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Edu-Metaverse: A Structured Architecture and Workflow Model for Digital and Blended Learning

Edu-Metaverse: A Structured Architecture and Workflow Model ...

引用

international Symposium on advanced computing and Communication (ISACC)

作者： Sanskar Garg Rishi Dev Ashwini Kumar Singh Sahil Sahani Kiran Kumari Anjan Bandyopadhyay School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Law Kalinga Institute of Industrial Technology Bhubaneswar India School of Law Mody University Lakshmangarh India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The Metaverse is transforming digital engagement across sectors like education, healthcare, and technology. This survey examines how edge computing, combined with 5G, IoT, and AI, supports real-time, immersive experiences. In education, the Edu-Metaverse uses virtual platforms for simulations, STEM learning, language acquisition, and digital education, while addressing challenges such as privacy and scalability. In healthcare, it employs federated learning for secure applications like remote monitoring and diagnostics. Decentralized edge computing is key to solving issues related to interoperability and data security. The framework proposed identifies four essential hubs—design, knowledge, technology, and training connected through infrastructure, accessibility, and policy. The review emphasizes the importance of collaboration across disciplines and the need for infrastructure and training to fully realize the Metaverse’s potential.

关键词： Training Surveys Metaverse Reviews Computational modeling Scalability Medical services Real-time systems Remote monitoring Edge computing

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Thermal Information acquisition based on intelligent Gateway

Thermal Information Acquisition based on Intelligent Gateway

引用

2nd international conference on the Frontiers of intelligent Control, Measurement, and Signal systems, FICMSS 2025

作者： Li, Mingming Chen, Zhixuan Ma, Chi Academy of Aerospace Solid Propulsion Technology Xi'an710025 China College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China

This paper presents a wireless sensor network (WSN) for thermal information monitoring, utilizing edge computing intelligent gateways. The system consists of sensor nodes, an edge computing intelligent gateway, and a data centre, with Zigbee and Wi-Fi communication protocols employed for efficient data exchange. In the proposed monitoring mode, the sensor nodes acquire thermal information signals, which are then aggregated, processed, and forwarded by the edge gateway to the data center. A comparative experiment is conducted between the proposed monitoring mode and a traditional multi-hop cluster-based network topology under the same operating conditions. The experiment demonstrates that the monitoring mode with edge computing achieves a 40% reduction in response delay compared to the traditional system, with the transmission time being reduced by approximately 35% in the two-hop network configuration. The results highlight the advantages of edge computing in reducing transmission delays and improving overall system efficiency, especially in scenarios with multiple data nodes and high transmission demands. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Gateways (computer networks)

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Spatio-Temporal Traffic Prediction in Urban Area using Convolutional Temporal Network System 6

Spatio-Temporal Traffic Prediction in Urban Area using Convo...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Priya, K. Omkar, K. Naresh, R. Venkatesh, K. Dept. of Artificial Intelligence and Data Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Traffic prediction in urban areas is essential for smart city applications, enabling efficient transportation management, reduced congestion, and improved quality of life. Spatiotemporal traffic prediction is particularly challenging due to complex spatial and temporal dependencies in traffic data. This study introduces a Convolutional-Temporal Neural Net- work System (CTNS) model that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture spatial and temporal features simultaneously for more accurate traffic forecasting. CNNs are employed to learn spatial correlations between different locations in the urban area, while RNNs, specifically Long Short-Term Memory (LSTM) networks, capture the sequential dependencies in temporal traffic patterns. Using real-world traffic data, the hybrid CTNS model demonstrates superior performance in accurately predicting traffic flow compared to traditional models. This approach highlights the potential of deep learning architectures for enhancing the accuracy of traffic predictions, which is critical for urban planning and intelligent transportation systems. Extensive experiments on real-world urban traffic datasets validate the model’s ability to achieve high accuracy, scalability, and robustness in handling complex spatio-temporal interactions. © 2025 ieee.

关键词： Convolutional neural networks

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Research on EEG acquisition system of smart internet of things with enhanced Mamba

Research on EEG acquisition system of smart internet of thin...

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5th international conference on Signal Processing and Machine Learning (CONF SPML 2025)

作者： Zhe Li Wuhan University of Engineering Science Wuchang District Wuhan City Hubei Province People's Republic of China

ISBN: (数字)9781837243143

This paper explores an advanced system for the acquisition of brain electrical signals within the framework of intelligent systems. By integrating a sensor network, cloud platform, brain electrical signal acquisition system, data processing algorithms, and deep learning models, the system facilitates real-time monitoring, data transmission, storage, and intelligent recognition of brain electrical signals. In addition, we investigate the application of the Mamba deep learning model in the intelligent medical domain. To enhance the performance of the Mamba model, this paper proposes an Multi-branch Attention module and an multiscale Convolution module. On the UCI dataset, the Mamba model strengthened with these two modules significantly outperformed other traditional models, achieving an accuracy of 98.3%, with a model loss below 10e-7 and an AUC of 0.995. On the eegmmidb dataset, the enhanced new model improved the accuracy by 2.4% compared to the baseline model.

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intelligent Security Monitoring: Machine Learning-based Intrusion Detection

Intelligent Security Monitoring: Machine Learning-based Intr...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Rekha, M. Rani, P.Shobha Aashida, S. Elakkiya, S. Ezhil Arasi, S. Amirtha, A. R.M.K. Engineering College Department of Information Technology Chennai India R.M.K. Engineering College Department of Artificial Intelligence And Data Science Chennai India

ISBN: (纸本)9798331509828

The rapid growth in the field of Information Technology has definitely caused a steep rise in the rate and sophistication of cyber-attacks;therefore, cybersecurity is an issue of major concern. Recently, Machine Learning and Deep Learning methodologies have turned out to be really potent tools for network intrusion detection. This paper presents a detailed review of various ML and DL models used in Intrusion Detection systems (IDS). The models like Naïve Bayes, Artificial Neural Networks, Support Vector Machines, Decision Tree, K-Nearest Neighbors, and methods based on Reinforcement Learning are covered. In this paper, the MRF model and examine the model with other ML and DL models are focused. In this analysis, the authors use the KDD dataset, a very popular standard in intrusion detection research. This dataset has different attributes divided into four main classes. Such as Content, Host, Basic, and Traffic. All these are vital classes in deciding the false alarm rate and detection rate of IDS. The empirical evidence that the MRF model outperforms traditional models in both measures of accuracy and reliability are provided. How contribution analysis for each class of attributes towards DR and FAR can be utilized in optimizing the MRF model for better performance of intrusion detection systems are shown. The results obtained in this research work have been pointing toward the importance of choosing suitable data attributes and models so that maximum DR can be achieved with a minimum FAR, which would increase the effectiveness of IDS. The proposed Modified Random Forest-based Intrusion Detection System uses advanced feature selection and preprocessing to improve detection accuracy and reduce false alarms. The real-time adaptability is done by dynamic selection of parameters in order to prevent evolving cyber threats. © 2025 ieee.

关键词： Network intrusion

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Wireless Portable Dry Electrode Multi-channel sEMG acquisition System 18th

Wireless Portable Dry Electrode Multi-channel sEMG Acquisiti...

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18th international conference on Wireless Artificial intelligent computing systems and Applications, WASA 2024

作者： Gao, Bin Han, Yubing Zhou, You Yu, Jiguo Li, Sufang Dong, Anming Jinan250014 China Jinan250353 China Jinan250014 China Technology Department Shandong HiCon New Media Institute Co. Ltd. Jinan China

ISBN: (纸本)9783031714634

Surface Electromyography (sEMG) is a biological potential signal to measure and record the electrical activity of muscles near the surface of the skin. By analyzing sEMG signals, it is possible to identify the contraction and relaxation patterns of the muscles, and thus discriminate the gestures that generate these patterns. However, obtaining sEMG signals is by no means an easy task, due to their weak and high-impedance source signals, especially when using dry electrodes. It is critical to use high-quality sEMG signals to recognize the patterns. In this paper, we present a compact sEMG acquisition method. Most of the existing surface signal acquisition is designed with integrated operational amplifiers or instrument amplifiers, requiring additional hardware filtering circuits, making optimizing the volume and further reducing the power consumption challenging. At the same time, the separation of components will use more intermediate wires, which will cause more bloated volume and more interference. In addressing these challenges, a sEMG acquisition system based on KS1801 and STM32F103 is designed in this paper. Specifically, a sEMG acquisition sensor circuit based on Bioelectric signal acquisition analog front-end chip KS1801 is designed better to amplify the high-resistance weak signal from the dry electrode, reduce the circuit’s complexity, and reduce the system’s size. Furthermore, digital high-pass, digital low-pass, and 50 Hz digital notch filters are applied to eliminate signal noise effectively. A dual-buffer storage structure was implemented to temporarily store filtered data, providing ample time for subsequent complex recognition algorithms to process the collected data. A Bluetooth module was integrated to facilitate the transmission of cached data to a PC. Experimental results demonstrate the effectiveness of the sEMG acquisition system in reliably capturing signals from dry electrodes. It exhibits notable advantages, including robust anti-interference

关键词： Electromyography

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Integration of Artificial Intelligence and BIM in intelligent Decision Support System for Water Conservancy Projects

Integration of Artificial Intelligence and BIM in Intelligen...

引用

Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Zekun Chang Shandong Vocational College of Industry Zibo China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

A kind of intelligent DSS based on AI and Architecture Information Modeling (BIM) is put forward. The system is composed of several modules, including data collection, processing, intelligence decision-making, feedback and optimizing. The data acquisition and processing module collects environmental data, equipment status and water flow information of water conservancy projects in real time through a variety of sensors and remote sensing technologies; the intelligent decision module uses deep learning algorithms, especially GCN is to model and predict the collected data, so as to make effective risk assessment and scheduling decisions; the feedback and optimization module continuously optimizes the decision process according to real-time feedback information, and improves decision accuracy and response speed. Through a series of experimental simulations, this paper verifies the effectiveness of the system. The GCN-based model improves the forecasting precision by 15% compared with the conventional ANN, and the calculation efficiency is improved 20%. Furthermore, it is proved by experiments that the system has remarkable superiority in dealing with complicated data in real time, data fusion and decision-making.

关键词： Decision support systems Accuracy Graph convolutional networks Building information management Water conservation Predictive models Prediction algorithms Real-time systems Artificial intelligence Water resources

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User Behavior Analysis and Prediction Algorithm in intelligent Tourism Management System

User Behavior Analysis and Prediction Algorithm in Intellige...

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Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Juan Yin Dongqin Li Wuhan Technology and Business University Wuhan China Hubei Business Service Development Research Center Wuhan China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The goal of this project is to construct an intelligent tourist management system, and to make use of big data, AI, IOT and cloud computing to realize the digital transformation of tourism industry. The system can be used in the tourism resource management, the real-time information push, the personalized recommendation, the data analysis, etc. The system includes three main modules: tourist terminal, management platform and data center, which can realize intelligent management and real-time monitoring of scenic spots, transportation, accommodation and other information. Then, this paper designs an improved algorithm combining collaborative filtering algorithm and LSTM based on the complexity and nonlinear characteristics of tourist behavior data. To prove the validity of the algorithm, we have done several simulation experiments. The data are obtained from the real user interaction data set of an intelligent tourist platform. The experiment results indicate that the proposed algorithm has better performance than the conventional LSTM. The recommendation precision is 12.5% and the recall rate is 10.8%. In addition, the system model also shows good performance in computational efficiency and prediction robustness, and is suitable for large-scale tourism data scenarios.

关键词： Accuracy Heuristic algorithms Collaborative filtering Predictive models Prediction algorithms Robustness Real-time systems Computational efficiency Resource management Long short term memory

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Performance Analysis of MIMO Using Diversity Technique in VLC FSO Communication systems

Performance Analysis of MIMO Using Diversity Technique in VL...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Bajpai, Anurag Khunteta, Ajay Rajasthan Technical University Department Electronics Engineering Kota India

ISBN: (纸本)9798331527495

Visible Light Communication (VLC) is a growing trend in future communication technology. In VLC systems, Free Space Optics (FSO) is used as the transmission medium. These VLC FSO systems offer solutions to congestion problems in RF communication by providing similar frequency range scalability, cost-effectiveness, and mobility. However, challenges such as weather conditions like smog and storms, as well as the need for a clear line of sight, pose significant hurdles. In this paper, we purpose a reliable VLC system that uses MIMO with RZ, NRZ and QPSK coding with filters and amplifiers to improve performance. The system is tested over a 1500-meter link distance, achieving a simulated data rate of 10 Gbps. This proposed system is suitable for high data rate communication in VLC, with the potential to extend transmission distances by reducing signal fading in free space © 2025 ieee.

关键词： Visible light communication

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Remote Environmental Monitoring Based on Beidou Mobile Monitoring Terminal with an Early Warning Platform

Remote Environmental Monitoring Based on Beidou Mobile Monit...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 ieee international conference on

作者： Xingjian Liu Zhicai Zheng Guangdong Business and Technology University Zhaoqing Guangdong China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

With the increasingly serious environmental problems, traditional monitoring methods are faced with challenges such as poor real-time performance and insufficient data accuracy. Based on the Beidou Navigation Satellite System (BDS) and mobile monitoring terminal, a remote environmental monitoring and early warning platform is designed in this paper. The platform combines Beidou positioning, sensor data acquisition, wireless communication and cloud computing analysis to monitor and warn environmental changes in real time. By integrating the Beidou positioning module, the platform is able to accurately obtain the location of equipment and collect environmental data such as climate, air quality and noise, and ensure stable operation in remote areas using low-power wide-area networks such as NB-loT. Experimental results show that the platform performs well in terms of data acquisition, transmission efficiency and early warning accuracy.

关键词： Wireless communication Cloud computing Accuracy Disasters data acquisition Real-time systems Stability analysis Environmental monitoring Telemetry Meteorology

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