Software, Information Systems, Computer Science, Education
156
Scopus Publications
5131
Scholar Citations
33
Scholar h-index
145
Scholar i10-index
Scopus Publications
Resilience through crisis: an integrated framework for entrepreneurial STEM education in conflict-affected contexts Serhiy O. Semerikov, Pavlo P. Nechypurenko, Tetiana A. Vakaliuk, Iryna S. Mintii Discover Education, 2026 The intersection of educational resilience and entrepreneurial competencies presents unique opportunities for maintaining STEM education quality during crises. This systematic review synthesizes evidence from 301 sources to develop an integrated framework for implementing entrepreneurial STEM education in conflict-affected contexts. Through comparative analysis of pandemic and armed conflict impacts, we identify distinct adaptation strategies and their effectiveness. Our findings reveal that crisis conditions accelerate entrepreneurial competency development by 34–45% compared to normal contexts, while problem-based learning approaches maintain 78% of educational outcomes despite severe resource constraints. The Ukrainian case demonstrates how educational systems can transform adversity into innovation through community-integrated projects, digital resilience strategies, and trauma-informed pedagogies. We present a comprehensive implementation framework validated across multiple crisis contexts, offering evidence-based guidance for educators, policymakers, and international organizations. The study contributes a novel theoretical integration of crisis pedagogy, entrepreneurial education, and STEM resilience, providing actionable insights for building antifragile educational systems capable of thriving through disruption.
From science fiction to smart reality: a technology foresight review of smartdust evolution through the lens of Hamilton’s Great North Road Serhiy O. Semerikov, Tetiana A. Vakaliuk, Iryna S. Mintii, Olha V. Bondarenko, Olga B. Kanevska European Journal of Futures Research, 2026 The convergence of science fiction imagination and technological reality offers unique insights for urban technology planning and policy development. This technology foresight review examines the evolution of smartdust technology from its conceptual origins in hard science fiction to current research trajectories, using Peter F. Hamilton’s Great North Road as a focal analytical framework. While the concept of autonomous micro-scale machines in fiction traces back at least to Stanisław Lem’s The Invincible (Lem, The Invincible. A Continuum Book, 1964), Hamilton’s detailed treatment provides the most comprehensive framework for analyzing contemporary smartdust trajectories. Through systematic analysis of publications spanning smartdust research, edge computing, urban surveillance, and science fiction studies, we trace the technological pathways between fictional speculation and emerging reality. Our analysis reveals that while current smartdust prototypes operate at millimeter scale with limited autonomous capabilities, the convergence of edge computing, energy harvesting advances, and distributed artificial intelligence suggests achievable progression toward micron-scale, intelligent sensor networks within 30–50 years, though such projections remain contingent on breakthroughs and investments that are far from guaranteed. However, technical challenges in miniaturization and energy autonomy pale compared to the social, ethical, and governance barriers that fiction often glosses over. This review contributes a systematic methodological framework for extracting actionable technology insights from hard science fiction and demonstrates how speculative narratives can inform evidence-based urban planning while highlighting the critical importance of addressing privacy, behavioral autonomy, and democratic governance concerns proactively rather than reactively.
Student elective course selection patterns and satisfaction determinants identified through educational data mining Serhiy O. Semerikov, Olha V. Bondarenko, Pavlo P. Nechypurenko, Tetiana A. Vakaliuk, Iryna S. Mintii Scientific Reports, 2026 Digital transformation in higher education is reshaping how institutions design and deliver their curricula, with a growing emphasis on student agency and personalized learning paths. This study employs educational data mining techniques to analyze student preferences and satisfaction with elective courses at Kryvyi Rih State Pedagogical University in Ukraine. We investigate patterns in course selection, satisfaction determinants, and the effectiveness of the university's individual educational trajectory framework among 1,089 students. Our analysis reveals four distinct student segments with varying preferences and satisfaction profiles. Information availability before selection, alignment with career goals, teaching quality, and course relevance emerge as significant predictors of student satisfaction. We propose a data-driven framework for optimizing elective course systems that incorporates learning analytics, personalized recommendation engines, and enhanced information platforms. This research contributes to understanding how educational technology can better support student agency in curriculum customization while addressing critical issues of accessibility, equity, and educational quality. The findings align with Sustainable Development Goal 4 (Quality Education) by promoting inclusive and personalized educational opportunities that prepare students for future employment challenges.
Multivariate analysis of Moodle components and grade distribution patterns for adaptive learning environments Serhiy O. Semerikov, Pavlo P. Nechypurenko, Tetiana A. Vakaliuk, Iryna S. Mintii, Liliia O. Fadieieva Discover Education, 2026 The increasing sophistication of Learning Management Systems (LMS) has generated vast amounts of educational data with untapped potential for enhancing teaching and learning. While previous research has examined relationships between LMS components and overall student performance, less attention has been paid to how these components influence specific grade distributions. This study employs multivariate analysis techniques to investigate how Moodle components relate to distinct grade categories (A-F) across 985 courses at a Ukrainian pedagogical university. Using multiple regression models, canonical correlation analysis, and cluster analysis, we identify significant relationships between specific Moodle components and grade outcomes. Information components (particularly teacher presence) strongly predict excellence, while Activity x Communication interactions support satisfactory performance. Resource diversity combined with structured activities correlates with reduced failure rates. We further identify four distinct course design clusters with characteristic grade distribution patterns. The findings demonstrate how learning analytics can move beyond descriptive insights toward prescriptive guidance. This nuanced understanding of grade distribution patterns provides practical guidance for tailoring course design to diverse pedagogical objectives and student needs, thus contributing to the emerging field of personalized digital learning environments.
Energy-efficient neuromorphic computing for ultra-low latency cognitive radio: a hardware-software co-design framework for 6 G spectrum intelligence Serhiy O. Semerikov, Pavlo P. Nechypurenko, Tetiana A. Vakaliuk, Iryna S. Mintii, Andrii O. Kolhatin Discover Artificial Intelligence, 2026 Sixth-generation (6 G) wireless networks demand cognitive radio systems that simultaneously achieve sub-millisecond latency and sustainable energy consumption – requirements conventional artificial intelligence approaches cannot meet. This paper presents a hardware-software co-design framework integrating neuromorphic computing with cognitive radio to address both constraints through brain-inspired spiking neural networks (SNNs). We systematically analyze five neuromorphic platforms – Intel Loihi 2, IBM TrueNorth, SpiNNaker, SpiNNaker 2, and Intel Hala Point – using standardized benchmarks from the Intel Neuromorphic Deep Noise Suppression (N-DNS) Challenge, demonstrating sub-millisecond spectrum decisions (50-170 \(\mu \) s end-to-end latency) with energy consumption reduced by 100-1000 \(\times \) (31 pJ per spike) compared to conventional GPU-based approaches (2.5−12.5 \(\mu \) J per operation). Our framework provides three novel contributions: (1) a unified co-design methodology optimizing spike encoding, network topology, and hardware mapping jointly to achieve 3 \(\times \) efficiency gains over independent optimization; (2) quantitative design rules for encoding selection – rate coding for signal-to-noise ratios below -10 dB, temporal coding for latency requirements below 100 \(\mu \) s, and population coding for reliability exceeding 99.9%; and (3) experimental validation achieving 97.6% classification accuracy on real-world spectrum data from industrial IoT deployments consuming only 31 mW average power. Through five detailed case studies spanning industrial automation (99.9% uptime over 6 months), vehicle-to-everything communications (98.7% collision avoidance), defense applications (95% reliability under 40 dB jamming), smart cities (100,000 sensors), and healthcare (15-year implant lifetime), we demonstrate neuromorphic cognitive radio’s practical viability. The framework addresses critical deployment barriers including device variability mitigation (±20% threshold compensation), cross-platform algorithm portability, and RF-to-spike conversion interfaces. These results establish neuromorphic computing as a foundational technology for energy-constrained, latency-critical 6 G wireless systems, with implications extending to radar processing, electronic warfare, and satellite communications.
The bio-edge: a survey and research agenda for the Internet of Bio-Nano Things, 2026–2035 Serhiy O. Semerikov, Tetiana A. Vakaliuk Journal of Edge Computing, 2026 The Internet of Bio-Nano Things (IoBNT) extends the Internet of Things into the biochemical domain of living systems through nanoscale bio-engineered devices that sense, actuate, and communicate primarily via molecular signalling. Eleven years after the founding vision of Akyildiz et al. [4], the field has accumulated working architectures, microfluidic testbeds, and mature channel models, but its system-integration challenges - latency, privacy, and energy - are substantially edge-computing challenges: in-body decision loops cannot tolerate cloud round-trip latency, biomolecular data cannot safely stream to remote servers, and harvested-power devices cannot continuously transmit raw high-rate signals. This survey reframes the IoBNT layer stack as a five-layer bio-edge reference architecture in which the bio-cyber interface (BCI, distinct from brain-computer interface) is upgraded from a transduction gateway to a first-class compute layer with its own latency, energy, and trust accounting. We construct a DOI-deduplicated bibliometric snapshot of 311 entries, identify three under-occupied subtopics that constitute the field's strategic white space - TinyML on harvested power, federated learning across edge gateways, and Bio-SDN orchestration - and survey the technical state of each. The centrepiece is a ten-prediction research agenda for 2026-2035 with each prediction stated as a dated metric, a causal mechanism, and a falsifier, designed to give the IoBNT community a structured object that subsequent work can measure itself against.
SIMPLIFIED CONVOLUTIONAL MODEL FOR DETECTING VIBRATION ANOMALIES IN HELICOPTERS OPERATION Yurii Hodlevskyi, Tetiana Vakaliuk Radioelectronic and Computer Systems, 2026 The challenge in vibration analysis for helicopters is to automate anomaly detection to indicate possible equipment failure, especially in high-stakes scenarios where labelled failure data is rare and highly desirable for training modern models such as convolutional neural networks or autoencoders which require a lot of such data and are often used for this kind of analysis. The study focuses on vibration data generated by helicopters, focusing on anomalies in these vibrations that may indicate imminent equipment failure. These vibrations are often complex and change under different operating conditions, making them a valuable source of diagnostic information. Objective and Approach. This study presents a novel model for detecting abnormal vibrations in helicopter operations, minimising dependency on failure data. The model uses Fast Fourier Transform (FFT) and convolutional techniques to reduce high-dimensional vibration data into a three-dimensional vector, enabling effective anomaly detection through distribution metrics and Z-score-based thresholding. Case Study. The model was evaluated using a real-world dataset of helicopter vibration signals. The study successfully differentiated between normal and abnormal conditions without relying on explicit failure labels, validating its applicability in maintenance scenarios where early fault detection is critical. Method. Model was developed using Fast Fourier Transform (FFT) and convolutions to reduce vibration data to a low-dimensional vector representation that allows anomaly detection using distribution metrics. This method increases adaptability and reduces data requirements, making it suitable for expensive equipment with limited or no fault data. In addition, this model can be very flexible and extended to a model with additional training. It uses the Fast Fourier Transform as the first step to get the frequency components of a signal buried in vibrations. After FFT, we can represent the vibration data as a multidimensional vector. An example can consist of around 45000 dimensions after that vector simplifies to reduce the dimensions. The model uses convolutions to get only a 3-dimensional vector from the 45,000-dimensional vector. After convolutions, the data saves a clear correlation, and it is still possible to represent the vibration sample as a vector in 3-dimensional space and save its properties. Three dimensions were chosen for visualisation comfort, and it still works correctly. These vectors are located in space in two separate clusters for 0 and 1 corresponding labels, and they can be used for the next iteration of analysis. On the next iteration, the model gets a mean from x,y, and z and checks the distributions for class 0 and class 1. This step is possible because values after all convolutions for x,y, and z are close to each other. As distributions of these vectors are close to a bell shape, the model uses distribution metrics to predict fault by using a Z-score of the distribution of normal vectors. In this case, the model is close to the zero-shot learning concept because the model requires only data about expected behaviour. Results. The developed model showed high accuracy at a particular configuration when using different matrix convolutions and a different approach to setting the limit of anomalous values. At the final stage of the testing, the model reached an accuracy of 99.568%, which, under specific conditions, is not considered an overfitted model since the initial input parameters were clearly defined and limited by specific data. The model also demonstrated that when changing the input conditions, the model can be easily configured to new parameters. Discussion. The proposed model proved highly adaptable and accurate in detecting anomalies with minimal historical data. While it showed robustness across different configurations, future improvements could focus on refining sensitivity thresholds and integrating additional predictive techniques for enhanced performance. Conclusions. Tests validate the model's adaptability and scalability, recommending its use for fault detection in inexpensive, data-limited environments like helicopters.
Vision-Based Autonomous UAV Landing: A Comprehensive Review of Technologies, Techniques, and Applications Serhiy O. Semerikov, Pavlo P. Nechypurenko, Tetiana A. Vakaliuk, Iryna S. Mintii, Andrii O. Kolhatin Journal of Intelligent and Robotic Systems Theory and Applications, 2025 Autonomous landing capabilities are critical for unmanned aerial vehicles (UAVs) operating in challenging environments, yet remain among UAV operations’ most technically demanding aspects. This paper presents a comprehensive review of vision-based autonomous landing systems for UAVs, emphasising fiducial marker-based approaches. After systematically examining 143 papers published between 2018 and 2025, we critically analyse the evolution from traditional landing methods to advanced vision-based systems and evaluate their performance across diverse operational conditions. The review provides detailed analyses of marker design considerations, detection algorithms, performance metrics, and the impact of environmental factors like illumination, weather, and terrain on landing accuracy. We explore hardware implementation challenges, comparing FPGA, SoC, and GPU-based solutions for real-time vision processing on resource-constrained platforms. The integration of artificial intelligence accelerators and multi-sensor fusion approaches is examined, with quantitative comparisons of landing accuracy improvements across different methodologies. Contemporary trends, including swarm-based collaborative landing systems, self-reconfiguring markers, and machine learning-based adaptation to different environments, are discussed alongside regulatory frameworks and safety considerations. Real-world industrial implementations and technology transfer challenges are analysed to bridge the gap between academic research and commercial applications. This systematic review targets researchers, engineers, and stakeholders developing autonomous UAV landing systems for diverse applications, including emergency response, logistics, inspection, and environmental monitoring.
Educational transformation in the Digital Age: artificial intelligence, digital competence, and innovation trends from DigiTransfEd 2025 Ceur Workshop Proceedings, 2025
Learning under pressure: game-based, AI-driven, and crisis-responsive pedagogies in focus of the 12th Workshop on Cloud Technologies in Education Ceur Workshop Proceedings, 2025
Enhancing personal financial management skills through a machine learning-powered business simulator Ceur Workshop Proceedings, 2025
LLM on the edge: the new frontier Ceur Workshop Proceedings, 2025
Immersive technologies, AI integration, and STEAM pedagogical innovations at AREdu 2025 Ceur Workshop Proceedings, 2025
A novel neuro-fuzzy approach for evaluating educational programme quality and institutional performance in higher education Ceur Workshop Proceedings, 2025
Rapid digital competence development through emergency remote teaching: a three-wave analysis of teacher training during COVID-19 Ceur Workshop Proceedings, 2025
Immersive cloud-based mobile learning tools in higher education: a systematic review of integration frameworks and implementation strategies Ceur Workshop Proceedings, 2025
Use of wireless technologies in IoT projects Tetiana A. Vakaliuk, Oleksandr V. Andreiev, Oleksandr F. Dubyna, Oksana L. Korenivska, Yevheniya O. Andreieva Journal of Edge Computing, 2024
A model of application and learning of cloud technologies for future Computer Science teachers Ceur Workshop Proceedings, 2024
Selecting cloud-based learning technologies for developing professional competencies of bachelors majoring in statistics Ceur Workshop Proceedings, 2024
From digital strategies to innovative integration: advancing educational practices through technology at DigiTransfEd 2024 Ceur Workshop Proceedings, 2024
Edge computing applications: using a linear MEMS microphone array for UAV position detection through sound source localization Ceur Workshop Proceedings, 2024
Gamification method using Minecraft for training future teachers of computer science Ceur Workshop Proceedings, 2024
Shifting sands: analyzing trends in educational technology research published in Educational Technology Quarterly (2021-2023) Ceur Workshop Proceedings, 2024
Wireless technologies in IoT projects with distributed computing Ceur Workshop Proceedings, 2024
Approaches to Solving Proxy Performance Problems for HTTP and SOCKS5 Protocols for the Case of Multi-Port Passwordless Access Ceur Workshop Proceedings, 2024
Emulation and Detection of ARP Attacks in GNS3 Environment: Modelling and Development of a Defense Strategy Ceur Workshop Proceedings, 2024
Development and implementation of virtual physics laboratory simulations for enhanced learning experience in higher education Ceur Workshop Proceedings, 2024
Designing an immersive cloud-based educational environment for universities: a comprehensive approach Ceur Workshop Proceedings, 2024
Development of digital competencies in immersive cloud-based educational environment Ceur Workshop Proceedings, 2024
Immersive cloud-based educational environment of the university: Design principles Ceur Workshop Proceedings, 2024
From the digital transformation strategy to the productive integration of technologies in education and training: Report 2023 Ceur Workshop Proceedings, 2023
An algorithm for compiling an electronic multilingual terminological dictionary Ceur Workshop Proceedings, 2023
A secondary school's experience of a cloud-based learning environment deployment Ceur Workshop Proceedings, 2023
Application of Polarization‐Time Model Seismic Signal for Remote Monitoring of Potential Sources Emergencies by Three-Component Seismic Station Ceur Workshop Proceedings, 2023
Software Development for Field Level Industrial Process Control Network System Ceur Workshop Proceedings, 2023
Evaluation of the Effectiveness of the Integrated Security System as an Information System Ceur Workshop Proceedings, 2023
Implementation of STEM learning technology in the process of calibrating an NTC thermistor and developing an electronic thermometer based on it Ceur Workshop Proceedings, 2023
Vulnerabilities and Methods of Unauthorized Gaining Access to Video Surveillance Systems Ceur Workshop Proceedings, 2023
The impact of intangible assets on the financial performance of Slovak ICT companies: a panel data regression analysis Ceur Workshop Proceedings, 2023
Finding Anomalies in the Operation of Automated Control Systems Using Machine Learning Ceur Workshop Proceedings, 2023
Honeypot and cyber deception as a tool for detecting cyber attacks on critical infrastructure Ceur Workshop Proceedings, 2023
Study of Cyber Security Approaches in Organizing Digital Voting Ceur Workshop Proceedings, 2023
Advancing lifelong learning and professional development through ICT: insights from the 3L-Person 2023 workshop Ceur Workshop Proceedings, 2023
Revolutionizing education: using computer simulation and cloud-based smart technology to facilitate successful open learning Ceur Workshop Proceedings, 2023
Development of CRM system with a mobile application for a school Ceur Workshop Proceedings, 2022
Development of a simulator to determine personal financial strategies using machine learning Ceur Workshop Proceedings, 2022
Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach Ceur Workshop Proceedings, 2022
The didactic potential of cloud technologies in professional training of future teachers of Ukrainian language and literature Ceur Workshop Proceedings, 2022
Using a business simulator with elements of machine learning to develop personal finance management skills Ceur Workshop Proceedings, 2022
The use of digital tools by secondary school teachers for the implementation of distance learning in the context of digital transformation in Ukraine Ceur Workshop Proceedings, 2022
Mask and emotion: Computer vision in the age of COVID-19 Serhiy O. Semerikov, Tetiana A. Vakaliuk, Iryna S. Mintii, Vita A. Hamaniuk, Vladimir N. Soloviev, Olga V. Bondarenko, Pavlo P. Nechypurenko, Svitlana V. Shokaliuk, Natalia V. Moiseienko, Vitalii R. Ruban ACM International Conference Proceeding Series, 2021
Immersive E-learning resources: Design methods Serhiy O. Semerikov, Tetiana A. Vakaliuk, Iryna S. Mintii, Vita A. Hamaniuk, Vladimir N. Soloviev, Olga V. Bondarenko, Pavlo P. Nechypurenko, Svitlana V. Shokaliuk, Natalia V. Moiseienko, Dmytro S. Shepiliev ACM International Conference Proceeding Series, 2021
Development of career guidance quests using WebAR D S Shepiliev, S O Semerikov, Yu V Yechkalo, V V Tkachuk, O M Markova, Ye O Modlo, I S. Mintii, M M Mintii, T V. Selivanova, N K Maksyshko, T A Vakaliuk, V V Osadchyi, R O Tarasenko, S M Amelina, A E Kiv Journal of Physics Conference Series, 2021
Analysis of tools for the development of augmented reality technologies Ceur Workshop Proceedings, 2021
Algorithm of analysis and conversion of input data of a two-factor multi-variative transport problem with weight coefficients Ceur Workshop Proceedings, 2021
Quantum information technology on the edge Ceur Workshop Proceedings, 2021
Graph model of Fog Computing system Ceur Workshop Proceedings, 2021
Methodology for evaluation the performance indicators of the ergatic information system functioning Ceur Workshop Proceedings, 2021
Method of assessing the influence of personnel competence on institutional information security Ceur Workshop Proceedings, 2021
Architecture for edge devices for diagnostics of students' physical condition Ceur Workshop Proceedings, 2021
Selection of online tools for creating math tests Ceur Workshop Proceedings, 2021
Current state and prospects of distance learning development in Ukraine Ceur Workshop Proceedings, 2021
Qualitative analysis of pulsograms by fractility indices Tatyana N. Nikitchuk, Oksana L. Korenivska, Dmytro Kh. Shtofel, Jamalbek A. Tussupov, Vasyl B. Benedytskyi, Tetiana A. Vakaliuk, Iryna M. Kobylianska Proceedings of SPIE the International Society for Optical Engineering, 2021
Information and communication technologies in the study of mathematical methods in psychology Ceur Workshop Proceedings, 2020
The selection of cloud services for er-diagrams construction in it specialists databases teaching Ceur Workshop Proceedings, 2020
The state of ICT implementation in institutions of general secondary education: A case of Ukraine Ceur Workshop Proceedings, 2020
Possibilities of application of augmented reality in different branches of education Ceur Workshop Proceedings, 2020
Model of using cloud-based environment in training databases of future IT specialists Ceur Workshop Proceedings, 2020
Development of a model of the solar system in AR and 3D Ceur Workshop Proceedings, 2020
Using massive open online courses in teaching the subject "computer networks" to the future IT specialists Ceur Workshop Proceedings, 2020
Using game simulator Software Inc in the Software Engineering education Ceur Workshop Proceedings, 2020
Using game dev tycoon to develop professional soft competencies for future engineers-programmers Ceur Workshop Proceedings, 2020
WebAR development tools: An overview Ceur Workshop Proceedings, 2020
Development of a web-based system of automatic content retrieval database Ceur Workshop Proceedings, 2019
Import test questions into Moodle LMS Ceur Workshop Proceedings, 2019
RECENT SCHOLAR PUBLICATIONS
Development of soft skills among IT students through project-based work using the Kanban methodology SO Didkivska, TA Vakaliuk Discover Education , 2026 2026
The bio-edge: a survey and research agenda for the Internet of Bio-Nano Things, 2026-2035 SO Semerikov, TA Vakaliuk Journal of edge computing 5 (1), 1-46 , 2026 2026
АНАЛІЗ ІНСТИТУЦІЙНИХ РЕПОЗИТАРІЇВ ТА БІБЛІОТЕЧНИХ СИСТЕМ ДЛЯ ЗБЕРІГАННЯ Й ОПРАЦЮВАННЯ FAIR-ДАНИХ У ГАЛУЗІ ОСВІТНІХ НАУК І МІНТІЙ, Т ВАКАЛЮК Засоби навчальної та науково-дослідної роботи, 70-83 , 2026 2026
Штучний інтелект в освітніх дослідженнях: бібліометричний аналіз публікаційної активності в базах Scopus та Web of Science (2022–2026 рр.) ІС Мінтій, ТА Вакалюк, ОЮ Сіренко, ОМ Спірін Інноваційна педагогіка 2 (93), 292-298 , 2026 2026
МЕТОД MAX-MIN СЕМАНТИЧНОЇ СЕГМЕНТАЦІЇ ТЕКСТУ ДЛЯ ІНФОРМАЦІЙНОГО ПОШУКУ В ТЕХНІЧНІЙ ДОКУМЕНТАЦІЇ: ДОСЛІДЖЕННЯ НА ПРИКЛАДІ ДОКУМЕНТАЦІЇ HAZELCAST МО ФАНТ, ТА ВАКАЛЮК Вісник Херсонського національного технічного університету, 405-410 , 2026 2026
Перспективи розвитку цифрової компетентності викладачів закладів вищої освіти засобами генеративного штучного інтелекту І Мінтій, Т Вакалюк, С Іванова, О Спірін, В Олексюк, А Кільченко Електронне наукове фахове видання “ВІДКРИТЕ ОСВІТНЄ Е-СЕРЕДОВИЩЕ СУЧАСНОГО … , 2026 2026
Energy-efficient neuromorphic computing for ultra-low latency cognitive radio: a hardware-software co-design framework for 6 G spectrum intelligence SO Semerikov, PP Nechypurenko, TA Vakaliuk, IS Mintii, AO Kolhatin Discover Artificial Intelligence , 2026 2026 Citations: 1
From science fiction to smart reality: a technology foresight review of smartdust evolution through the lens of Hamilton’s Great North Road SO Semerikov, TA Vakaliuk, IS Mintii, OV Bondarenko, OB Kanevska European Journal of Futures Research , 2026 2026
Student elective course selection patterns and satisfaction determinants identified through educational data mining: SO Semerikov et al. SO Semerikov, OV Bondarenko, PP Nechypurenko, TA Vakaliuk, IS Mintii Scientific Reports , 2026 2026
Simplified convolutional model for detecting vibration anomalies in helicopters operation Y Hodlevskyi, T Vakaliuk Radioelectronic and Computer Systems 2026 (1), 99-110 , 2026 2026
Mapping the Emergence: A Bibliometric Analysis of the Convergence Gap in GeoAI Teacher Education Research SO Semerikov, TA Vakaliuk, IS Mintii, OV Bondarenko, OB Kanevska SN Computer Science 7 (1), 114 , 2026 2026 Citations: 2
Artificial Intelligence in Modelling Communicative Situations in Foreign Language Classes for Future Management Professionals O Osova, T Vakaliuk, O Spirin, D Antoniuk, Y Avramchuk Digital Technologies in Education II: Selected Cases, 245-265 , 2026 2026
Using Project Management ICTs in the Process of Non-formal Education of IT Specialists I Rantyuk, T Vakaliuk, V Kovalevskyi Digital Technologies in Education II: Selected Cases, 91-108 , 2026 2026
Evaluation of Collective Educational Research Based on Digital Technologies O Spirin, V Oleksiuk, S Ivanova, T Novytska, T Vakaliuk Digital Technologies in Education II: Selected Cases, 269-289 , 2026 2026
Classification of artificial intelligence tools for educational research by the criterion of research autonomy T Vakaliuk, S Semerikov, O Spirin, V Oleksiuk, V Osadchyi CTE Workshop Proceedings, 221-235 , 2026 2026
Філософсько-методологічні засади інтеграції штучного інтелекту в освітні дослідження КП Осадча, ОМ Спірін, ВП Олексюк, ТА Вакалюк, ІС Мінтій, ... Проблеми інженерно-педагогічної освіти 1 (86) , 2026 2026
Google Apps Script як засіб автоматизації конструювання діагностичного інструментарію науково-педагогічного працівника ІС Мінтій, ТА Вакалюк, ОЮ Сіренко, ОМ Спірін Інститут цифровізації освіти НАПН України , 2026 2026
Використання генеративного штучного інтелекту для розвитку цифрової компетентності викладачів закладів вищої освіти: аналітичні матеріали СМ Іванова, АВ Кільченко, ОМ Спірін, ВП Олексюк, ТА Вакалюк, ... Information Technologies in Education 2 (58), 146-158 , 2026 2026
СТАН ДОСЛІДЖЕНЬ У ГАЛУЗІ ЗАСТОСУВАННЯ МАШИННОГО НАВЧАННЯ З ПІДКРІПЛЕННЯМ ДЛЯ ТЕСТУВАННЯ ЗАХИЩЕНОСТІ ІНФОРМАЦІЙНИХ СИСТЕМ ГВ Шпита, ТА Вакалюк ВЧЕНІ ЗАПИСКИ, 12026437 , 2026 2026
Технічне завдання на наукове дослідження «Технології формування і використання FAIR даних в галузі освітніх наук»(номер державної реєстрації 0126U000521)(01.01. 2026-31.12. 2027) СМ Іванова, ІС Мінтій, ОМ Спірін, ВП Олексюк, КП Осадча, ... Інститут цифровізації освіти НАПН України , 2026 2026
MOST CITED SCHOLAR PUBLICATIONS
Revolutionizing education: using computer simulation and cloud-based smart technology to facilitate successful open learning S Papadakis, AE Kiv, HM Kravtsov, VV Osadchyi, MV Marienko, ... CEUR Workshop Proceedings 3358, 1-18 , 2023 2023 Citations: 127
Можливості використання хмарних технологій в освіті ТА Вакалюк Актуальні питання сучасної педагогіки. Матеріали міжнародної науково … , 2013 2013 Citations: 123
Our sustainable coronavirus future S Semerikov, S Chukharev, S Sakhno, A Striuk, V Osadchyi, V Solovieva, ... E3SWC 166, 00001 , 2020 2020 Citations: 122
Unlocking the power of synergy: the joint force of cloud technologies and augmented reality in education S Papadakis, A Kiv, H Kravtsov, V Osadchyi, O Marienko, O Pinchuk, ... CEUR Workshop Proceedings. Vol. 3364: 1–23. , 2023 2023 Citations: 115
Features of distance learning of cloud technologies for the organization educational process in quarantine TA Vakaliuk, OM Spirin, NM Lobanchykova, LA Martseva, IV Novitska, ... Journal of Physics: Conference Series 1840 (1), 012051 , 2021 2021 Citations: 112
Хмарні технології в освіті: навчально-методичний посібник для студентів фізико-математичного факультету ТА Вакалюк Вид-во ЖДУ , 2016 2016 Citations: 111
Using game simulator Software Inc in the Software Engineering education TA Vakaliuk, VV Kontsedailo, DS Antoniuk, OV Korotun, IS Mintii, ... Proceedings of the 2nd International Workshop on Augmented Reality in … , 2019 2019 Citations: 103
Development of career guidance quests using WebAR DS Shepiliev, SO Semerikov, YV Yechkalo, VV Tkachuk, OM Markova, ... Journal of Physics: Conference Series 1840 (1), 012028 , 2021 2021 Citations: 101
Analysis of tools for the development of augmented reality technologies TA Vakaliuk, SI Pochtoviuk CEUR Workshop Proceedings , 2021 2021 Citations: 95
Критерії добору відкритих Web-opiєнтованих технологій навчання основ програмування майбутніх учителів інформатики ОМ Спірін, ТА Вакалюк Інформаційні технології і засоби навчання 4 (60), 275-287 , 2017 2017 Citations: 95
Possibilities of application of augmented reality in different branches of education SI Pochtoviuk, T Vakaliuk, AV Pikilnyak Augmented Reality in Education: Proceedings of the 2nd International … , 2020 2020 Citations: 86
Import test questions into Moodle LMS IS Mintii, SV Shokaliuk, MM Mintii, VN Soloviev, TA Vakaliuk CEUR Workshop Proceedings, 529-540 , 2019 2019 Citations: 72
Model of using cloud-based environment in training databases of future IT specialists OV Korotun, TA Vakaliuk, VN Soloviev Proceedings of the 7th Workshop on Cloud Technologies in Education (CTE 2019 … , 2019 2019 Citations: 67
Current state and prospects of distance learning development in Ukraine IS Mintii, T Vakaliuk, SM Ivanova, OA Chernysh, S Hryshchenko, ... Proceedings of the 4th International Workshop on Augmented Reality in … , 2021 2021 Citations: 66
Using Game Dev Tycoon to Develop Professional Soft Competencies for Future Engineers-Programmers T Vakaliuk, V Kontsedailo, D Antoniuk, O Korotun, S Semerikov, I Mintii Proceedings of the 16th International Conference on ICT in Education … , 2020 2020 Citations: 62
WebAR development tools: An overview DS Shepiliev, YO Modlo, YV Yechkalo, VV Tkachuk, MM Mintii, IS Mintii, ... CEUR Workshop Proceedings 2832, 84-93 , 2020 2020 Citations: 58
Soft Skills in Software Engineering Technicians Education IP Varava, AP Bohinska, TA Vakaliuk, IS Mintii Journal of Physics: Conference Series 1946 (1), 012012 , 2021 2021 Citations: 57
Green IT as a tool for design cloud-oriented sustainable learning environment of a higher education institution T Vakaliuk, D Antoniuk, A Morozov, M Medvedieva, M Medvediev E3S Web of Conferences 166, 10013 , 2020 2020 Citations: 56
Possibilities of Using AR and VR Technologies in Teaching Mathematics to High School Students TA Vakaliuk, LD Shevchuk, BV Shevchuk Universal Journal of Educational Research 8 (11B), 6280-6288 , 2020 2020 Citations: 54
Теоретико-методичні засади проектування і використання хмаро орієнтованого навчального середовища у підготовці бакалаврів інформатики ТА Вакалюк Житомирський державний університет імені Івана Франка , 2019 2019 Citations: 54
