Кафедра інформаційних технологій і систем (ДМетІ)

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UK: Кафедра інформаційних технологій і систем (Дніпровський металургійний інститут, ДМетІ) EN: Department of Information Technologies and Systems (DMetI)

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Data Flow Management in Information Systems Using Blockchain Technology
(Dnipro University of Technology, Dnipro, 2024) Sytnyk, Roman; Hnatushenko, Viktoriia V.
ENG: Purpose. Improving the process of information transfer for critical infrastructure sectors and enterprises through new approaches to real-time tracking of goods, services, and equipment, ensuring secure and transparent data integration and auditing of data flows in information systems using blockchain technologies. Methodology. This research moves away from traditional centralized data management systems based on SQL and no-SQL databases by implementing a decentralized, immutable system built on blockchain technology. This uses the principles of the Merle tree in a digital ledger within blockchain technology to verify data integrity and smart contracts to automate key data flow processes. By tracking goods and equipment through supply chains on the blockchain, this approach ensures product authenticity, provenance, and transparency in real time. In addition, it creates a secure and transparent audit trail for all data in the system compared to conventional centralized data management systems based on SQL and no-SQL databases. Findings. The developed blockchain-based approach improves data security, transparency, automation, and trust in managing data flows. Compared to traditional systems, it offers unique advantages such as immutability, decentralized management, and improved traceability. But while offering numerous advantages, blockchain also faces some limitations in terms of scalability and system complexity. Originality. Digital ledger and blockchain methods have been further developed in the context of designing information systems and data flow management systems based on blockchain algorithms in the context of Industry 4.0. This allows increasing data security, transparency, automation, and trust in data flow management. Practical value. The proposed approach is used to design information and data flow management systems based on blockchain algorithms. This improves the quality of data flow management in industrial enterprises and critical infrastructure, as well as supply chains.
Decentralized Information System for Supply Chain Management Using Blockchain
(RWTH Aachen, Germany, 2022) Sytnyk, Roman; Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.
ENG: Development of international and domestic trade, globalization, creation of longer and more complex supply chains, increase in sales of goods and similar trends lead to an increase in requirements and load on information systems that manage and monitor the shipments of goods, resources and products. The aim of this paper is to make improvements to the existing approaches of building and designing logistics information systems. The paper proposes usage of blockchain technology in order to simplify and make more transparent the processes of monitoring and managing the movement of products between different equal participants in logistics supply chain information systems. A prototype of the supply chain information system based on the use of blockchain technology and smart-contracts using a decentralized Ethereum virtual machine was developed and studied in comparison with traditional approaches.
Detection of Forest Fire Consequences on Satellite Images Using a Neural Network
(German Society for Photogrammetry, Remote Sensing and Geoinformation, 2023) Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.; Kashtan, Vita; Heipke, Christian
ENG: The objective of this research is the detection of burnt forest areas from Sentinel-2 imagery. The proposed algorithm uses an approach based on convolutional neural networks (CNN). The functionality of the created system allows solving the task, starting from the moment of receiving the input data, image preprocessing and ending with the export of a hot-spot fire polygonal file describing the area that was burnt. These results are compared to methods based on the dNBR and a variant of BAIS2 called dBAIS2, which are generated from measurements in the near and middle IR channels of the Sentinel images. The proposed algorithm was tested on Sentinel satellite images acquired from June to September 2021for the Tizi Ouzou region, Algeria. We found it to have an overall accuracy of 97%, outperforming the results obtained from dNBR and dBAIS2 by large margins.
Development and Research of a Chatbot Using the Linguistic Core of Amazon Lex V2
(CEUR-WS Team, Aachen, Germany, 2024) Hnatushenko, Viktoriia V.; Ostrovska, Kateryna Yu.; Nosov, Valerii
ENG: The main of this research is to develop and explore the configuration of a text and voice recognition system, integrate it into a specialized application, and deploy the application in a cloud environment. Amazon Lex service is built on chatbots that support Natural Language Understanding (NLU) and voice recognition. The developed chatbot elevates the user experience while engaging with voice consultants by offering flexible customization options. A chatbot has been designed with interactive text input fields and voice recording functions. The server architecture of the application is configured for seamless data transmission through the AWS SDK to Amazon Lex. The input information undergoes processing to ensure the generation of responses that are dynamically displayed on the web page. The structure of all intents – simulating banking services such as checking card balance, transaction history, and more. Testing the intents was done by creating a dataset with possible user statements and automated runs. The developed chatbot was tested through 6 runs, each consisting of up to 5 statements for recognition. The accuracy of text input recognition ranged from 60% to 99%, with voice input recognition accuracy being 10% lower.
Enhancing the Quality of CNN-Based Burned Area Detection in Satellite Imagery through Data Augmentation
(Copernicus GmbH (Copernicus Publications) on behalf of the International Society of Photogrammetry and Remote Sensing, 2023) Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.; Soldatenko, Dmytro V.; Heipke, Christian
ENG: This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes the impact of image flipping, rotation, and noise addition on the overall accuracy for different classes of burned areas in a forest: fire, burned, smoke and background. Results demonstrate that while single augmentation techniques such as flipping and rotation alone did not result in significant improvements, a combined approach and the addition of noise resulted in an enhancement of the classification accuracy. Moreover, the study shows that augmenting the dataset through the use of multiple augmentation methods concurrently, resulting in a fivefold increase in input data, also enhanced the recognition accuracy. The study also highlights the need for further research in developing more efficient CNN models and in experimenting with additional augmentation methods to improve the accuracy of burned area detection, which would benefit environmental protection and emergency response services.
Land Cover Mapping with Sentinel-2 Imagery Using Deep Learning Semantic Segmentation Models
(CEUR-WS Team, Aachen, Germany, 2024) Hnatushenko, Viktoriia V.; Honcharov, Oleksandr
ENG: Land cover mapping is essential for environmental monitoring and evaluating the effects of human activities. Recent studies have demonstrated the effective application of particular deep learning models for tasks such as wetland mapping. Nonetheless, it is still ambiguous which advanced models developed for natural images are most appropriate for remote sensing data. This study focuses on the segmentation of agricultural fields using satellite imagery to distinguish between cultivated and non-cultivated areas. We employed Sentinel-2 imagery obtained during the summer of 2023 in Ukraine, illustrating the nation's varied land cover. The models were trained to differentiate among three principal categories: water, fields, and background. We chose and optimised five advanced semantic segmentation models, each embodying distinct methodological methods derived from U-Net. Upon examination, all models exhibited robust performance, with total accuracy spanning from 80% to 89.2%. The highest-performing models were U-Net with Residual Blocks and U-Net with Residual Blocks and Batch Normalisation, whereas U-Net with LeakyReLU Activation exhibited much quicker inference times. The findings suggest that semantic segmentation algorithms are highly effective for efficient land cover mapping utilising multispectral satellite images and establish a dependable benchmark for assessing future advancements in this domain.
Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification
(ISPRS, Hannover, Germany, 2024) Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.; Soldatenko, Dmytro V.
ENG: Today's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalanced sample distribution is a critical problem in this regard. Traditional machine learning algorithms in particular have limitations in dealing with sample imbalance. In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. To illustrate our method, we use Sentinel-2 remote sensing (RS) images covering a number of regions in Ukraine, and then we create an image dataset of the region and for training and testing make data augmentation. The models with different architectural features were investigated. The results demonstrate that the proposed CNN has a higher classification accuracy than the ones discussed in the paper: the classification accuracy on the test dataset reached 96.7% with intersection-over-union values of up to 89.7%. This opens the way for further research in the direction of refining algorithms for classify satellite data with an imbalanced class structure.
Non-Relational Approach to Developing Knowledge Bases of Expert System Prototype
(Dnipro University of Technology, Ukraine, 2022) Hnatushenko, Volodymyr V.; Hnatushenko, Viktoriia V.; Dorosh, Natalja L.; Solodka, N. O.; Liashenko, O. A.
ENG: Purpose. Use of a non-relational database management system is proposed while developing a database of a prototype of expert system with using a semantic model of the knowledge. Methodology. The study compares traditional relational approach with the proposed non-relational one in terms of the formation of certain queries. The following indices are used to compare efficiency of two management systems for the databases: particular query set (in MySQL and Cypher languages); runtime for the specified record size (i.e. their processing speed); ease of understanding: and software support of the queries. Findings. It has been identified that the graph model is a more expedient solution in the process of designing semantic networks and their development where complex hierarchical relationships between objects have to be stored and processed. Architecture of the graph database has been applied in terms of the specific example. A prototype of an expert system has been developed to demonstrate the capabilities of the created system of logical inference. The classifier of sciences was chosen as an example in the subject area. Originality. A prototype of the expert system, using the proposed non-relational approach, has been designed involving modern service-oriented architecture (SOA). The abovementioned helped separate the database from the inference engine and the user interface, facilitate perception as well as update and code debugging. Service-oriented architecture makes the system more flexible and robust. Practical value. The developed software is meant to develop both simple expert systems and medium-complex ones.
Relationship between Economic Development, Forest Resources, and Forest Fires: European Context
(LLC “Consulting Publishing Company “Business Perspectives”, Sumy, Ukraine, 2024) Dobrovolska, Olena; Schmidtke, Knut; Hnatushenko, Viktoriia V.; Sytnyk, Svitlana; Dmytriieva, Iryna S.
ENG: Conservation of forest resources is a prerequisite for sustainable development of human society, both in the context of preventing negative climate change and for economic growth. The study aims to establish or refute the co-dependence between the level of forest cover in European countries and the production of gross domestic product. The study object is the socio-economic systems of the national economies of European countries in relation to the totality of forest resources of the continent. Studying the dynamics of forest cover indicators (the share of forests in the total area of the country and forest area per capita), weighted within the internationally recognized regions of Europe, it is confirmed that the level of forest cover of European countries is gradually increasing. The analysis of forest fire area maps identifies three main groups by the level of vulnerability to forest fires: safe (Northern European countries), conditionally safe (Western European countries), and dangerous (Eastern and Southern European countries). Denmark, Finland, France, Norway, and Finland show a direct correlation between the level of forest cover of a country’s territory and gross domestic product. The results of cluster analyses based on the data from 2000, 2010, 2015, and 2020 confirm the existence of a stable cluster of European countries (34 countries) in which there is one type of relationship between the production of gross domestic product and the level of forest cover of the territory.

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