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检索条件"任意字段=5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019"

共 603 条记录，以下是71-80 订阅

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A Short-term Traffic Flow Forecasting Method Based on SVM-PSO-BP Combined Model 5

A Short-term Traffic Flow Forecasting Method Based on SVM-PS...

引用

5th international conference on Robotics, Intelligent Control and Artificial intelligence, RICAI 2023

作者： Ding, ChunLing Juanatas, Roben A. College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China

ISBN: (纸本)9798350357950

the accuracy of traffic flow forecasting has a significant impact on intelligent traffic control. In recent years, scholars pay more and more attention to traffic flow forecasting based on combined models. Considering the strengths and weaknesses of the nonlinear regression SVM and BP neural network, this paper puts forward a short-term traffic flow forecasting method that use the combination of SVM and BP models. To get better forecasting performance of the BP neural network model, the article uses PSO(Particle Swarm Optimization) algorithm to optimize some parameters of the BP model, finally construct the SVM-PSO-BP combined model. Using RMSE(Root Mean Square Error) and MAE (Mean Absolute Error) as indicators to evaluate the model performance, and traffic flow data collected from British highways as the dataset, the models are respectively trained and used for prediction on weekdays and weekends. through experiments, it is found that the proposed SVM-PSO-BP outperforms a single prediction model in both evaluation indicators, and can better reflect the changes and trends of traffic flow. © 2023 ieee.

关键词： back propagation neural network short-term traffic flow prediction Support Vector Machine SVM-PSO-BP model

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Dynamic Remote Networking for Modular data Centres 5

Dynamic Remote Networking for Modular Data Centres

引用

5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Periola, A. A. Alonge, A. A. Ogudo, K. A. Univ Johannesburg Dept Elect & Elect Engn Technol Johannesburg South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Remote networking is required for accessing data from modular data centres that are sited where there is no terrestrial network access. the use of satellite networks for modular data centres (MDC) is suitable in this regard MDCs benefit from using mega-constellation satellite networks. this also increases the application and relevance of small satellite mega-constellation networks. the use in this manner is accompanied with the benefit of reduced latency. A reduced latency is obtained compared to the case where geostationary communication satellites are used However, the use of mega-constellations is subject to high risk of collisions because of high space junk in the low earth orbit (LEO). A hybrid architecture that infers the occurrence of collision in LEO to enhance MDC communications is proposed the hybrid architecture enables the MDC to transit to the use of geostationary communication satellites for continued MDC communications. the proposed architecture enhances of successful MDC related data transmissions by at least 38. 7% and up to 80.6% on average.

关键词： computing Platforms Modular data Centres Mega-Constellations Remote Networks Satellites

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Student Behavior Recognition Using Video-Based Deep Learning 5

Student Behavior Recognition Using Video-Based Deep Learning

引用

5th international conference on Robotics, Intelligent Control and Artificial intelligence, RICAI 2023

作者： Shi, Xiangui Mariano, Vladimir Y. College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei230088 China

ISBN: (纸本)9798350357950

the student classroom behavior analysis system based on deep learning can automatically monitor and analyze students' actions and behaviors in the classroom. this method utilizes a camera to collect classroom teaching videos, and uses the Yolo v5 algorithm to track students' movement information in real-time, in order to identify students' classroom behavior patterns. And evaluate the learning status of students. the design of this system aims to provide teachers with more information about students' learning situations and apply AI technology to education and teaching. A student classroom behavior analysis system based on deep learning can help teachers better understand students' learning situations and needs, improve classroom teaching effectiveness, and provide more effective learning guidance and feedback for students. © 2023 ieee.

关键词： AI Education Classroom behavior testing Deep learning Yolo-v5

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引用

5th ieee international conference on Artificial intelligence and big data, ICAIBD 2022

作者： Zhengfang, He MacHica, Ivy Kim D. Zhimin, Bi Faculty College of Information and Computing Davao City Philippines Yunnan Technology and Business University School of Intelligent Science and Engineering Kunming China

ISBN: (纸本)9781665499132

Textual similarity measurement has become even more important in natural language processing for information retrieval. the improvement in the accuracy of machine translation is a prime reason for developing a two-stage model of fine-tuning Bidirectional Encoder Representation from Transformers (BERT) and training using the Double Siamese Text Convolutional Neural Networks (DSTCNN). the first stage task is improved to use the output of each word of BERT as the input of the DSTCNN model. the DSTCNN model is proposed in the second stage task to determine the sequential and reverse relationship. the design motivation and detailed structure of the DSTCNN model are provided. Also, the STS-B dataset was used for the experiment. the Two-stages model achieved 88.1% Spearman's rank correlation coefficient, which increased by 1.6%, compared with the first-stage model. the experimental results show that using the two-stage model yields an improved performance compared to other algorithms. © 2022 ieee.

关键词： Convolutional neural networks

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Research on University Sports Information Management System Based on big data and Cloud computing 5

Research on University Sports Information Management System ...

引用

5th international conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Lei, Ming Wang, Yong YiBin Vocational and Technical College Sichuan Yibin China

ISBN: (纸本)9798331527662

this paper proposes a sports information management system based on Hadoop platform, which integrates convolutional neural network (CNN) technology to achieve efficient data management and intelligent decision support. through this architecture system can effectively process and analyze the massive data from university sports activities, so as to improve the efficiency and accuracy of data processing. Secondly, Convolutional Neural Network (CNN) technology is introduced to realize face recognition function. the application of this technology not only enhances the security of the system, but also improves the speed and accuracy of athlete identity verification. the automatic feature extraction capability of CNN enables the system to quickly identify key information in complex image data, which provides a strong technical support for the intelligence of sports management. In addition, the system provides rich data API interfaces, which can seamlessly connect various sports resources in colleges and universities, and realize data sharing and exchange. By building a unified sports big data environment, the system promotes the optimal allocation and efficient use of resources, and provides comprehensive data support for sports teaching, training and competition. this system builds a powerful sports information management platform by integrating Hadoop platform and CNN technology. this not only improves the ability of data processing and storage, but also greatly promotes the modernization process of college sports management through intelligent decision support. © 2024 ieee.

关键词： Convolutional neural networks

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Enhancing Diabetic Retinopathy Diagnosis with Inception v4: A Deep Learning Approach 5

Enhancing Diabetic Retinopathy Diagnosis with Inception v4: ...

引用

5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Athilakshmi, R. Jansi, R. Upadhyay, Roshan Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350321425

An automated system for the classification of Diabetic Retinopathy (DR) is introduced in this study. DR is a prominent cause of blindness globally, primarily impacting individuals aged 20 to 65. the proposed system can be an essential tool in the sustainable healthcare industry, providing timely and accurate diagnosis and treatment to patients with DR. Current manual screening by ophthalmologists can be time-consuming. Accurate categorization of DR is crucial to provide appropriate care and reduce the likelihood of permanent blindness. the use of big data and deep learning models in DR detection has the potential to revolutionize screening and diagnosis, leading to improved patient outcomes and reduced healthcare costs. this study compares traditional and CNN models, including Decision Trees, Random Forest, KNN, SVM, and Inception V4, to identify the best model for classifying retinal images into five stages of DR severity: No DR, Mild, Moderate, Severe, and Proliferative DR. Among the models tested, Inceptionv4 demonstrated the highest accuracy of 92.83% in classifying DR data. these findings demonstrate the potential of an automated system to improve DR diagnosis and management. © 2023 ieee.

关键词： Computer vision

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Dynamic Resource Allocation for Streaming Services using Deep Reinforcement Learning: User-Centric Approach to Mobile Usage 38

Dynamic Resource Allocation for Streaming Services using Dee...

引用

38th international conference on Information Networking (ICOIN)

作者： Song, Ha Eun Park, Yu Min Hong, Choong Seon Kyung Hee Univ Dept Comp Sci & Engn Yongin 446701 South Korea

ISBN: (纸本)9798350330946;9798350330953

In this work, we propose a novel method to dynamically adjust bandwidth, power, and quality while considering the user's residual data availability in a video streaming environment utilizing cellular data. the method is designed to ensure the best video quality without exceeding the user's data usage limit through deep reinforcement learning. the main goal is to maximize the user's quality of experience (QoE) through efficient resource allocation. To validate the proposed method, we conducted comparative experiments with existing algorithms that do not consider data usage and a fixed resource allocation method. the experimental results show that the proposed method increases QoE by 55.01% compared to the methods that do not consider data usage and by 444.21% compared to the fixed resource allocation method, providing users with a superior streaming experience.

关键词： Adaptive bitrate video streaming computing power allocation bandwidth allocation and mobile data usage

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AI Based Ontology-Driven Information Retrieval for Healthcare Information System 5

AI Based Ontology-Driven Information Retrieval for Healthcar...

引用

5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Suguna, M. Sangeetha, M. Shanmugapriya, N. Pavithra, G. Anandkumar, V. Tamil Nadu Chennai India V.S.B. Engineering College Cse Department Tamilnadu Karur India Coimbatore India Dayananda Sagar College of Engg Ece Dept Bangalore India Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350383522

Over the years, advancements in the work have been incorporated towards professor relationships and data collection techniques to model a wide variety of human activities and behaviors. Most sensor data come from smart devices such as cleaners and cleaners that provide the ability to manipulate and extract data from monitoring data for monitoring and healthcare. Due to the high popularity and use of smart devices as respondents, performance recognition systems are more accurate and easier to use. Identifying smooth designs is a difficult task in contrasting environments and scattering tube data. this work presents a knowledge-based model based on job descriptions that reflects the work of the worker. the knowledge model is based on two new approaches: to consider a functional degree scheme between measuring sensor energy and functional consciousness to model controversial sensor data and establish the relationship between them. Low efficiency (easy work) and high level (weak). In this article we make a case why ontology can contribute to blockchain design. To support this issue, we make an analysis translate the tonnage ontology and some of its representations into the smart contracts that enable it implement traceability restrictions on the original traceability feature and Ethereum blocking platform. © 2024 ieee.

关键词： Artificial intelligence big data Healthcare Information Retrieval Ontology

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Proceedings - 5th ieee international conference on big data Security on Cloud, bigdataSecurity 2019, 5th ieee international conference on High Performance and Smart computing, HPSC 2019 and 4th ieee international conference on Intelligent data and Security, IDS 2019

Proceedings - 5th IEEE International Conference on Big Data ...

引用

5th ieee international conference on big data Security on Cloud, 5th ieee international conference on High Performance and Smart computing and 4th ieee international conference on Intelligent data and Security, bigdataSecurity/HPSC/IDS 2019

ISBN: (纸本)9781728100067

the proceedings contain 55 papers. the topics discussed include: an analysis of differential privacy research in location data;speech emotion recognition based on multi-task learning;high performance data encryption with AES implementation on FPGA;blockchain " can it solve the security issues and fraud expenses for credit card commerce?;designing security system for iot;research on 3d interactive model selection and customization of ceramic products based on big data cloud service platform;raw cardinality information discovery for big datasets;and streaming machine learning for real-time gas concentration prediction.

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Research on data Acquisition of Intelligent Network Industry Chain Based on data Integration and Self-Acquisition Technology 5

Research on Data Acquisition of Intelligent Network Industry...

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2023 5th international conference on Artificial intelligence and Computer Applications, ICAICA 2023

作者： Liu, Lei Feng, Qianlong Wu, Song Zhang, Fan China Automotive Technology and Research Center Co. Ltd. Tianjin China Co. Ltd. Tianjin China

ISBN: (纸本)9798350323313

the process modeling method of supply chain is established. this project intends to adopt 'access to associated information-assimilation and integration of multi-source information-large-scale information analysis and processing - multi-agent interactive application' as the main line to study the industrial collaborative support method based on industrial chain collaborative support services. the calculation methods of supply and demand chain, enterprise chain, value chain and space chain are presented. By collecting the basic information of the enterprise, itself, the link between the supply chain and the enterprise is found. the main research contents include large-scale data mining and analysis processing;data analysis based on domain ontology;Information coding specification. the system uses *** MVC system architecture, and carries on the structured database design with SQLServer2012. An integrated application simulation experiment platform is constructed with distributed cloud computing architecture. this paper discusses the development and use of intelligent monitoring and big data collection system of supply chain from the aspects of enterprise management process and human power. through the experiment simulation, the matching algorithm and the corresponding optimization method of the industry cooperation support can lay the foundation for the reasonable and reasonable development of enterprises. © 2023 ieee.

关键词： Informatization

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