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International Conference on Problems of Cybernetics and Informatics (PCI)

作者： Khanlar Gamzaev Sergey Leonov Sevil Huseynzade Sevinc Kerimova Department of General and Applied Mathematics Azerbaijan State Oil and Industry University Baku Azerbaijan Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" (of Affiliation) Kharkiv Ukraine

The process of unsteady flow of a single-phase liquid in a cylindrical reservoir arising under the elastic mode of reservoir development is considered. To describe this process, a power law of filtration is proposed for a single-phase flow in a porous medium. Within the framework of the proposed model, the task of identifying one parameter of the model characterizing the simulated reservoir system is set. To construct a discrete analog of the inverse problem, the finite difference method is used using an explicit–implicit approximation in time. To solve the resulting system of nonlinear equations, a special representation is proposed that allows reducing the nonlinear system to two independent systems of linear equations. As a result, an explicit formula is obtained for determining the approximate value of the desired reservoir parameter. Based on the proposed method, numerical experiments were carried out for model problems.

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The Method of Dynamic Routing in Self-Healing Networks

The Method of Dynamic Routing in Self-Healing Networks

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Shyman Anna Nina Kuchuk Andriy Kovalenko Svitlana Kashkevich Stanislav Partyka Oleksandr Yankovskyi Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of Electronic Computers Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Computerized Management Systems National Aviation University Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The report proposes a dynamic routing method for Self-healing Networks (ShN). The method takes into account the specific features of ShN. During routing, the flow of service information is reduced. The search for the optimal route is carried out using a convolutional neural network. The method significantly outperforms existing methods when some network nodes fail.

关键词： Receivers Performance gain Routing Routing protocols Maintenance Convolutional neural networks Standards

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Word-Based Turkish Lip Reading System with Two Stream 3dcnn and Bilstm

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SSRN 2023年

作者： Erbey, Ali Barışçı, Necaattin Department of Computer Programming Distance Education Vocational School Usak University Usak64200 Turkey Department of Information Systems Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara06500 Turkey

Lip reading is a technique that aims to understand spoken words by analyzing people's lip movements. Deep learning algorithms are used as a powerful tool for detecting and recognizing lip movements more accurately. In this study, we present a lip-reading architecture for the Turkish language. The architecture is trained to recognize Turkish words using 3DCNN (3D Convolutional Neural Network) architecture and two-stream deep learning methods. The model was trained on video data representing lip movements and learned to classify words accurately. The Turkish lip reading system has been trained and tested on a large Turkish lip reading dataset. In the experiments, an accuracy of 87.12% was achieved with the proposed model, indicating successful results in the Turkish language. The proposed model demonstrates high accuracy rates in recognizing Turkish words, showcasing its effectiveness. This paper introduces a novel architecture, a lip-reading system that employs deep learning techniques, providing a robust tool for successful Turkish lipreading applications. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Design and Implementation of an Instrumented walking cane for Detection of Freezing of Gait 3

Design and Implementation of an Instrumented walking cane fo...

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3rd IEEE International Multidisciplinary Conference on engineering Technology, IMCET 2021

作者： Haidar Ahmad, Joelle El-Asmar, Alain Abi Zeid Daou, Roy Hayek, Ali Boercsoek, Josef Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon MART Learning Education and Research Center Chananir Lebanon University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany

ISBN: (纸本)9781665405874

Independent mobility is an important feature of an individual's normal life and with the elderly population continually growing, assistive devices such as walkers and canes provide help and guidance to users. Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of an age-related disease known as Parkinson Disease (PD). This paper is headed toward implementing a detection, diagnosis and correction system for FoG episodes. The proposed cane will generate visual, auditory and tactile cues to help patients overcome this problem, therefore reducing fall risks. In addition, a vibrating calf cuff will be attached to the calf for muscle stimulation. A compatible android phone application is designed to display heart rate measurements of the patient as well as his exact location in case of fall detection. The system is considered to be an electrically safe, portable, comfortable and a low-cost device. The performance of this system is analyzed thoroughly and the accuracy of all methodologies is considered carefully and evaluated. The results have shown no faulty errors and good accuracy and detection percentages. © 2021 IEEE.

关键词： Diagnosis

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Characteristics of Rational Classifications in Game-Theoretic Algorithms of Pattern Recognition for Unmanned Vehicles 3

Characteristics of Rational Classifications in Game-Theoreti...

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

作者： Kalinin, Yevhen Kozhushko, Andrii Rebrov, Oleksii Zakovorotniy, Alexander National University of Life and Environmental Sciences of Ukraine Tractors Cars and Bioenergy Resources Department Kyiv Ukraine National Technical University 'Kharkiv Polytechnic Institute' Car and Tractor Industry Department Kharkiv Ukraine National Technical University 'Kharkiv Polytechnic Institute' Computer Engineering and Programming Department Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

In pattern recognition, depending on how the training sample is represented, a theory associated with the standard representation of the training sample (information) and the structural theory are distinguished. In the first case it is convenient to set and describe objects that are independent of each other, objects that have a rigid, static nature. In the second case, when information is specified in a structural way, it is convenient to study, describe and recognize objects whose basis presents an invariable internal or external structure. This paper considers a fundamentally different case of setting learning information. It deals with objects and processes that are based on conflict. Such features are peculiar to the data sets that an unmanned vehicle receives while driving, which requires minute-by-minute formation and decision-making in the context of the current traffic situation. Naturally, it is necessary to involve game-theoretic models to describe conflicts. It is these issues, i.e., the systematic study of game-theoretic models in the problems of recognition and classification, that the work is devoted to. It is emphasized that the classification of recognizable objects, as some speculative system, must meet the requirements of sufficiency of grounds and coverage of the totality of existing and possible recognizable objects. The most important purpose of classification is to describe the properties of its classes and subclasses, types and subspecies of recognized objects, which makes it possible for it to be used for recognizing specific objects, which subjects encounter in certain areas of activity. The paper introduces the concept of stable classification, formulates and proves the theorems based on the introduced concept, the sufficiency of the conditions for the correctness of the algorithm of pattern recognition, using the game-theoretic approach and evaluation of the degree of stability of the classification underlying it. The generated theorems a

关键词： Decision making

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Social engineering Attacks Detection Approach

Social Engineering Attacks Detection Approach

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Sergii Lysenko Volodymyr Vorobiov Oleksandr Bokhonko Piotr Gaj Oleg Savenko Jacek Wołoszyn Department of Computer Engineering and Information Systems Khmelnitsky National University Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Department of Computer Engineering and System Programming Khmelnitsky National University Khmelnitsky Ukraine Department of Informatics and Teleinformatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy.

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Runtime Verification of Autonomous Systems Utilizing Digital Twins as a Service

Runtime Verification of Autonomous Systems Utilizing Digital...

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Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), IEEE International Conference on

作者： Morten Haahr Kristensen Alberto Bonizzi Cláudio Gomes Simon Thrane Hansen Carlos Isasa Hannes Iven Eduard Kamburjan Peter Gorm Larsen Martin Leucker Prasad Talasila Valdemar Trøjgård Tang Stefano Tonetta Lars B. Vosteen Thomas Wright Department of Electrical and Computer Engineering Aarhus University Denmark Fondazione Bruno Kessler Italy Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Institute for Software Engineering and Programming Languages Universität zu Lübeck Germany Department of Informatics University of Oslo Norway

ISBN: (数字)9798350389760

ISBN: (纸本)9798350389777

Autonomous Systems (AS) enable systems to adapt to drastic and unprecedented environmental changes, a capability that can be enhanced through the utilization of Digital Twins (DTs). However, the additional capabilities of AS come at the cost of explainability, as the expanding adaptation space complicates the reasoning about the system's behavior. For certain types of systems, it is crucial to ensure that specific properties are upheld despite the system's autonomous behavior. To facilitate the monitoring of these properties, we propose the use of Runtime Verification (RV). This tutorial demonstrates the integration of RV tools into the Digital Twins as a Service (DTaaS) platform to monitor and verify the behavior of AS in real-time. By exploring various methods to incorporate RV tools within a DT context, the tutorial aims to advance the application of RV technologies in autonomic computing and self-adaptive system design. Specifically, we demonstrate how the behavior of a self-configuring DT can be verified utilizing RV. This is accomplished through the DTaaS platform, which supports seamless deployment of DT-based AS.

关键词： Runtime Costs Autonomous systems Tutorials Real-time systems Cognition Digital twins Distributed computing Monitoring System analysis and design

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Information technology of remote monitoring and control

Information technology of remote monitoring and control

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2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2021

作者： Mnushka, Oksana Savchenko, Volodymyr Leonov, Serhii Shaposhnikova, Olena Kharkiv National Automobile and Highway University Computer Technology and Mechatronics Department Kharkiv Ukraine Globallogic Ukraine Kharkiv Ukraine National Technical University 'Kharkiv Polytechnical Institute' Computer Engineering and Programming Department Kharkiv Ukraine

ISBN: (纸本)9781665412629

Remote monitoring of objects or technological processes is used in many industries and service-oriented companies to obtain up-to-date information about the state of things or technical processes. The article is devoted to developing, analyzing, and implementing information technology (IT) for remote monitoring. The high-level design of IT and system for its implementation is based on the IDEF0 Functional Modeling methodology that describes the system hierarchy and establishes horizontal links between various activities. The achieved results confirm the possibility of implementing the proposed technology. The results will be of interest to technical specialists involved in the development of relevant applications but also to business analysts and managers for a better understanding of modern applied information technologies. © 2021 IEEE.

关键词： Remote control

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Methods for Improving the Quality of Classification on Imbalanced Data

Methods for Improving the Quality of Classification on Imbal...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Zozulia Vladislav Nataliia Khatsko Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate methods of balancing classes and to develop a classification method on imbalanced data to increase the level of network security. The methods used are: methods of artificial intelligence, machine learning. The following results were obtained: Class balancing methods based on Undersampling, Oversampling and their combinations were studied. The following methods were chosen for further research: SMOTEENN, SVMSMOTE, BorderlineSMOTE, ADASYN, SMOTE, KMeansSMOTE. The UNSW-NB 15 set was used as the source data, which contains information about the normal functioning of the network and during intrusions. A decision tree based on the CART (Classification And Regression Tree) algorithm was used as the basic classifier. According to the research results, it was found that the use of the SMOTEENN method provides an opportunity to improve the quality of detection of intrusions in the functioning of the network. Conclusions. The scientific novelty of the obtained results lies in the complex use of data balancing methods and the method of data classification based on decision trees to detect intrusions into computer networks, which made it possible to reduce the number of Type II errors.

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Optimizing machine learning algorithms to enhance blood donation for liver patients: a case study

Optimizing machine learning algorithms to enhance blood dona...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Maad M. Mijwil El-Sayed M. El-kenawy Indu Bala Mohammad Aljanabi Mostafa Abotaleb Abdelhameed Ibrahim Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood.

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