Кафедра інформаційних технологій і систем (ДМетІ)

Permanent URI for this communityhttp://crust.ust.edu.ua/handle/123456789/14593

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

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Aircraft Detection in Aerial Imagery Based on YOLO Architectures
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Radionov, Yevhen; Hnatushenko, Volodymyr V.
ENG: The study is devoted to determining the most efficient YOLO-based architecture for the task of aircraft detection in high-resolution aerial imagery. A comparative analysis was conducted across YOLO models v8 through v11 under three experimental conditions: using pre-trained (raw) models, fine-tuning the models on a domain-specific dataset, and fine-tuning models to a dataset enhanced through a proposed image preprocessing method. The evaluation considered both accuracy and inference performance metrics. The proposed methodology reduced the false negative rate from 19.5% to 3.2% at a confidence threshold of 0.75, underscoring its effectiveness in enhancing target visibility under challenging imaging conditions such as low contrast or background clutter.
Aircraft Detection with Deep Neural Networks and Contour-Based Methods
(National University "Zaporizhzhia Polytechnic", Zaporizhzhia, 2024) Radionov, Y. D.; Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Kazymyrenko, O. V.
ENG: Context. Aircraft detection is an essential task in the military, as fast and accurate aircraft identification allows for timely response to potential threats, effective airspace control, and national security. The use of deep neural networks improves the accuracy of aircraft recognition, which is essential for modern defense and airspace monitoring needs. Objective. The work aims to improve the accuracy of aircraft recognition in high-resolution optical satellite imagery by using deep neural networks and a method of sequential boundary traversal to detect object contours. Method. A method for improving the accuracy of aircraft detection on high-resolution satellite images is proposed. The first stage involves collecting data from the HRPlanesv2 dataset containing high-precision satellite images with aircraft annotations. The second stage consists of preprocessing the images using a sequential boundary detection method to detect object contours. In the third stage, training data is created by integrating the obtained contours with the original HRPlanesv2 images. In the fourth stage, the YOLOv8m object detection model is trained separately on the original HRPlanesv2 dataset and the dataset with the applied preprocessing, which allows the evaluation of the impact of additional processed features on the model performance. Results. Software that implements the proposed method was developed. Testing was conducted on the primary data before preprocessing and the data after its application. The results confirmed the superiority of the proposed method over classical approaches, providing higher aircraft recognition accuracy. The mAP50 index reached 0.994, and the mAP50-95 index reached 0.864, 1% and 4.8% higher than the standard approach. Conclusions. The experiments confirm the effectiveness of the proposed method of aircraft detection using deep neural networks and the process of sequential boundary traversal to detect object contours. The results indicate this approach’s high accuracy and efficiency, which allows us to recommend it for use in research related to aircraft recognition in high-resolution images. Further research could focus on improving image preprocessing methods and developing object recognition technologies in machine learning.
Analysis of Methodologies for Carbon Stock Estimation in Forests
(Український державний університет науки і технологій, ННІ «Інститут промислових та бізнес технологій», ІВК «Системні технології», Дніпро, 2022) Kavats, Olena O.; Khramov, Dmitriy A.; Sergieeiva, Kateryna L.; Vasyliev, Volodymyr V.
ENG: Current approaches to carbon stock estimation in forest ecosystems are discussed. Datasets containing biomass and carbon stock estimates that can be used for training/validation in machine learning are described. Examples of applying the remote approach to assessing forest biomass over large areas are analyzed. To estimate the forest carbon stocks in Ukraine, the most promising is the remote approach, which combines ground-based and satellite measurements for forest classification and statistical modeling of carbon stocks. For training and validation of machine learning algorithms, it is proposed to use the GEDI Biomass Map covering most of the territory of Ukraine — from the southern borders to the latitude of Chernihiv in the north. A prototype of forest biomass estimating product in Ukraine can be based on publicly available MODIS NBAR data, SRTM DEM, ECMWF climate data and use the Random Forest machine learning method.
Analysis of Monolithic and Microservice Architectures Features and Metrics
(Хмельницький національний університет, Україна, 2021) Selivorstova, Tatjana V.; Klishch, Sergey M.; Kyrychenko, Serhii; Guda, Anton I.; Ostrovskaya, Kateryna Yu.
ENG: In this paper the information technologies stack is presented. These technologies are used during network architecture deployment. The analysis of technological advantages and drawbacks under investigation for monolithic and network architectures will be useful during of cyber security analysis in telecom networks. The analysis of the main numeric characteristics was carried out with the aid of Kubectl. The results of a series of numerical experiments on the evaluation of the response speed to requests and the fault tolerance are presented. The characteristics of the of monolithic and microservice-based architectures scalability are under investigation. For the time series sets, which characterize the network server load, the value of the Hurst exponent was calculated. The research main goal is the monolithic and microservice architecture main characteristics analysis, time series data from the network server accruing, and their statistical analysis. The methodology of Kubernetes clusters deploying using Minikube, Kubectl, Docker has been used. Application deploy on AWS ECS virtual machine with monolithic architecture and on the Kubernetes cluster (AWS EKS) were conducted. The investigation results gives us the confirmation, that the microservices architecture would be more fault tolerance and flexible in comparison with the monolithic architecture. Time series fractal analysis on the server equipment load showed the presence of long-term dependency, so that we can treat the traffic implementation as a self-similar process. The scientific novelty of the article lies in the application of fractal analysis to real time series: use of the kernel in user space, kernel latency, RAM usage, caching of RAM collected over 6 months with a step of 10 seconds, establishing a long-term dependence of time series data. The practical significance of the research is methodology creation of the monolithic and microservice architectures deployment and exploitation, as well as the use of time series fractal analysis for the network equipment load exploration.
Analysis of Relationships between Parameters of the National Forest Inventory of Finland: Case Study of Mesic Forest
(Geological Society Publishing House, London, UK, 2025) Kavats, Olena O.; Khramov, Dmitriy; Sergieieva, Kateryna L.
ENG: The use of satellite images and machine learning in addition to in situ data in national forest inventories enables covering large areas and significantly reduces costs. However, such combined inventories provide modelled stand properties, the relationships between which are not well understood. An approach to investigating linear and non-linear relationships between forest inventory parameters is proposed. It is applied to a study of the Multi-Source National Forest Inventory (MS-NFI) stand properties for the case of mesic forests. The relationships between MS-NFI parameters and stand reflectance in the visible, red edge, near infrared and short-wave infrared spectral regions were investigated for the Sentinel-2 satellite sensor. Linear models of canopy reflectance as a function of forest stand and elevation properties were developed. These models allowed to assess the comparative influence of MS-NFI parameters on stand reflectance as well as the monthly dynamics of this influence during the season (May–August 2019). Linear relationships between forest inventory parameters were investigated using a correlation matrix. Generalized additive models were used to investigate non-linear pairwise relationships between forest inventory parameters. The proposed approach can be applied to assess the impact of stand features obtained from conventional ground-based forest inventory on forest canopy reflectance.
Application of Neural Networks for Prediction Financial Time Series
(Scientific Publishing Center “Sci-conf.com.ua”, Perfect Publishing, 2024) Prokofiev, Taras; Ostrovska, Kateryna Yu.
ENG: The article discusses some aspects and features of the use of neural networks for forecasting financial time series for the purpose of making a profit. The use of neural networks to analyze financial information is a promising alternative (or complement) to traditional research methods. Due to their adaptability, the same neural networks can be used to analyze several instruments and markets, while the patterns found by a player for a specific instrument using technical analysis methods may work worse or not work at all for other instruments.
Application of Two-Dimensional Padé-Type Approximations for Image Processing
(National University «Zaporizhzhia Polytechnic», Zaporizhzhia, 2023) Olevskyi, V. I.; Hnatushenko, Volodymyr V.; Korotenko, G. M.; Olevska, Yu. B.; Obydennyi, Ye. O.
ENG: Context. The Gibbs phenomenon introduces significant distortions for most popular 2D graphics standards because they use a finite sum of harmonics when image processing by expansion of the signal into a two-dimensional Fourier series is used in order to reduce the size of the graphical file. Thus, the reduction of this phenomenon is a very important problem. Objective. The aim of the current work is the application of two-dimensional Padé-type approximations with the aim of elimination of the Gibbs phenomenon in image processing and reduction of the size of the resulting image file. Method. We use the two-dimensional Padé-type approximants method which we have developed earlier to reduce the Gibbs phenomenon for the harmonic two-dimensional Fourier series. A definition of a Padé-type functional is proposed. For this purpose, we use the generalized two-dimensional Padé approximation proposed by Chisholm when the range of the frequency values on the integer grid is selected according to the Vavilov method. The proposed scheme makes it possible to determine a set of series coefficients necessary and sufficient for construction of a Padé-type approximation with a given structure of the numerator and denominator. We consider some examples of Padé approximants application to simple discontinuous template functions for both formulaic and discrete representation. Results. The study gives us an opportunity to make some conclusions about practical usage of the Padé-type approximation and about its advantages. They demonstrate effective elimination of distortions inherent to Gibbs phenomena for the Padé-type approximant. It is well seen that Padé-type approximant is significantly more visually appropriate than Fourier one. Application of the Padé-type approximation also leads to sufficient decrease of approximants’ parameter number without the loss of precision. Conclusions. The applicability of the technique and the possibility of its application to improve the accuracy of calculations are demonstrated. The study gives us an opportunity to make conclusions about the advantages of the Padé-type approximation practical usage.
Applying Machine Learning Techniques to Analyze Forest Fire Impacts on Sentinel-2 Imagery across Ukraine
(Український державний університет науки і технологій, ННІ ≪Інститут промислових та бізнес технологій≫, ІВК ≪Системні технології≫, Дніпро, 2026) Hnatushenko Viktoriia V.; Udovyk, Iryna M.; Heipke, Christian; Hnatushenko, Maksym V.
ENG: Forest fires pose severe ecological and socio-economic threats, necessitating efficient tools for rapid damage assessment. This study presents a machine learning approach for detecting burnt forest areas in Ukraine using multispectral Sentinel-2 imagery. A new manually annotated dataset was developed for training semantic segmentation models, addressing the scarcity of open data for the region. The proposed convolutional neural network, based on an encoder–decoder architecture with Xception blocks, effectively captures spectral patterns associated with fire damage. Experiments conducted on Sentinel-2 Level-2A imagery of the Kinburn Peninsula (October 2022) demonstrate high detection performance, achieving an Intersection over Union (IoU) of 95%. The results confirm the model’s capability for accurate burnt-area mapping and highlight its potential for broader applications in regional fire monitoring and environmental management.
Attributes and Metrics of Trust Based Models in Cloud Security
(Publishing House "Helvetica", 2026) Bobrenok, Viacheslav V.; Guda, Anton I.
ENG: Cloud environments became a popular solution for hosting and managing infrastructure and data for businesses in different domains: cloud computing service providers’ revenue in 2018 amounted to approximately 217 billion US dollars, in 2022 - 481 billion, and the forecast for 2028 includes a profit figure of more than 1 trillion US dollars. But as organizations migrate from traditional on-premises infrastructures to cloud platforms, conventional perimeter-based security approaches have become insufficient due to the absence of clear network boundaries and the rise of remote access. It introduced a new set of security challenges. For example, in 2021, losses to companies from the leakage of confidential information amounted to an average of 3.5 million US dollars, and losses from attacks aimed at destroying or damaging IT infrastructure amounted to 4.6 million US dollars. Thus, these issues must be resolved to facilitate further adoption of cloud technologies. Trust based models might be a solution for some of these challenges as the evolution of trust models in cloud security reflects a shift from static to dynamic and adaptive mechanisms. By shifting from implicit trust to continuous verification and contextual awareness, these models provide a more robust framework for protecting sensitive information and maintaining secure access in cloud ecosystems. This work is an attempt to discover key attributes of trust based models and how they can be used to create a mechanism for securing data and workloads in cloud environments. It is achieved by conducting an extensive review of existing security threats in cloud environments as well as a systematic analysis of key characteristics of trust based models and their applicability for mitigation of these threats. As a result, this work discovers key attributes of trust based models which can be used to implement new security mechanisms for cloud environments. Such mechanisms should be better suited for handling the dynamic nature of such environments. Even though securing cloud environments remains a complex task, the attributes described in this research can be used to create new tools and methodologies which can greatly simplify it and facilitate further adoption of cloud technologies.
Automated Building Damage Detection on Digital Imagery Using Machine Learning
(Dnipro University of Technology, Ukraine, 2023) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: Purpose. To develop an automated method based on machine learning for accurate detection of features of a damaged building on digital imagery. Methodology. This article presents an approach that employs a combination of unsupervised machine learning techniques, specifically Principal Component Analysis (PCA), K-means clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to identify building damage resulting from military conflicts. The PCA method is utilized to identify principal vectors representing the directions of maximum variance in the data. Subsequently, the K-means method is applied to cluster the feature vector space, with the predefined number of clusters reflecting the number of principal vectors. Each cluster represents a group of similar blocks of image differences, which helps to identify significant features associated with fractures. Finally, the DBSCAN method is employed to identify areas where points with similar characteristics are located. Subsequently, a binary fracture mask is generated, with pixels exceeding the threshold being identified as fractures. Findings. The introduced methodology attains an accuracy rate of 98.13 %, surpassing the performance of conventional methods such as DBSCAN, PCA, and K-means. Furthermore, the method exhibits a recall of 82.38 %, signifying its ability to effectively detect a substantial proportion of positive examples. Precision of 58.54 % underscores the methodology’s capability to minimize false positives. The F1 Score of 70.90 % demonstrates a well-balanced performance between precision and recall. Originality. DBSCAN, PCA and K-means methods have been further developed in the context of automated detection of building destruction in aerospace images. This allows us to significantly increase the accuracy and efficiency of monitoring territories, including those affected by the consequences of military aggression. Practical value. The results obtained can be used to improve automated monitoring systems for urban development and can also serve as the basis for the development of effective strategies for the restoration and reconstruction of damaged infrastructure.
Comparative Analysis of Activation Functions in U-Net for Binary Water Segmentation using Sentinel-2 Imagery
(CEUR-WS Team, Aachen, Germany, 2025) Kundenko, Pavlo; Hnatushenko, Viktoriia V.; Tsaryk, Vladyslav Yu.; Dmytriieva, Iryna S.
ENG: The study examines how different activation functions influence the performance of a U-Net model applied to binary water-body segmentation in Sentinel-2 imagery. Using an identical training setup for each experiment, six nonlinearities—ReLU, Leaky ReLU, ELU, PReLU, Swish and RReLU—are individually substituted into the network while all other parameters remain fixed. Comparative evaluation on a held-out validation set reveals that Leaky ReLU provides the most balanced trade-off between precision and recall, making it the preferred choice for accurate water-mask generation. PReLU offers a similar but slightly lower performance, whereas ELU excels at capturing additional water pixels at the cost of more false positives. The findings highlight the importance of activation-function selection in remote-sensing segmentation tasks and suggest further exploration of advanced nonlinearities and larger, more diverse datasets to enhance generalization.
Comparative Analysis of Classification Methods for High-Resolution Optical Satellite Images
(Khmelnytskyi National University, Khmelnytskyi, 2024) Hnatushenko, Volodymyr V.; Kashtan, Vita; Chumychov, Denys; Nikulin, Serhii
ENG: High-resolution satellite image classification is used in various applications, such as urban planning, environmental monitoring, disaster management, and agricultural assessment. Traditional classification methods are ineffective due to the complex characteristics of high-resolution multichannel images: the presence of shadows, complex textures, and overlapping objects. This necessitates selecting an efficient classification method for further thematic data analysis. In this study, a comprehensive assessment of the accuracy of the most well-known classification methods (parallelepiped, minimum distance, Mahalanobis distance, maximum similarity, spectral angle map, spectral information difference, binary coding, neural network, decision tree, random forest, support vector machine, K-nearest neighbour, and spectral correlation map) is performed. This study comprehensively evaluates various classification algorithms applied to high-resolution satellite imagery, focusing on their accuracy and suitability for different use cases. To ensure the robustness of the evaluation, high-quality WorldView-3 satellite imagery, known for its exceptional spatial and spectral resolution, was utilized as the dataset. To assess the performance of these methods, error matrices were generated for each algorithm, providing detailed insights into their classification accuracy. The average values along the main diagonal of these matrices, representing the proportion of correctly classified pixels, served as a key metric for evaluating overall effectiveness. Results indicate that advanced machine learning approaches, such as neural networks and support vector machines, consistently outperform traditional techniques, achieving superior accuracy across various classes. Despite their high average accuracy, a deeper analysis revealed that only some algorithms are universally optimal. For instance, some methods, such as random forests or spectral angle mappers, exhibited strength in classifying specific features like vegetation or urban structures but performed less effectively for others. This underscores the importance of tailoring algorithm selection to the specific objectives of individual classification tasks and the unique characteristics of the target datasets. This study can be used to select the most effective method of classifying the earth's surface, depending on the tasks of further thematic analysis of high-resolution satellite imagery. Furthermore, it highlights the potential of integrating machine learning-based approaches to enhance the accuracy and reliability of classification outcomes, ultimately contributing to more practical applications.
Complex of Mathematical Models and Methods to Calculate Pressure Effect on Sulfide Distribution in Steel
(Хмельницький національний університет, Україна, 2021) Selivyorstova, Tetjana V.; Selivyorstov, Vadim Yu.; Kuznecov, Vitaliy V.
ENG: Primary objective is to develop computational method to analyze digital pictures of sulfide prints, helping obtain qualitative image characteristics, and to formulate mathematical model of the distribution of sulphide inclusions to determine specific features of the pressure effect on the macrostructure formation of carbon steel castings flooded into the uncooled mold. The research was carried out using images of sulfide prints of templates cut of steel cylindrical castings; L500 steel was applied. The castings result from industrial tests of a method of gas-dynamic effect on the fusion in the foundry forms under the conditions of a casthouse of Dnipropetrovsk aggregate plant PJSC. Digital pictures of sulfide prints, obtained in terms of the increased rate of gas pressure and maximum pressure, were binarized; defective fra gments were removed; and zo ning took place. The developed computational method has been applied for fragments of images, representing different zones; data arrays have been received containing sizes and amounts of inclusions in the fragment. The developed computational method to analyze digital images of sulfide prints has been implemented. ASImprints software support has helped obtain qualitative characteristics of images; namely, distribution of amount of the certain-size sulfide inclusions. The computational method to analyze digital images of sulfide prints has made it possible to study the set of patterns of sulfide prints. The dependences have been obtained, describing specific features of sulfide inclusion distribution while varying gas-dynamic pressure method in terms of fusion in the casting form. It has been demonstrated that the distribution describes effectively the power-series distribution to compare with the exponential one. Mathematical model of the power -series distribution parameter dependence upon pressure has been developed. Deviation of the distribution parameters in terms of the experimental values and the model values has been evaluated. The research demonstrates the ways to apply an algorithm of simple recursive casting for quantitative analysis of digital images of sulfide prints. Use of ASImprints, being software implementation of the computational method to analyze digital images of sulfide prints making it possible to obtain qualitative characteristics of images, has helped identify that the increased pressure within a casting-device for gas injection system results in the increased specific amount of inclusions and the decreased specific zone of sulfide inclusions respectively. It has been defined that exponential function describes reliably the nature of sulfide inclusion distribution in the digital image of sulfide print. The research has demonstrated that fragments of a sulfide print, belonging to one zone, are statistically homogeneous. Thus, it is possible to analyze quantitively digital image zone of a sulfide print on its fragment. Mathematical model of dependence of sulfide inclusion distribution in carbon-steel castings in terms of gas-dynamic effect on fusion solidifying in a mold has been developed. The model may be applied to predict sulfide inclusion distribution within the selected zones of cross section of the cylindrical castings solidifying in the uncooled mold in terms of the preset mode of gas-dynamic effect.
Computer Modeling of Territory Flooding in the Event of an Emergency at Seredniodniprovska Hydroelectric Power Plant
(Dnipro University of Technology, Ukraine, 2022) Ivanov, D. V.; Hnatushenko, Volodymyr V.; Kashtan, Vita Yu.; Garkusha, I. M.
ENG: Purpose. Computer modeling of territory flooding in the event of an emergency at Seredniodniprovska Hydroelectric Power Plant (HPP). Methodology. The computer model of possible territory flooding at Seredniodniprovska HPP is developed using simulation modeling methods and geometric and hydrological approaches and considers initial boundary conditions of the water-engineering system. Calculations of the wave break height and the half-divided cross-sectional area of the river bed were made and a three-dimensional model of the territory flooding was built using the Python language and ArcGIS Desktop software. Findings. The data for each creation of the hydraulic node, namely the depth and width of the flooded territory, were calculated. This allowed analyzing the macro level considering the triangulation model of the surface. The wave break parameters and flaps (intersections) were taken into account in case of a dam break at a hydroelectric power plant or a rise in the water level. A mathematical model, and a 3D model were developed, and a forecast of the flood zone due to an emergency was made using satellite survey data. Originality. The mathematical method received further development for calculating flood territories in the event of an emergency at Seredniodniprovska Hydroelectric Power Plant, taking into account the parameters of the breakthrough wave and the calculation of cross-sections for the cases when a hydroelectric dam breaks or the water level rises; the method uses one-dimensional and two-dimensional systems of Saint-Venant equations, and geometric and hydrological approaches. A three-dimensional model of the territory flooding is developed to predict possible consequences. Practical value. The obtained results can be used to model the flooding of the territory located near dangerous hydro-technical objects, such as dams, dikes as well as to forecast flooded territories during the construction of drainage and protective structures.
Computer Technology for Satellite Imagery Processing in Nature Management Problem Solving Using Lineament Analysis
(Український державний університет науки і технологій, ІВК «Системні технології», Дніпро, 2023) Kashtan, Vita Yu.; Nikulin, Serhii; Hnatushenko, Volodymyr V.; Sergieieva, Kateryna; Korobko, Olha; Ivanov, Denys
ENG: This study focuses on analyzing the techniques used to highlight lineaments in images. Various mask algorithms, including the widely used optimal Kenny detector, were employed to identify brightness boundaries. Additionally, several quality criteria were developed to assess the accuracy of boundary selection. Based on the results of the analysis, conclusions were drawn regarding the effectiveness of different pre-processing methods for space images, along with recommendations to streamline data processing and analysis and enhance the reliability of results. Our analysis of image processing methods for selecting brightness boundaries revealed that the most effective approach involves applying filters to the source images to increase the number of selected boundaries while maintaining their integrity and length.
Construction of a Kinetic Equation of Carbon Removal for Controlling Steel Melting in the Metallurgical System "Cupola Furnace – Small Converter"
(TECHNOLOGY CENTER PC, Kharkiv, 2025) Makarenko, Dmytro M.; Selivorstova, Tetiana V.; Dotsenko, Yuriy V.; Osypenko, Iryna O.; Dzevochko, Oleksandr M.; Pereverzieva, Alevtyna M.; Dzevochko, Alona I.
ENG: The object of research in the paper is the process of steelmaking in a small converter, which works in tandem with a cupola furnace. The existing problem is that the control of the process of obtaining steel in an oxygen converter is complicated by the need to determine in real time the current chemical composition of the melt, in particular carbon. This is due to the fact that the rate of carbon removal is too high, as a result of which the process of carbon removal is transient. Therefore, it is too difficult to implement regulation based on feedback on continuous measurement. The presence of the specified problem requires solutions related to the possibilities of developing or improving software control of the process. It is shown that in certain sections of the process within each time section of oxygen purging of the melt in the converter, the kinetic curve has a linear form with a constant coefficient value in front of the inlet mine. But the value of the initial coefficient for each equation that describes the process within its limits changes. This allows to state that in case of a change in the initial condition, the kinetic curves shift relative to each other in parallel. On this basis, a system of equations has been constructed that describes the process of carbon removal in a small oxygen converter that receives liquid iron from a cupola furnace. It has been shown that to use the obtained system of equations, it is necessary to know the initial carbon content in the melt discharged from the cupola furnace, and it depends on the method of oxygen supply to the cupola furnace. Based on the modeling of this process in two variants – using a “sharp blow” and supplying oxygen to the air blown into the tuyeres, a nomogram has been constructed. It allows to determine the initial carbon content for the practical use of the obtained system of equations. Using the obtained system makes it possible to determine the time after which oxygen cutoff should be made. This will allow to decide to implement software control of the melt blowing process in the converter. The presented study will be useful for machine-building enterprises that have foundry shops in their structure, where cast iron is smelted for the manufacture of castings.
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.
Deep Learning-Based Segmentation of Multi-Temporal Satellite Imagery for Flood Detection
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: A deep learning-based pixel-based flood zone segmentation approach is proposed using multi-temporal satellite images and topographic and hydrological information. It is proposed to combine heterogeneous data (satellite images before and after the flood, digital elevation model, and hydrographic characteristics) into a single input tensor, allowing the neural network to consider the area's spatial and temporal dynamics and morphometric features. The architecture of the model ensures the preservation of the spatial detail of the flooded area through skip-connection mechanisms, which contributes to the correct identification of flood boundaries. Comparative analysis with FCNN, DeepLabv3, and BASNet confirmed the superiority of the proposed approach (F1-score 82%, Dice 82% for the category 'flooded areas'), which indicates its effectiveness for accurately detecting flooded areas.
Detecting Extraordinary Application Memory Use by Analyzing Memory Screenshots
(Public Organization "Ukrainian Assembly of Doctors of Sciences in Public Administration", Kyiv, 2024) Guk, Natalia A.; Mitikov, Nikolay Y.; Selivyorstova Tatjana V.
ENG: This study investigates excessive memory consumption in .NET applications, with a focus on identifying inefficiencies in memory allocations that lead to unnecessary resource usage. Real-world processing system memory snapshots were gathered using ProcDump, and managed heaps were thoroughly inspected with WinDBG to uncover memory usage patterns and the distribution of space among different types. ClrMD was employed to further analyze runtime data and offer optimization recommendations targeted at reducing the overall memory footprint. To validate these optimizations, Benchmark.NET performance tests were conducted using 'application' data to measure memory usage before and after the suggested changes. The analysis uncovered that user-defined types were responsible for consuming significantly more memory than required. This overconsumption was due to overallocation, largely driven by an overestimation of the necessary data range for the objects, despite domain-specific data consistently fitting within smaller numeric ranges. The mismatch between object design and actual data requirements led to memory inefficiencies. After conducting targeted optimizations based on the real characteristics of the stored data and adhering to memory alignment principles, the study succeeded in significantly reducing the application's memory consumption. These optimizations resulted in memory savings potentially measured in gigabytes, demonstrating the effectiveness of aligning object design with data representation. The research underscores the value of memory snapshot analysis as a tool for identifying and mitigating excessive memory usage in .NET applications. It advocates for a more deliberate object design strategy, one that takes into account the actual range and size of the data being handled. Such an approach can result in significant performance improvements and more efficient memory management. This study offers practical insights for developers aiming to enhance the memory efficiency of .NET applications, contributing to more sustainable and scalable software systems.
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.
Development of a JFET Model with Increased Accuracy: Measurements of Wrangling Data, Acquisition and Model Analysis
(CEUR-WS Team, Aachen, Germany, 2025) Hnatushenko, Viktoriia V.; Guda, Anton I.; Zimoglyad, Andrew Yu.; Zhurba, Anna O.
ENG: Junction gate field – effect transistors have a significant role in the modern electronics. Simulation of the electronic schematics is a crucial part of the modern devices development. At the present time an existent model are used. A drawbacks of the exiting JFET models, with are commonly used during electronic schematics simulation a described. For the tasks of precision simulation the simple approximation functions and switching conditions lead to accuracy loss. A general purpose and specialized hardware and software complex was created to acquire measurement data. This measurement complex gives as possibility to acquire measurement data in automatic and semi-automatic modes. A bulk amount of data about selected JFET species was collected. According to this data a new model was proposed. This model allows us identify parameters in sequence, which significantly decreases the possibility of the identification errors. Proposed model requires more complex calculations to achieve results, and more data to conduct parametric identification. But as the result, new model provides better agreement with experimental data, especially in low-voltage regimes. New model allows us to decrease simulation error level from the 20% to 1—5%. The proposed model provides better qualitative conforming to the experimental data.
Development of a Method for Using Graphical Diagrams for Queries to Large Language Models
(International Scientific Unity, Lisbon, Portugal, 2025) Dmytriieva, Iryna S.; Yushkovskyi, Pavlo
ENG: This paper is devoted to the development of an innovative method that combines the visual structure of graphic diagrams with the power of large language models (LLMs). Based on experiments with GPT-4, Claude 3, and Gemini 1.5 Pro, it has been demonstrated that the use of formalized diagrams as queries increases the coherence and accuracy of responses by 16–25% compared to traditional text instructions. The proposed approach opens up new opportunities for structured human-artificial intelligence interaction in education, science, and analytics.
Development of Software Module for Analysis of It Specialists’ Labor Market
(Odesa National University of Technology, 2022) Dub, Anhelina; Zhurba, Anna O.
ENG: The paper describes how software module was developed to analyze the labor market of IT professionals using the Python programming language in the integrated programming environment. The software module screens, crawls, parses and exports data from specialised sites. The software module allows to find, structure and export data to CSV and TXT files. The sample of key parameters was investigated by means of business analytics. The developed program can be expanded with additional functions, supplemented by a graphical interface, uploaded to the web hosting. The software module provides processing of a large array of unstructured information from vacancy announcement sites, reduces the amount of routine manual operations and provides an opportunity to focus stakeholders’ attention on key priorities. The aim of the work is to develop a parsing software module for automated collection and analysis of open position data to determine which knowledge and skills are most important for employers in the IT industry.
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.
Entropy-Cryptographic Approach for Transmission of Satellite Data in Telecommunication Networks
(Khmelhytskyi National University, Khmelhytskyi, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: Amid rapid advances in satellite observations and increasing risks associated with high-resolution multispectral data downloads, stronger satellite image encryption techniques are becoming increasingly important. To address these challenges, the paper proposes a new cryptographic methodology based on entropy and nonlinear methods. This approach offers high resistance against brute-force attacks, generates a high-entropy key, and employs a dynamic non–linear transformation in multi-layered encryption, which is AES-256-bit encrypted. This comprehensive approach is designed to safeguard the security of satellite data in transit from modern cryptanalysis as it traverses communication networks. The approach begins with entropy-based initializations, iteratively expands the key space, and applies adaptive transformations to render the encrypted data highly unpredictable. Experiments with satellite images of various resolutions demonstrate that this approach is resistant to cryptanalysis. The evaluation included measuring entropy, analysing the correlation between neighbouring pixels, and testing resistance to statistical and frequency-based attacks. The encrypted images achieved entropy values close to the theoretical maximum and showed almost no correlation between adjacent pixels, demonstrating the strength and uniformity of the encryption process. Performance tests on systems with multiple threads and processors revealed clear links between execution time, data size, and the level of parallelization. Moderate parallelism provided the best speed improvements, and the method remained scalable for large datasets, making it suitable for high-throughput environments. This approach ensures strong satellite-image robustness in image size, content, or spectral characteristics. The flexible structure and good performance make it a promising candidate for future telecom networks and secure satellite data distribution systems.
Flood Forecasting with Sentinel-2 Images Using Machine Learning
(ISPRS, Hannover, Germany, 2025) Hnatushenko, Viktoriia V.; Kashtan, Vita Y.; Hnatushenko, Volodymyr V.; Heipke, Christian
ENG: This paper proposes a methodology for detecting flooded areas using Sentinel-2 images, followed by flood forecasting based on a combination of the deep neural network U-Net and a support vector machine (SVM). The U-Net architecture classifies a given Sentinel image into the two classes “water” and “no water”, the SVM subsequently performs a near-future prediction of flooded areas based on the U-Net results and additional information (DEM, land use information, precipitation data etc.). Experimental results demonstrate that for a test site in Ukraine the U-Net/SVM model achieves the highest overall accuracy (98.8%), slightly outperforming other models, including Random Forest and SVM. The resulting flood maps provide valuable information for planning rescue operations and territory management, allowing for rapidly identifying areas of flooding. It can thus contribute to a significant reduction in economic losses and an increase in emergency preparedness.
Fractal Analysis of Satellite Night-Time Imagery for Environmental and Urban Monitoring
(ISPRS, Hannover, Germany, 2025) Hnatushenko, Viktoriia V.; Zhurba, Anna O.; Hnatushenko, Volodymyr V.
ENG: Satellite remote sensing technologies offer a powerful means for acquiring high-resolution imagery that captures both natural and anthropogenic features across large geographic regions. One particularly valuable application of satellite data is the analysis of night-time lights, which reflect human activity, urban infrastructure and energy consumption. Traditional image processing methods often struggle to describe the irregular and complex geometries inherent to urban environments and natural landscapes. This research proposed the application of fractal analysis as a methodological tool for characterizing such complexity in night-time satellite images. By applying this to satellite images of Ukraine and the city of Kyiv before and after major attacks on energy infrastructure, it demonstrated how reductions in night-time illumination correspond with significant decreases in fractal dimension. Additionally, the method was applied to assess urban expansion in Dubai and Dnipro across multi-decadal periods. Results show a correlation between increased urbanization and changes in fractal characteristics, providing evidence that fractal metrics can serve as proxies for urban morphology and development. Fractal analysis as an effective method for environmental monitoring, crisis assessment, and urban planning opens pathways for integrating fractal parameters into larger geospatial intelligence systems and provides a foundation for further research on scale-invariant properties of urban and ecological patterns in satellite imagery.
Homomorphic Filtering in Digital Multichannel Image Processing
(Dnipro University of Technology, Dnipro, Ukraine, 2023) Hnatushenko, Volodymyr V.; Spirintseva, O. V.; Spirintsev, V. V.; Kravets, O. V.; Spirintsev, Dmytro V.
ENG: Purpose. The purpose of this article is to develop a preprocessing method for digital multispectral remote sensing images obtained through optical and infrared means in the electromagnetic spectrum. The method aims to ensure invariance with respect to positional formation conditions that determine spatial and radiometric resolution. By implementing homomorphic filtering in this method, we can significantly increase the informative value of processed imagery. Methodology. The problem solving, including the development of the spatial and radiometric resolution increase ways for multispectral geospatial data are based on the methods of brightness spatial distribution fusion, methods of data dimension reduction, de-correlation techniques and geometric correction of image spatial distributions. Findings. The method of preprocessing digital remote sensing data has been developed, which is a component of the methodology for identifying geometric shapes (GS) of objects in multi-channel aerospace images, allowing for a significant improvement in their recognition efficiency when noise is present. Originality. The method of preprocessing photogrammetric scenes using homomorphic filtering to enhance their informational significance is proposed. The method ensures invariance to positional conditions of fixation, improves the accuracy of further recognition, eliminates the drawbacks of known methods associated with the existence of parametric uncertainty dependence, the features of fixation of species information, low values of information indices of synthesized images, and computational process peculiarities. Practical value. Practical value consists in improving of identification accuracy of objects GS in digital geospatial data, in significant increasing of raster multispectral images information value and in rising of automated image processing efficiency. The use of the method can greatly enhance the value and usefulness of multispectral photogrammetric images in a wide range of applications, from environmental monitoring to urban planning.
A Hybrid Neural Network Architecture for Semantic-Contextual Analysis of Emotions in Social Media
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Ovcharenko, Maksym; Ivanko, Artem
ENG: The paper proposes a hybrid neural network architecture for multi-class classification of the emotional state of social media texts, which combines contextual vector representations generated by the BERT model with extended structured features related to the user and message metadata. The proposed approach uses a multilayer perceptron network that combines linguistic and contextual information in a single representation. The results of the experimental study confirm the superiority of the proposed approach over traditional LSTM and CNN architectures, as well as over the separate use of BERT embeddings. The achieved classification accuracy is 90%, and the F1-measure is 0.91, which indicates the high efficiency of the model in conditions of high variability of language structures and stylistic features of social content.
Hybrid Quantum CNN-Based Information Technology for Building Semantic Segmentation in Aerial Imagery
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Babets, Dmytro; Cyran, Krzysztof; Wereszczyński, Kamil
ENG: This paper presents information technology for semantic segmentation of building objects in high-resolution aerospace images using a hybrid quantum convolutional neural network (QCNN). Quantitative and visual evaluations demonstrate the effectiveness of the proposed approach compared to classical segmentation models such as U-Net, CNN, and FCN. The hybrid QCNN model achieved high pixel classification accuracy, with a mean accuracy of 0.98 on the training set and 0.97 on the validation set, indicating robust object recognition. Loss values reached minimal levels (0.05 training, 0.07 validation), confirming efficient training without overfitting. Intersection over Union (IoU) scores were 0.90 and 0.89 for training and validation sets, respectively, demonstrating precise building contour delineation. Testing on diverse urban scenes yielded 100% detection accuracy without false positives or negatives. Training dynamics showed rapid convergence, with mean average precision (mAP) increasing to 0.35–0.45 and mAP@50:95 reaching 0.20–0.30, stabilizing after 50 epochs. The proposed technology effectively segments buildings under varying density, geometric complexity, shadows, and background noise. The hybrid QCNN approach enhances segmentation quality and generalization to new images. The results confirm the practical applicability of the developed technology for geospatial monitoring, urban planning, and mapping based on aerospace imagery.
Identification of Objects on Satellite Images Using the Image Texture Properties
(CEUR-WS Team, Aachen, Germany, 2023) Hnatushenko, Volodymyr V.; Shedlovska, Yana; Shedlovsky, Igor; Gorev, Vyacheslav
ENG: This paper focuses on identifying objects in satellite images using image texture properties, which is an important problem in agriculture. Texture segmentation can distinguish areas that correspond to tree plantations. Orchards and tree plantations can cover vast areas with thousands of trees, making the automation of harvest estimation crucial. Satellite images enable the creation of an effective automatic system for counting trees in plantations. In this work, we applied image texture segmentation to identify areas corresponding to agricultural plantations. We calculated textural properties of the image using the gray-level cooccurrence matrix, including mean value, variance, homogeneity, second angular moment, correlation, contrast, divergence, and entropy. These characteristics were used for segmentation, with multi-scale segmentation employed to distinguish areas of the image with specific textures. We proposed an algorithm for counting objects in satellite images, based on identifying individual objects that create a texture according to their spectral characteristics. The images used in this work primarily featured three object classes: trees, soil, and tree shadows. Since trees in gardens and plantations are arranged uniformly and have the same size, they can be easily distinguished from other image pixels based on their spectral characteristics. We analyzed NDVI and NSVDI spectral indices for tree detection and used the automatic spectral index histogram splitting method to distinguish objects with a high index value corresponding to trees.
Improvement of the Algorithm for Setting the Characteristics of Interpolation Monotone Curve
(Lublin University of Technology, Lublin, 2023) Kholodniak, Yuliia; Havrylenko, Yevhen; Halko, Serhii; Hnatushenko, Volodymyr V.; Suprun, Olena; Volina, Tatiana; Miroshnyk, Oleksandr; Shchur, Taras
ENG: Interpolation of a point series is a necessary step in solving such problems as building graphs de-scribing phenomena or processes, as well as modelling based on a set of reference points of the line frames defining the surface. To obtain an adequate model, the following conditions are imposed upon the interpolating curve: a minimum number of singular points (kinking points, inflection points or points of extreme curvature) and a regular curvature change along the curve. The aim of the work is to develop the algorithm for assigning characteristics (position of normals and curvature value) to the interpolating curve at reference points, at which the curve complies with the specified conditions. The characteristics of the curve are assigned within the area of their possible location. The possibilities of the proposed algorithm are investigated by interpolating the point series assigned to the branches of the parabola. In solving the test example, deviations of the normals and curvature radii from the corresponding characteristics of the original curve have been determined. The values obtained confirm the correctness of the solutions proposed in the paper.
Information System for Abandoned Arable Land Detection From Sentinel-2 Images
(CEUR-WS Team, Aachen, Germany, 2025) Akymenko, Karyna; Sergieieva, Kateryna L.; Kavats, Yurii V.; Kovrov, Oleksandr S.
ENG: An information system for the automated detection of abandoned arable land, based on Sentinel-2 satellite images, is developed. The system provides monitoring of agricultural land, even in areas where ground surveys are challenging to conduct. Integrated with Google Earth Engine (GEE), the system classifies agricultural areas as cultivated or abandoned in near real time based on Normalized Difference Vegetation Index (NDVI) time series. It supports two modes of operation: local analysis of GeoTIFF files and cloud analysis using an interactive map. Its classification method compares the maximum NDVI values for the target and reference years, enabling the detection of the characteristics of the vegetation cover degradation of abandoned land. The results were experimentally validated for a sample of agricultural areas in the Dnipropetrovsk and Donetsk Oblasts. The proposed system can detect abandoned arable land with an accuracy of up to 92.5% (F1-score: 0.898), even in areas of military conflict where ground observations are unavailable.
Information System for Air Quality Assessment and Data Processing: Design and Implementation
(CEUR-WS Team, Aachen, Germany, 2024) Hnatushenko, Volodymyr V.; Bulana, Tetiana; Gomilko, Igor; Molodets, Bohdan; Boldyriev, Daniil
ENG: In recent years, the issue of air quality has become increasingly critical. This article presents a hardware and software-based solution for collecting and processing data from ground stations monitoring air quality. The system is designed to collect real-time data from monitoring stations, store the data in a central database, perform data processing, and detect anomalies. Additionally, the system offers data visualization through maps and tables. The developed solution consists of three core components: a web server, a web application, and ground stations. A key feature of the software is its ability to handle real-time data aggregation and fill data gaps using custom-built aggregators. This is achieved through real-time data parsing, managed by Celery workers and queued via RabbitMQ. All data is stored in an SQL database, with PostgreSQL and Django frameworks facilitating database management and administration. Python scripts are used to process raw data into user-friendly formats such as AQI indices and graphical representations. The system is designed for seamless deployment across multiple remote servers, ensuring high flexibility and reliability for researchers. This software architecture enables scalable, conflict-free deployment, enhancing the efficiency and accuracy of air quality assessments.
Information System of Air Quality Assessment Based of Ground Stations and Meteorological Data Monitoring
(CEUR-WS Team, Aachen, Germany, 2023) Molodets, Bohdan; Hnatushenko, Volodymyr V.; Boldyriev, Daniil; Bulana, Tetiana
ENG: Monitoring ground stations and collecting meteorological data are essential solutions for assessing air quality. A developed information system can aggregate and process the data obtained. The data is transformed into a unified format and returned through written application programming interfaces (APIs). Client interfaces were created for convenient display of the results. The project infrastructure is designed for easy deployment. The architectural solution for creating the system proposes a toolkit that optimizes system operation when performing complex tasks through asynchronous execution. The use of Docker during deployment provides additional capabilities. To calculate the distribution of emissions in Kryvyi Rih, the CALPUFF model was employed for data processing. The article describes the client part structure and interface description. It also displays the processed data, which is the result of applying a mathematical model to the meteorological and station data.
Information System of Air Quality Assessment Using Data Interpolation from Ground Stations
(CEUR-WS Team, Aachen, Germany, 2023) Molodets, Bohdan; Hnatushenko, Volodymyr V.; Boldyriev, Daniil; Bulana, Tetiana
ENG: Monitoring ground stations is crucial for creating interactive maps that assist in assessing air quality. A developed information system can aggregate and process the data obtained, which is then transformed into a unified format and used as input data for interpolation methods that create raster imagery. After processing, the data is stored in Amazon Simple Storage Service or database and can be retrieved using application program interfaces (APIs). The proposed architectural solution for creating the system includes a toolkit that can work with different volumes of data with ease. Using Docker during deployment provides additional capabilities for creating a flexible and scalable system. Specific tools such as PostGis and Geospatial Data Abstraction Library (GDAL) simplify the processing of data. For instance, GDAL helps with the interpolation, cropping, and tiling of the air quality raster image. The article describes the structure of the client part and the interface in detail. By using the Mapbox Graphics Library system, the system can easily visualize big data as a vector layer, helping users recognize hazardous zones and find safe places.
Information System to Enhance Agricultural Production Efficiency Based on Sustainable Development Principles
(CEUR-WS Team, Aachen, Germany, 2024) Hnatushenko, Volodymyr V.; Shuleshko, Victor; Bulana, Tetiana; Molodets, Bohdan
ENG: The increasing global demand for sustainable agricultural practices necessitates innovative solutions to optimize resource use and minimize environmental impact. This paper presents the information technology developed to automate and optimize the operation of a hydroponic system to improve energy and resource efficiency in sustainable agriculture. An automated control platform at the system's base continuously monitors key environmental parameters within the hydroponic facility, including water level, nutrient distribution, lighting, and energy consumption. The system can precisely regulate the processes by collecting real-time data on these variables, providing ideal conditions for plant growth. The hydroponic system is equipped with a set of sensors and actuators that control water flow, fertilizer supply, and lighting according to the needs of each plant. The developed spectral composition is based on combining the spectra of HPS and two peaks of photosynthetic efficiency. This combination of the spectrums complements each other, which allows photosynthesis to proceed as efficiently as possible. The system makes it possible to save up to 25% of electricity consumption without losing quality and quantity characteristics, to reduce the number of hours of illumination, and to be placed in cost-effective climatic conditions.
Information Technologies in IT Education as a Factor of Digitalization of Ukrainian Society
(CEUR-WS Team, Aachen, Germany, 2024) Sokolova, Natalya; Hnatushenko, Volodymyr V.
ENG: The article investigates the digitization of Ukrainian society, the role of digital skills in labor market demand, digital literacy among the population, and the key audience of educators - teenagers. It outlines the approaches and experiences of the Department of Information Technology and Computer Engineering at Dnipro University of Technology in transforming IT education. It specifically examines the teaching of programming courses to first-year students to build foundational competencies for continued learning and the use of digital technologies in training specialists for government bodies. The article also highlights the integration of Cisco International Network Academy courses into the curriculum and the training of specialists through educational partnership projects with businesses.