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Документ Hybrid recommender for virtual art compositions with video sentiments analysis(Національний технічний університет "Харківський політехнічний інститут", 2024) Kuchuk, Heorhii; Kuliahin, AndriiTopicality. Recent studies confirm the growing trend to implement emotional feedback and sentiment analysis to improve the performance of recommender systems. In this way, a deeper personalization and current emotional relevance of the user experience is ensured. The subject of study in the article is a hybrid recommender system with a component of video sentiment analysis. The purpose of the article is to investigate the possibilities of improving the effectiveness of the results of the hybrid recommender system of virtual art compositions by implementing a component of video sentiment analysis. Used methods: matrix factorization methods, collaborative filtering method, content-based method, knowledgebased method, video sentiment analysis method. The following results were obtained. A new model has been created that combines a hybrid recommender system and a video sentiment analysis component. The average absolute error of the system has been significantly reduced. Added system reaction to emotional feedback in the context of user interaction with virtual art compositions. Conclusion. Thus, the system can not only select the most suitable virtual art compositions, but also create adaptive and dynamic content, which will increase user satisfaction and improve the immersive aspects of the system. A promising direction of further research may be the addition of a subsystem with a generative neural network, which will create new virtual art compositions based on the conclusions of the developed recommendation system.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 Biometric authentication utilizing convolutional neural networks(Національний технічний університет "Харківський політехнічний інститут", 2023) Datsenko, Serhii; Kuchuk, HeorhiiCryptographic algorithms and protocols are important tools in modern cybersecurity. They are used in various applications, from simple software for encrypting computer information to complex information and telecommunications systems that implement various electronic trust services. Developing complete biometric cryptographic systems will allow using personal biometric data as a unique secret parameter instead of needing to remember cryptographic keys or using additional authentication devices. The object of research the process of generating cryptographic keys from biometric images of a person's face with the implementation of fuzzy extractors. The subject of the research is the means and methods of building a neural network using modern technologies. The purpose of this paper to study new methods for generating cryptographic keys from biometric images using convolutional neural networks and histogram of oriented gradients. Research results. The proposed technology allows for the implementation of a new cryptographic mechanism - a technology for generating reliable cryptographic passwords from biometric images for further use as attributes for access to secure systems, as well as a source of keys for existing cryptographic algorithms.Документ Generating currency exchange rate data based on Quant-GAN model(Національний технічний університет "Харківський політехнічний інститут", 2023) Bao, Dun; Zakovorotnyi, Oleksandr; Kuchuk, N. G.This paper discusses the use of machine learning algorithms to generate data that meets the demands of academia and industry in the context of exchange rate fluctuations. Research results. The paper builds a Quant-GAN model using temporal convolutional neural networks (CNN) and trains it on end-of-day and intraday high-frequency rates of currency pairs in the global market. The generated data is evaluated using various statistical methods and is found to effectively simulate the real dataset. Experimental results show that data generated by the model effectively fits statistical characteristics and typical facts of real training datasets with good overall fit. The results provide effective means for global FX market participants to carry out various tasks such as stress tests and scenario simulations. Future work includes accumulating data and increasing computing power, optimizing and improving GAN models, and establishing evaluation standards for generating exchange rate price data. As computing power continues to grow, the GAN model’s ability to process ultra-large-scale datasets is expected to improve.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.