检索结果-内蒙古大学图书馆

data augmentation method for insulators based on Cycle-GAN

Journal of electronic science and Technology 2024年第2期22卷 36-47页

作者： Run Ye Azzedine Boukerche Xiao-Song Yu Cheng Zhang Bin Yan Xiao-Jia Zhou School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Electrical Engineering and Computer Science University of OttawaOttawaK1N6N5Canada Yangtze River Delta Research Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313001China

data augmentation is an important task of using existing data to expand data *** generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated ***,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator *** solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator *** attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect *** experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.

关键词： data expansion Deep learning Generate confrontation network Insulator

来源：评论

学校读者我要写书评

暂无评论

Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass

Improving Heart Disease Prediction with Stacked Ensemble Lea...

引用

Women in Computing (InCoWoCo), International Conference for

作者： Amirthaa M Gerardine Immaculate Mary Anitha Julian Anto Lourdu Xavier Raj Selvarathinam School of Electronics Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Saveetha Engineering College Chennai Tamilnadu India Data Science and Analytics Grand Valley State University Michigan USA

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Heart disease prediction remains a critical area of research due to its significant impact on public health. This paper, titled “Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass,” presents a comprehensive study comparing the performance of binary and multiclass classification models for heart disease prediction. Utilizing two distinct datasets, one for binary classification and one for multiclass classification. The study evaluates various machine learning and deep learning models, with a focus on stacked ensemble methods. The Binary Classification Results leveraging the Preprocessing-and-PCA-on-Heart-Disease-dataset, the study evaluated several models including Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, Deep Learning model, and Logistic Regression. The results indicate that the Stacking Ensemble model achieved the highest accuracy of 90.2%, outperforming individual models where Random Forest (88.5%), SVM (89.1%), XGBoost (89.7%), Gradient Boosting (88.0%) and Deep Learning (89.1%). The Multiclass Classification study using the UCI Heart Disease dataset, when applied the same models, the Stacking Ensemble model achieved an accuracy of 64.1%, which, although superior to other individual models such as Random Forest (58.1%), SVM (54.3%), Gradient Boosting (59.2%) and Deep Learning model (57.6%) fell short compared to XGBoost, which attained an accuracy of 65.8%. This outcome highlights the challenges associated with multiclass classification in heart disease prediction.

关键词： Heart Support vector machines Deep learning Accuracy Stacking Predictive models Boosting Ensemble learning Random forests Diseases

来源：评论

学校读者我要写书评

暂无评论

Truncated representation graph with adaptive weighted and manifold regularization for agglomerative clustering 5

Truncated representation graph with adaptive weighted and ma...

引用

5th International Conference on data science and Information Technology, DSIT 2022

作者： Cai, Zhiling Li, Ruijia Wu, Hong Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Agglomerative clustering has emerged as a promising technique with its ability to generate the informative cluster hierarchies. The graph learning of data is very common in many agglomerative clustering algorithms. In this problem, the construction of the affinity graph with high quality is a vital element to improve the performance of clustering. To this end, we propose a new method named truncated representation graph with adaptive weighted and manifold regularization. On the one hand, an adaptive weighting matrix is embedded to the reconstruction error term to reinforce the effect of important features during graph learning. It encourages our method to obtain a robust representation graph of the data. On the other hand, the manifold regularization is introduced to capture the geometric structure of the data so that the representation coefficients of data are smooth with respect to the intrinsic data manifold. Moreover, we truncate the trivial representation coefficients to achieve a block-diagonality and more effective graph for agglomerative clustering. The experimental results on various benchmark datasets verify the superiority of our proposed method with respect to the state-of-the-art approaches. © 2022 IEEE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

SINDyG: Sparse Identification of Nonlinear Dynamical Systems from Graph-Structured data

arXiv

引用

arXiv 2024年

作者： Basiri, Mohammad Amin Khanmohammadi, Sina Data Science and Analytics Institute University of Oklahoma NormanOK United States School of Computer Science University of Oklahoma NormanOK United States

The combination of machine learning (ML) and sparsity-promoting techniques is enabling direct extraction of governing equations from data, revolutionizing computational modeling in diverse fields of science and engineering. The discovered dynamical models could be used to address challenges in climate science, neuroscience, ecology, finance, epidemiology, and beyond. However, most existing sparse identification methods for discovering dynamical systems treat the whole system as one without considering the interactions between subsystems. As a result, such models are not able to capture small changes in the emergent system behavior. To address this issue, we developed a new method called Sparse Identification of Nonlinear Dynamical Systems from Graph-structured data (SINDyG), which incorporates the network structure into sparse regression to identify model parameters that explain the underlying network dynamics. We showcase the application of our proposed method using several case studies of neuronal dynamics, where we model the macroscopic oscillation of a population of neurons using the extended Stuart-Landau (SL) equation and utilize the SINDyG method to identify the underlying nonlinear dynamics. Our extensive computational experiments validate the improved accuracy and simplicity of discovered network dynamics when compared to the original SINDy approach. © 2024, CC BY.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Advancing Healthcare IoT: Blockchain and Federated Learning Integration for Enhanced Security and Insights

Advancing Healthcare IoT: Blockchain and Federated Learning ...

引用

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Malik, Rida Ur-Rehaman, Atta Razzaq, Hamza Bhatt, Chandradeep Kaushik, Keshav Khan, Inam Ullah Riphah Institute Of System Engineering Department Of Information Security Islamabad Pakistan Riphah Institute Of System Engineering Department Of Data Science Islamabad Pakistan Graphic Era Hill University Department Of Cse Uttarakhand India Graphic Era Deemed To Be University Uttarakhand Dehradun India School Of Computer Science University Of Petroleum And Energy Studies Dehradun India Seas Isra University Department Of Electronic Engineering Islamabad Campus Pakistan

ISBN: (纸本)9798350366846

Fast-growing IoT devices in healthcare have ushered in a new era of data-driven patient care and treatment. These gadgets capture too much sensitive medical data, a major issue. We need to boost data analysis, security, and privacy. This research uses cutting-edge concepts like federated learning and blockchain technology to improve Healthcare IoT analytics and security. This study examines blockchain and federated learning's numerous integration aspects. It begins with data security, employing blockchain's immutability and decentralization to prevent manipulation. Federation learning was utilized to solve privacy concerns while adhering to tight healthcare data security regulations, providing strong data analysis without releasing raw, patient-specific information. This study examines how federated learning and blockchain technologies enhance healthcare IoT systems' analytics. This connection allows safe, collaborative analysis among healthcare professionals and devices, offering more insights for research, diagnostics, and tailored patient care. How to integrate these cutting-edge technologies into healthcare IoT networks is also explored. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

An Iterative Optimizing Framework for Radiology Report Summarization With ChatGPT

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4163-4175页

作者： Ma, Chong Wu, Zihao Wang, Jiaqi Xu, Shaochen Wei, Yaonai Liu, Zhengliang Zeng, Fang Jiang, Xi Guo, Lei Cai, Xiaoyan Zhang, Shu Zhang, Tuo Zhu, Dajiang Shen, Dinggang Liu, Tianming Li, Xiang Northwestern Polytechnical University School of Automation Xi'an710072 China University of Georgia School of Computing AthensGA30602 United States Northwestern Polytechnical University School of Computer Science Xi'an710072 China Massachusetts General Hospital Department of Radiology BostonMA02114 United States University of Electronic Science and Technology of China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu611731 China The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonVA76019 United States ShanghaiTech University School of Biomedical Engineering Shanghai201210 China Shanghai United Imaging Intelligence Company Ltd. Department of Research and Development Shanghai200030 China Shanghai Clinical Research and Trial Center Shanghai201210 China

The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Typically written by radiologists, this part is derived from the 'Findings' section, which can be laborious and error-prone. Although deep-learning-based models, such as bidirectional encoder representation from transformers (BERT), have achieved promising results in automatic impression generation (AIG), such models often require substantial amounts of medical data and have poor generalization performance. Recently, large language models (LLMs) like Chat Generative Pre-trained Transformer (ChatGPT) have shown strong generalization capabilities and performance, but their performance in specific domains, such as radiology, remains under-investigated and potentially limited. To address this limitation, we propose ImpressionGPT, leveraging the contextual learning capabilities of LLMs through our dynamic prompt and iterative optimization algorithm to accomplish the AIG task. ImpressionGPT initially employs a small amount of domain-specific data to create a dynamic prompt, extracting contextual semantic information closely related to the test data. Subsequently, the iterative optimization algorithm automatically evaluates the output of LLMs and provides optimization suggestions, continuously refining the output results. The proposed ImpressionGPT model achieves superior performance of AIG task on both the Medical Information Mart for Intensive Care - Chest X-ray database (MIMIC-CXR) and Open Access Biomedical Image Search Engine (OpenI) datasets without requiring additional training data or fine-tuning the LLMs. This work presents a paradigm for localizing LLMs that can be applied in a wide range of similar application scenarios, bridging the gap between general-purpose LLMs and the specific language processing needs of various domains. © 2020 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A GTFS data acquisition and processing framework and its application to train delay prediction

引用

International Journal of Transportation science and Technology 2023年第1期12卷 201-216页

作者： Jianqing Wu Bo Du Zengyang Gong Qiang Wu Jun Shen Luping Zhou Chen Cai School of Computing and Information Technology University of WollongongNSW 2522Australia SMART Infrastructure Facility University of WollongongNSW 2522Australia School of Civil Mining and EnvironmentalUniversity of WollongongNSW 2522Australia Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China School of Electrical and Information Engineering University of SydneyNSW 2006Australia CSIRO’s Data61 NSW 2015Australia

With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Term Memory is developed to predict train delay with minor anomaly in Sydney as a case study. The contribution of this new framework will render great potential for broader applications and deeper research.

关键词： General transit feed specification Delay prediction Train delay Long short-term memory data fusion

来源：评论

学校读者我要写书评

暂无评论

Task Offloading and Multidimensional Resource Allocation for Semantic-Assisted High-Speed Railway with Multi-access MEC 3rd

Task Offloading and Multidimensional Resource Allocation for...

引用

3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Qu, Jiaming Guo, Qichang Yuan, Jiabin Zhang, Wei Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing China China Datang Corporation Science and Technology General Research Institute Ltd. Beijing China

ISBN: (纸本)9789819624676

In high-speed railway communication scenarios, most of the computing tasks generated by trains are compute-intensive and delay-sensitive. Multi-access mobile edge computing (MEC) technology is very promising in resolving the contradiction between this situation and the limited local resources. Traditionally, MEC servers are deployed at base stations along the railway. We introduce dynamic MEC servers assisted by unmanned aerial vehicles (UAVs) additionally to deal with excessive server loads and the real-time nature of trains and tasks. Moreover, to complete real-time tasks, trains usually need abundant resources to transmit a large amount of raw data to MECs. However, limited local resources cannot meet the needs. Therefore, we propose a semantic-assisted communication model, introducing semantic encoding, decoding, extraction and feature aggregation to reduce resource overhead and computing delay while ensuring the accuracy of semantic understanding. Next, we conduct research on multi-dimensional resource allocation with the goal of minimizing delays and maximizing cache resource utilization by presenting our problem formulation. We propose a scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enable MEC agents to collaborate in offloading tasks and allocate resources. Lastly, we conduct simulation experiments and compare with two schemes: centralized resource management and random offloading resource allocation, verifying that the proposed scheme can achieve higher resource utilization and QoS satisfaction rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

A Hybird Alignment Loss for Temporal Moment Localization with Natural Language

A Hybird Alignment Loss for Temporal Moment Localization wit...

引用

2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Guo, Chao Liu, Daizong Zhou, Pan The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Electronic Information and Communication Huazhong University of Science and Technology China Wangxuan Institute of Computer Technology Peking University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

This paper addresses the problem of temporal moment localization with natural language, which aims to localize a target video moment according to a language description. A key challenge in this task is how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description. Traditional methods generally utilize the vanilla attention mechanism to simulate soft alignment between extracted video and sentence features, however, they lack the training signal to alleviate the domain gap between video and sentence domains. In this paper, we propose a novel cross-domain alignment module to deeply align the features in two domains, yielding to learn better representations. We evaluate our model on three public datasets: Charades-STA, ActivityNet Captions and TACoS, and the experiments prove that our proposed cross-domain alignment module can bring significant improvement, and can be also applied to other baseline models. © 2022 IEEE.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment 21

HKTGNN: Hierarchical Knowledge Transferable Graph Neural Net...

引用

21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhou, Zhanting Bi, Kejun Zhong, Yuyanzhen Tang, Chao Li, Dongfen Ying, Shi Wang, Ruijin University of Electronic Science and Technology of China School of Information Science and Engineering Chengdu China Sichuan University School of Computer Science Chengdu China Chongqing Ceprei Industrial Technology Research Institute Co. Ltd. Chongqing China Sichuan Changhong Electronic Holding Group Co. Ltd. Chengdu China Chengdu University of Technology College of Computer Science and Cyber Security Chengdu China Wuhan University School of Computer Science Wuhan China

ISBN: (纸本)9798350329223

The strength of a supply chain is an important measure of a country's or region's technical advancement and overall competitiveness. Establishing supply chain risk assessment models for effective management and mitigation of potential risks has become increasingly crucial. As the number of businesses grows, the important relationships become more complicated and difficult to measure. This emphasizes the need of extracting relevant information from graph data. Previously, academics mostly employed knowledge inference to increase the visibility of links between nodes in the supply chain. However, they have not solved the data hunger problem of single node feature characteristics. We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN1) supply chain risk assessment model to address these issues. Our approach is based on current graph embedding methods for assessing corporate investment risk assessment. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module, resulting in a directed homogeneous graph with just product nodes. This reduces the complicated supply chain network into a basic product network. It addresses difficulties using the domain difference knowledge transferable module based on centrality, which is presented by the premise that supply chain feature characteristics may be biased in the actual world. Meanwhile, the feature complement and message passing will alleviate the data hunger problem, which is driven by domain differences. Our model outperforms in experiments on a real-world supply chain dataset. We will give an equation to prove that our comparative experiment is both effective and fair.1Accepted by The 21st IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA 2023). © 2023 IEEE.

关键词： Supply chains

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：