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

作者： Wei, Xuan Li, Wenyun He, Xiaoming Sun, Jiaqi China Telecom Research Institute Guangzhou510630 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

The new cloud-network operation system is based on cloud, distributed technology and service-oriented architecture, aiming to improve the operation efficiency of cloud-network through all-digital transformation. Unified service modelling is a prerequisite for the construction of the new cloud-network operation system. This paper proposes a PSR (Product, Service, Resource) layered decoupling model and encapsulates standardized network capabilities across the network based on the PSR model in order to realize the intelligent opening and operation of the cloud-network. By introducing China Telecom's PSR model and proposing the basic principles and design methods of IP network capabilities encapsulation design under the PSR model architecture, as well as sharing the practical cases and exploratory experiences of IP network capabilities design and capabilities opening, this paper lays a solid technical foundation for the enterprise's digital transformation and the construction of intelligent, automated, and visualized operation and service system. © 2025 SPIE.

关键词： Internet protocols

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Design of mine low power wireless movable column shrinkage sensor 5

Design of mine low power wireless movable column shrinkage s...

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

作者： Lu, Ping China Coal Technology Engineering Group Chongqing Research Institute Chongqing400039 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Addressing the issue of measuring the expansion and contraction of hydraulic support movable columns in coal mine working faces, this paper designs a low-power wireless movable column shrinkage sensor for mines. The sensor employs a pull rope displacement sensing probe, achieving an accuracy of ±0.2%FS and a repeatability of ±0.05%FS. This wireless movable column shrinkage sensor utilizes LoRa wireless transmission technology and WaveMesh network to facilitate long-distance ad hoc networking, offering ease of installation and use. Experimental results demonstrate that the sensor exhibits advantages such as high-precision measurement, low energy consumption, and wireless ad hoc network communication capabilities, making it suitable for widespread use in monitoring the expansion and contraction of movable columns in mine hydraulic supports. © 2025 SPIE.

关键词： Coal mines

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PDM: A Simple Approach for Identifying Influential Users in Social networks 24

PDM: A Simple Approach for Identifying Influential Users in ...

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

作者： Yang, Ruisi Bu, Chunfen Zhang, Yunfei School of Information Engineering Kunming University Yunnan Kunming China Faculty of Science University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798400709999

In the realm of social computing, identifying highly influential users holds significant theoretical and practical *** methods frequently depend on centrality measures such as degree, closeness, betweenness, and K-shell within social *** these approaches are straightforward, they often fall short due to their reliance on static topological *** address these issues, a method for rapidly constructing user features based on user historical data and social network topology was designed, and a classifier for high-influence users was *** aggregating the features of neighboring nodes using the social network topology, the proposed PDM framework performs rapid calculations based on the Laplacian matrix of the network structure, making it more efficient than using GCN *** model demonstrates superior performance compared to existing methods across two real-world social network *** effectively leveraging both historical data, this approach offers a more robust strategy for identifying high influential users. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： network topology

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network Traffic Anomaly Detection Based on DGBi-SA Model

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Engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, international Association of Engineers. All rights reserved.

关键词： Long short-term memory

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A comprehensive review on detection of depression through social media analysis 4th

A comprehensive review on detection of depression through so...

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4th international conference on Computational Methods in science and technology, ICCMST 2024

作者： Dogra, Shriya Kaur, Parneet Sharma, Tejpal Kumari, Nisha Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781032911571

Social media platforms like Instagram, Twitter, and Facebook have completely changed our world. People today exhibit a kind of digital character and are more linked than ever. While social media undoubtedly offers many noteworthy advantages, there are also unavoidable drawbacks. Later Research has demonstrated a link between elevated use of social media platforms and heightened levels of depression. The goal of the current study is to study the existing machine learning approaches to identify potentially sorrowful Twitter users based on their tweets and network behaviour. To accomplish this, we assessed classifiers that identify whether or not a person is depressed based on information extracted from their tweets and network behaviour. It is observed that as a greater number of features are incorporated, the F-score and accuracy in classifying depressed persons also improve. This technique is a predictive, data-driven tool for early mental health diagnosis of depression or any type of psychiatric ailments. The main contribution of the article is the examination of the traits and how they affect the ability to gauge depression severity. © 2025 the Author(s).

关键词： Tweets

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Research on Financial Performance Evaluation of Agricultural Listed Companies Based on BP Neural network Model 24

Research on Financial Performance Evaluation of Agricultural...

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

作者： Li, Liujuan Yangpu Branch Shanghai Open University Shanghai China

ISBN: (纸本)9798400709999

This study aims to create a financial performance evaluation model for agricultural listed companies using the BP neural network, addressing traditional systems' inaccuracies. Based on the characteristics of these companies, a financial performance evaluation indicator system is constructed, with 42 A-share listed agricultural enterprises as the sample. The model includes an input layer with 17 neurons for key financial indicators, an optimized hidden layer, and an output layer for financial performance. Trained using the Levenberg-Marquardt algorithm, the model's performance is evaluated by MSE and R-squared metrics. Results show high accuracy, with MSE near zero and R2 reaching one, indicating a strong fit between predictions and actual data. This BP neural network model offers a reliable tool for assessing agricultural enterprises' financial performance, providing valuable insights for management and decision-making. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Decentralized finance

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MEL Frequency Features Driven Deep Learning for Speech Emotion Detection: A Novel Model

MEL Frequency Features Driven Deep Learning for Speech Emoti...

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2025 IEEE international conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Rani, Rashmi Tiwari, Kalyani Ramaiya, Manoj Kumar Nagar, Rajesh Kumar Shrivastava, Neeraj Hardia, Garima Indore India IPS Academy Institute of Engineering and Science Department of Computer Science & Engineering India Indore India Indore India

ISBN: (纸本)9798331512965

Speech Emotion Recognition (SER) is a critical area of research in Human-computer Interaction (HCI), aiming to develop systems capable of identifying and responding to human emotions conveyed through speech. This paper focuses on training a Deep Neural network (DNN) model using Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, a widely used and effective technique in this domain. To address the complexity of speech data, we utilized a deep learning algorithm, specifically employing the Long Short-Term Memory (LSTM) network, in conjunction with MFCC features. © 2025 IEEE.

关键词： Emotion Recognition

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Research on network Security Game Based on System Dynamics 24

Research on Network Security Game Based on System Dynamics

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

作者： Zeng, Qiufen Pei, Tingrui School of Mechanical Engineering and Mechanics Xiangtan University Hunan Xiangtan China School of Information Science and Engineering Hunan Institute of Engineering Hunan Xiangtan China College of Information Science and Technology Jinan University Guangdong Zhuhai China School of Computer Science School of Cyberspace Science Xiangtan University Hunan Xiangtan China

ISBN: (纸本)9798400711237

network security game was usually considered as attack-defense confrontation;however, we need to think out of the box because any network entity may get a chance to launch an attack. The problem of network security which was described as the game between regulatory authority and network entity needs to be analyzed from the perspective of macro management. The model of system dynamic which was combined with traditional game theory was established. Several strategies which were most commonly used were verified and evaluated through simulation model. In order to weaken the instability of network security game, selective punishment strategy was proposed and the simulation result showed that not only in astringency but also in network security maintenance the proposed strategy can achieve good results. © 2024 Copyright held by the owner/author(s).

关键词： System theory

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Research on marine litter identification model based on convolutional neural network 24

Research on marine litter identification model based on conv...

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

作者： Huang, Xin Zheng, Zhiguo Liu, Qianyu College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

The problem of marine litter is becoming more and more serious, which poses a great threat to the marine ecological environment. The research of marine litter identification and classification problem is imminent, and the identification and classification of images are especially critical. The advantage of convolutional neural network, one of the deep learning models, lies in its ability to automatically learn the features in the image, thus improving the accuracy of image classification. Therefore, the study of marine litter recognition model based on convolutional neural network is of some significance. Collecting and pre-processing multiple types of marine litter image data, extracting features in terms of colour, shape and texture, constructing a marine litter recognition model based on convolutional neural network, training and analysing the comparison results, effectively verifying the accuracy and precision of the model. The proposed model has high accuracy and stability in marine litter identification, which can provide effective technical support for marine environmental protection as well as a technical basis for the protection of marine ecosystems. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Trust-Based Identity Privacy for Edge Computing 24

Trust-Based Identity Privacy for Edge Computing

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14th international conference on Communication and network Security, ICCNS 2024

作者： Dong, Jie Wen Kang, Xi Nai Peng, Jin Jiang, Xuan Ming Zhu, Tao Jun Information Technology Department National Key Laboratory of Seience and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute BeiJing China School of Computer Science Beijing University of Technology BeiJing China

ISBN: (纸本)9798400717994

College of computer science Beijing University of technology, Beijing 100124, China, 1374622525@*** This paper proposes a trust collaboration technology for edge computing, addressing trust isolation and security issues through a unified trust value calculation method and blockchain technology. The designed trust management model evaluates the identity, capability, and behavior of terminal nodes using static and dynamic trust measurements. A consortium blockchain stores trust information to ensure immutability and efficient sharing. Simulation experiments validate the scheme's effectiveness in countering malicious node attacks, improving task completion rates, and reducing system risks. This solution offers an efficient and secure approach to trust management in edge computing environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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