificial intelligence methods, specifically an adaptive neuro-fuzzy inference system (ANFIS). The multi-layered ANFIS implements the Takagi-Sugeno fuzzy inference with the ability to learn using gradient methods. The methodology is based on a hybrid approach that integrates expert knowledge with the automated training of the model on real data. Results. The research resulted in the development of a hybrid adaptive model for software code quality assessment based on fuzzy logic and the ANFIS. This model allows for automated, objective, and flexible code quality assessment in refactoring tasks. The model uses eight key code smell metrics: WMC, DIT, RFC, LCOM, NOA, NOC, CBO, and FANOUT. Their normalization and processing are performed using fuzzy logic based on the Takagi-Sugeno algorithm. This ensures that the uncertainty and subjectivity of expert evaluations are taken into account. The ANFIS architecture allows the model to learn from real data, with subsequent automated adjustment of the membership function parameters and rule weights. This enables the model to adapt to various technology stacks and projects. The use of trapezoidal membership functions increases the accuracy of modeling critical code smell zones, while the hybrid learning algorithm based on gradient descent ensures high precision in determining code quality, ultimately contributing to improved software efficiency, maintainability, scalability, and security. The scientific novelty of the research lies in the development of a hybrid adaptive model for software code quality assessment. Unlike existing models, this one is based on fuzzy logic and an ANFIS, which combines expert knowledge with automated training on real data to enhance the objectivity and efficiency of the refactoring process. The proposed ANFIS architecture with trapezoidal membership functions is used to process eight key code smell metrics (WMC, DIT, RFC, LCOM, NOA, NOC, CBO, FANOUT) within the context of Takagi-Sugeno fuzzy inference. This provides a flexible, interpretable, and adaptive assessment of code quality with the ability to automatically tune model parameters based on gradient learning, which significantly increases the accuracy of code quality determination and the model's suitability for various technology stacks and projects. The practical significance of the research lies in the direct implementability and integration of the developed hybrid adaptive model for software code quality assessment into existing static analysis tools and DevOps processes, specifically as plugins for Continuous Integration/Continuous Delivery (CI/CD) systems. This will enable automated, objective, and adaptive monitoring of code quality in real time. In addition, the model has significant potential for extension to various programming languages and technology stacks by analyzing large datasets from open-source repositories, which will enhance its universality and accuracy. A promising direction for future work is to improve the ANFIS architecture by incorporating deep learning methods, which would allow for the automatic detection of new code smells and their interdependencies. The development of interpretable mechanisms to explain the model's decisions will increase developer trust in the system and promote its widespread adoption in both industrial software development and educational processes in software engineering and cybersecurity.
  • IMPROVING SAFETY AND EFFICIENCY FOR FIXED-WING UAVS BY UTILIZING AN UNMANNED GROUND PLATFORM
    Nazar Pedchenko, Alina Yanko, Oleksandr Laktionov, Bohdan Boriak
    Technology Audit and Production Reserves, 2025
    The object of this research was the launch process of fixed-wing unmanned aerial vehicles. Military unmanned aerial vehicle systems are rapidly improving and becoming increasingly effective on the battlefield and in the enemy's rear. However, the complex and dynamic environment of modern warfare significantly impacts the preparation and launch of UAVs. Therefore, ensuring the maximum safety of these operations is one of the key factors influencing the overall effectiveness of these systems. At the same time, the launch operation requires personnel to be in an open area, making it a critical task to find solutions to protect UAV crews from enemy attacks. A possible solution is the remote control of the UAV launch. This article proposes using unmanned ground platforms for the remote launch of fixed-wing UAVs to reduce the probability of enemy strikes against crews and equipment. The research included modeling and comparing the launch of a fixed-wing UAV from a runway and with the help of an unmanned ground platform. The modeling results showed that launching from the platform reduces the takeoff distance by 39.1% (from 273.6 m to 166.7 m) and the operation time by more than half (from ~23 s to 9.2 s). This overall reduction will decrease the probability of the unmanned equipment being struck by the enemy. An additional advantage of this method is reduced fuel consumption. It also allows for the use of a propeller that is more efficient for flight, which is not possible with a traditional runway takeoff. Reducing the strength requirements for the drone's airframe allows for a decrease in its mass, which, in turn, increases the mass of the warhead or reconnaissance equipment.
  • DEVELOPMENT OF A FUZZY RISK ASSESSMENT MODEL FOR INFORMATION SECURITY MANAGEMENT
    Yurii Zdorenko, Alina Yanko, Mykhailo Myziura, Nadiia Fesokha
    Technology Audit and Production Reserves, 2025
    The object of research is the process of assessing information security risks of information resources during the functioning of information activity objects, which is the basis of effective security management. One of the most problematic areas of classical probabilistic risk assessment models is high subjectivity in determining quantitative values of indicators. To eliminate these shortcomings, it is proposed to create universal, scalable and trainable risk assessment models based on qualitative characteristics. The study used an adaptive neuro-fuzzy logical inference system (ANFIS). A mathematical model of information security risk assessment was obtained, which expands existing solutions by scaling. The approach used in the model allows to automatically adapt to dynamic changes in the functioning of the information activity object. The proposed model has the following features: automated generation of the rule base and retraining of the fuzzy system. The use of artificial neural networks to automate the adjustment of the parameters of the fuzzy system allows to avoid the subjectivity characteristic of expert assessments. This provides the ability to obtain current values of the information security risk level. The conducted experimental studies quantitatively confirmed the effectiveness of the model, which demonstrated classification accuracy of up to 95% and a significant reduction in the mean square error to 0.01 compared to classical probabilistic models and traditional fuzzy expert systems. This is due to the fact that the proposed model has a number of features, in particular, automated generation of the rule base and the possibility of retraining the fuzzy system, which is provided by the use of artificial neural networks. Due to this, automatic adaptation to dynamic changes in the object and accurate obtaining of current values of the risk level are ensured. Compared to similar known models, this provides automated adjustment of parameters based on the results of retraining (with an error of > 1–2%) and reliable information security management by prioritizing protective measures and responding promptly to threats.
  • NEUROCOMPUTER OPERATING IN THE RESIDUE CLASS SYSTEM
    Alina Yanko, Viktor Krasnobayev, Alina Hlushko, Stanislav Goncharenko
    Advanced Information Systems, 2025
    Objective. The aim is to justify the possibility of creating a data processing neurocomputer (NC) based on the use of one kind of non-positional machine arithmetic residue class system (RCS). Methodology. In the basis of research of the problem of NC creation there is a methodology based on the use of methods of synthesis of non-positional code representation structures (NPCRS), as well as on the realization of data processing methods in RCS. The totality of these methods is realized on the basis of using three basic principles of data processing in RCS: independence of processing of numerical values of the residue content; equality of functioning of data processing channels; low-digit data in the numerical representation of the residue content. Results. The results of the conducted research confirm the possibility of creation of NC using RCS as a basis for information processing. Formal (semantic) similarity of mathematical models, as well as analytical similarity of artificial neural systems representation with the basic formulas of data processing represented in RCS is presented. The correspondence of the operation of weighted summation in neuron to the operation of addition by modules in RCS is established. It is shown that the activation function of a neuron can be efficiently approximated using multiplication operations by modules in RCS. It is analytically shown that the representation of synapse weights in NPCRS elements allows to realize parallel computations similar to parallel information processing in the human brain. Scientific Novelty. For the first time, a comprehensive study of the influence of RCS on such key characteristics of NK neurocomputers as performance in processing large amounts of data, reliability of information storage and transmission, and overall fault tolerance has been carried out. A new approach to the construction of NC based on the use of neural network mathematical basis based on non-positional RCS codes is proposed. The introduction of this mathematical apparatus into the structure of neural networks provides the possibility of achieving higher accuracy and naturalness in modeling the hierarchical organization inherent in biological neural networks of the human cognitive system. Practical Significance. The prospects for further research are the development of specific hardware implementations of super-performance and highly fault-tolerant NC based on RCS, as well as the study of the possibility of applying this approach to solve specific problems of artificial intelligence, such as pattern recognition and natural language processing.
  • DEVELOPMENT OF A CLUSTERING ALGORITHM FOR PARAMETERS OF EXPLOSIVE OBJECTS BASED ON A COMPREHENSIVE INDICATOR
    O Laktionov, A Yanko, N Pedchenko
    Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2025
    Purpose. To enhance the efficiency of clustering parameters of explosive objects through the development of hybrid clustering elements. Methodology. A classifier for explosive objects based on a comprehensive indicator, serving as the main principle for classifier improvement, was developed using mathematical modeling. Data processing was carried out using the Python programming language and scikit-learn libraries. The research methodology involves grouping explosive objects into two clusters with the aim of improving the existing algorithms for detecting explosive objects. Findings. The proposed comprehensive indicator demonstrates a standard deviation 8.2 % less than the existing one. The improved clustering algorithm exhibits Davis-Bouldin index values of 0.517 and 0.525, while the existing ones show 0.572 and 0.572, respectively. This indicates that the output estimations of the new algorithm are less susceptible to noise, which enhances clustering quality and reduces the number of errors during practical application. Originality. A parameter clusterer for explosive objects is proposed which, unlike the existing ones, incorporates complex estimates built on the basis of a linear model with combined parameters as input data. Practical value. The practical significance of the proposed solution lies in the fact that improving existing algorithms for detecting explosive objects will increase the efficiency of computer vision in solving reconnaissance and demining tasks. The proposed solutions can be used as an addition to existing approaches for monitoring and managing national security to prevent emergencies.
  • IMPROVING THE PROCESS OF CONTROL AND CORRECTION OF ERRORS IN NON-POSITIONAL CODE STRUCTURES
    Alina Yanko, Victor Krasnobayev, Alina Hlushko
    Eastern European Journal of Enterprise Technologies, 2025
    The object of this study is the processes of operational control and correction of data errors in non-positional code structures (NCS). Based on a critical analysis of the existing data control method based on the use of the projection of a number in RCS, limited control efficiency and the ability to detect only single errors have been established. The study improves methods for rapid control and data correction of a real-time computer system (CS) operating in a non-positional number system, in the so-called residual class system (RCS). A comprehensive approach to control and eliminate errors in RCS is built on the basis of non-positional coding, underlying which is the Chinese residual theorem. This theorem proves that NSC is the next stage in the development of the theory of information control using arithmetic control by modulus. The use of the property of complete arithmetic of NSC has made it possible to improve the method and increase the efficiency of data control due to information processing in RCS without controlling each intermediate result obtained. Comparison with the most efficient existing method has made it possible to establish that the devised method provides an increase in the speed of data control by 1.2–1.3 times. An effective process of operational and accurate error detection based on an improved method of data control in RCS, which is based on the use of the corrective properties of NCS, has been proposed. Parallel error correction in NCS increases the efficiency of error correction by 2 times, due to a decrease in the number of intermediate operations in the improved method. At the same time, with an increase in the bit grid of the operands being processed, the efficiency of the application of the considered error correction process improves
  • MODELS FOR INDUSTRY DIFFERENTIATION IN DECISION-MAKING SYSTEMS WITH AN APPLICATION TO THE UKRAINIAN ECONOMY
    Alina Hlushko, Oleksandr Laktionov, Alina Yanko, Oleksandr Isaiev
    Radioelectronic and Computer Systems, 2025
    This article is devoted to the study of the problem of using adaptive models of differentiation of sectors of the real sector of the economy as a key component of modern decision support systems (DSS). The subject of the study is models of differentiation of real sectors of the Ukrainian economy for integration into decision support systems to optimize public administration. This research aims to develop and validate adaptive models of industry differentiation into clusters (groups) to improve the effectiveness of decision-making systems applied to the real sector of Ukraine’s economy. The research object is the process of sectoral differentiation, which allows determining the structural features and patterns of economic sector functioning. DSS architecture is proposed that integrates multifactor analysis and machine learning algorithms for automated selection of strategic scenarios. For clustering, we used production volume indicators and the number of strategically important enterprises in Ukraine for the pre-war period (2015–2021), which serve as a benchmark model for comparative analysis. A comparative assessment of the effectiveness of the classical K-means, DBSCAN, and Ensemble model algorithms was conducted with quantitative verification of the results using the Silhouette Score and Davies-Bouldin Score metrics. Empirical analysis showed that the DBSCAN and Ensemble models provide the highest quality of clustering (Silhouette Score 0.8387; Davies-Bouldin Score 0.0777), forming a reliable grouping of economic sectors. DSS module was developed based on the results obtained to form indicative tactical support measures, in particular, infrastructure strengthening of high-potential clusters and structural reorganization of vulnerable ones. Conclusions. The developed models form a universal methodological framework that is suitable for use in different countries, particularly in countries with a “peaceful” economy. DSS specialists can use the research results to identify key sectors of the economy, develop adaptive policies, and increase the stability and competitiveness of economic systems in a dynamic environment.
  • Modeling of a Neural Network-Based Motor Position Controller in a System for Tracking Objects of Complex Shapes
    Ceur Workshop Proceedings, 2025
  • Implementation of Cryptographic Transformations for Digital Security Using the Residue Number System
    Ceur Workshop Proceedings, 2025
  • PREDICTING ROBOTIC PLATFORM MISSIONS USING A KERNEL ACTIVATION NETWORK WITH AN ASYMMETRIC KERNEL
    Oleksandr Laktionov, Alina Yanko, Bohdan Boriak, Oleksii Mykhailichenko
    Eastern European Journal of Enterprise Technologies, 2025
    This study considers those processes predicting the functional efficiency of robotic platforms that affect the optimization of their mission planning. Given the growing demand for autonomous mobile systems, a critical task is to ensure high efficiency of their dynamics under different loads, terrains, and speeds, which requires reliable tools for decision-making even before physical launch. To solve the task, a method based on a customized Kernel Activation Network (KAN) was devised and programmatically implemented to predict the functional efficiency of the platform. The results demonstrate a significant increase in accuracy: KAN achieves an MSE of 0.00055727 on synthetic data and 0.00041720 on the experimental sample, while other architectures demonstrate 0.00105989 and higher. The key innovation of KAN is the use of an asymmetric chi-square kernel in parallel with the Gaussian kernel, as well as the integration of input estimates that take into account the triple interaction of factors. This explains the network's ability to effectively capture complex nonlinear dependences between numerous platform parameters (rolling resistance, aerodynamic drag, climbing force, etc.) and environmental conditions. The use of an asymmetric kernel significantly simplifies the network architecture, allowing for high accuracy at lower computational complexity. In practice, the results serve as an additional tool for optimizing mission planning of robotic platforms. This makes it possible to optimize equipment selection, construct strategic logistics routes, and increase the safety and reliability of autonomous systems under actual conditions. The achieved Technology Readiness Level is 4
  • Secondary Software Faults Detection Models
    Oleksandr Rudenko, Alina Yanko, Olena Haitan, Yurii Zdorenko, Zinaida Rudenko
    Lecture Notes in Networks and Systems, 2025
  • DEVELOPMENT OF A HARDWARE-SOFTWARE SOLUTION FOR DETECTION OF COMPLEX-SHAPED OBJECTS IN VIDEO STREAM
    Oleksandr Laktionov, Alina Yanko, Alina Hlushko
    Technology Audit and Production Reserves, 2024
  • MODEL OF AN AUTOMATED CONTROL SYSTEM FOR THE POSITIONING OF RADIO SIGNAL TRANSMISSION/RECEPTION DEVICES
    Bohdan Boriak, Alina Yanko, Oleksandr Laktionov
    Radioelectronic and Computer Systems, 2024
  • IDENTIFICATION OF AIR TARGETS USING A HYBRID CLUSTERING ALGORITHM
    Oleksandr Laktionov, Alina Yanko, Nazar Pedchenko
    Eastern European Journal of Enterprise Technologies, 2024
  • A Method of Control and Operational Diagnostics of Data Errors Presented in a Non-positional Number System in Residual Classes
    Ceur Workshop Proceedings, 2024
  • Fault-Tolerant Operation of an Integer Data Processing System
    Victor Krasnobayev, Alina Yanko, Illia Fil
    Lecture Notes in Networks and Systems, 2024
  • ENHANCING THE PROTECTION OF AUTOMATED GROUND ROBOTIC PLATFORMS IN THE CONDITIONS OF RADIO ELECTRONIC WARFARE
    A Yanko, N Pedchenko, O Kruk
    Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2024
  • Synthesis of a Mathematical Model of a Fault-Tolerant Real-Time Computer System Operating in Non-positional Arithmetic in Residual Classes
    Victor Krasnobayev, Alina Yanko, Dmytro Kovalchuk, Illia Fil
    Lecture Notes in Networks and Systems, 2024
  • Method for detection of the modified DDoS cyber attacks on a web resource of an Information and Telecommunication Network based on the use of intelligent systems
    Ceur Workshop Proceedings, 2024
  • Economic cyber security of business in Ukraine: Strategic directions and implementation mechanism
    Svitlana Onyshchenko, Alina Yanko, Alina Hlushko, Oleksandra Maslii
    Economic and Cyber Security, 2023
  • Cyberspace protection system based on the data comparison method
    Krasnobayev Victor, Alina Yanko, Alina Hlushko, Oleg Kruk, Oleksandr Kruk, et al.
    Economic and Cyber Security, 2023
  • Economic and cyber security
    Krasnobayev Victor, Alina Yanko, Alina Hlushko, Oleg Kruk, Oleksandr Kruk, et al.
    Economic and Cyber Security, 2023
  • Information Security of the National Economy Based on an Effective Data Control Method
    Victor Krasnobayev, Alina Yanko, Alina Hlushko
    Journal of International Commerce Economics and Policy, 2023
  • An Improved Method for Performing the Arithmetic Operations of Modulo Addition of the Remainders of Numbers
    Victor Krasnobayev, Alina Yanko, Dmytro Kovalchuk
    2023 13th International Conference on Dependable Systems Services and Technologies Dessert 2023, 2023
  • INFLUENCE OF THE NUMBER SYSTEM IN RESIDUAL CLASSES ON THE FAULT TOLERANCE OF THE COMPUTER SYSTEM
    Alina Yanko, Viktor Krasnobayev, Anatolii Martynenko
    Radioelectronic and Computer Systems, 2023
  • Improving the efficiency of diagnosing errors in computer devices for processing economic data functioning in the class of residuals
    Svitlana Onyshchenko, Alina Yanko, Alina Hlushko
    Eastern European Journal of Enterprise Technologies, 2023
  • Control, diagnostics and error correction in the modular number system
    Victor Krasnobayev, Alina Yanko, Dmytro Kovalchuk
    Ceur Workshop Proceedings, 2023
  • Business Information Security
    Svitlana Onyshchenko, Stanislav Bilko, Alina Yanko, Svitlana Sivitska
    Lecture Notes in Civil Engineering, 2023
  • Method for Computing Exponentiation Modulo the Positive and Negative Integers
    Ceur Workshop Proceedings, 2023
  • The Mechanism of Information Security of the National Economy in Cyberspace
    Svitlana Onyshchenko, Alina Yanko, Alina Hlushko, Oleksandra Maslii, Vitaliia Skryl
    Lecture Notes in Civil Engineering, 2023
  • CYBERSECURITY AND IMPROVEMENT OF THE INFORMATION SECURITY SYSTEM
    Journal of the Balkan Tribological Association, 2023
  • Method of Tabular Implementation of the Arithmetic Operation of Multiplying Two Numbers Represented in the System of Residual Classes
    Victor Krasnobayev, Alina Yanko, Dmytro Kovalchuk
    2022 IEEE 9th International Conference on Problems of Infocommunications Science and Technology Pic S and T 2022 Proceedings, 2022
  • Increasing Information Protection in the Information Security Management System of the Enterprise
    Svitlana Onyshchenko, Alina Yanko, Alina Hlushko, Svitlana Sivitska
    Lecture Notes in Civil Engineering, 2022
  • The Procedure for Implementing the Operation of Multiplying Two Matrices Using the Residual Number System
    Victor Krasnobayev, Alexandr Kuznetsov, Alina Yanko, Tetiana Kuznetsova
    2020 IEEE International Conference on Problems of Infocommunications Science and Technology Pic S and T 2020 Proceedings, 2021
  • Solving the Shortest Path Problem Using Integer Residual Arithmetic
    Victor Krasnobayev, Alexandr Kuznetsov, Alina Yanko, Tetiana Kuznetsova
    2020 IEEE International Conference on Problems of Infocommunications Science and Technology Pic S and T 2020 Proceedings, 2021
  • Processing of the residuals of numbers in real and complex numerical domains
    Victor Krasnobayev, Alexandr Kuznetsov, Alina Yanko, Bakhytzhan Akhmetov, Tetiana Kuznetsova
    Lecture Notes on Data Engineering and Communications Technologies, 2021
  • The data errors control in the modular number system based on the nullification procedure
    Victor Krasnobayev, Alexandr Kuznetsov, Alina Yanko, Kateryna Kuznetsova
    Ceur Workshop Proceedings, 2020
  • The analysis of the methods of data diagnostic in a residue number system
    Victor Krasnobayev, Alexandr Kuznetsov, Alina Yanko, Tetiana Kuznetsova
    Ceur Workshop Proceedings, 2020
  • Correction codes in the system of residual classes
    Victor Krasnobayev, Alexandr Kuznetsov, Alina Yanko, Kateryna Kuznetsova
    2019 IEEE International Scientific Practical Conference Problems of Infocommunications Science and Technology Pic S and T 2019 Proceedings, 2019
  • Methods of nulling numbers in the system of residual classes
    Ceur Workshop Proceedings, 2019
  • Influence of diagnostics errors on safety: Indicators and requirements
    Yuriy Ponochovniy, Eugen Bulba, Alina Yanko, Egor Hozbenko
    Proceedings of 2018 IEEE 9th International Conference on Dependable Systems Services and Technologies Dessert 2018, 2018
  • Method of Error Control of the Information Presented in the Modular Number System
    Victor Krasnobayev, Sergey Koshman, Alina Yanko, Anatolii Martynenko
    2018 International Scientific Practical Conference on Problems of Infocommunications Science and Technology Pic S and T 2018 Proceedings, 2018
  • Algorithms of data processing in the residual classes system
    Alina Yanko, Sergey Koshman, Victor Krasnobayev
    2017 4th International Scientific Practical Conference Problems of Infocommunications Science and Technology Pic S and T 2017 Proceedings, 2017
  • A Method for Arithmetic Comparison of Data Represented in a Residue Number System
    V. A. Krasnobayev, A. S. Yanko, S. A. Koshman
    Cybernetics and Systems Analysis, 2016

RECENT SCHOLAR PUBLICATIONS

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    ОВ Михайліченко, АС Янко, ОІ Лактіонов
    Вісник Вінницького політехнічного інституту, 78–82-78–82 , 2025
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    Технологічний центр, м. Харків , 2025
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    АС Янко, АД Глушко, ВА Краснобаєв, ОІ Лактіонов
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    CEUR Workshop Proceedings , 2025
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  • Predicting robotic platform missions using a kernel activation network with an asymmetric kernel= Прогнозування місій роботизованих платформ за допомогою мережі активації ядра …
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    Технологічний центр, Харків , 2025
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  • Optimization of stepper motor control in various operating modes
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  • Optimization of stepper motor control in various operating modes= Оптимізація керування кроковим двигуном у різних режимах роботи
    ОІ Лактіонов, АС Янко
    Одеський національний технологічний університет , 2025
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    ОІ Лактіонов, НМ Педченко, АС Янко, ОО Крук
    Гельветика, м. Херсон , 2025
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    АС Янко, ОВ Михайліченко, ОІ Крук
    Національний університет" Полтавська політехніка імені Юрія Кондратюка" , 2025
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    АС Янко, МС Мизюра
    Національний університет" Полтавська політехніка імені Юрія Кондратюка" , 2025
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  • Розробка CRM-системи для клубу віртуальної реальності Mr. VR на мові програмування C
    АС Янко, ПС Сабельнікова
    Національний університет" Полтавська політехніка імені Юрія Кондратюка" , 2025
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  • Використання LoRaWAN технології як основи для побудови розумного міста
    АС Янко, СО Гончаренко
    Національний університет" Полтавська політехніка імені Юрія Кондратюка" , 2025
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  • Features of the development and implementation of a ground robotic complex motion control system
    АС Янко, ОІ Лактіонов, БР Боряк
    2025
  • Features of the development and implementation of a ground robotic complex motion control system= Особливості розроблення та реалізації системи керування рухом наземного …
    БР Боряк, АС Янко, ОІ Лактіонов
    Національний технічний університет «Дніпровська політехніка» , 2025
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  • Цифрова трансформація бізнес-процесів: безпековий аспект
    АС Янко, СС Білько, АД Глушко
    2025
  • Modern methods for protecting and storing data in computer systems to ensure their fault tolerance
    АС Янко, ОВ Михайліченко, АД Глушко
    2025
  • Modern methods for protecting and storing data in computer systems to ensure their fault tolerance= Сучасні методи захисту та зберігання даних комп'ютерних систем для …
    АС Янко, ОВ Михайліченко, АД Глушко
    Національний технічний університет України «Київський політехнічний інститут … , 2025
    2025

MOST CITED SCHOLAR PUBLICATIONS

  • Правове регулювання досудового розслідування: проблеми теорії та практики
    ВГ Дрозд
    Харківський національний університет внутрішніх справ , 2018
    2018
    Citations: 156
  • A Method for arithmetic comparison of data represented in a residue number system
    VA Krasnobayev, AS Yanko, SA Koshman
    Cybernetics and Systems Analysis 52 (1), 145-150 , 2016
    2016
    Citations: 132
  • Algorithms of data processing in the residual classes system
    A Yanko, S Koshman, V Krasnobayev
    2017 4th International Scientific-Practical Conference Problems of … , 2017
    2017
    Citations: 30
  • Correction codes in the system of residual classes
    V Krasnobayev, A Kuznetsov, A Yanko, K Kuznetsova
    2019 IEEE International Scientific-Practical Conference Problems of … , 2019
    2019
    Citations: 23
  • Система захисту комп’ютерної мережі
    АС Янко, РА Вигівський
    Національний університет" Полтавська політехніка імені Юрія Кондратюка" , 2022
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    Citations: 9
  • Conception of realization of criptographic rsa transformations with using of the residue number system.
    V Krasnobayev, A Yanko, S Koshman
    Computer Science and Cybersecurity, 5-12 , 2016
    2016
    Citations: 8
  • Аспект інформаційної безпеки в сучасних CRM-системах в епоху діджиталізації економіки та бізнесу
    АС Янко, ВО Шахно
    Таврійський науковий вісник. Серія: Технічні науки 4, 28-33 , 2022
    2022
    Citations: 6
  • Метод арифметического сравнения данных, представленных в системе остаточных классов
    ВА Краснобаев, АС Янко, СА Кошман
    Кибернетика и системный анализ, 157-162 , 2016
    2016
    Citations: 6
  • Цифрова трансформація бізнес-процесів: безпековий аспект
    АД Глушко, АС Янко, СС Білько
    Причорноморський науково-дослідний інститут економіки та інновацій , 2025
    2025
    Citations: 5
  • Расчет и сравнительный анализ производительности компьютерной системы обработки целочисленных данных, функционирующей в системе остаточных классов
    ВА Краснобаев, АС Янко, ПН Гроза, СА Кошман, АП Гроза, ...
    Системи обробки інформації, 111-117 , 2015
    2015
    Citations: 5
  • Забезпечення відмовостійкості комп'ютерних систем на основі коригувальних властивостей непозиційних кодових структур
    АС Янко, ІВ Філь
    Національний технічний університет" Харківський політехнічний інститут" , 2023
    2023
    Citations: 4
  • Моделювання базової конструкції робототехнічної платформи
    О ЛАКТІОНОВ, П Назар, А ЯНКО, Б БОРЯК
    ВИМІРЮВАЛЬНА ТА ОБЧИСЛЮВАЛЬНА ТЕХНІКА В ТЕХНОЛОГІЧНИХ ПРОЦЕСАХ, 95-99 , 2024
    2024
    Citations: 3
  • Концепція системи виявлення та запобігання вторгнень до мережі
    O Makarenko, A Yanko
    Системи управління, навігації та зв’язку. Збірник наукових праць 2 (68), 59-67 , 2022
    2022
    Citations: 3
  • The method of error detection and correction in the system of residual classes.
    V Krasnobayev, A Yanko, S Koshman
    Computer science and cybersecurity, 58-66 , 2016
    2016
    Citations: 3
  • Метод табличной реализации операции умножения в классе вычетов
    ВА Краснобаев, АС Янко, СА Кошман
    Системи обробки інформації, 121-127 , 2014
    2014
    Citations: 3
  • Основные свойства непозиционной системы счисления
    ВА Краснобаев, СВ Сомов, АС Янко
    Системи управління, навігації та зв'язку, 146-149 , 2013
    2013
    Citations: 3
  • Конструктивний аналіз бюджетних рішень для керування робототехнічними платформами
    АС Янко, ОВ Михайліченко, ОО Крук
    Таврійський науковий вісник. Серія: Технічні науки, 170-178 , 2024
    2024
    Citations: 2
  • Виявлення атак типу LDDoS за допомогою SDN мереж з елементами машинного навчання
    А ЯНКО, А ПРОКУДІН, О КРУК
    ВИМІРЮВАЛЬНА ТА ОБЧИСЛЮВАЛЬНА ТЕХНІКА В ТЕХНОЛОГІЧНИХ ПРОЦЕСАХ, 287-296 , 2024
    2024
    Citations: 2
  • Метод виявлення та виправлення помилок на основі часових числових перерізів. Матеріали міжнародної науково-технічної конференції Сучасні напрями розвитку інформаційно …
    АС Янко, ПС Сабельнікова
    Impress , 2024
    2024
    Citations: 2
  • Примеры определения ранга числа, представленного в непозиционной системе счисления остаточных классов= Приклади визначення рангу числа, представленого в непозиционній системі …
    ВА Краснобаєв, ОА Замула, АС Янко
    Харківський національний університет радіоелектроніки , 2018
    2018
    Citations: 2