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Functional Model of an Electrocardiological Study 9

Functional Model of an Electrocardiological Study

引用

9th IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2022

作者： Filatova, Anna Povoroznyuk, Anatoliy Fahs, Mohamad National Technical University Kharkiv Polytechnic Institute Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (纸本)9798350398915

The purpose of this research is to develop a functional model of the electrocardiological study using the methodology of functional modeling IDEF0. The functional model of the electrocardiological study are developed in the form of a contextual diagram, its decomposition, and decomposition of the activities To perform registration and analysis of electrocardiograms and To perform diagnostics. Using the proposed functional model of the electrocardiological study, the structural diagram of the cardiological decision support system is developed based on the morphological analysis of biomedical signals with locally concentrated features. The modules of the proposed cardiological decision support system, as well as the modes of its operation, are considered. Further research is aimed at developing an information model of the electrocardiological study for the development of an informational structure of the cardiological decision support system based on the morphological analysis of electrocardiograms. © 2022 IEEE.

关键词： Decision support systems

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Fast Two-Factor Authentication Method in Systems With a Centralized User's Database 3

Fast Two-Factor Authentication Method in Systems With a Cent...

引用

3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Rezanov, Bohdan Kuchuk, Heorhii National Technical University 'Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

work proposes a two-factor authentication method by integrating the second factor into the authentication service in system with a centralized user's database. This approach made it possible to achieve the universality of the process and reduce the authentication time. In this case, the compromise of the first factor becomes inexpedient. Simulation showed that the rest of the authentication parameters meet the requirements defined by international standards for two-factor authentication procedures. © 2022 IEEE.

关键词： Java programming language

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Development of a Navigation System on the University Territory Using Augmented Reality Technology 9

Development of a Navigation System on the University Territo...

引用

9th IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2022

作者： Chernykh, Olena Zakovorotniy, Oleksandr Hrianyk, Heorhii Kosmin, Leonid Bondarenko, Yelizaveta National Technical University Kharkiv Polytechnic Institute Computer Engineering and Programming Department Kharkiv Ukraine

ISBN: (纸本)9798350398915

AR navigation is one of the interactive ways to use augmented reality. By displaying virtual guides in physical space using a smartphone, users can navigate from point to point more naturally than by comparing the map to their immediate environment. Thanks to this great advantage, AR navigation can help in searching both inside the academic building and on the territory of the institute. In this paper, the authors used 3DUnity and AR Foundation to create a navigation system around the territory of NTU KhPI using augmented reality technology. This development will allow you to navigate the terrain of the KhPI campus, find the location of the desired building, and also look the route from building to building on the map. The whole process is displayed on the smartphone screen. Real-time synchronization allows users in real world to feel virtual space, thereby enhancing poignancy and interaction, and making effect more vivid and almost real. © 2022 IEEE.

关键词： Augmented reality

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Optimal speed controller design with interval type-2 fuzzy sets 2

Optimal speed controller design with interval type-2 fuzzy s...

引用

2nd IEEE KhPI Week on Advanced Technology, KhPI Week 2021

作者： Zakovorotniy, Alexander Kharchenko, Artem National Technical University Kharkiv Polytechnic Institute Computer Engineering and Programming Department Kharkiv Ukraine

ISBN: (纸本)9781665412056

Interval type-2 fuzzy sets are often used in the designing of fuzzy systems associated with the speed control under uncertainty. In this paper, we consider the possibilities of using interval type-2 fuzzy sets to describing the uncertainties that arise when defining input variables. These uncertainties can be presented as the different meanings on the same dynamic system or changes in the technical parameters that occur due to external factors. We also present complex interval type-2 fuzzy sets and give the examples of their use to reduce the number of fuzzy sets and rules in a fuzzy knowledge base. The necessary conditions for the construction of complex interval type-2 fuzzy sets and methods for determining lower and upper membership functions in them are also given. The footprint of uncertainty for the complex interval type-2 fuzzy set is presented with upper and lower membership functions of different fuzzy sets. All methods which are described in this work can be used in decision support systems design, to select the optimal parameters of a nonlinear system, or in fuzzy speed controller design. © 2021 IEEE.

关键词： Fuzzy control

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Study of methods for improving the meta-algorithm of the bagging classifier 4

Study of methods for improving the meta-algorithm of the bag...

引用

4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Gavrylenko, Svitlana Hornostal, Oleksii Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine

ISBN: (纸本)9798350395532

In this paper, we explored approaches that improve the performance of ensemble bagging classifiers for identifying the state of a computer system. The following algorithms are considered: Ensemble pruning, Advanced Voting Algorithms, Dynamic Voting Strategies, Confidence Calibration, Adaptation through Meta-Features and Meta-Learning, which allow improving the voting procedure of the bagging meta-algorithm. Artificial data was generated as initial data, which complicates the classification task and contains an increased amount of noise. In the Google Collab environment, software models of algorithms have been developed, and their quality has been assessed. It was found that the use of the Ensemble pruning and Adaptation through Meta-Features algorithm is the most qualitative. In addition, the Ensemble pruning algorithm reduces the number of basic ensemble classifiers, and, as a result, increases the efficiency of computer system identification. Based on the results of the study, a method for identifying a computer system was proposed through the integrated use of a bagging classifier and an optimization procedure based on the Ensemble pruning algorithm. © 2023 IEEE.

关键词： Calibration

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DETECTION computer NETWORK INTRUSION USING DEEP NEURAL NETWORKS 5

DETECTION COMPUTER NETWORK INTRUSION USING DEEP NEURAL NETWO...

引用

5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Gavrylenko, Svitlana Poltoratskyi, Vadym Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine

ISBN: (纸本)9798350384499

In this work, the effectiveness of using classical machine learning methods and modern deep neural network models for intrusion detection in computer networks has been investigated. The purpose of this work is to develop a model for detecting intrusions into computer networks based on the Transformer model using tabular input data. In this work, the UNSW-NB15 dataset is used as the source data. This dataset contains information about normal network behaviour as well as during synthetic intrusions. Models for intrusion detection in computer networks based on machine learning methods were investigated: Decision Tree, KNN, Logistic Regression, SVM, Gradient Boosting, Random Forest. A method of converting tabular data into images was developed, which made it possible to build intrusion detection models based on Vision Transformer and Vision Transformer for small-size datasets on modern Transformer architecture. The research results showed that developed model based on Vision Transformer and Vision Transformer for small-size datasets improves the quality of identification, and eliminates the need for a preprocessing step such as dimensionality reduction. ©2024 IEEE.

关键词： Logistic regression

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Research of Intelligent Data Analysis Methods for Identification of computer System State 30

Research of Intelligent Data Analysis Methods for Identifica...

引用

30th International Scientific Symposium Metrology and Metrology Assurance, MMA 2020

作者： Gavrylenko, Svetlana Chelak, Viktor Hornostal, Oleksii National Technical University Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (纸本)9781728197197

Classification is one of the methods of the intelligent data analysis (data mining), which allows to study the existing data set and create a model for each class based on its data features. To increase the accuracy of classification, ensemble methods are used, including some of the most effective ensemble methods based on boosting and bagging. This study presents the results of a comparative analysis of machine learning methods for classifying the state of a computer system. The ensemble methods based on boosting and bagging were modified using a special procedure for selecting the main classification parameters: The number of trees (the number of learners), the number of branches (the number of splits or leaves nodes), and the learning speed (the learning rate). Modified methods were tested with the following initial data of the computer system functioning: loading of the central processor and memory, the amount of incoming and outgoing traffic (network utilization), the number of read / write operations to disk, the statistical data of the system events analysis (the number of operations with the system registry, file system, number of processes, etc.). It was found that the classification accuracy with the test sample was more than 99%. © 2020 IEEE.

关键词： bagging decision trees ensemble classification methods boosting

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Research of Methods of Identifying the computer Systems State based on Bagging Classifiers 3

Research of Methods of Identifying the Computer Systems Stat...

引用

3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Gavrylenko, Svitlana Hornostal, Oleksii Chelak, Viktor National Technical University 'Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

Peculiarities of constructing ensemble bagging classifiers for identifying the state of a computer system under conditions of noisy data are studied. Decision trees and multilayer perceptron were used as basic classifiers. It was found that the accuracy of the bagging algorithm with decision trees as basic classifiers with standard settings ranges from 84.4% to 88.7%. The use of Bootstrap algorithms for the formation of data samples: Pasting, Bootstrapping, Random Subspace, Random Patches Ensemble and the selection of the number of basic classifiers in the ensemble made it possible to increase the classification accuracy to 90.2%. The following parameters were added to improve the accuracy of bagging classifiers based on the multilayer perceptron: the algorithm for forming data samples, the number of basic classifiers in the ensemble, the function of optimizing the neural network, the function of activating hidden layer, size of hidden layers. The recommendation was made to choose the value of the analyzed parameters for the creation of bagging ensembles with multilayer perceptrons, which made it possible to increase the accuracy of computer system identification up to 92.2%. The obtained results have further practical significance and can be used in improving the methods of identifying the state of computer systems. © 2022 IEEE.

关键词： Multilayer neural networks

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A botnet detection approach based on the clonal selection algorithm 9

A botnet detection approach based on the clonal selection al...

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9th IEEE International Conference on Dependable Systems, Services and Technologies, DESSERT 2018

作者： Lysenko, Sergii Bobrovnikova, Kira Savenko, Oleg Department of Computer Engineering and System Programming Khmelnitsky National University Instytutska 11 Khmelnitsky Ukraine

ISBN: (纸本)9781538659038

The paper presents a new technique for the botnets' detection in the corporate area networks. It is based on the usage of the algorithms of the artificial immune systems. Proposed approach is able to distinguish benign network traffic from malicious one using the clonal selection algorithm taking into account the features of the botnet's presence in the network. An approach present the main improvements of the BotGRABBER system. It is able to detect the IRC, HTTP, DNS and P2P botnets. © 2018 IEEE.

关键词： Botnet

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DDoS botnet detection technique based on the use of the semi-supervised fuzzy C-means clustering 14

DDoS botnet detection technique based on the use of the semi...

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14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, ICTERI 2018

作者： Lysenko, Sergii Savenko, Oleg Bobrovnikova, Kira Department of Computer Engineering System Programming Khmelnitsky National University Instytutska 11 Khmelnitsky Ukraine

A new technique for the DDoS botnet detection based on the botnets network features analysis is proposed. It uses the semi-supervised fuzzy c-means clustering. The proposed approach includes the learning and the detection stages. Analysis is based on the extracted from the network traffic features that may indicate the presence of the DDoS botnets' presence in the network. Experimental results demonstrated that the proposed technique ensures the DDoS botnet detection at the rate at about 95%. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Botnet

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