检索结果-内蒙古大学图书馆

A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

Computers, Materials & Continua 2025年第4期83卷 1195-1218页

作者： Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and TechnologyChizhou247000China

Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关键词： Fuzzy rough sets feature selection cross-similarity fuzzy relations

来源：评论

学校读者我要写书评

暂无评论

The Mass Assembly History for Galaxies with MaNGA

引用

research in Astronomy and Astrophysics 2024年第3期24卷 58-69页

作者： Xue Ge Hong-Tao Wang Cheng-Long Lei Yun-Jun Guo Yi-Long Jiang Xiao-Xiao Cao School of Physics and Information Engineering Jiangsu Second Normal University Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal University Key Laboratory of Modern Astronomy and Astrophysics Nanjing UniversityMinistry of Education School of Science Langfang Normal University

How galaxies assemble masses through their own star formation or interaction with the external environment is still an important topic in the field of galaxy formation and *** use Value Added Catalogs with galaxy features that are spatially and temporally resolved from Sloan Digital Sky Survey data Release 17 to investigate the mass growth histories of early-type galaxies(ETGs)and late-type galaxies(LTGs).We find that the mass growth of ETGs is earlier than that of LTGs for massive galaxies(M_(*)>10^(10)M_⊙),while low-mass(M_(*)≤10^(10)M_⊙)ETGs have statistically similar mass assembly histories as low-mass *** stellar metallicity of all massive galaxies shows a negative gradient and basically does not change with ***,in low-mass galaxies,the stellar metallicity gradient of elliptical galaxies is negative,and the stellar metallicity gradient of lenticular and spiral galaxies evolves from positive to *** are not all in a high-density environment,but exhibit mass *** the tidal strength increases,the star formation rate of low-mass ETGs rapidly *** results support a picture where massive galaxies exhibit inside-out quenching mode,while low-mass galaxies show outside-in quenching *** effects play an important role in regulating the mass assembly histories of low-mass ETGs.

关键词： Galaxy evolution-galaxies star formation-Galaxy formation

来源：评论

学校读者我要写书评

暂无评论

application of U-Net and Optimized Clustering in Medical Image Segmentation:A Review

引用

Computer Modeling in Engineering & Sciences 2023年第9期136卷 2173-2219页

作者： Jiaqi Shao Shuwen Chen Jin Zhou Huisheng Zhu Ziyi Wang Mackenzie Brown School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200China School of Data Science Perdana UniversitySerdangSelangor43400Malaysia

As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so *** with natural images,medical images have a variety of ***,the emphasis of information which is conveyed by images of different modes is quite *** it is time-consuming and inefficient to manually segment medical images only by professional and experienced ***,large quantities of automated medical image segmentation methods have been ***,until now,researchers have not developed a universal method for all types of medical image *** paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent *** the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation *** is the improved strategies based on traditional clustering *** other is the research progress of the improved image segmentation network structure model based on *** power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional *** paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.

关键词： Medical image segmentation clustering algorithm U-Net

来源：评论

学校读者我要写书评

暂无评论

Optimization Scheme of Active Reconfigurable Intelligent Surface Assisted Internet of Vehicles System

Optimization Scheme of Active Reconfigurable Intelligent Sur...

引用

2024 International Workshop on Automation, Control, and Communication Engineering, IWACCE 2024

作者： Xu, Zhuoran Ni, Yiyang Sun, Wenxue Liu, Yaxuan Mo, Zhaoying Zhao, Haitao Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal University Nanjing210013 China

ISBN: (数字)9781510685420

ISBN: (纸本)9781510685413

In recent years, the Integrated Satellite Aerial Terrestrial (I-SAT) network has emerged as an innovative and comprehensive communication system and has gained wide attention. However, in the face of a complex external environment, it will still be affected by other factors. This paper considers the integration of Unmanned Aerial Vehicle (UAV) with Reconfigurable Intelligent Surfaces (RIS) and introduces a novel sub-connected active RIS architecture under the energy consumption constraints of the system. Firstly, a joint RIS phase shift, amplification, and UAV trajectory optimization algorithm is proposed to achieve a high achievable sum rate. Secondly, a Deep Deterministic Policy Gradient (DDPG) algorithm is utilized to solve the optimization problem and converge in a continuous action space. Finally, simulation results confirm that the proposed approach significantly enhances the system performance compared to the other schemes. © 2024 SPIE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Advance of Thyroid Nodule Ultrasound Diagnosis Based on Deep Learning 29th

Advance of Thyroid Nodule Ultrasound Diagnosis Based on Deep...

引用

29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Wan, Huiling Chen, Shuwen Ni, Yiyang Qi, Shaojia Qu, Hui School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China

ISBN: (纸本)9783031449468

The incidence of thyroid nodules is increasing rapidly worldwide. In order to reduce the burden on doctors and improve the accuracy of diagnosis, ultrasound imaging plays an important role in the diagnosis and follow-up of thyroid nodules. In recent years, artificial intelligence (AI) technology based on machine learning and deep learning has improved significantly. With the help of medical imaging screening tools, physicians can make clinical decisions about thyroid nodules more efficiently and provide the best protection for patients as early as possible. This paper mainly discusses the research progress of computer vision and related algorithms in ultrasonic imaging of thyroid nodules in recent years from the aspects of image preprocessing, detection and segmentation of focal areas, feature extraction, and identification of benign/malignant nodules. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

A Survey of COVID-19 Diagnosis Based on Deep Learning from X-ray Images and CT-Scan Images 29th

A Survey of COVID-19 Diagnosis Based on Deep Learning from X...

引用

29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Wang, Jiaji Chen, Shuwen Zhu, Huisheng School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (纸本)9783031449468

The Coronavirus pandemic has been going on for three years. Undoubtedly, it caused huge damage to people, physically and mentally. The earlier the doctor diagnoses, the sooner the patient will recover In this paper, we review the more advanced applications of deep learning in COVID-19 diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network

引用

Computer Modeling in Engineering & Sciences 2023年第9期136卷 2595-2616页

作者： Jiaji Wang Shuwen Chen Yu Cao Huisheng Zhu Dimas Lima School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200China Department of Electrical Engineering Federal University of Santa CatarinaFlorianópolis88040-900Brazil

This paper presents a 6-layer customized convolutional neural network model(6L-CNN)to rapidly screen out patients with COVID-19 infection in chest CT *** model can effectively detect whether the target CT image contains images of pneumonia *** this method,6L-CNN was trained as a binary classifier using the dataset containing CT images of the lung with and without pneumonia as a *** results show that the model improves the accuracy of screening out COVID-19 *** to othermethods,the performance is *** addition,the method can be extended to other similar clinical conditions.

关键词： COVID-19 custom convolutional neural network medical images

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis for Configured Active RIS-Aided Communication Systems

Performance Analysis for Configured Active RIS-Aided Communi...

引用

2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Li, Longze Zhao, Haitao Ni, Yiyang Xu, Zhuoran Xun, Wei Xu, Bangning Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Jiangsu Second Normal University Nanjing Jiangsu Province Engineering Research Center of Basic Education Big Data Application 210003 China

ISBN: (纸本)9798350345384

The reconfigurable intelligent surface (RIS) is a crucial wireless channel control technology in 6G systems. Unlike traditional passive RIS (PRIS), active RIS (ARIS) can overcome the "multiplicative fading"effect. However, the configuration of the reflecting elements can significantly impact the system performance. In this paper, we focus on the ARIS equipped with uniform planar array (UPA) reflecting elements. We provide the ergodic achievable rate (EAR) for both unconfigured and configured ARIS system with closed-form. The effects of reflecting element area on the signal-to-noise ratio (SNR) are investigated, and a model selection scheme is proposed to determine whether to use ARIS or PRIS. Moreover, we analyze the average received power (ARP) and amplification power in ARIS. Based on the results of the analysis, a joint reflecting element area and quantity optimization scheme is proposed to improve system performance. The simulation results verify the accuracy of the analysis and highlight the superiority of the ARIS-assisted communication system. © 2023 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Fine-grained Personalized Hierarchical Federated Learning towards Heterogeneous Internet of Vehicles

引用

IEEE Internet of Things Journal 2025年第11期12卷 16349-16362页

作者： Zhang, Puning Shao, Shuman Wu, Chao Yang, Zhigang Zhang, Ruoying Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing400065 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd National Engineering Laboratory for Industrial Big-data Application Technology Chongqing400707 China China Merchants Testing Vehicle Technology Research Institute Co. Ltd Chongqing404100 China Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China China China Academy of Information and Communications Beijing100083 China

High mobility and non-independent and identically distributed (non-iid) data in the Internet of Vehicles (IoV) pose challenges for the convergence speed and accuracy of federated learning model training. Traditional federated learning approaches fail to account for the unique characteristics of local vehicle data, limiting the model performances. To address this, we propose a personalized hierarchical federated learning method tailored for heterogeneous IoV. Our approach customizes models for each vehicle, enhancing their performance on local data. We introduce a network-aware client selection method that dynamically chooses vehicle clients based on their spatiotemporal distribution and changing channel conditions. Additionally, our fine-grained personalized learning method leverages frequency domain analysis to extract and retain both fundamental and personalized knowledge from local model parameters. This allows us to build highly accurate personalized models suited to each vehicle's data characteristics. Experimental results show that our method improves model accuracy by up to 10.31% and reduces training time by up to 24.93% compared to the latest personalized federated learning algorithms. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Deep learning approaches in flow visualization

引用

Advances in Aerodynamics 2022年第1期4卷 327-340页

作者： Can Liu Ruike Jiang Datong Wei Changhe Yang Yanda Li Fang Wang Xiaoru Yuan Key Laboratory of Machine Perception(Ministry of Education) and School of AIPeking UniversityBeijingChina China Aerodynamics Research and Development Center MianyangChina National Engineering Laboratory for Big Data Analysis and Application Peking UniversityBeijingChina

With the development of deep learning(DL)techniques,many tasks in flow visualization that used to rely on complex analysis algorithms now can be replaced by DL *** reviewed the approaches to deep learning technology in flow visualization and discussed the technical benefits of these *** also analyzed the prospects of the development of flow visualization with the help of deep learning.

关键词： Deep learning Flow visualization data management Feature extraction Particle tracing

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：