Myroslav Komar

Scopus Publications

Multilingual news dataset about Ukraine (2022–2025): data collection and documentation
Khrystyna Lipianina-Honcharenko, Myroslav Komar, Ihor Ihnatiev, Hennadii Bohuta, Khrystyna Yurkiv
Scientific Data, 2026
This Data Descriptor introduces a multilingual news dataset about Ukraine spanning 2022-2025. It contains 120,617 articles gathered from publicly available online news sources and organized to support research on the information environment surrounding the war. The dataset includes article metadata and text fields, as well as thematic labels that help researchers study how key issues are discussed over time and across outlets. It is intended to support research on media coverage and information environments, supporting research on misleading narratives, and analysing trends in media coverage. The collection can also be used to develop and evaluate natural language processing methods for text classification, topic analysis, and comparative studies of reporting across languages. By providing a large, structured corpus focused on a high-impact geopolitical context, the dataset enables reproducible experiments and offers a practical foundation for researchers and practitioners interested in media analysis, disinformation studies, and information resilience.
Semantic Core for Sensor Telemetry Ingestion for Digital Twins
Oleksandr Osolinskyi, Khrystyna Lipianina-Honcharenko, Myroslav Komar
Smart Cities, 2026
Digital twin platforms for smart cities must continuously receive different types of data from sensors, gateways, and services, but in real situations these data are heterogeneous in terms of indicator names, measurement units, time rules, and object identification, which makes integrations expensive and fragile, while second verification becomes complicated. In this paper, a minimal semantic core for “first-stage” telemetry receiving of the DTwin platform, where semantics are used as operational rules during data ingestion. The core includes a machine-readable model of entities and relationships, dictionaries of metrics and measurement units, a unified event format with separation into a stable envelope and payload, formal validation against data schemas, a mapping table for transforming raw fields into standardized measurements [name, value, unit], as well as an ingestion service with canonicalization of the event record and integrity control through the SHA-256 cryptographic hash. The implementation ensures ingestion of correct events, rejection of incorrect ones without recording, and reproducible verification through control examples, a testing protocol, and evidence snapshots. In smart city settings, such a telemetry ingestion foundation can support reliable monitoring of municipal buildings and infrastructure, including energy efficiency, indoor environmental quality, and data-driven operational decision-making. The proposed approach establishes a core for the stable integration of different sensor data into digital twins and further scaling of the platform.
Edge-Cloud Information-Quanta Ontology for Vision-Language Surveillance: Data-Centric Tokenization for Low-Latency IoT Deployment
Ihor Tkach, Yevhen Petrov, Myroslav Komar, Dekhtyarenko Dmitro
2025 IEEE 16th Annual Ubiquitous Computing Electronics and Mobile Communication Conference Uemcon 2025, 2025
Applications of the Internet of Things (IoT) increasingly demand video analytics that operate under strict bandwidth, privacy, and latency-energy constraints. This work presents an edge-cloud architecture tailored to Applications of IoT in which raw CCTV frames are transformed on the edge into compact, semantically stable Information-Quanta (IQ) events, while semantic reasoning and policy evaluation are executed in the cloud. The edge stage performs markerless pose estimation and IQ detection; only structured events (JSON over MQTT/HTTP[S]) are transmitted, thereby reducing network load and confining sensitive imagery to the device. Methodologically, a data-centric pipeline is employed: (i) automatic curation (near-duplicate removal, noise estimation, class rebalancing), (ii) label-noise-tolerant training, and (iii) calibration-aware, decision-relevant evaluation alongside latency/energy measurement. In a reference deployment with two 1080p streams on a CPU-only edge node, the system sustains <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 25 \text{fps}$</tex> and achieves sub-3 s median alert latency; tokenized cloud queries yield <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 110 ms$</tex> typical reasoning time per event. The resulting IoT-oriented design provides auditable triggers, predictable end-to-end performance, and a practical latencyenergy envelope suitable for resource-constrained environments, supporting explainability and privacy without sacrificing responsiveness.
Intelligent Methods and Means of Supporting User-System Interaction Based on its Pattern Analysis
Ceur Workshop Proceedings, 2025
Knowledge engineering information technology for cultural-educational scenarios based on RAG
Ceur Workshop Proceedings, 2025
Emotion-aware film recommendation with heterogeneous graph neural networks
Ceur Workshop Proceedings, 2025
Increasing the efficiency of decision-making in a decentralized autonomous organization in the warehousing sector
Ceur Workshop Proceedings, 2025
A GENERAL METHOD FOR REAL-TIME DETECTION OF INFORMATION THREATS WITH A UKRAINE CASE STUDY
Khrystyna Lipianina-Honcharenko, Myroslav Komar, Hennadii Bohuta, Ihor Ihnatiev, Khrystyna Yurkiv, Oleg Illiashenko, Lesia Bilovus
Radioelectronic and Computer Systems, 2025
The subject matter is a general set of methods and system architecture for text analytics, enabling real-time detection and monitoring of information threats, validated through a Ukrainian case study. It integrates sentiment analysis, polarity-inversion handling, and machine-learning–based thematic classification. The research is especially relevant in the context of hybrid warfare, where the information environment becomes a battlefield of disinformation, manipulative campaigns and cognitive influence. The goal is to develop and experimentally validate a comprehensive information technology system for automated threat detection in the Ukrainian information space, built on the principles of Responsible Artificial Intelligence (Responsible AI) and modern natural language processing techniques. The objectives: the formation of a multilingual corpus of news and social media texts; implementation of a sentiment analysis module that incorporates polarity inversion; development of a hybrid thematic classification method that combines keyword dictionaries with machine learning model ensembles; and the construction of a Responsible AI Evaluation (RAIE) framework with indicators for fairness, transparency and user satisfaction. The obtained results confirm all five proposed hypotheses: the developed sentime оьnt analysis module achieves macro-F1 = 0.85 and reduces MAE by 18.2% compared to the baseline model; the polarity inversion detection algorithm allows automatic reversal of sentiment score in manipulative texts, improving the detection of hostile narratives; the hybrid thematic classification achieves macro-F1=0.83, with latency of 55 ms/document and throughput of 18 documents/second; integration of all modules into a unified pipeline improves recall by 10.4% without significant increase in latency; the RAIE conceptual model ensures ΔF1 ≤ 5%, an expert user satisfaction score of 4.14/5 and less than 10% latency overhead. The conclusions demonstrate that the proposed system effectively combines high accuracy in identifying information threats with the principles of ethical AI, transparency and user trust, making it practically valuable for national cybersecurity centres, CERTs and OSINT platforms. Conclusions. The scientific novelty lies in the development of novel methods: a context-sensitive sentiment analysis approach tailored to military-related vocabulary; a polarity inversion algorithm for detecting covert hostility; a hybrid thematic classification model combining machine learning with expert dictionaries; an integrated information processing architecture with >17 documents/second throughput; a Responsible AI evaluation model incorporating Fairness Gap, Model Cards and User Satisfaction Score.
Sustainable Information System for Enhancing Virtual Company Resilience Through Machine Learning in Smart City Socio-Economic Scenarios
Khrystyna Lipianina-Honcharenko, Myroslav Komar, Nazar Melnyk, Roman Komarnytsky
Economics Innovative and Economics Research Journal, 2024
This paper introduces an innovative framework for the management of virtual companies in smart urban environments, with an emphasis on socio-economic resilience facilitated by Sustainable Information Systems. The system aims to equip virtual enterprises in smart cities with tools for robust operations amid socio-economic challenges. Its effectiveness is evidenced by improvements in investment risk assessment, business process simulation, and HR project management, enhancing efficiency and foresight. A key feature is predictive analytics for crisis demand forecasting, enabling swift market adjustments and strategic inventory management. It also helps identify alternative clients and suppliers, ensuring business continuity. Integrating machine learning and augmented reality, the system supports automation and strategic decision-making, significantly benefiting the e-commerce sector by addressing fluctuating demand, supply chain issues, and market adaptations during crises. The Sustainable Information System for Virtual Company Management in Smart Cities offers crucial support for e-businesses facing these socio-economic challenges, facilitating their navigation through turbulent times. Its meticulously designed architecture and functionalities make it a powerful instrument for assisting virtual companies in crisis conditions, fostering their sustainable growth within the socio-economic framework of smart urban settings. Comparative studies with existing models underscore this system’s superior efficiency and holistic approach, highlighting its contribution to enhancing the operational efficiency of virtual companies by 95%, reducing the time needed for critical activities like investment risk analysis and business process simulation, and bolstering the socio-economic resilience of smart cities against crises
Intelligent Waste-Volume Management Method in the Smart City Concept
Khrystyna Lipianina-Honcharenko, Myroslav Komar, Oleksandr Osolinskyi, Volodymyr Shymanskyi, Myroslav Havryliuk, Vita Semaniuk
Smart Cities, 2024
This research paper proposes an innovative approach to urban waste management using intelligent methods of classification, clustering, and forecasting. The application of this approach allows for more efficient waste management and contributes to the sustainable development of the urban environment. The aim of this research is to develop an intelligent method for urban waste management, which includes clustering of waste sources, accurate forecasting of waste volumes, and evaluation of forecast results. To achieve this goal, a real dataset with city characteristics and waste data was used. On account of the war in Ukraine, the authors faced the problem of obtaining open data on waste in Ukraine, so it was decided to use data from another city (Singapore). The results show the high efficiency of the developed method. Comparison of the obtained results with the results of the nearest similar works shows that the main feature of this study is the high accuracy of waste-volume forecasting using the XGBoost model, which reached a level of up to 98%.
Enhancing the Efficiency of Decision Support Systems in the Warehousing Sector
Ceur Workshop Proceedings, 2024
Intelligent System For Visual Testing Of Software Products
Ceur Workshop Proceedings, 2024
Regression-based method for real-time solar power plant efficiency forecasting
Ceur Workshop Proceedings, 2024
Evaluation of the Keyword Selection Methods Effectiveness for the Fake News Classification
Ceur Workshop Proceedings, 2024
DESIGN OF AN INTELLIGENT SYSTEM FOR ENHANCING URBAN SOCIAL RESILIENCE
Khrystyna Lipianina-Honcharenko, Myroslav Komar, Roman Madarash, Stanislav Novosad, Volodymyr Zhabiuk, Nazar Mykhalchuk, Kostiantyn Koshytskii, Dmytro Lendiuk, Nazar Melnyk, Oles Telikhovskyi
Eastern European Journal of Enterprise Technologies, 2024
Intelligent Method for Counting Cars from Satellite Images
Ceur Workshop Proceedings, 2023
Emerging Digital Technologies Driven Approach to Increase the Supply Chains Competitivity
Vladyslav Dombrovskyi, Mykhailo Dombrovskyi, Myroslav Komar, Vita Semaniuk, Galyna Liakhovych
Proceedings of the IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs, 2023
Neural Network Method of Items Catalog Forming for Online Store
Ivan Kit, Hrystyna Lipyanina-Goncharenko, Taras Lendyuk, Anatoliy Sachenko, Myroslav Komar
Lecture Notes on Data Engineering and Communications Technologies, 2022
An Intelligent Method for Forming the Advertising Content of Higher Education Institutions Based on Semantic Analysis
Khrystyna Lipianina-Honcharenko, Taras Lendiuk, Anatoliy Sachenko, Oleksandr Osolinskyi, Diana Zahorodnia, Myroslav Komar
Communications in Computer and Information Science, 2022
Model of an Autonomous Airmobile Complex for Measuring Air Pollution Concentrations by Vehicles
Vasyl Tymchyshyn, Frank Otoo, Myroslav Komar, Volodymyr Shpak, Vita Semaniuk, Volodymyr Fronchko
Proceedings International Conference on Advanced Computer Information Technologies Acit, 2022
Access Distribution to the Evaluation System Based on Fuzzy Logic
Lesia Dubchak, Nadiia Vasylkiv, Iryna Turchenko, Myroslav Komar, Tetiana Nadvynychna, Rudolf Volner
Proceedings International Conference on Advanced Computer Information Technologies Acit, 2022
The Task of Parametric Identification the Interval Models with Nonlinear Parameters
Mykola Dyvak, Andriy Pukas, Volodymyr Manzhula, Natalia Kasatkina, Myroslav Komar, Vadym Zabchuk
Proceedings International Conference on Advanced Computer Information Technologies Acit, 2022
Evaluation the Efficiency of Information Technology of Big Data Intelligence Analysis and Processing
Ceur Workshop Proceedings, 2022
Deep convolutional neural network for detection of solar panels
Vladimir Golovko, Alexander Kroshchanka, Egor Mikhno, Myroslav Komar, Anatoliy Sachenko
Lecture Notes on Data Engineering and Communications Technologies, 2021
Neural Network Approach for Semantic Coding of Words
Vladimir Golovko, Alexander Kroshchanka, Myroslav Komar, Anatoliy Sachenko
Advances in Intelligent Systems and Computing, 2020
A new approach for missing data imputation in big data interface
Chunzhi Wang, Nataliya Shakhovska, Anatoliy Sachenko, Myroslav Komar
Information Technology and Control, 2020
Deep multilayer neural network for predicting the winner of football matches
International Journal of Computing, 2020
Artificial Intelligence for Sport Activitity Recognition
Sergei Bezobrazov, Andrei Sheleh, Sergei Kislyuk, Vladimir Golovko, Anatoliy Sachenko, Myroslav Komar, Vitaliy Dorosh, Volodymyr Turchenko
Proceedings of the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs 2019, 2019
Knowledge management applications based on user activities feedback
Mykola Fisun, Mykhailo Dvoretskyi, Hlib Horban, Myroslav Komar
International Journal of Computing, 2019
Textures maps complex for 3D character model development
Ceur Workshop Proceedings, 2019
Method of Creating the 3D Face Model of Character Based on Textures Maps Module
Denys Zolotukhin, Anatoliy Sachenko, Artur Hermanowich, Myroslav Komar, Pavlo Bykovyy
Communications in Computer and Information Science, 2019
Deep convolutional neural network for recognizing the images of text documents
Ceur Workshop Proceedings, 2019
Development of the quantitative method for automated text content authorship attribution based on the statistical analysis of N-grams distribution
Vasyl Lytvyn, Victoria Vysotska, Ihor Budz, Yaroslav Pelekh, Nataliia Sokulska, Roman Kovalchuk, Lyudmyla Dzyubyk, Oksana Tereshchuk, Myroslav Komar
Eastern European Journal of Enterprise Technologies, 2019
Information technologies based on wavelet transform for soldered joints diagnostic of printed circuit boards
Ceur Workshop Proceedings, 2019
Design of a recommendation system based on Collaborative Filtering and machine learning considering personal needs of the user
Vasyl Lytvyn, Victoria Vysotska, Viktor Shatskykh, Ihor Kohut, Oksana Petruchenko, Lyudmyla Dzyubyk, Vitaliy Bobrivetc, Valentyna Panasyuk, Svitlana Sachenko, Myroslav Komar
Eastern European Journal of Enterprise Technologies, 2019
Deep neural network for detection of cyber attacks
Myroslav Komar, Vitaliy Dorosh, Grygoriy Hladiy, Anatoliy Sachenko
2018 IEEE 1st International Conference on System Analysis and Intelligent Computing Saic 2018 Proceedings, 2018
Deep Neural Network for Image Recognition Based on the Caffe Framework
Myroslav Komar, Pavlo Yakobchuk, Vladimir Golovko, Vitaliy Dorosh, Anatoliy Sachenko
Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing Dsmp 2018, 2018
Parallel Deep Neural Network for Detecting Computer Attacks in Information Telecommunication Systems
Vitaliy Dorosh, Myroslav Komar, Anatoliy Sachenko, Vladimir Golovko
2018 IEEE 38th International Conference on Electronics and Nanotechnology Elnano 2018 Proceedings, 2018
Compression of network traffic parameters for detecting cyber attacks based on deep learning
Myroslav Komar, Anatoliy Sachenko, Vladimir Golovko, Vitaliy Dorosh
Proceedings of 2018 IEEE 9th International Conference on Dependable Systems Services and Technologies Dessert 2018, 2018
Development of Solar Panels Detector
Vladimir Golovko, Alexander Kroshchanka, Sergei Bezobrazov, Anatoliy Sachenko, Myroslav Komar, Oleksandr Novosad
2018 International Scientific Practical Conference on Problems of Infocommunications Science and Technology Pic S and T 2018 Proceedings, 2018
Convolutional neural network based solar photovoltaic panel detection in satellite photos
Vladimir Golovko, Sergei Bezobrazov, Alexander Kroshchanka, Anatoliy Sachenko, Myroslav Komar, Andriy Karachka
Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs 2017, 2017
High performance adaptive system for cyber attacks detection
Myroslav Komar, Volodymyr Kochan, Lesia Dubchak, Anatoliy Sachenko, Vladimir Golovko, Sergei Bezobrazov, Ihor Romanets
Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs 2017, 2017
Intelligent cyber defense system using artificial neural network and immune system techniques
Myroslav Komar, Anatoliy Sachenko, Sergei Bezobrazov, Vladimir Golovko
Communications in Computer and Information Science, 2017
Improving of the security of intrusion detection system
Myroslav Komar, Volodymyr Kochan, Anatoly Sachenko, Victor Ababii
2016 13th International Conference on Development and Application Systems Das 2016 Conference Proceedings, 2016
Increasing the resistance of computer systems towards virus attacks
Myroslav Komar, Anatoly Sachenko, Volodymyr Kochan, Taras Skumin
2016 IEEE 36th International Conference on Electronics and Nanotechnology Elnano 2016 Conference Proceedings, 2016
The methods of artificial intelligence for malicious applications detection in android OS
International Journal of Computing, 2016
Intelligent cyber defense system
Ceur Workshop Proceedings, 2016
Artificial immune system for Android OS
Sergei Bezobrazov, Anatoly Sachenko, Myroslav Komar, Vladimir Rubanau
Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs 2015, 2015
Development of neural network immune detectors for computer attacks recognition and classification
Myroslav Komar, Vladimir Golovko, Anatoly Sachenko, Sergei Bezobrazov
Proceedings of the 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems Idaacs 2013, 2013
Methods and tools for reducing the risk of damage the reverse laryngeal nerve during the surgical operation on a thyroid
Mykola Dyvak, Andriy Pukas, Myroslav Komar
Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs 2011, 2011
Intelligent system for detection of networking intrusion
Myroslav Komar, Vladimir Golovko, Anatoly Sachenko, Sergei Bezobrazov
Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs 2011, 2011
Evolution of immune detectors in intelligent security system for malware detection
Vladimir Golovko, Sergei Bezobrazov, Vasilii Melianchuk, Myroslav Komar
Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs 2011, 2011
Principles of neural network artificial immune system design to detect attacks on computers
Modern Problems of Radio Engineering Telecommunications and Computer Science Proceedings of the 10th International Conference Tcset 2010, 2010
Approach to neural-based identification of multisensor conversion characteristic
I. Turchenko, O. Osolinsky, V. Kochan, A. Sachenko, R. Tkachenko, V. Svyatnyy, M. Komar
Proceedings of the 5th IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications Idaacs 2009, 2009

MOST CITED SCHOLAR PUBLICATIONS

Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user
V Lytvyn, V Vysotska, V Shatskykh, I Kohut, O Petruchenko, L Dzyubyk, ...
Восточно-Европейский журнал передовых технологий, 6-28 , 2019
2019
Citations: 78
Deep multilayer Neural Network for Predicting the Winner of Football Matches.
S Anfilets, SV Bezobrazov, VA Golovko, A Sachenko, M Komar, R Dolny, ...
Int. J. Comput. 19 (1), 70-77 , 2020
2020
Citations: 71
Convolutional neural network based solar photovoltaic panel detection in satellite photos
V Golovko, S Bezobrazov, A Kroshchanka, A Sachenko, M Komar, ...
2017 9th IEEE International Conference on Intelligent Data Acquisition and … , 2017
2017
Citations: 71
Deep neural network for image recognition based on the Caffe framework
M Komar, P Yakobchuk, V Golovko, V Dorosh, A Sachenko
2018 IEEE Second International Conference on Data Stream Mining & Processing … , 2018
2018
Citations: 68
The methods of artificial intelligence for malicious applications detection in Android OS
S Bezobrazov, A Sachenko, M Komar, V Rubanau
Computing, 184-190 , 2016
2016
Citations: 59
Development of neural network immune detectors for computer attacks recognition and classification
M Komar, V Golovko, A Sachenko, S Bezobrazov
2013 IEEE 7th International Conference on Intelligent Data Acquisition and … , 2013
2013
Citations: 55
A new approach for missing data imputation in big data interface
C Wang, N Shakhovska, A Sachenko, M Komar
Information Technology and Control 49 (4), 541-555 , 2020
2020
Citations: 47
Intelligent system for detection of networking intrusion
M Komar, V Golovko, A Sachenko, S Bezobrazov
Proceedings of the 6th IEEE International Conference on Intelligent Data … , 2011
2011
Citations: 43
Compression of network traffic parameters for detecting cyber attacks based on deep learning
M Komar, A Sachenko, V Golovko, V Dorosh
2018 IEEE 9th International Conference on Dependable Systems, Services and … , 2018
2018
Citations: 42
High performance adaptive system for cyber attacks detection
M Komar, V Kochan, L Dubchak, A Sachenko, V Golovko, S Bezobrazov, ...
2017 9th IEEE international conference on intelligent data acquisition and … , 2017
2017
Citations: 41
Development of solar panels detector
V Golovko, A Kroshchanka, S Bezobrazov, A Sachenko, M Komar, ...
2018 International Scientific-Practical Conference Problems of … , 2018
2018
Citations: 38
Intelligent cyber defense system using artificial neural network and immune system techniques
M Komar, A Sachenko, S Bezobrazov, V Golovko
International Conference on Information and Communication Technologies in … , 2016
2016
Citations: 33
Principles of neural network artificial immune system design to detect attacks on computers
V Golovko, M Komar, A Sachenko
2010 International Conference on Modern Problems of Radio Engineering … , 2010
2010
Citations: 30
Development of the quantitative method for automated text content authorship attribution based on the statistical analysis of N-grams distribution
V Lytvyn, V Vysotska, I Budz, Y Pelekh, N Sokulska, R Kovalchuk, ...
Eastern-European Journal of Enterprise Technologies 6 (2), 28-51 , 2019
2019
Citations: 28
Deep neural network for detection of cyber attacks
M Komar, V Dorosh, G Hladiy, A Sachenko
2018 IEEE First International Conference on System Analysis & Intelligent … , 2018
2018
Citations: 26
Deep convolutional neural network for detection of solar panels
V Golovko, A Kroshchanka, E Mikhno, M Komar, A Sachenko
Data-Centric Business and Applications: ICT Systems-Theory, Radio … , 2020
2020
Citations: 22
Approach to neural-based identification of multisensor conversion characteristic
I Turchenko, O Osolinsky, V Kochan, A Sachenko, R Tkachenko, ...
2009 IEEE International Workshop on Intelligent Data Acquisition and … , 2009
2009
Citations: 20
Evolution of immune detectors in intelligent security system for malware detection
V Golovko, S Bezobrazov, V Melianchuk, M Komar
Proceedings of the 6th IEEE International Conference on Intelligent Data … , 2011
2011
Citations: 17
Artificial intelligence for sport activitity recognition
S Bezobrazov, A Sheleh, S Kislyuk, V Golovko, A Sachenko, M Komar, ...
2019 10th IEEE International Conference on Intelligent Data Acquisition and … , 2019
2019
Citations: 16
Intelligent waste-volume management method in the smart city concept
K Lipianina-Honcharenko, M Komar, O Osolinskyi, V Shymanskyi, ...
Smart Cities 7 (1), 78-98 , 2023
2023
Citations: 15

Myroslav Komar

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS