检索结果-内蒙古大学图书馆

7th International Conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Yang, Chai Wei Lee, Angela Siew-Hoong School of Engineering and Technology Sunway Unversitiy Department of Computing and Information Systems Malaysia

ISBN: (纸本)9781665487115

The increasing of aging population has caused the rising need for healthcare and assistance through smart wearables health monitoring systems in order to attend impairments in cognitive psychosocial functioning of the elderly, as well as their ability and mobility. To facilitate and encourage the use of smart wearable systems among older adults, this study investigated the factors that may have contributed to the acceptance of such systems, and smart wearables acceptance model for elderly people will be developed by using structural equation modeling. The model will be validated with 250 samples. Validation and reliability of the constructs in the model will be tested through Partial Least Square method. The results will indicate what are the factors that have significant positive effect on the intention of the elderly people in using smart wearable systems. © 2022 IEEE.

关键词： Technology acceptance model Wearable computers Sociology Medical services Mathematical models Reliability Older adults

来源：评论

学校读者我要写书评

暂无评论

ELAINE: rELiAbility and evIdence-aware News vErifier 1

ELAINE: rELiAbility and evIdence-aware News vErifier

引用

1st Workshop on NLP Applied to Misinformation, NLP-MisInfo 2023

作者： Badenes-Olmedo, Carlos Saquete, Estela Sepúlveda-Torres, Robiert Bonet-Jover, Alba Ontology Engineering Group Universidad Politécnica de Madrid Spain Department of Software and Computing Systems University of Alicante Spain

Disinformation is one of the main problems of today’s society, and specifically the viralization of fake news. This research presents ELAINE, a hybrid proposal to detect the veracity of news items that combines content reliability information with external evidence. The external evidence is extracted from a scientific knowledge base that contains medical information associated with coronavirus, organized in a knowledge graph created from a CORD-19 corpus. The information is accessed using Natural Language Question Answering and a set of evidences are extracted and their relevance measured. By combining both reliability and evidence information, the veracity of the news items can be predicted, improving both accuracy and F1 compared with using only reliability information. These results prove that the approach presented is very promising for the veracity detection task. © 2023 CEUR-WS. All rights reserved.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Online Shopping Behavior during Pandemic 2022

Online Shopping Behavior during Pandemic

引用

6th International Conference on Compute and Data Analysis, ICCDA 2022

作者： Teh, Phoey Lee Siew Hoong Lee, Angela Teo, Jie Ying Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia

ISBN: (纸本)9781450395472

Many industries have been impacted, and some have been force to shut down as a result of the pandemic outbreak, . Meanwhile, e-commerce has survived and thrived. Since then, online shopping grown in popularity. Consumers are encouraged to stay at home and order their daily necessities online to reduce the spread of the virus. This paper aims to revisit online shopping behaviour by investigating financial risk, convenience risk, privacy risk, delivery risk, environmental risk, and information trust towards shopping behaviour. We collected a total of 250 questionnaires during the lockdown period in the year 2020. Findings shown that participants agree that information trust, privacy risk, convenient risk, and delivery risk can influence their online shopping behaviour, but the only not is environmental risk. © 2022 ACM.

关键词： Electronic commerce

来源：评论

学校读者我要写书评

暂无评论

Link Prediction of biomedical data using Hetrosim GCN-GAT

Link Prediction of biomedical data using Hetrosim GCN-GAT

引用

2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Mythili, R. Parthiban, N. College of Engineering and Technology School of Computing SRM Institute of Science and Technology Department of DataScience and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350319200

With the emergence of large number of Electronic Medical Records (EMRs), the researchers turned their attention towards the prediction, recommendation systems with these available data. This paper deals with the input of the patient details and the drugs administered to them for the different health conditions. The Drug to Drug Interaction (DDI) prediction is essential in order to avoid the unintended adverse reactions and making the drugs less effective. It is represented in the form of graphs with nodes and edges/links. Link prediction is often used to detect the existence of link or edges present in between the specified nodes. Graph Neural Network based algorithm HetrosimGCN-GAT is proposed to address the underlying link prediction tasks. A heterogeneous network is initially built using the drug-drug, protein-protein similarity matrices and drug protein matrices. The embedded representation of the drug and protein is then obtained using the graph convolutional neural network (GCN). Graph attention mechanism provides a weighting factor for every connect in order to assess the significance or highlight the contribution of distinct neighbourhood nodes to the central node. The unknown DDI and Drug Protein Interaction is predicted by applying the inner product decoder. The performance of HetrosimGCN-GAT is evaluated against the baseline methods and its outperforms the baseline methods. © 2023 IEEE.

关键词： drug to drug interactions GNNs knowledge graph link prediction

来源：评论

学校读者我要写书评

暂无评论

Fast Surface Error Diagnosis for Radiotelescopes Using Linearized Microwave Holography

引用

IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3527-3536页

作者： Romero, Celia Fonta Varela, Fernando Rodriguez Iraguen, Belen Galocha Lopez-Perez, Jose-Antonio Universidad Politécnica de Madrid Information Processing and Telecommunications Center Radiation Group Department of Signals Systems and Radiocommunications MadridE-28040 Spain Universidad Rey Juan Carlos Group of Microwave Engineering and Radiocommunication Systems Department of Signal Theory and Communications and Telematic Systems and Computing MadridE-28942 Spain Centro Astronómico de Yebes Dirección General del Instituto Geográfico Nacional Guadalajara19001 Spain

Radiotelescopes are typically composed of large reflector antennas located outdoors. These antennas are designed to achieve high levels of gain, which is possible due to their large size and RF systems designed for working at high frequencies. However, various sources of error (such as mounting imperfection or deformations caused by gravity and weather effects) are difficult to identify and can significantly degrade the performance of the radiotelescope, reducing its gain. The correct diagnosis of these errors has been a challenge in radioastronomy. The most widely used technique is microwave holography, in which mechanical errors are detected through long electromagnetic acquisitions. The measured far-field is processed to obtain the aperture field and thus the errors affecting the surface or the phase distribution. In this paper, a novel methodology is proposed to diagnose reflector errors using holography. The proposed approach defines a system that models all possible aperture errors as a combination of basis functions, directly relating the errors to the acquired far-field and reducing the number of samples required for analysis. © 1963-2012 IEEE.

关键词： Reflection

来源：评论

学校读者我要写书评

暂无评论

ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation

ICON: Improving Inter-Report Consistency in Radiology Report...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hou, Wenjun Cheng, Yi Xu, Kaishuai Hu, Yan Li, Wenjie Liu, Jiang Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798891761681

Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated *** this paper, we emphasize another crucial quality that it should possess, i.e., inter-report consistency, which refers to the capability of generating consistent reports for semantically equivalent *** quality is even of greater significance than the overall report accuracy in terms of ensuring the system's credibility, as a system prone to providing conflicting results would severely erode users' ***, existing approaches struggle to maintain inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion *** address this issue, we propose ICON, which Improves the inter-report CONsistency of radiology report *** to enhance the system's ability to capture similarities in semantically equivalent lesions, our approach first involves extracting lesions from input images and examining their ***, we introduce a lesion-aware mixup technique to ensure that the representations of the semantically equivalent lesions align with the same attributes, achieved through a linear combination during the training *** experiments on three publicly available chest X-ray datasets verify the effectiveness of our approach, both in terms of improving the consistency and accuracy of the generated reports. © 2024 Association for Computational Linguistics.

关键词： Radiology

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants 2nd

COVID-19 Lung CT Image Segmentation: A Comparison of Various...

引用

2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Alebiosu, David Olayemi Folayan, Adeola Chen, Wei Tolulope, Abejide Ajibade, Samuel-Soma M. School of Engineering and Technology Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia School of Medical and Health Science Monash University Petaling Jaya Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China College of Science and Engineering University of Derby Derby United Kingdom Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkey

ISBN: (纸本)9789819639489

Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Distributed and Localized Model Predictive Control - Part II: Theoretical Guarantees

引用

IEEE Transactions on Control of Network systems 2023年第3期10卷 1113-1123页

作者： Alonso, Carmen Amo Li, Jing Shuang Matni, Nikolai Anderson, James California Institute of Technology Computing and Mathematical Sciences Department PasadenaCA91106 United States University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States Columbia University Department of Electrical Engineering and the Data Science Institute New YorkNY10027 United States

Engineered cyberphysical systems are growing increasingly large and complex. These systems require scalable controllers that robustly satisfy state and input constraints in the presence of additive noise: such controllers should also be accompanied by theoretical guarantees on feasibility and stability. In our companion paper, we introduced distributed and localized model predictive control (DLMPC) for large-scale linear systems;DLMPC is a scalable closed-loop MPC scheme in which subsystems need only exchange local information in order to synthesize and implement local controllers. In this article, we provide recursive feasibility and asymptotic stability guarantees for DLMPC. We leverage the system level synthesis framework to express the maximal positive robust invariant set for the closed-loop system and its corresponding Lyapunov function in terms of the closed-loop system responses. We use the invariant set as the terminal set for DLMPC, and show that this guarantees feasibility with minimal conservatism. We use the Lyapunov function as the terminal cost, and show that this guarantees stability. We provide fully distributed and localized algorithms to compute the terminal set offline, and also provide necessary additions to the online DLMPC algorithm to accommodate coupled terminal constraint and cost. In all algorithms, only local information exchange is necessary, and computational complexity is independent of the global system size: we demonstrate this analytically and experimentally. This is the first distributed MPC approach that provides minimally conservative yet fully distributed guarantees for recursive feasibility and asymptotic stability in the nominal and robust settings. © 2014 IEEE.

关键词： Lyapunov methods

来源：评论

学校读者我要写书评

暂无评论

Singe Image Dehazing using Deep Neural Networks 2

Singe Image Dehazing using Deep Neural Networks

引用

2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350315905

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values. © 2023 IEEE.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

引用

IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：