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2nd international conference on Artificial Intelligence, Systems and network Security, AISNS 2024

作者： He, Dandan Zeng, Weijia Wang, Congwen Dalian University of Science and Technology Liaoning Dalian China People?s Liberation Army No. 91524 Liaoning Dalian China

ISBN: (纸本)9798400711237

This paper discusses the core features of computer database technology and elaborates on its practical strategies in the field of information management. It aims to optimize information management processes, enhance the application efficiency of computer database technology, ensure the accuracy and efficiency of data processing, and promote the overall optimization and upgrading of information management systems. © 2024 Copyright held by the owner/author(s).

关键词： Data accuracy

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Real-Time Intrusion Detection Method Based on Frequency Domain Simplified Dual-PINN 24

Real-Time Intrusion Detection Method Based on Frequency Doma...

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8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Zhang, Bohan Wang, Zhiwen Miao, Wei Lanzhou University of Science and Technology Gansu Lanzhou China Gansu Ziguang Intelligent Transportation and Control Technology Co Gansu Lanzhou China

ISBN: (纸本)9798400710094

To address the limitations of traditional intrusion detection systems that do not conform to the physical laws governing actual industrial processes, we propose a frequency domain simplified Dual-Physical-Informational Neural network (Dual-PINN) methodology. This methodology reduces training costs and enhances real-time detection performance, allowing the lightweight detection system to be applicable in resource-constrained industrial environments. Additionally, drawing on the concept of meta-learning, we have partitioned the model into a teacher network and a student network to enhance the robustness of detection. Experimental results demonstrate that our method achieves over 99% detection accuracy on a real-world network traffic dataset, with an average detection time of 0.098 seconds per data packet, effectively enhancing the performance and efficiency of intrusion detection. © 2024 Copyright held by the owner/author(s).

关键词： network intrusion

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5G communication architecture and key technologies 5

5G communication architecture and key technologies

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5th international conference on Signal Processing and computer science, SPCS 2024

作者： Gao, Ruihan School of Civil and Resource Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Under the background of the rapid development of big data and artificial intelligence, the rapid development of mobile communication has accelerated the development process of 5G technology. 5G has comprehensively affected people's production and life, and brought opportunities and challenges to intelligent network systems. From the perspective of 5G mobile communication network architecture and key technologies, this paper presents the core points and advantages of 5G through the newly improved and upgraded 5G network architecture and various updated and iterative key technologies, and looks forward to the future development trend of 5G. © 2025 SPIE.

关键词： 5G mobile communication systems

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A Transferable-experience-based Routing Mechanism for Named Computing Power network

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第4期52卷 1235-1243页

作者： Dai, Yakai Zhu, Yi School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China

Computing-aware routing is the core technology of Computing Power network. Through independently determining the forwarding target in each router, computingaware routing aims at scheduling the computing task to an optimal Computing Service Provider (CSP). However, current computing-aware routing schemes only depend on the collected computing and network resource information, do not consider the experience of CSPs in handling similar tasks, resulting in excessive invocation time. Facing this problem, this paper proposes a novel routing mechanism based on transfer learning experience in named computing power network (NRS-TLE). NRS-TLE introduces the accuracy of trained classification models as the experience of CSP, and design a tcce-function to evaluate the task completion capabilities of different CSPs at NDN routers. Based on the evaluation results of the tcce- function, NDN routers further perform a probabilistic routing scheme. The simulation results show that, comparing with traditional routing scheme SAC-NSGA-II, the computing service invocation time under NRS-TLE will decrease by a minimum of 26.42% and a maximum of 40.22%. © 2025 IAENG international Journal of computer science

关键词： Computing power

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On-site smart maintenance based on collaboration between network large model and AI models 24

On-site smart maintenance based on collaboration between net...

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5th international conference on computer science and Management technology, ICCSMT 2024

作者： Zhang, Chao Li, Donghao Dong, Bin China Telecom Research Institute Beijing China

ISBN: (纸本)9798400709999

On-site maintenance is the end of telecom operators' network maintenance, and it is an important means of ensuring the quality of telecom network operation and user ***, improving the quality and efficiency of on-site maintenance is *** paper designs an on-site smart maintenance scheme based on the network large model, according to the actual maintenance processes of telecom *** also proposes practical use cases such as maintenance Q&A assistant, on-site troubleshooting and analysis assistant, and on-site maintenance *** giving full play to generative AI and orchestrating the ability of small models, the network large model helps maintenance personnel solve practical problems in *** not only improves the maintenance efficiency but also effectively guarantees the safe operation of the *** improving customer satisfaction, it can also reduce the maintenance costs of operators to a certain extent. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Scheduled maintenance

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Research on Service Composition Based on DQN in Multiple Clouds 24

Research on Service Composition Based on DQN in Multiple Clo...

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5th international conference on computer science and Management technology, ICCSMT 2024

作者： Yang, Yanhua Zhan, Xinrong College of Computer Engineering Jimei University Fujian Xiamen China School of Data and Computer Science Xiamen Institute of Technology Fujian Xiamen China

ISBN: (纸本)9798400709999

Service organized all kinds of services to one for the *** the cloud environment, the services and related QoSs (Quality of Services) in every cloud may be *** this paper, how to compose those services together in the multiple clouds environment is the *** use a SCDQN (Service Composition based a Deep-Q-network) method, and consider the execution time, privacy level, cost and availability of the service and the service request in the multiple *** show that SCDQN has a lower value in the execution time and cost while satisfying privacy level and availability requirement. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： DQN

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A Multi-feature Co-distillation Algorithm Based on Human Skeletal Point Behavior Recognition 24

A Multi-feature Co-distillation Algorithm Based on Human Ske...

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2nd international conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Pan, Xuefeng Tian, Zhiqiang School of Computer Wuhan Qingchuan University Wuhan China School of Information Wuhan Software Engineering Vocational College Wuhan Open University Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400712883

Depth model-based behavior estimation of human skeletal points is widely used in the field of behavior recognition. In order to improve the accuracy of behavior recognition, the complexity and computation of the designed network model are increasing, while the mobile platforms have limited arithmetic power and memory, and thus cannot be deployed. To solve the above problems, the AGCN network, a skeletal point-based behavior recognition model, is clipped to compress the network size so that it can support common mobile platforms;and a multi-feature co-distillation method is proposed to efficiently migrate the performance of the original network to the target network, so that the performance of the clipped lightweight network tends to be close to that of the original network. The model training, testing, visualization, and ablation experiments are completed on the NTU-RGB+D60 data set. The experimental results show that the algorithm after lightweighting (clipping network and multi-feature co-distillation), can significantly reduce the computational amount of behavior recognition. © 2024 Copyright held by the owner/author(s).

关键词： Distillation

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Hybrid ARIMA and DQN Approaches for Stabilizing network Traffic Flows 24

Hybrid ARIMA and DQN Approaches for Stabilizing Network Traf...

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8th international conference on computer science and Artificial Intelligence, CSAI 2024

作者： Yang, Jiuling Shi, Jiayi Kuang, Ping Feng, Zhikun Xiong, Kun Shi, Yuan University of Electronic Science and Technology of China Sichuan Chengdu China China Electronic Technology Cyber Security Co. Ltd. Sichuan Chengdu China

ISBN: (纸本)9798400718182

In the increasingly complex context of network traffic management, achieving steady-state traffic regulation has become essential for enhancing network stability and resource utilization. How to optimize network performance while ensuring end-to-end latency and link utilization has become an urgent issue within steady-state traffic regulation. This paper proposes a method for network steady-state regulation, which combines the Autoregressive Integrated Moving Average (ARIMA) model with Deep Q-network (DQN). This method is divided into two stages: (1) future network traffic state is predicted by using ARIMA model through analyzing historical traffic data;(2) the optimal path is learned as well as the routing traffic is reallocated by a DQN agent to enhance network utilization. The experimental results demonstrate that our proposed method effectively reduces the packet loss rate to 0.1% and decreases latency by a factor of five. © 2024 Copyright held by the owner/author(s).

关键词： DQN

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network Traffic Prediction Mechanism Based on Variational Mode Decomposition and GA-ELM 24

Network Traffic Prediction Mechanism Based on Variational Mo...

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8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Zhou, Jinghe Shi, Jinmei Chen, Xue Chen, Mengyao College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China Hainan Province Wisdom Internet of Things Engineering and Technology Research Center Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

Accurate prediction of network traffic is crucial to network resource management, whether it’s network resource provisioning or malicious attack detection, reliable network traffic prediction mechanisms are needed for advance warning existing network traffic prediction methods still have some obvious limitations, existing network traffic prediction methods still have some obvious limitations, such as high computational complexity, VMD decomposition parameter optimization problem, premature convergence into local optimum solutions, and integrated prediction model prediction accuracy is not high many problems. In response to the above, this paper presents a model that combines Variation Mode Decomposition (VMD) and Extreme Learning Machine (ELM) for predicting network traffic based on Genetic Algorithm (GA). © 2024 Copyright held by the owner/author(s).

关键词： Variational mode decomposition

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DSSNet: a transformer-based network for dense scene text detection and recognition in complex environments 5

DSSNet: a transformer-based network for dense scene text det...

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5th international conference on Signal Processing and computer science, SPCS 2024

作者： Li, Jing Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This paper presents a novel approach for dense scene text detection called DSSNet (Dense Script Spotter network). The network leverages ResNet and FPN for feature extraction, employing multi-scale feature fusion and Transformer-based feature processing to enhance text recognition across varying sizes. The method generates text instance shapes using Bézier central curves and performs text recognition by integrating positional query information. Experimental results on the DSTD1500 and ICDAR2015 datasets demonstrate that DSSNet outperforms existing methods in terms of text localization accuracy, recognition accuracy, and annotation flexibility. © 2025 SPIE.

关键词： Feature extraction

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