Перегляд за Автор "Liubchenko, Nataliia"
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Документ Performance comparison of U-Net and LinkNet with different encoders for reforestation detection(Національний технічний університет "Харківський політехнічний інститут", 2024) Podorozhniak, Andrii; Onishchenko, Daniil; Liubchenko, Nataliia; Grynov, DenysThe subject of study is analysis of performance of artificial intelligence systems with different architectures for reforestation detection. The goal is to implement, train and evaluate system with different models for deforestation and reforestation detection. The tasks are to study problems and potential solutions in forestry for reforestation detection and present own solution. As part of model comparison, results are presented for different artificial neural network architectures with different encoders. For training and testing purpose custom dataset was created, which includes different areas of territory of Ukraine within different timestamps. Main research methods are literature analysis, experiment and case study. As a result of analysis of modern artificial intelligence methods, machine learning, deep learning and convolutional neural networks, high-precision algorithms U-Net and LinkNet were chosen for system implementation. Conclusions. The studied problem was stated formally and broken down in smaller steps; possible solutions were studied and proposed solution was described in details. Necessary mathematical background for analysis of the performance was provided. As part of the development, accurate deforestation/reforestation module was created. All analysis results were listed and a comparison of the studied algorithms was presented.Документ Research application of the spam filtering and spammer detection algorithms on social media and messengers(Національний технічний університет "Харківський політехнічний інститут", 2023) Podorozhniak, Andrii; Liubchenko, Nataliia; Oliinyk, Vasyl; Roh, ViktoriiaIn the current era, numerous social networks and messaging platforms have become integral parts of our lives, particularly in relation to work activities, due to the prevailing COVID-19 pandemic and russian war in Ukraine. Amidst this backdrop, the issue of spam and spammers has become more pertinent than ever, with a continuous rise in the incidence of spam within work-related text streams. Spam refers to textual content that is extraneous to a specific text stream, while a spammer denotes an individual who disseminates unsolicited messages for personal gain. The proposed article is devoted to address this scientific and practical challenge of identifying spammers and detecting spam messages within the textual context of any social network or messenger. This endeavor encompasses the utilization of diverse spam detection algorithms and approaches for spammer identification. Four algorithms were implemented, namely a naive Bayesian classifier, Support-vector machine, multilayer perceptron neural network, and convolutional neural network. The research objective was to develop a spam detection algorithm that can be seamlessly integrated into a messenger platform, exemplified by the utilization of Telegram as a case study. The designed algorithm discerns spam based on the contextual characteristics of a specific text stream, subsequently removing the spam message and blocking the spammeruser until authorized by one of the application administrators.Документ Usage of intelligent methods for multispectral data processing in the field of environmental monitoring(Національний технічний університет "Харківський політехнічний інститут", 2021) Podorozhniak, Andrii; Liubchenko, Nataliia; Kvochka, Mykyta; Suarez, IvanThe subject of study in the article is artificial intelligence methods that can be used for recognition of specific areas of the earth's surface in multispectral images provided by Earth remote sensing systems (ERS). The goal is to automate data analysis for recognizing areas affected by fire on multispectral remote sensing images. The task is to study and formulate a method for processing multispectral data, which makes it possible to automate the process of operational recognition of areas of burned-out areas in images, for the development of an eco-monitoring software system using artificial intelligence tools such as deep learning and neural networks. As a result of the analysis of modern methods of processing multispectral data, an investigation of the supervised learning strategy was chosen. The choice of the described method for solving an applied problem is based on the high flexibility of these method, as well as, provided that there is a sufficient amount of used training input data and correct training strategies, the possibility of analyzing heterogeneous multispectral data with ensuring high accuracy of results for each individual sample. Conclusions: the application of methodologies for intelligent processing of multispectral images has been investigated and substantiated. The theoretical foundations of the construction of neural networks are considered, the applied area of application is selected. An architectural model of a software product is analyzed and proposed, taking into account its scalability, the model of software system is developed and the results of its work are shown. The obtained results show the efficiency of proposed system and prospects of the proposed algorithms, which is a reason for further research and improvement of the used algorithms, with their possible use in industrial and enterprise eco-monitoring systems.Документ Usage of Mask R-CNN for automatic license plate recognition(Національний технічний університет "Харківський політехнічний інститут", 2023) Podorozhniak, Andrii; Liubchenko, Nataliia; Sobol, Maksym; Onishchenko, DaniilThe subject of study is the creation process of an artificial intelligence system for automatic license plate detection. The goal is to achieve high license plate recognition accuracy on large camera angles with character extraction. The tasks are to study existing license plate recognition technics and to create an artificial intelligence system that works on big shooting camera angles with the help of modern machine learning solution – deep learning. As part of the research, both hardware and software-based solutions were studied and developed. For testing purposes, different datasets and competing systems were used. Main research methods are experiment, literature analysis and case study for hardware systems As a result of analysis of modern methods, Mask R-CNN algorithm was chosen due to high accuracy. Conclusions. Problem statement was declared; solution methods were listed and characterized; main algorithm was chosen and mathematical background was presented. As part of the development procedure, accurate automatic license plate system was presented and implemented in different hardware environments. Comparison of the network with existing competitive systems was made.Different object detection characteristics, such as Recall, Precision and F1-Score, were calculated. The acquired results show that developed system on Mask R-CNN algorithm process images with high accuracy on large camera shooting angles.