2024 № 1 Сучасні інформаційні системи

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Construction of a diagnostic algorithm for soil identification according to the international soil classification system WRB
(Національний технічний університет "Харківський політехнічний інститут", 2024) Hasanova, Samira
Topicality. In this article discusses the identification of soils according to the international soil classification World Reference Base for Soil Resources system (WRB). The World Reference Base for Soil Resources was developed to identify soils and use the obtained data in different areas of everyday life: agriculture, forestry, animal husbandry, etc. The purpose of the work Note that the WRB, developed by a group of soil scientists, is not meant to replace national classification systems. Besides this classification system, there are also different soil classifications designed by national soil science schools. The difference in the structures of these classifications necessitated the development of a diagnostic algorithm to correlate them with each other. Results Three options for determining whether a soil belongs to reference soil groups are considered, depending either on soil parameters only, or on a combination of diagnostic horizons and soil parameters, or only on diagnostic horizons. A group of scientists headed by M. Babayev also developed a national soil classification system for Azerbaijan. In order to compare these two systems, this study proposes a soil data structure, as well as an algorithm for soil identification according to the WRB classification on the basis of the proposed structure. Conclusion A soil diagnostic algorithm is developed, which will allow identifying any soil type with the corresponding WRB Reference Soil Group. Three variants of allocating soils to WRB Reference Soil Groups based only on soil parameters, or on the combination of diagnostic horizons and soil parameters, or only on diagnostic horizons are considered.
Intrusion detection model based on improved transformer
(Національний технічний університет "Харківський політехнічний інститут", 2024) Gavrylenko, Svitlana; Poltoratskyi, Vadym; Nechyporenko, Alina
The object of the study is the process of identifying the state of a computer network. The subject of the study are the methods of identifying the state of computer networks. The purpose of the paper is to improve the efficacy of intrusion detection in computer networks by developing a method based on transformer models. The results obtained. The work analyzes traditional machine learning algorithms, deep learning methods and considers the advantages of using transformer models. A method for detecting intrusions in computer networks is proposed. This method differs from known approaches by utilizing the Vision Transformer for Small-size Datasets (ViTSD) deep learning algorithm. The method incorporates procedures to reduce the correlation of input data and transform data into a specific format required for model operations. The developed methods are implemented using Python and the GOOGLE COLAB cloud service with Jupyter Notebook. Conclusions. Experiments confirmed the efficiency of the proposed method. The use of the developed method based on the ViTSD algorithm and the data preprocessing procedure increases the model's accuracy to 98.7%. This makes it possible to recommend it for practical use, in order to improve the accuracy of identifying the state of a computer system.
Practical principles of integrating artificial intelligence into the technology of regional security predicting
(Національний технічний університет "Харківський політехнічний інститут", 2024) Shefer, Oleksandr; Laktionov, Oleksandr; Pents, Volodymyr; Hlushko, Alina; Kuchuk, Nina
Objective. The aim is to enhance the efficiency of diagnostics for determining the level of air attack safety through the practical integration principles of artificial intelligence. Methodology. Models and technologies for safety diagnostics of the region (territorial community) have been explored. The process of building an artificial intelligence model requires differentiation of objects at a level to accumulate assessments-characteristics of aerial vehicles. The practical integration principles of artificial intelligence into the forecasting technology are based on the Region Safety Index, used for constructing machine learning models. The optimal machine learning model of the proposed approach is selected from a list of several models. Results. A technology for predicting the level of regional safety based on the Safety Index has been developed. The recommended optimal model is the Random Forest model ([('max_depth', 13), ('max_features', 'sqrt'), ('min_samples_leaf', 1), ('min_samples_split', 2), ('n_estimators', 79)]), demonstrating the most effective quality indicators of MAE; MAX; RMSE 0.005; 0.083; 0.0139, respectively. Scientific Novelty. The proposed approach is based on a linear model of the Region Safety Index, which, unlike existing ones, takes into account the interaction of factors. This allows for advantages of the proposed method over existing approaches in terms of the root mean square error of 0.496; 0.625, respectively. In turn, this influences the quality of machine learning models. Practical Significance. The proposed solutions are valuable for diagnosing the level of safety in the region of Ukraine, particularly in the context of air attacks.
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.
Digital business transformation: analysis of the effect artificial intelligence in E-Commerce's product recommendation
(Національний технічний університет "Харківський політехнічний інститут", 2024) Louis, Jeffry Vincent; Noerlina; Syahchari, Dicky Hida
The purpose of this study is to determine whether artificial intelligence used in E-Commerce influences product recommendations for users. This study explains how much influence artificial intelligence on product recommendations supplied by E-commerce in terms of consumer behavior in making purchasing decisions. Research methods. This research used bibliometric analysis to find the mapping of this topic with articles period 2017 to 2023 from Scopus database. Of the 103 articles were showed by keyword and analyzing the articles according to the relate of the content about 29 articles were finally obtained. The research result is Artificial Intelligence has influence for E-commerce, recommendation system, decision support system, customer behaviour's, and customer trust. Product recommendations have an impact on E-Commerce. Conclusion. However, from the literature review, founded that there are still a few journals discussing related to considerations to the implementation regarding the use of AI in e-commerce "Consumer behaviour", "Customer Trust", "Purchasing decisions". This study is also useful to generate additional AI-related research in e-commerce and unquestionably for a fresh subject will be covered especially in context of product recommendations on E-commerce.
Advantages and drawbacks of two-step approach to providing desired parameters in lossy image compression
(Національний технічний університет "Харківський політехнічний інститут", 2024) Li, Fangfang; Abramov, Sergey; Dohtiev, Ihor; Lukin, Vladimir
The object of the study is the process of lossy image compression. The subject of the study is the two-step approach to providing desired parameters (quality and compression ratio) for different coders. The goals of the study are to review advantages of the two-step approach to lossy compression, to analyze the reasons of drawbacks, and to put forward possible ways to get around these shortcomings. Methods used: linear approximation, numerical simulation, statistical analysis. Results obtained: 1) the considered approach main advantage is that, in most applications, it provides substantial improvement of accuracy of providing a desired value of a controlled compression parameter after the second step compared to the first step; 2) the approach is quite universal and can be applied for different coders and different parameters of lossy compression to be provided; 3) the main problems and limitations happen due to the use of linear approximation and essential difference in behavior of rate/distortion curves for images of different complexity; 4) there are ways to avoid the approach drawbacks that employ adaptation to image complexity and/or use certain restrictions at the second step. Conclusions: based on the results of the study, it is worth 1) considering more complex approximations of rate-distortion curves; 2) paying more attention to adequate and fast algorithms of characterizing image complexity before compression; 3) using quality metrics that have quasi-linear rate/distortion curves for a given coder.
Proposals to improve the information capabilities of coastal-based radar stations for surveillance of surface and air objects
(Національний технічний університет "Харківський політехнічний інститут", 2024) Kuznietsov, Oleksandr; Kolomiitsev, Oleksii; Nos, Ivan; Biesova, Oksana; Krykhovetskyi, Heorhii
Sea-based radar stations (RS) are widely used for solving the tasks of radar surveillance of surface objects (SO) and air objects (AO). The subject of the article is the mechanisms of radio wave propagation in the boundary layer of the atmosphere. The aim is to investigate the possibilities of improving the accuracy of measuring the range and radial velocity of SO and AO observed beyond the line-of-sight of coastal-based RS. Objective: to analyse the spatial and temporal parameters and properties of waveguide layers above the water surface. Methods used: maximum likelihood and frequency. The following results were obtained. The results of experimental studies of seasonal and daily changes in the parameters of the lower troposphere layer in the Black Sea coastal zone and the parameters of tropospheric radio waveguides are presented. The procedure for calculating the energy transmission losses during radio wave propagation in the boundary layer of the atmosphere is presented, and the conditions for detecting SO and AO beyond the radar line-of-sight are determined. Recommendations for increasing the range of detection of SO and AO are given, which are associated with the possibility of predicting the existence of tropospheric radio waveguides by using data on the current conditions of radio wave propagation over the sea based on the signals of the automatic ship identification system AIS. Conclusions. Proposals have been developed to improve the accuracy of measuring the range and radial velocity of SO and AO at waveguide propagation of radio waves over the sea surface. A promising area for further research may be to identify ways to optimise the measurement of angular coordinates in modern RS during waveguide propagation of radio waves over the sea surface.
Performance evaluation of python libraries for multithreading data processing
(Національний технічний університет "Харківський політехнічний інститут", 2024) Krivtsov, Serhii; Parfeniuk, Yurii; Bazilevych, Kseniia; Meniailov, Ievgen; Chumachenko, Dmytro
Topicality. The rapid growth of data in various domains has necessitated the development of efficient tools and libraries for data processing and analysis. Python, a popular programming language for data analysis, offers several libraries, such as NumPy and Numba, for numerical computations. However, there is a lack of comprehensive studies comparing the performance of these libraries across different tasks and data sizes. The aim of the study. This study aims to fill this gap by comparing the performance of Python, NumPy, Numba, and Numba.Cuda across different tasks and data sizes. Additionally, it evaluates the impact of multithreading and GPU utilization on computation speed. Research results. The results indicate that Numba and Numba.Cuda significantly optimizes the performance of Python applications, especially for functions involving loops and array operations. Moreover, GPU and multithreading in Python further enhance computation speed, although with certain limitations and considerations. Conclusion. This study contributes to the field by providing valuable insights into the performance of different Python libraries and the effectiveness of GPU and multithreading in Python, thereby aiding researchers and practitioners in selecting the most suitable tools for their computational needs.
Complex method of determining the location of social network agents in the interests of information operations
(Національний технічний університет "Харківський політехнічний інститут", 2024) Herasуmov, Serhii ; Tkachov, Andrii; Bazarnyi, Sergii
The researcher developed a method for determining the location of social network agents in the interest of conducting an information operation based on a comprehensive approach to data analysis of the information system. The relevance of the method is determined by the need to specify the enemy's target audience in the area of the information operation. Results. The author proposed a complex method for determining the location of social network agents, which is based on the combination of data from the analysis of the social connections of the specified agent, geotags and the time of registration of his friends in the social network, databases of IP addresses and geolocations of social network agents. The advantage of the developed method is the possibility of its application without direct access to the devices of agents of the social network that use the data of global positioning satellite systems. Conclusion. The application of the proposed complex method of determining the location of agents of social networks makes it possible to increase the effectiveness of information operations due to a more accurate definition of the enemy's target audience in the area of operations. The direction of improvement of the developed method can be its integration with complex information systems of psychological influence, as well as the use of machine learning methods and algorithms.