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On the Automated Selection of Neural Network Architectures in the Task of Pattern Recognition for Intelligent Transportation systems

On the Automated Selection of Neural Network Architectures i...

引用

2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Moseva, Alesya Gorodnichev, Mikhail Moseva, Marina Kharrasov, Kamil Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

Exponential growth in the number of computing resources, their cheapening, and the wide availability of large data sets have ensured a great demand for information systems based on neural networks. Such systems are widely used in many spheres of life: in medicine, detecting diseases at early stages and helping to design new drugs;in the financial sector, detecting fraudulent transactions and calculating risks, including the development of intelligent transportation systems. Intelligent transportation systems (ITS) actively use computer vision technologies to solve such tasks as object recognition, traffic analysis, traffic forecasting and road safety. However, the development of effective neural network models for these tasks requires significant resources and high qualification of specialists. The system of automated training and selection of neural network architecture can significantly simplify and speed up this process. Data collection, selection of the necessary model and tuning of hyperparameters requires specialized knowledge and skills. This work is aimed at developing a tool for automated selection of neural network architecture, which will accelerate the development and implementation of models, reduce development costs, improve the quality and reliability of solutions, provide scalability and adaptability to changing conditions. © 2025 IEEE.

关键词： Intelligent systems

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Development and Research of a Modified Gradient Boosting Method Effectiveness to Solve Applied Problems of Time-Series Forecasting

Development and Research of a Modified Gradient Boosting Met...

引用

2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Fatkhulin, Timur Leokhin, Yuri Zanegin, Alexey Rakhmatova, Aziza Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

The article considers the problem of increasing the accuracy of the gradient boosting method when forecasting timeseries (including demand for equipment or other goods). The aim of the study is to develop a modified gradient boosting method of decision trees for this forecasting. The relevance of the work is due to the fact that the subject area under consideration is characterized by the presence of many different noises and anomalies in datasets. The use of machine learning methods in forecasting such data leads to a decrease in the accuracy of forecasts and the occurrence of the overfitting effect. Despite the tunings of the existing parameters in existing gradient boosting algorithms, this problem remains and leads to the need for additional improvements. The object of the study is time-series. The subject of the study is the indicators (or metrics) of the effectiveness of machine learning methods used to forecast these time-series. As a result, the most suitable methods for reducing the effect of overfitting and the influence of anomalies were considered and identified, which formed the basis for the development of a modified method of gradient boosting trees. Finally, conclusions are drawn. © 2025 IEEE.

关键词： Adversarial machine learning

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The Impact of Heterogeneity on Network Resource Allocation Process for Managing Education in Real Time

The Impact of Heterogeneity on Network Resource Allocation P...

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2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Dawood, Tammam Z. Muzata, Artwell R. Stepanov, Mikhail S. Singh, Ghanshyam Kanishcheva, Margarita G. Moscow Technical University of Communications and Informatics Moscow Russia University of Johannesburg Department of Electrical and Electronic Engineering Science Johannesburg South Africa

ISBN: (纸本)9798331532635

The use of technology in education has increased In recent years, especially after the emergence of the COVID-19 pandemic and the trend towards digital learning. Solutions such as e-learning, blended learning, and game-based learning are being used. Virtual Reality (VR) and Augmented Reality (AR) can help significantly change the style of education towards making it more interesting, useful, and possible. Of course, this type of application requires specific traffic characteristics that must be taken into account when allocating resources in the network in order to obtain an acceptable quality of service. The article provides a numerical study of the influence of the heterogeneity property on the characteristics of the joint servicing of information flows of various types in modern networks. To conduct the study, a generalized mathematical model of a network access node is used, and parameters and characteristics are discussed. © 2025 IEEE.

关键词： Virtual environments

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Researching the Efficiency of Machine Learning Methods Used in Forecasting Demand for Certain Types of Goods

Researching the Efficiency of Machine Learning Methods Used ...

引用

2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Leokhin, Yuri Fatkhulin, Timur Zanegin, Alexey Rakhmatova, Aziza Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

The article deals with the problem of improving the efficiency of forecasting demand for certain types of goods. The goal of the study is to determine the most effective machine learning method for this forecasting. The relevance of the work is due to the fact that traditional statistical methods for forecasting demand do not provide the ability to process effectively the currently received volumes of information on sales and trade;besides, not all machine learning methods for predicting numerical values are suitable for the subject area under consideration. The object of the study is the arising demand for certain types of goods. The subject of the study is the effectiveness indicators (or metrics) of machine learning methods used to forecast demand for certain types of goods. When analyzing the subject area, mean absolute error, the root mean square error and coefficient of determination were selected as metrics used to evaluate the effectiveness of a particular method. As a result, the most suitable algorithms for the most effective forecast of demand for various types of goods were identified. Finally, conclusions are drawn. © 2025 IEEE.

关键词： Adversarial machine learning

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Analyzing the Potential of AI-Powered Writing Evaluation Tools for Developing Students' Analytical Skills

Analyzing the Potential of AI-Powered Writing Evaluation Too...

引用

2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Maltseva, Svetlana N. Pavlova, Anna Y. Moscow Technical University of Communications and Informatics Moscow Russia Foreign Languages Department Moscow Russia

ISBN: (纸本)9798331532635

In the contemporary context of learning, the utilisation of artificial intelligence (AI) has emerged as a pivotal concern, particularly concerning the diligent employment of neural networks by students as a medium to augment learning, as opposed to their utilisation as a mere executor of tasks derived from textbooks. This paper delves into the realm of students' engagement with AI-powered tools, offering feedback on their written compositions, summaries, articles, etc. during their university education. The scientific significance of this study lies in its analysis of the potential of modern automated writing evaluation services using large language models, such as ProWritingAid, Quillbot, MagicSchool and DeepSeek, to encourage students to develop their analytical skills while actively participating in corrective assessment of their writing based on feedback received from the AI tools. The necessity of employing automatic feedback mechanisms is further examined within the context of foreign language acquisition for technical students pursuing higher education, with a focus on electronics and electrical engineering as case studies. The analysis yielded recommendations for enhancing the efficacy of feedback provision to students. © 2025 IEEE.

关键词： Contrastive Learning

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Trajectory Mining for Vehicle Speed Characteristics Using Traffic Video-Analysis

Trajectory Mining for Vehicle Speed Characteristics Using Tr...

引用

2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Yashina, Marina V. Serov, Pavel G. Korshunov, Kirill S. Smyk, Aleksandra F. Lazarev, Nikita S. Moscow Technical University of Communications and Informatics Moscow Russia Moscow Russia Moscow Automobile and Road Construction State Technical University Russia Moscow Russia

ISBN: (纸本)9798331532635

The modern development of information technology and artificial intelligence leads to the accumulation of large volumes of spatiotemporal data in various areas of human activity. This is especially true in the field of transport, where intelligent transport system devices accumulate data in various formats, including video and satellite positioning data. This article presents an approach for real-time traffic analysis for trajectory using computer vision techniques. The proposed method involves object detection, tracking, and trajectory estimation of vehicles. A deep learning-based YOLO11 model is employed for vehicle detection, while CSRT (Channel and Spatial Reliability Tracking) tracking is used for continuous monitoring of vehicle movements. Additionally, instantaneous speed estimation is integrated into the system, providing a more accurate representation of traffic dynamics. Comparison of trajectories is carried out based on Lp-norms. Experimental results demonstrate the effectiveness of the proposed method in various urban traffic scenarios. © 2025 IEEE.

关键词： Deep learning

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Content Delivery Network for E-Learning Based on Enterprise Service Bus

Content Delivery Network for E-Learning Based on Enterprise ...

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2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Prokurovsky, Alexey Toutova, Natalia Andreev, Iliya Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

Learning management systems are an important element of the educational process, providing students with a variety of educational material and giving teachers the opportunity to monitor students' knowledge and assess their progress. However, an increase in the number of students leads to the growth of the load on data transmission channels, which can negatively affect the quality of education. To solve this problem, content delivery networks are used, which speed up the loading of web resources and reduce the workload on servers. An enterprise service bus can significantly improve the performance of content distribution algorithms in a content delivery network. It is especially useful in distance learning, where fast and reliable delivery of educational material, feedback, performance analytics and other functions are required. In this work the features and benefits of using an integration bus to optimize content delivery network operation within learning management systems are discussed. The results of experiments using the content delivery network with an algorithm for optimizing content delivery based on the integration bus 1C:Bus are presented. It is shown that the average time of content distribution across network nodes is reduced by 25%. Implementation of an integration data bus in a distance learning system can significantly improve the efficiency and quality of the educational process. © 2025 IEEE.

关键词： Students

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Analysis of Explainable Artificial Intelligence Methods in Security Information and Event Management systems

Analysis of Explainable Artificial Intelligence Methods in S...

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2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Bulgakova, Elena V. Vasilevskiy, Pavel A. Doynikov, Daniil S. Kubankov, Alexander N. Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

The present article engages with the ongoing discourse surrounding the implementation of artificial intelligence and explainable artificial intelligence within the domain of information security systems. In the context of escalating cyber threats and mounting data volumes, conventional defense methodologies are proving to be inadequate. Consequently, the integration of artificial intelligence has emerged as a pivotal strategy to enhance the efficacy of threat detection and response processes. The article delves into the intricacies of explainability in artificial intelligence decision-making, exploring explainable artificial intelligence techniques such as local interpretable model-agnostic explanations, shapley additive explanations, gradient-weighted class activation mapping, and layer-wise relevance propagation. These techniques facilitate a more profound comprehension of the rationale behind artificial intelligence models' decisions, thereby enhancing the interpretability of these models. The discourse further encompasses the trade-offs between the accuracy and explainability of artificial intelligence models, along with their potential applications in security information management systems to enhance the efficiency and quality of incident analysis. The integration of explainable artificial intelligence into information security systems has the potential to enhance user confidence in artificial intelligence technologies and optimize human-machine interaction. © 2025 IEEE.

关键词： Cyber attacks

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Modeling of Multiservice Traffic Service Process in a High-Throughput Satellite Network

Modeling of Multiservice Traffic Service Process in a High-T...

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2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Troegubov, Aleksandr Stepanov, Sergey Malikova, Elena Thu, Do Xuan Moscow Technical University of Communications and Informatics Moscow Russia University of Transport Technology Hanoi Viet Nam

ISBN: (纸本)9798331532635

The demand for satellite communication services is increasing every year. One promising solution is high-throughput satellite (HTS) networks in geostationary Earth orbit (GEO). To provide high-speed communications over a large area on the Earth's surface, HTS nodes use multi-beam onboard antennas. Modern communication systems handle multiservice traffic, including real-time and elastic traffic, requiring efficient radio resource management. This paper addresses the problem of servicing multiservice traffic in an HTS node, where elastic traffic service rates are constrained. To evaluate quality-of-service (QoS) metrics, a mathematical model based on Markov process theory is developed. Efficient resource utilization while meeting strict QoS requirements is crucial;therefore, an approximate method is introduced to estimate key service parameters. The proposed method is both fast and accurate, enabling real-time computation for resource planning and optimization tasks. Its performance is evaluated through numerical experiments, demonstrating its effectiveness in the low-loss region of request servicing. Furthermore, the method is applied to a channel resource planning problem, illustrating its efficiency in determining the minimum resource requirements needed to meet predefined QoS targets. © 2025 IEEE.

关键词： Markov processes

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Application of Virtualization and Containerization Technologies in IP Telephony

Application of Virtualization and Containerization Technolog...

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2025 systems of signals generating and processing in the field of on board communications, SOSG 2025

作者： Antonova, V.M. Malikova, E.E. Malikov, A.Y. Fartyshev, M.A. Moscow Technical University of Communications and Informatics Moscow Russia V.A. Kotelnikov Institute of Radio Engineering and Electronics The Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9798331532635

The article considers the contemporary trends that telecommunication companies are facing in the rapidly changing technological environment. One of the trends is the technology of network virtualization, which allows to significantly increase the efficiency of network resources through the organization of multiple virtual resources based on one physical resource. The objective of this study is to analyze new tools available for the implementation of a virtual IP PBX. Virtualization and containerization technologies that affect the efficient operation of VoIP services are studied in the paper. The implementation of these technologies has been shown to facilitate more efficient use of server resources and more secure interaction with multiple tools for analyzing system operation. The principles of integrating software telephone exchanges into the virtualized environment are considered. Special attention is paid to the integration of a virtual Asterisk exchange into the containerized environment. Approaches to automating Asterisk's deployment and management in the microservice architecture using a Kubernetes platform are also considered. It is recommended that virtualization and containerization technologies should be incorporated into the curricula of technical universities.) © 2025 IEEE.

关键词： Telephone exchanges

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