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检索条件"任意字段=2023 International Conference on Communication Network and Machine Learning, CNML 2023"

共 14679 条记录，以下是51-60 订阅

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Optimal research on UAV information transmission combined with resource allocation in natural disaster scenarios - a natural disaster rescue and relief-oriented UAV ad-hoc communication network 4

Optimal research on UAV information transmission combined wi...

引用

4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Liu, Yiyang Network Engineering Southwest University Chongqing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

With the integration and development of computer and communication technologies, intelligent communication is playing an increasingly important role in various fields. In response to the intelligent, flexible, and robust communication demands in emergency scenarios, this paper proposes a network for constructing a self-organizing communication network based on Unmanned Aerial Vehicle (UAV). In this network, the network consists of a master node UAV and a branch node UAV, forming a master-slave structure. This UAV communication network can be applied in scenarios like forest wildfires, where the master node UAV dynamically schedules the branch node UAV to achieve coverage and monitoring of ground fire scenes, providing support for forest fire prevention, control, and assessment. Simulation experiments demonstrate that the proposed network exhibits significant effects in ensuring network connectivity, coverage, and mobility efficiency. © 2024 SPIE.

关键词： Antennas

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A Survey on machine learning for Space-Air-Ground Integrated network: Key Technologies and Challenges

A Survey on Machine Learning for Space-Air-Ground Integrated...

引用

2024 international conference on Electronics, Information, and communication, ICEIC 2024

作者： Seo, Junggon Kim, Yeonwoong Kim, Donghyeon Jung, Haejoon Kyung Hee University Department of Electronics and Information Convergence Engineering Yongin-si17104 Korea Republic of

ISBN: (纸本)9798350371888

In the realm of advanced mobile communications, the integration of space, air, and ground networks, referred to as space-air-ground integrated network (SAGIN), is expected to be an essential ingredient of the sixth generation (6G) mobile communication systems. Moreover, given the increasing significance of artificial intelligence (AI) applications, it is imperative to establish SAGIN with a service-oriented framework that leverages machine learning. Motivated by this fact, in this paper, we introduce the applications of machine learning technologies to address the multifaceted challenges encountered in SAGIN. © 2024 IEEE.

关键词： Mobile telecommunication systems

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Recurrent Attentive Neural networks for Sequential Recommendation

Recurrent Attentive Neural Networks for Sequential Recommend...

引用

2023 international conference on communication network and machine learning, cnml 2023

作者： Tan, Lei Xu, Jinmao Gong, Daofu Liu, Fenlin Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001 China Henan University of Engineering Zhengzhou450001 China

ISBN: (纸本)9798400716683

Sequential recommendation is essential in modern online service platforms. By modeling the evolving preferences of a user from the historical behavior sequence, sequential recommendation aims to predict the next interaction of the user in the near future. For sequential recommendation, it is challenging to comprehensively characterize user preferences based on historical behavior sequences. In this paper, we propose a Recurrent Attentive Neural networks model (RANN) for sequential recommendation, which characterizes the user preference from the long-term preference, short-term interest, and current sequential pattern. Specifically, the long short-term memory network is employed to generate the cell state and hidden state at each time step for each user, which represent the long-term preference and the current sequential pattern of the user, respectively. And the scaled dot-product attention is utilized to capture the short-term interest feature among the most recent hidden states. Finally, user preference features and item embeddings are integrated into a next-item prediction network for sequential recommendation. Experimental results on the real-world dataset verify the superiority of RANN against state-of-the-art baselines. © 2023 ACM.

关键词： Neural network models

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Intelligent Traffic-Service Mapping of network for Advanced Industrial IoT Edge Computing 20

Intelligent Traffic-Service Mapping of Network for Advanced ...

引用

20th international conference on Factory communication Systems (WFCS)

作者： Liu, Bowen Zheng, Tao Thar, Kyi Gidlund, Mikael Ma, Xiaoting Lei, Bo Zhang, Hongke Guizani, Mohsen Beijng Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Mid Sweden Univ Dept Comp & Elect Engn Ostersund Sweden China Telecom Corp Ltd Res Inst Beijing Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350319347;9798350319354

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machine learning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios.

关键词： Intelligent edge computing network service perception machine learning Explainable AI

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Edge-cloud Collaboration Based Service Deployment and Resource Allocation in Digital Twin 6G network

Edge-cloud Collaboration Based Service Deployment and Resour...

引用

2023 international conference on communication network and machine learning, cnml 2023

作者： Liu, Tong Jiang, Haitao Liu, Yu Department of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing402260 China

ISBN: (纸本)9798400716683

In this paper, we propose an Edge-Cloud based and Artificial Intelligence (AI) supported Scheme (ECAIS) in Digital Twin-enabled 6G network. Specifically, we first analyze the traffic consumption of the backbone network based on the construction workflow of DT-6G. Then, the process of tasks solving in DT-6G is detailed discussed, based on which the traffic consumed in updating and services providing is discussed. Subsequently, the network response time for task settling is derived. Then, in order to enhance the performance of DT-6G, a utility function which is represented together by throughput consumption and network latency is formulated. Thereafter, a mathematical optimization model is put forward to minimize the utility function. In view of the complexity, an AI-based algorithm is designed to resolve the optimization model. Numerical results corroborate the efficiency of our scheme. © 2023 ACM.

关键词： Resource allocation

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Research on communication network Intrusion Detection Method based on machine learning Algorithm

Research on communication Network Intrusion Detection Method...

引用

international conference on Internet of Things and machine learning, IoTML 2023

作者： Hao, Fengping Li, Yi Wang, Jingjing Jiangsu Jinken Vocational and Technical College Nanjing211100 China Army Logistics Academy Chongqing400050 China

ISBN: (纸本)9781510671805

communication network is an important part of social life, but it is extremely vulnerable to various network attacks, and the energy of data nodes in the network is limited, and abnormal behavior in the network cannot be detected in the high-dimensional traffic data mode. Therefore, an intrusion behavior detection method based on machine learning algorithm was proposed to detect attack behavior in communication network in real time. Firstly, through the restricted Boltzmann machine algorithm model of machine learning, high-dimensional traffic data features are extracted and dimension reduction operation is carried out. Then, combined with the characteristics of communication network cluster structure, a multi-layer support vector machine classification model is established to detect network abnormal state. Finally, the public intrusion detection data set is used to verify the proposed intrusion behavior detection model. The experimental results show that the detection accuracy of the model for network traffic can reach 99.63%, in order to provide reference for further research on intrusion behavior detection methods in the future. © 2023 SPIE.

关键词： Intrusion detection

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Towards Programmable networking With CoopNet: A Horizontal Parallel Multipath Approach

Towards Programmable Networking With CoopNet: A Horizontal P...

引用

international conference on Computing, networking and communications (ICNC)

作者： Voicu, Razvan Cristian Chang, Yusun Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Kennesaw State Univ Sch Elect Engn Atlanta GA USA

ISBN: (纸本)9781665457194

The advent of the Internet of Things (IoT) and machine-to-machine (M2M) communication provide a system for collecting and manipulating big data and a platform for sensing, actuating, and automating the environment. IoT, M2M communication, social networking, and mass multimedia severely strain the communication infrastructure. Thus, the archaic communication frameworks require necessary improvements. One such improvement is the simultaneous usage of parallel communication links of differing radio access networks. This paper presents a machine learning (ML) optimization for link selection and use in CoopNet, a horizontal programmable communication architecture. Programmable networking paves the way for advancing communication to improve performance, reliability, security, and policy-based applications, including network decoupling. The ML implementation in CoopNet improves throughput by over 17%, delay by 10%, and reduces individual link utilization.

关键词： Heterogeneous networks Device to Device network Architecture communication Spectrum Efficiency Multipath machine learning RNN FFNN Infrastructure as a Service

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network Traffic Classification Using Supervised learning Algorithms

Network Traffic Classification Using Supervised Learning Alg...

引用

international conference on Computer, Electrical and communication Engineering (ICCECE)

作者： Choudhury, Mira Rani Muraleedharan, N. Acharjee, Parimal George, Aleena Terese Natl Inst Technol Dept Elect Engn Durgapur India Ctr Dev Adv Comp C DAC Bangalore Karnataka India

ISBN: (纸本)9781665452519

network traffic classification is crucial for traffic monitoring and application-based policy enforcement. However, the widespread use of encrypted protocols has greatly challenged conventional traffic classification techniques using packet payload and port numbers. For the network application in this paper, two machine learning algorithms, Decision Tree (DT) and Random Forest (RF) are used. An open-access Kaggle dataset with six different types of applications is used for this study. To achieve the best values for model training, loop iteration is used rather than the hyper-parameter optimization technique. When compared to DT, RF has the highest accuracy (99.72%). In order to improve the classification process and various hidden patterns connected with the statistical features, more statistical features were taken into account in comparison to other related works that had already been done. The outcomes demonstrate the potency of supervised learning algorithms for categorizing network traffic.

关键词： Traffic classification supervised learning machine learning hyper-parameter tuning decision tree random forest

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Application of machine learning and Artificial Intelligence Technology in Mobile communication network

Application of Machine Learning and Artificial Intelligence ...

引用

2023 IEEE international conference on network, Multimedia and Information Technology, NMITCON 2023

作者： Huang, Shutong The Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong

ISBN: (纸本)9798350300826

The social and economic development is rapid, and the technological level is constantly improving. The demand for information services has developed into multiple aspects. This paper describes various basic principles, analysis methods, and results of data analysis through testing methods to distinguish and compare the advantages and disadvantages of machine learning methods in mobile networks, and provide guidelines for appropriate analysis. The main content of this paper includes the introduction of the latest technology of mobile communication network and the development of machine learning and artificial intelligence technology as well as the importance of the application of machine learning and artificial intelligence technology in mobile communication network. The data sources and analysis methods, model development process, model performance analysis process, and the results of analyzing the data are comprehensively described in each stage of multiple analysis. Experiments have verified that the oil painting algorithm in this paper is more stable in network distribution, and the accuracy of face recognition is as high as 98%. © 2023 IEEE.

关键词： machine learning

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A Survey of Multi-Transmission Path Optimization

A Survey of Multi-Transmission Path Optimization

引用

2023 international conference on communication network and machine learning, cnml 2023

作者： He, Bingyu Fu, Hao Li, Jinhua Zhu, Xiaomin Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250300 China Shandong Institute of Bigdata Jinan250000 China

ISBN: (纸本)9798400716683

The newly developed networks hope to bring some specific features, such as wide coverage, high bandwidth, and provide varieties of media services. Multipath Transport Protocol builds on the TCP protocol and utilizes the transmission capacity of multiple paths for data transmission. It improves the efficiency of bandwidth usage and the resilience of the connection. What's more, it can adaptively move data from more congested path to less congested path. It effectively improves the transmission capacity and stability of the network, which is of great significance. However, heterogeneous physical network interfaces have different resource allocation characteristics, different subflows have the problem of partial transmission path overlap in the network layer, and the data packets of upper applications are different in terms of cut-off time, importance, distortion rate and so on. all these will affect the intelligent cooperation between subflows in end-to-end multipath transmission. To this end, this survey first introduces the challenges / problems of supporting multipath transmission and possible solutions. In this paper, the researches on multipath transmission optimization in recent years are compared, and the future research trend is prospected. © 2023 ACM.

关键词： network layers

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