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Energy Efficiency Enhancement of Ultra Dense Multiuser MIMO System by Using Wyner Model 14

Energy Efficiency Enhancement of Ultra Dense Multiuser MIMO ...

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14th international conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Abose, Tadele A. Mariye, Yihenew W. Demissie, Abinet M. Olwal, thomas O. Mettu University Mettu Ethiopia Addis Ababa University Addis Ababa Ethiopia Dire Dawe University Dire Dawa Ethiopia Tshwane University of Technology Pretoria South Africa

ISBN: (纸本)9798350335095

In modern wireless communication, energy consumption has emerged as a major problem for wireless network operators. With the advent of fifth generation (5G) wireless communication, energy efficiency (EE), one of key performance measurement indicators of wireless networks, has gained recognition. Multi-user multiple-input multiple-output (MU-MIMO) and cell densification are some of the most recent techniques that have proven to be highly effective in maximizing the EE of 5G wireless communications. Most of the research has been done on either MIMO or ultra-dense network (UDN), utilizing different precoding techniques. However, works that consider MU-MIMO, UDN, and precoding techniques jointly are rarely investigated. the cooperative design of UD MU-MIMO systems using the Wyner model, taking into account the power needs of the base station, user equipment, and the coverage area to increase the EE is the concern of this research. In this research, the optimal partial zero forcing (p-ZF) processing is used to deploy the combination of MU-MIMO and UDN with Wyner model. Numerical results show that the design consisting of the combination of MU-MIMO, UDN and precoding techniques (p-ZF) is more energy efficient compared to MU-MIMO with normalized p-ZF precoding. © 2023 IEEE.

关键词： MIMO systems

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Integrating deep learning and reinforcement learning into a digital twin architecture for medical predictions. 15

Integrating deep learning and reinforcement learning into a ...

引用

15th international conference on Emerging Ubiquitous systems and Pervasive networks / 14th international conference on Current and Future Trends of Information and communication Technologies in Healthcare, EUSPN/ICth 2024

作者： Alpysbay, Nursultan Kolesnikova, Kateryna Chinibayeva, Tolganay International IT University 34/1 Manas str. Almaty Kazakhstan

In the course of the research, a digital patient twin architecture based on deep reinforcement learning techniques was developed. the aim of the paper is to develop and validate a digital patient twin architecture that integrates deep learning and reinforcement learning techniques to improve the accuracy of medical predictions and individualize treatment. the existing information-analytical systems for health monitoring were analyzed, which allowed us to identify their limitations and define areas for improvement. the developed digital twin architecture integrates data from various sources including electronic health records, biometric sensors and genetic information. A deep reinforcement learning model is trained on patient data and tested to evaluate its ability to accurately predict health status. Results showed high accuracy, completeness, and F-measure scores, indicating the model's ability to effectively classify and predict health risks. the proposed system provides physicians with a tool for personalized diagnostics and development of individual treatment plans. the architecture of the digital twin of the patient is a promising solution for improving the accuracy of medical prognoses and optimizing treatment processes in clinical practice. © 2024 the Authors.

关键词： Deep reinforcement learning

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Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection systems 14

Comparative Evaluation of Machine Learning-Based Controller ...

引用

14th international conference on Information and communication systems, ICICS 2023

作者： Al-Jarrah, Omar Y. Ramadan, Omar A. Alsaify, Baha Alkhushayni, Suboh Jordan University of Science and Technology Faculty of Computer and Information Technology Dept. Network Engineering and Security P.O. Box 3030 Irbid22110 Jordan Jordan University of Science and Technology Faculty of Engineering Dept. Mechanical Engineering P.O. Box 3030 Irbid22110 Jordan Yarmouk University Dept. Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

the controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). these IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. this study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN. © 2023 IEEE.

关键词： Intrusion detection

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Enhanced Lifetime with less energy consumption in WSN Using a Genetic Algorithm-based approach 14

Enhanced Lifetime with less energy consumption in WSN Using ...

引用

14th international conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Bejjam, Komuraiah Bollena, Navya Jhanvitha, B. Electronics & Communication Dept. Telangana Warangal India

ISBN: (纸本)9798350335095

Enhanced lifetime means to increase or im- prove the value, quantity, desirability, or attractiveness. Energy efficiency simply refers to using less energy to complete the same task, thereby reducing energy waste. Energy efficiency protocols are used in wireless sensor networks to improve system energy conservation and lifespan. In a WSN, instead of using physical media for communication, transmission is accomplished through the deployment of nodes. the deployed nodes send data to the destination node or to the sink node. the process of gathering the nodes for improved communication is referred to as clustering. During clustering, neighboring nodes that are part of a compatible cluster send data to the cluster head, aggregating it and sending it to the sink. For the enhancement of WSN, many clustering algorithms have been created. In the case of the traditional approach, the node prefers a straight-line path hence it consumes more energy a reduction the lifetime. Hence, another genetic algorithm-based approach is VGDRA. Since the proposed method is dynamic rather than static, it has a higher energy efficiency than LEACH, because it takes the shortest path. Balanced load and optimization increase the likelihood of better results in lower loops, which other techniques cannot achieve. the proposed method's simulation results are generated using MATLAB R2022 b. © 2023 IEEE.

关键词： Energy efficiency

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A Cryptographic and Key Management Glance at Cybersecurity Challenges of the Future European Railway System 14

A Cryptographic and Key Management Glance at Cybersecurity C...

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14th international conference on Cyber Conflict (CyCon) - Keep Moving

作者： Kiviharju, Mikko Lassfolk, Christina Rikkonen, Sanna Kari, Hannu Finnish Def Res Agcy Riihimaki Finland CCD COE Tallinn Estonia Natl Def Univ Helsinki Finland

ISBN: (纸本)9789916978917;9789916978900

In today's globalized economy, the transport of people and goods is essential. Railways form a critical infrastructure that constitutes one of the backbones of modern society. Furthermore, a shift from air and road transport to electric railway enables a cost-efficient reduction of the negative climate impact caused by transport. thus, the utilization of existing railway systems must become more efficient. Consequently, the importance, complexity, and smartness of these systems increases, but the systems simultaneously become more vulnerable to attacks. Within the European Union, there is a quest for a pan-European standardized control system for railway traffic. Old, national, non-interoperable railway communication systems need renewal. the European Rail Traffic Management System (ERTMS) is the European standard for automatic train protection as well as for command and control systems. the next-generation development effort, the Future Railway Mobile communication System (FRMCS), builds on a 5G service standard. this article focuses on two key aspects of cybersecurity, namely cryptographic and key management challenges in proposed railway communication systems.

关键词： cybersecurity railway communication cryptography

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Intrusion Detection systems in IoT Based on Machine Learning: A state of the art 15

Intrusion Detection Systems in IoT Based on Machine Learning...

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作者： Berhili, Mohammed Chaieb, Omar Benabdellah, Mohammed ACSA Laboratory FSO Mohammed First University Oujda60000 Morocco

Nowadays, connected devices are progressively integrating our lifestyle, known as the Internet of things (IoT). there are several domains where these IoT can be found, including industry, smart cities, energy, healthcare, smart homes, and so on. these devices bring with them a host of security threats, due to their many weaknesses such as limited resources and the fact that it is often out of date. this is why researchers have progressively turned to innovative techniques to face these threats, such as Intrusion Detection systems (IDS). In addition, IDS can be enhanced through machine learning (ML), which improves detection accuracy, adapts to new and evolving threats, identifies unknown threats, reduces false positives, and more. In this study, we conduct a review of the recent articles focusing on the integration of IDS and ML. Each method is evaluated based on attributes such as feature selection method, algorithms used, detection and false positive rates, security threats, and so on. this involves presenting a critical analysis of each approach. Furthermore, we present the most prevalent datasets containing deliberate attacks on test IoT networks. In addition, we cover the anomaly detection-based IDS method in depth, focusing on papers that use ML. Subsequently, we engage in a discussion by comparing all methods and identifying open re-search questions. As a result, summarized tables are provided to highlight each model. © 2024 the Authors.

关键词： Anomaly Detection Deep Learning Feature Selection Internet of things Intrusion Detection System Machine Learning

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communication-wise Comparison of the Online Resource Allocation Methods in CAV Fleets 14

Communication-wise Comparison of the Online Resource Allocat...

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14th international conference on Ambient systems, networks and Technologies networks, ANT 2023 and the 6th international conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Daoud, Alaa Picard, Gauthier Alqasir, Hiba Gianessi, Paolo Balbo, Flavien CNRS UMR 6158 LIMOS Institut Henri Fayol Mines Saint-Etienne France ONERA DTIS Universite de Toulouse France UMR CNRS 5516 Laboratoire Hubert Curien Universite Jean Monnet Saint-Etienne France

Deploying fleets of Connected Autonomous Vehicles (CAVs), with limited peer-to-peer communication ranges, in order to provide On-demand Transport (ODT) services, requires a careful choice and evaluation of solution methods for resource allocation problems. We adopt in this paper a multiagent approach consists of: 1) defining a generic model to ODT's dynamic resource allocation problem in connected autonomous vehicle fleets, taking into account the limited connectivity and communication constraints, 2) behavior abstraction of AV Agents, and 3) abstracting the solution methods as Coordination Mechanisms (CMs) to define the characteristics of a solution method and its requirements to implement the corresponding planning sub-behavior. Using this methodology, we were able to compare the performance of a variety of solution methods with a set of evaluation criteria, namely for the solution Quality of Service (QoS) and Quality of Business (QoB). In this work, we focus on the communication-wise evaluation criteria, such as connectivity and network load. © 2023 Elsevier B.V.. All rights reserved.

关键词： Multi agent systems

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Enhancing Breast Cancer Detection with Dimensionality Reduction Techniques: A Study Using PCA and LDA on Wisconsin Breast Cancer Data 15

Enhancing Breast Cancer Detection with Dimensionality Reduct...

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作者： Esen, Gani Altaibek, Aizhan Amankulov, Jandos Matkerim, Bazargul Nurtas, Marat International Information Technology University Manas st.34/1 Almaty050040 Kazakhstan Institute of Ionosphere Kamenskoye Plateau "Ionosphere" 117 Almaty050020 Kazakhstan Kazakh Institute of Oncology and Radiology Abai av. 91 Almaty490078 Kazakhstan Al Farabi Kazakh National University Al-Farabi Avenue 71 Almaty050040 Kazakhstan

Breast cancer is the leading cause of cancer-related mortality among women, presenting a significant health challenge. this study introduces a method that leverages dimensionality reduction, specifically Principal Component Analysis (PCA), to enhance the accuracy of existing breast cancer detection techniques. By applying PCA and Linear Discriminant Analysis (LDA), our research utilizes the Wisconsin breast cancer dataset to validate the improved performance of these methods. the findings indicate that incorporating PCA into the detection models significantly enhances prediction accuracy and model performance. © 2024 the Authors.

关键词： Lung cancer

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Automating hybrid business processes with RPA: optimizing warehouse management 14

Automating hybrid business processes with RPA: optimizing wa...

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14th international conference on Emerging Ubiquitous systems and Pervasive networks / 13th international conference on Current and Future Trends of Information and communication Technologies in Healthcare, EUSPN/ICth 2023

作者： Nalgozhina, Nurgul Uskenbayeva, Raissa International Information Technology University Manas st. 34/1 Almaty050000 Kazakhstan

In the modern business world, where hybrid business processes are widespread, effective resource and operations management defines the success of companies. One of the most critical areas is logistics, which is a critical component for many organizations, regardless of size or industry. the management of logistics processes is becoming more complex every year, as the demands on the industry are becoming more complicated and today consists not only of efficiency issues but also of environmental friendliness, and globalization. In this situation, RPA technology is one of the main solutions, enabling companies to achieve new levels of efficiency, accuracy, and speed in managing warehouse processes. In this article, we will consider modern approaches to managing hybrid business processes using the example of warehouses and explore how RPA technology has become the main optimization tool. the paper observes specific examples of the use of RPA in the context of warehouse management, identifies the benefits and risks associated with automation, and defines the development prospects and future of this innovative technology in logistics. © 2024 the Authors. Published by Elsevier B.V.

关键词： Supply chains

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Metauniversity as a Tool for Inclusive Learning 15

Metauniversity as a Tool for Inclusive Learning

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作者： Turzhanov, Umitkhan Daineko, Yevgeniya Tsoy, Dana Ipalakova, Madina International Information Technology University Manast St. 34/1 Almaty050000 Kazakhstan

this paper explores the concept of a Metauniversity as an innovative tool for inclusive learning in higher education. Leveraging cutting-edge virtual reality (VR) and augmented reality (AR) technologies, particularly Unity and Meta XR platforms, the Metauniversity creates a comprehensive and accessible educational environment. the study focuses on two key aspects: the inclusive nature of the platform and its multiplayer capabilities. By allowing students to create and customize avatars, the Metauniversity accommodates diverse physical and cultural characteristics, promoting equality and comfort in the learning process. the multiplayer features facilitate real-time interaction, collaborative projects, and knowledge sharing, enhancing the overall educational experience. the paper also delves into the technical architecture of the multiplayer system, demonstrating how various components interact to create a seamless, immersive learning environment. this research highlights the potential of Metauniversity to revolutionize educational approaches, offering new opportunities for personal and professional development while fostering a tolerant and harmonious learning community. © 2024 the Authors.

关键词： Virtual environments

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