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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Khidirova, Charos Sadikova, Shakhnoza Mukhsinov, Shamil Nashvandova, Gulrukhsor Mirzaeva, Sarvinoz Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of System and Applied Programming Tashkent Uzbekistan Tashkent State Technical University Department of Information Processing and Control Systems Tashkent Uzbekistan

ISBN: (纸本)9781665432580

Machine learning (ML) methods are the main tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of big data, to reveal hidden or non-obvious patterns on this basis, and to extract new knowledge. The review presents an analysis of scientific literature on the use of ML methods for diagnosing and predicting the clinical course of coronary heart disease. Provides information on reference databases, the use of which allows you to develop models and validate them (European ST-T Database, Cleveland Heart Disease database, Multi-Ethnic Study of Atherosclerosis, etc.). The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. The most promising ML methods include deep learning, which is implemented using multilayer artificial neural networks. It is assumed that the improvement of models based on ML methods and their introduction into clinical practice will help support medical decision-making, improve the effectiveness of treatment and optimize health care costs. © 2021 IEEE.

关键词： Support vector machines

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IoT Cyberattack Detection Approach Based on Energy Consumption Analysis

IoT Cyberattack Detection Approach Based on Energy Consumpti...

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

作者： Kira Bobrovnikova Oleg Savenko Sergii Lysenko Ivan Hurman Department of Computer Engineering and Information Systems Khmelnitsky National University Khmelnitsky Ukraine Department of Computer Engineering and System Programming Khmelnitsky National University Khmelnitsky Ukraine

ISBN: (纸本)9798350333053

Today, the Internet of Things has been widely used and is becoming an increasingly vital part in various areas of human activity, which helps to effectively solve various real-life tasks and problems. The infrastructure of the Internet of Things includes a lot of heterogeneous devices with limited capacities, which makes it difficult to control them in order to detect cyberattacks. The increase in the number of attacks on the infrastructure of the Internet of Things indicates the relevance of developing new solutions for detecting attacks on the networks of the Internet of Things. One of the possible approaches to identify attacks is to analyze the physical parameters associated with IoT devices that describe the behavior of devices on the network, such as power consumption. Power analysis allows you to detect power-hungry attacks such as crypto mining and denial-of-service attacks. The paper proposes a new method for cyberattack detection in the infrastructure of the Internet of Things that is based on the analysis of energy consumption. In order to improve the accuracy of detecting cyberattacks, the approach also uses the analysis of the actions of the IoT software. The proposed approach makes it possible to detect attacks such as DoS/DDoS with high efficiency with a level of detection up to 99.95%.

关键词： Energy consumption Power demand Software Behavioral sciences Internet of Things Object recognition Data mining

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Model-based Generation of Attack-Fault Trees

arXiv

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arXiv 2023年

作者： Groner, Raffaela Witte, Thomas Raschke, Alexander Hirn, Sophie Pekaric, Irdin Frick, Markus Tichy, Matthias Felderer, Michael Institute of Software Engineering and Programming Languages Ulm University Germany Department of Computer Science University of Innsbruck Austria Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Germany Department of Mathematics and Computer Science University of Cologne Germany

Joint safety and security analysis of cyber-physical systems is a necessary step to correctly capture inter-dependencies between these properties. Attack-Fault Trees represent a combination of dynamic Fault Trees and Attack Trees and can be used to model and model-check a holistic view on both safety and security. Manually creating a complete AFT for the whole system is, however, a daunting task. It needs to span multiple abstraction layers, e.g., abstract application architecture and data flow as well as system and library dependencies that are affected by various vulnerabilities. We present an AFT generation tool-chain that facilitates this task using partial Fault and Attack Trees that are either manually created or mined from vulnerability databases. We semi-automatically create two system models that provide the necessary information to automatically combine these partial Fault and Attack Trees into complete AFTs using graph transformation rules. © 2023, CC BY-NC-ND.

关键词： Trees (mathematics)

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Creating a Dynamic Online Learning Environment with AI Integration

Creating a Dynamic Online Learning Environment with AI Integ...

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International Computer Sciences and Information Technologies (CSIT)

作者： Oleksandra Bulgakova Zarina Viniarska Viacheslav Zosimov P.M Platonov Educational and Scientific Institute of Computer Engineering Automation Robotics and Computer Programming Odesa National University of Technology Odesa Ukraine Department of Applied information systems Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

Educational, online learning has become a common practice due to rapid digitalization and recent global events. This study focuses on developing a comprehensive online learning platform that addresses users' needs by integrating advanced AI capabilities for personalized support and adaptive learning. The platform supports user registration and authentication, course and lesson management, and offers interactive features like AI-driven chat support. In this study, we show the importance of creating user-friendly online learning platforms with the implementation of modern AI trends. The development and testing of this platform is ongoing to determine the adaptation of the system to different learning styles.

关键词： Adaptive learning Electronic learning Authentication Passwords Market research Real-time systems Artificial intelligence Information technology Standards Testing

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

SSRN

引用

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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Application of IoT in the study of biogas release parameters

Application of IoT in the study of biogas release parameters

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International Scientific Conference Electronics (ET)

作者： Neven K. Nikolov Anelia I. Tzanova Kiril A. Koparanov Elena V. Antonova Daniela V. Minkovska Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd. Sofia Bulgaria Department of Chemistry Faculty of Electronic Engineering and Technologies Technical University of Sofia 8 Kliment Ohridski blvd. Sofia Bulgaria

This paper describes the development of an IoT device prototype for measuring biogas release parameters. Through the device, measurements are made, and statistics are accumulated based on which calculations are perfor... 详细信息

ISBN: (数字)9798350376449

ISBN: (纸本)9798350376456

关键词： Performance evaluation Heating systems Prototypes Production Sensor phenomena and characterization Biogas Sensor systems Internet of Things Information technology Monitoring

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Input Data Time Range Impact on Forecast Quality Using Automated Procedures

Input Data Time Range Impact on Forecast Quality Using Autom...

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Computer Science (COMSCI), International Scientific Conference on

作者： Kiril Andreev Koparanov Elena Ventseslavova Antonova Ognyan Nakov Nakov Daniela Veleva Minkovska Krasin Krasimirov Georgiev Lyudmila Yordanova Stoyanova Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria Department of Computer Systems Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria Department of Air Transport Faculty of Transport Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria

Easy access to time series forecasting provides a strong competitive advantage for any company or organization. As automated forecasting environments become more prevalent in the market, it is crucial to explore their capabilities and features to utilize them competently. This study demonstrates the influence of data coverage on prediction accuracy and validates the initial assumption. Based on the findings, recommendations have been presented to enhance the quality of predictive processes.

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Algorithmic Support for Building a Distributed IoT System in a Cloud Service

Algorithmic Support for Building a Distributed IoT System in...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Mykola Zamkovyi Svitlana Gavrylenko Kyrylo Khatsko Nataliia Khatsko dept. Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

The Internet of Things (IoT) today already connects billions of different IoT devices with the Internet. With the advent of new services and services, new challenges arise: how to make a cloud service publicly available, and reliable, how and where to store data. The article considers an example of the implementation of an IoT application with cloud components, considers the implementation of connecting IoT components to a cloud provider, and presents the IoT architecture. The focus is on the architecture and implementation of data exchange protocols, the implementation of protocols? for data collection and management of remote system components Given the large amount of data transmitted by IoT devices to the cloud, data compression and interaction with cloud services is a very relevant topic. Therefore, the article also offers recommendations for managing the traffic of IoT devices. The purpose of this article is to provide a description and algorithm for connecting IoT devices to the cloud, to bring together disparate information, and make it open and understandable to beginners and academic researchers.

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Modeling of DC Traction Motor In Ansys RMXPRT

Modeling of DC Traction Motor In Ansys RMXPRT

引用

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Vitalii Herasymenko Vladyslav Pliuhin Valentyn Noskov Oleksandr Zakovorotnyi Mykola Mezentsev Tetyana Orlova Department of Electric Transport O. M. Beketov National University of Urban Economy in Kharkiv Kharkiv Ukraine Department of the Systems of Electricity Supply and Electricity Consumption of Cities O. M. BeketovNational University of Urban Economy in Kharkiv Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Computer Engineering and Programming Department National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

DC traction motors are widely used in all branches of urban economy. They began to be used in industry about a hundred years ago with the advent of the first calculation methods. When designing a DC motor, it is important to consider it comprehensively, together with the electric drive system. The article discusses the application of a more correct approach to modeling a series-excited DC motor using the ANSYS software package, in particular, the ANSYS RMxprt / Maxwell/ Simplorer bundle. The main stages of initial data input are considered and the main calculation results are shown. Using ANSYS Maxwell allows you to quickly and automatically build a typical machine geometry using RMxprt. When solving non-standard structures, it is possible to use 3D design with step-by-step elements. The analysis of the obtained graphic dependences of electric power parameters makes it possible to judge the efficiency of the electric motor and its operational properties in idling and under load. The simulation results show excellent agreement with the data obtained experimentally.

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A generative approach for user-centered, collaborative, domain-specific modeling environments

arXiv

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arXiv 2021年

作者： Zweiho, Philip Steffen, Bernhard Programming Systems Department of Computer Science Tu Dortmund University Dortmund44227 Germany

The use of low- and no-code modeling tools is today an established way in practice to give non- programmers an opportunity to master their digital challenges independently, using the means of model- driven software development. However, the existing tools are limited to a very small number of different domains such as mobile app development, which can be attributed to the enormous demands that a user has on such a tool today. These demands exceed the mere use of a modeling environment as such and require crosscutting concerns such as: easy access, direct usability and simultaneous collaboration, which result in additional effort in the realization of such tools. Our solution is based on the idea to support and simplify the creation of new domain-specific holistic tools by generating it entirely based on a declarative specification with a domain-specific meta-tool. The meta-tool Pyro demonstrated and analyzed here focuses on graph-based graphical languages to fully generate a complete, directly executable tool starting from a meta-model in order to meet all cross-cutting requirements. Copyright © 2021, The Authors. All rights reserved.

关键词： Specifications

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