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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 86242 条记录，以下是301-310 订阅

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Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

引用

2024 ieee international conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 ieee.

关键词： Electronic health record

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Access Control in E Health Care Records Employing Mobile Cloud Computing Model and Big data Analytics 1

Access Control in E Health Care Records Employing Mobile Clo...

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1st ieee international conference on Computing and data science, ICCDS 2024

作者： Saisree, Yerramaddu Lalitha, Nomula Reddy Felix, A. Yovan Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365337

Cloud service providers are currently storing large volumes of data on cloud. This requires verification mechanisms often employing cryptography by third party auditors. However, ensuring the security and privacy of e-health systems poses a significant challenge. To overcome this challenge, researchers are focusing on building a robust public auditing system. This privacy preserving protocol focuses on privacy preservation allowing third party auditors to evaluate out sourced data without accessing the content. The key to this approach is implementation of batch auditing that enables simultaneous tasks to be processed by trusted third party auditors. The proposed system provides scalable, flexible and efficient public auditing capabilities enhancing the levels of security of e-healthcare systems. The external auditors promote trust and privacy in cloud-based healthcare systems. The integration of mobile devices and cloud computing provides a vast range of opportunities for healthcare management. This innovation provides a way to meet the evolving changes of cloud-based healthcare data management. © 2024 ieee.

关键词： Mobile cloud computing

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SEMI-SUPERVISED METRICS-BASED SELF-TRAINING ROOT CAUSE ANALYSIS FOR CLOUD-NATIVE systems WITH CLASS-IMBALANCED data 49

SEMI-SUPERVISED METRICS-BASED SELF-TRAINING ROOT CAUSE ANALY...

引用

49th ieee international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huang, Ying Du, Qingfeng Han, Yongqi He, Cheng Tian, Fulong Tongji Univ Sch Software Engn Shanghai Peoples R China Di Matrix Shanghai Informat Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Root cause analysis is crucial for cloud-native systems. However, existing supervised approaches ignore the potential of unlabeled data, which is frequent in the cloud-native root cause analysis scenarios. Moreover, the class-imbalanced distribution of faults presents obstacles to applying semi-supervised learning. To overcome these limitations, we propose STRCA, a metrics-based semi-supervised self-training approach for root cause analysis. Furthermore, STRCA employs minority priority self-training, which selects pseudo-labels of high quality during generations. Additionally, the stepwise distribution alignment is introduced to rebalance the predicted distribution with gradually decreasing strength. These two strategies mitigate the class-imbalance of data in semi-supervised learning. Experiments on the public dataset show the effectiveness of STRCA with limited labels.

关键词： root cause analysis cloud-native system AIOps semi-supervised learning class-imbalanced data

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Attention based Convolutional Network for Traffic Flow Velocity Forecasting 25

Attention based Convolutional Network for Traffic Flow Veloc...

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25th ieee international conferences on High Performance Computing and Communications, 9th international conference on data science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Anqin Sun, Yong School of Computer Science Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9798350330014

Accurate and efficient traffic flow velocity prediction is very important for intelligent transportation system. But traffic flow velocity prediction faces the following challenges: the data is dynamic in time and space dimensions, and the error accumulation in long-term prediction. In this paper, we propose an Attention based Convolutional Network (ACN) for traffic flow velocity forecasting. ACN combines convolution with attention mechanism to capture the spatio-temporal features of data and uses the attention output layer to model the correlation between the generated forecast data and the historical data to grasp the important features to optimize the output. Experiments on two real data show that ACN can improve the accuracy of traffic speed prediction. © 2023 ieee.

关键词： Flow velocity

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Visual Analysis of the Bibliometric data Associated with the Calibration of Car-Following Models 20

Visual Analysis of the Bibliometric Data Associated with the...

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20th Annual international conference on Distributed Computing in Smart Sensor systems and the Internet of Things (DCOSS-IoT)

作者： Pop, Madalin-Dorin Micea, Mihai, V Politehn Univ Timisoara Comp & Informat Technol Dept Timisoara Romania

ISBN: (纸本)9798350369458;9798350369441

Intelligent transportation systems (ITS) use the latest technologies for real-time traffic control and monitoring to ensure efficient traffic management and reduce the risks of traffic accidents. The microscopic level of traffic modeling is the most appropriate level for controlling and monitoring the interaction between vehicles based on car-following scenarios. However, the data retrieved from sensor networks can be affected by measurement errors, and consequently the implementation of appropriate mechanisms to overcome their propagation to the control system is mandatory. This paper aims to analyse the current research in the calibration of car-following models and provide valuable insights of recent developments in this field. To achieve this goal, VOSviewer has been chosen as a visualisation tool to create bibliographic maps based on the output from the well-known scientific database Clarivate Analytics Web of science (WoS). The maps obtained provide a visual representation of the main institutions involved in this field of research and identify the research interests based on author and indexing keywords. Furthermore, this paper analyses the top five clusters identified based on the analysis of co-occurrence keywords, presenting discussions about the connections existing within these clusters.

关键词： bibliometric analysis calibration car-following model intelligent transportation systems VOSviewer

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Cloud data Acquisition from Shared-Use Facilities in A University-Scale Laboratory Information Management System 16

Cloud Data Acquisition from Shared-Use Facilities in A Unive...

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16th ieee/ACM international conference on Utility and Cloud Computing (UCC) / 10th ieee/ACM international conference on Big data Computing, Applications and Technologies (BDCAT)

作者： Hanai, Masatoshi Kawamura, Mitsuaki Ishikawa, Ryo Suzumura, Toyotaro Taura, Kenjiro Univ Tokyo Ctr Informat Technol Tokyo Japan Univ Tokyo Inst Engn Innovat Tokyo Japan

ISBN: (纸本)9798400702341

Effective data management across large-scale shared-use materials-science facility centers is crucial, as high-end facilities available at such centers are indispensable for cutting-edge research in modern materials science. However, establishing network connectivity for these facilities is often not practically feasible due to its uniqueness, forcing users to resort to the traditional manual method of data acquisition and transfer, such as the use of DVD disks. In this paper, we introduce an IoT device designed to facilitate direct data transfer from these non-networked and shared-use facilities to a cloud system. We propose a novel data transfer algorithm that integrates user management with data transfer processes for safely assigning each user's data to its private storage space on the cloud. The proposed technology serves as a fundamental building block in shaping a smarter laboratory information management system for the advancement of material researches and developments.

关键词： data acquisition data management systems internet of things cloud computing laboratory information management systems materials informatics

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CRT-Paillier Homomorphic Privacy Protection Scheme Based on BLS Signatures in Mobile Vehicular Networks 17

CRT-Paillier Homomorphic Privacy Protection Scheme Based on ...

引用

ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green Computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th ieee international conference on Smart data, Smartdata 2024

作者： Zhou, Yuquan Huang, Hejin Liao, Wenyu Liu, Jiyan Ying, Xiangdong Zhang, Chen Luo, Entao Hunan University of Science and Engineering Yongzhou China Xiangtan University Xiangtan China Hunan University of Technology and Business Changsha China Guangdong University of Technology Guangzhou China

ISBN: (纸本)9798350351637

Vehicular Ad-hoc Networks (VANETs) serve as an integral component within Intelligent Transportation systems (ITS), designed to augment traffic fluidity and ensure a secure, comfortable driving environment for both drivers and passengers. In this system, real-time data exchange between vehicles, Road Side Units (RSUs), and other infrastructure is essential. However, this process can potentially lead to serious data privacy issues due to the large-scale data exchange. This paper proposes an edge-assisted homomorphic privacy protection scheme in VANETs, based on BLS signatures for homomorphic authentication and group signatures to address the conflicting requirements of participant identity verification and anonymity. By utilizing the Chinese Remainder Theorem (CRT)-optimized Paillier encryption algorithm, it allows for homomorphic addition operations on encrypted data, aiming to support secure data aggregation in ciphertext states while ensuring data transmission and aggregation security. The incorporation of CRT decomposes the modulo operation in the Paillier algorithm into operations with smaller moduli, further enhancing the efficiency of the Paillier algorithm in handling large-scale datasets, and reducing computational complexity and time costs. Experimental results demonstrate that with a global precision of 1024 bits in gmpy2, for a 2048-bit key length CRT-Paillier encryption algorithm, the encryption time is 91.30 ms, and the decryption time is 9.96 ms. The proposed scheme significantly enhances data security and operational efficiency within the VANET domain. © 2024 ieee.

关键词： Vehicular ad hoc networks

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Research on Agent-Based data Acquisition Techniques for IT Infrastructure 7

Research on Agent-Based Data Acquisition Techniques for IT I...

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7th international conference on Computer Information science and Application Technology, CISAT 2024

作者： Pan, Junbing Tian, Yongtao Deng, Zhuoming Fu, Jiacheng Luo, Shijian Information Communication Branch of Guangxi Grid Company Guangxi Nanning530012 China Nanning Power Supply Bureau Guangxi Nanning530028 China

ISBN: (纸本)9798350375107

IT infrastructure contains many different systems and devices, in which the uncertainty and non-uniformity of data sources is an urgent problem to be solved. Therefore, this paper proposes the research of Agent-based IT infrastructure data collection technology. In the architectural design of the system for IT infrastructure data acquisition, the coordination and collaboration mechanism of multiple systems is analyzed, and heterogeneous data is converted into homogeneous data through data mapping. Communication between Agents is used to coordinate the work between them, so as to efficiently achieve the tasks of acquiring, converting and storing heterogeneous data. In addition, Agents are able to monitor the status of the IT infrastructure in real time, and detect and respond to system performance problems or security threats in time, so as to take actions quickly. The experimental results show that by deploying intelligent Agents, data can be collected, processed and transmitted in an automated manner, reducing manual intervention and improving the efficiency and accuracy of data acquisition. © 2024 ieee.

关键词： data collection

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Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial data 23

Rethinking Temporal Dependencies in Multiple Time Series: A ...

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23rd ieee international conference on data Mining (ieee ICDM)

作者： Owusu, Patrick Asante Tajeuna, Etienne Patenaudet, Jean-Marc Brun, Armelle Wang, Shengrui Univ Sherbrooke Dept Comp Sci Sherbrooke PQ Canada Laplace Insights Sherbrooke PQ Canada Univ Lorraine LOR1A Nancy France

ISBN: (纸本)9798350307887

These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects among multiple time series efficiently. Our two-step approach identities patterns and temporal influences with low execution lime, showcasing its potential in financial system incident prediction.

关键词： Temporal graph Multiple tune series Crosssectional patterns Series trajectory Financial data

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Cover More with Less: Eliminating Blind Spots for Surveillance Camera via Passive WiFi Sensing 30

Cover More with Less: Eliminating Blind Spots for Surveillan...

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30th ieee international conference on Parallel and Distributed systems, ICPADS 2024

作者： Mottakin, Khairul Guo, Jinhua Song, Zheng University of Michigan-Dearborn Dept. of Computer and Information Science MI United States

ISBN: (纸本)9798331515966

Surveillance cameras, even with Pan, Tilt, and Zoom (PTZ) capabilities, can only cover a limited directional range at a time, leading to inevitable blind spots. To mitigate these blind spots, users typically need to deploy additional cameras or sensors (such as motion sensors and microphones) to guide the PTZ platform's movement for capturing intruders, incurring additional deployment and maintenance costs. To address this challenge, this paper introduces a novel approach that utilizes the camera's built-in WiFi module to detect potential intruders and direct the PTZ platform's movements. The approach involves collecting WiFi Channel State Information (CSI) samples with humans positioned at various locations, and training a machine learning model to infer the real-time location of intruders. Given the intensive human effort required for collecting sample data, we developed an algorithm to optimize the selection of locations for collecting CSI samples. The algorithm assesses each location's contribution to the overall success rate of capturing intruders, thereby achieving optimal sample distribution. Our evaluation demonstrates that our approach achieves a capture rate of 7 8. 2 4%, which is up to 24% higher than baseline methods, despite being trained with data collected from only 13% of the locations. © 2024 ieee.

关键词： Wi-Fi

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