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Internet of Things data Processing and Analysis Based on Edge computing 2

Internet of Things Data Processing and Analysis Based on Edg...

引用

2nd international conference on data Science and Information System, ICDSIS 2024

作者： Liao, Junting Xie, Hui Xiangnan University School Of Computer And Artificial Intelligence Hunan Chenzhou China

ISBN: (纸本)9798350381665

With the widespread application of Internet of Things (IoT) systems in various fields such as healthcare, transportation, and industrial production, the data transmission efficiency and network resource occupancy of system managed devices and various sensors have become urgent issues to be solved. This paper introduces edge computing to solve these problems, and verifies the effect of edge computing in IoT system through comparative experiments. The study found that edge computing significantly improved the information processing speed of the IoT system. The average delay time of the experimental group was 3.03 milliseconds, while the control group without edge computing was 4.03 milliseconds. This shows that edge computing can significantly optimize the data processing and analysis capability and efficiency of IoT system. © 2024 ieee.

关键词： Edge computing

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Research on Medical Efficiency of Beijing-Tianjin-Hebei Based on SBM Model

Research on Medical Efficiency of Beijing-Tianjin-Hebei Base...

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2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Bai, Yanxin Dong, Yaru Ding, Haozhe Zhao, Chengyu Yang, Zixi Liu, Shuo Zhang, Xinpeng Song, Weiyou School of Economics and Management Hebei University of Technology 300131 China School of Artificial Intelligence and Data Science Hebei University of Technology 300131 China School of Mathematics and Statistics Hebei University of Economics and Trade 050064 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Based on the data of Beijing-Tianjin-Hebei region from 2011 to 2019,this paper measures the medical efficiency of Beijing-Tianjin-Hebei region in recent ten years by using the super efficiency SBM model, and uses the ML index decomposition method to conduct a dynamic analysis of medical efficiency, and further discusses the calculation results through convergence analysis, The medical efficiency in Beijing-Tianjin-Hebei region was studied. The study found that the overall medical efficiency in Beijing-Tianjin-Hebei region showed an increasing trend, but the increase was not large, and there was greater room for progress;The convergence of ML index changes is also high, indicating the overall development of Beijing-Tianjin-Hebei region. © 2025 SPIE.

关键词： Medical research data modeling Analytical research data acquisition Medical equipment Diseases and disorders systems modeling Biomedical applications Education and training Energy efficiency

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intelligent Operation and Maintenance Technology for High-Voltage Switches Based on Internal Characteristic Inversion 5

Intelligent Operation and Maintenance Technology for High-Vo...

引用

5th ieee international conference on advanced Electrical and Energy systems, AEES 2024

作者： Luo, Yingting Zhang, Shuai Jiang, Junfei Peng, Zaixing Guangdong Electric Power Research Institute Equipment and Environmental Monitoring Evaluation Office Guangzhou China Production Technology Research Center China South Power Grid International Co.LTD. Guangzhou China

ISBN: (纸本)9798331517724

High-voltage switches are key equipment in the power system. The evaluation of their reliability is related to the safe and stable operation of the power grid. Currently, in view of certain internal characteristic variations of high-voltage switches, there is a lack of external observable parameters that reflect the variations, and the equipment status variation cannot be effectively identified. Based on a certain model of 220 kV combined electrical appliances, this article systematically studies the relevant reliability indicators for evaluating the characteristic variation within the switch, including index acquisition, measurement, evaluation and judgment methods. Through relevant test analysis, the key internal characteristic parameters that cause sudden failures of high-voltage switches and cannot be directly measured are revealed, and the logical relationship between the precise inversion method inside and outside the high-voltage switch, the dynamic process digital mapping method, and the quality deviation feedback optimization method is clarified. It lays the method foundation for realizing the measurable, observable and controllable quality characteristics of the highvoltage switch throughout its life cycle. © 2024 ieee.

关键词： Zero voltage switching

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Deep Learning-Based Stock Price Prediction: A Comprehensive Approach Using Bi-LSTM 4

Deep Learning-Based Stock Price Prediction: A Comprehensive ...

引用

4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Chy, Md. Kalim Amzad Masum, Abdul Kadar Muhammad Alam, Mohammed Shamsul Azad, Md. Abul Kalam Hassan, Md. Maruf Bhuiyan, Touhid International Islamic University Dept. of Computer Science and Engineering Chittagong Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Islamic University of Technology Dept. of Business and Technology Management Gazipur Bangladesh School of It Washington University of Science and Technology VA United States

ISBN: (纸本)9798331529635

This study delves into the prediction of stock prices in the Bangladesh stock market using advanced deep learning frameworks, with a particular focus on the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Traditional statistical models often struggle to effectively capture the complex, non-linear characteristics of financial data, leading to inconsistent results. The Bi-LSTM model, however, addresses this challenge by processing data in both forward and backward sequences, making it particularly suitable for analyzing the intricate dynamics of the Bangladesh stock market. A rigorous feature engineering process is undertaken, incorporating 64 distinct features that include market trends, stock-specific indicators, technical and fundamental variables, as well as crucial macroeconomic factors like gold prices and foreign exchange reserves. The dataset spans historical stock market data from 2012 to 2022, with comprehensive preprocessing steps taken to ensure data quality and consistency. Hyperparameter tuning is executed through a grid search to optimize model performance. The evaluation of the Bi-LSTM model is carried out using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and the R2 score, which demonstrate its enhanced predictive power compared to traditional models. This research contributes to the growing field of deep learning in financial forecasting, offering new perspectives for stock price prediction in emerging markets, while also addressing ethical issues to minimize risks like market manipulation. © 2024 ieee.

关键词： Mean square error

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Analysis of data using hybridized K-means clustering with PSO-JAYA algorithm 1

Analysis of Data using hybridized K-means clustering with PS...

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1st ieee international conference on Advances in Electronics, Communication, computing and intelligent Information systems, ICAECIS 2023

作者： Revanna, Jai Keerthy Chowlur Niture, Nandkumar Al-Nakash, Nushwan Yousif B. Harrisburg University of Science and Technology Organization Information Systems Engineering and Management HarrisburgPA United States

ISBN: (纸本)9798350348057

Despite decades of intensive study on grouping for small to medium-sized data sets, research into huge data clustering is gaining more and more attention from both business and academia. Contrary to clustering algorithms on small and large amounts of data, big data grouping requires more processing power and resources to handle the massive data retention and complex analysis of data. This research suggests an optimal method for data clustering to address this problem. In addition to the combined model of Particle Swarm Optimization and JAYA Approach, this strategy used the idea of K-means clustering to initiate the search for optimal clusters. The simulations are run using a Python program using the Pendigits dataset. The efficacy of the scheme is shown when compared to current state-of-the-art methodologies by analyzing the simulation outcomes in terms of NMI and FMI score. © 2023 ieee.

关键词： K-means clustering

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Design of Computer Vision-Based Construction Progress Monitoring System 15

Design of Computer Vision-Based Construction Progress Monito...

引用

15th international conference on intelligent Human-Machine systems and Cybernetics, IHMSC 2023

作者： Li, Daping Tian, Hua Lang, Hongbo Niu, Ziyang Zhao, Qingxiang Wen, Lijun China Construction Fifth Engineering Bureau Co. Ltd. Changsha China School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350326178

The construction industry plays a crucial role in contributing to the economy. However, poor real-time monitoring of construction progress often leads to ineffective project management and budget overruns. To address this issue, this paper proposes a computer vision-based system for monitoring construction progress and presents its corresponding architecture. The study explores various technologies including portable data acquisition devices, cloud-edge collaborative computing architecture, progress calculation based on time evolution and multimodal fusion, and construction progress data structure based on lightweight format. These findings serve as the essential groundwork for subsequent development and validation of the system. © 2023 ieee.

关键词： data acquisition

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English Listening and Speaking Learning System Using LINE ChatBot in Cloud computing 3

English Listening and Speaking Learning System Using LINE Ch...

引用

3rd ieee international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Deng, Xiaoyan School Of Arts And Science Chengdu College Of University Of Electronic Science And Technology Of China Chengdu China

ISBN: (纸本)9798350318609

Cloud computing (CC) in a student learning system generates seamless access to educational resources and transfers their data to an offsite cloud server that is handled by a third-party provider. However, need to enhance the listening and speaking abilities of learners which maximizes the communication skills for professional, academic, and social success. Therefore, integrating listening and speaking development models with cloud-based learning systems is significant for comprehensive skill enhancement among students. In this research, the English listening and speaking learning system using LINE ChatBot in CC is analyzed to enhance students' abilities. Initially, 75 students are considered for participating in learning activities containing a four-week conversation exercise in English in both listening and speaking. To examine the competition's influence on language acquisition, competition characteristics are added to learning activities in a group of experiments to increase learning outcomes and motivation. With the help of LINE ChatBot environment of contextual learning, both student groups' performances are improved. Furthermore, extrinsic motivation in both control and experimental groups enhanced the learner's ability of English listening and speaking. © 2024 ieee.

关键词： Learning systems

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Towards LLM-Enhanced Digital Twins of intelligent computing Center 24

Towards LLM-Enhanced Digital Twins of Intelligent Computing ...

引用

24th ieee international conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Yang, Yi Liu, Manxian Jia, Kun Dong, Wenjie Wang, Jian Institute of Automation Chinese Academy of Sciences Beijing China The 33rd Research Institute of China Electronics Technology Group Corporation Taiyuan China

ISBN: (纸本)9798350365658

Digital twin techniques enable the digital modeling of the power and environmental systems of intelligent computing systems. However, the traditional analysis methods of digital twins do not integrated expert experience with data-driven approaches flexibly. They also analyze the information from multiple dimensions in isolation without considering their relations. This paper proposes a large language model-enhanced digital twin system for addressing these issues. The proposed system provides natural language-based interactive analysis, allowing experts to collaborate with algorithms for risk analysis;it employs a large language model-driven multi-agent system to simulate the interactions of the dynamic environmental systems, achieving multi-dimensional joint prediction. Additionally, this paper presents a case study to demonstrate the usefulness and effectiveness of the proposed system. © 2024 ieee.

关键词： Risk analysis

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intelligent FaultEdge: AI-Driven Fault-Tolerant Edge Framework for Smart Grid Monitoring in IoT 12

Intelligent FaultEdge: AI-Driven Fault-Tolerant Edge Framewo...

引用

12th ieee international conference on intelligent systems, IS 2024

作者： Singh, Tushar Kumar, Sudhakar Singh, Sunil K. Chilkoti, Vanshika Bansal, Shavi Chui, Kwok Tai Gupta, Brij B. Chandigarh College of Engineering & Technology Department of Cse Chandigarh India Insights2Techinfo United States Hong Kong Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan

ISBN: (纸本)9798350350982

Smart grid monitoring in IoT environments demands robust fault tolerance mechanisms to ensure uninterrupted operation and data accuracy. The integration of advanced machine learning with fault-tolerant strategies in the proposed intelligent FaultEdge framework represents a significant innovation. Unlike traditional reactive systems, intelligent FaultEdge adopts a proactive approach, leveraging predictive analytics, anomaly detection, and adaptive reconfiguration to anticipate and manage potential faults in real-time. This proactive framework not only enhances the reliability and resilience of smart grid operations but also minimizes downtime, improves safety, and optimizes energy efficiency. By continuously monitoring grid conditions and dynamically adjusting to evolving scenarios, intelligent FaultEdge supports sustainable and secure smart grid management. This paper presents a comprehensive framework aligned with the goals of modern intelligent systems, offering an efficient solution for enhancing the reliability and performance of IoT-enabled smart grids. Through its proactive fault management capabilities, intelligent FaultEdge contributes to advancing smart infrastructure, ensuring dependable energy supply, and supporting the evolution towards more resilient and efficient smart grids. © 2024 ieee.

关键词： Predictive analytics

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Genetic and Decision Tree-Based Secure data Dissemination Framework for Autonomous Vehicles 2

Genetic and Decision Tree-Based Secure Data Dissemination Fr...

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2nd ieee international conference on Contemporary computing and Communications, InC4 2024

作者： Patel, Tanisha Jadav, Nilesh Kumar Tanwar, Sudeep Garg, Deepak Institute of Technology Nirma University Department of Comp. Sci. & Engg. Ahmedabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (纸本)9798350383652

In this article, we proposed a hybrid model comprising a genetic algorithm and a decision tree for secure data exchange in autonomous vehicles (AVs). The integration of a genetic algorithm enables systematic feature optimization, enhancing the accuracy and efficiency of classification models. Extensive performance assessments, including metrics such as receiver operating characteristic (ROC) curve and a convergence curve, substantiate the effectiveness of the proposed framework in addressing classification tasks. A robust model is presented that could thrive in the dynamic environment of AV, as a result of which the security and efficiency of the intelligent Transportation systems is further strengthened. We focus on the practical implications of our research, and consequently, the real-world applications relating to the evolving AV environment. © 2024 ieee.

关键词： Adversarial machine learning

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