Сучасні інформаційні системи

Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/62915

У журналі публікуються результати досліджень з експлуатації та розробки сучасних інформаційних систем у різних проблемних галузях.

Рік заснування: 2017. Періодичність: 4 рази на рік. ISSN 2522-9052 (Print)

Новини

Включений до "Переліку наукових фахових видань України, в яких можуть публікуватися результати дисертаційних робіт на здобуття наукових ступенів доктора і кандидата наук" (технічні науки) наказом Міністерства освіти і науки України від 04.04.2018 № 326 (додаток 9, п. 56).

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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, Denys
The 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.
Hybrid recommender for virtual art compositions with video sentiments analysis
(Національний технічний університет "Харківський політехнічний інститут", 2024) Kuchuk, Heorhii; Kuliahin, Andrii
Topicality. 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.
Biometric authentication utilizing convolutional neural networks
(Національний технічний університет "Харківський політехнічний інститут", 2023) Datsenko, Serhii; Kuchuk, Heorhii
Cryptographic 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.
Generating currency exchange rate data based on Quant-GAN model
(Національний технічний університет "Харківський політехнічний інститут", 2023) Bao, Dun; Zakovorotnyi, A. Yu.; 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.
Usage of Mask R-CNN for automatic license plate recognition
(Національний технічний університет "Харківський політехнічний інститут", 2023) Podorozhniak, Andrii; Liubchenko, Nataliia; Sobol, Maksym; Onishchenko, Daniil
The 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.