检索结果-内蒙古大学图书馆

Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet

Computational intelligence and neuroscience 2022年第1期2022卷 6895833页

作者： Shervan Fekri-Ershad Mustafa Jawad Al-Imari Mohammed Hayder Hamad Marwa Fadhil Alsaffar Fuad Ghazi Hassan Mazin Eidan Hadi Karrar Salih Mahdi Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran. Big Data Research Center Najafabad Branch Islamic Azad University Najafabad Iran. Department of Medical Laboratory Techniques Al-Mustaqbal University College Hillah 51001 Babylon Iraq.

Cell phenotype classification is a critical task in many medical applications, such as protein localization, gene effect identification, and cancer diagnosis in some types. Fluorescence imaging is the most efficient tool to analyze the biological characteristics of cells. So cell phenotype classification in fluorescence microscopy images has received increased attention from scientists in the last decade. The visible structures of cells are usually different in terms of shape, texture, relationship between intensities, etc. In this scope, most of the presented approaches use one type or joint of low-level and high-level features. In this paper, a new approach is proposed based on a combination of low-level and high-level features. An improved version of local quinary patterns is used to extract low-level texture features. Also, an innovative multilayer deep feature extraction method is performed to extract high-level features from DenseNet. In this respect, an output feature map of dense blocks is entered in a separate way to pooling and flatten layers, and finally, feature vectors are concatenated. The performance of the proposed approach is evaluated on the benchmark dataset 2D-HeLa in terms of accuracy. Also, the proposed approach is compared with state-of-the-art methods in terms of classification accuracy. Comparison of results demonstrates higher performance of the proposed approach in comparison with some efficient methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

引用

IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

来源：评论

学校读者我要写书评

暂无评论

Unbalanced incomplete multi-view clustering via the scheme of view evolution: Weak views are meat;strong views do eat

arXiv

引用

arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem;2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://***/ZeusDavide/TETCI_UIMC. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Efficient Hardware-Assisted Crash Consistency in Encrypted Persistent Memory

Efficient Hardware-Assisted Crash Consistency in Encrypted P...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Zhan Zhang Jianhui Yue Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China Computer Science Department Michigan Technological University Houghton Michigan

ISBN: (数字)9783981926347

ISBN: (纸本)9781728144689

The persistent memory (PM) requires maintaining the crash consistency and encrypting data, to ensure data recoverability and data confidentiality. The enforcement of these two goals does not only put more burden on programmers but also degrades performance. To address this issue, we propose a hardware-assisted encrypted persistent memory system. Specifically, logging and data encryption are assisted by hardware. Furthermore, we apply the counter-based encryption and the cipher feedback (CFB) mode encryption to data and log respectively, reducing the encryption overhead. Our primary experimental results show that the transaction throughput of the proposed design outperforms the baseline design by up to 34.4%.

关键词： Encryption computer crashes Hardware Writing Programming

来源：评论

学校读者我要写书评

暂无评论

Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

引用

The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

来源：评论

学校读者我要写书评

暂无评论

TCIM: Triangle Counting Acceleration With Processing-In-MRAM Architecture

TCIM: Triangle Counting Acceleration With Processing-In-MRAM...

引用

Design Automation Conference

作者： Xueyan Wang Jianlei Yang Yinglin Zhao Yingjie Qi Meichen Liu Xingzhou Cheng Xiaotao Jia Xiaoming Chen Gang Qu Weisheng Zhao Fert Beijing Research Institute Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Department of Electrical and Computer Engineering University of Maryland College Park MD USA

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers. In this paper, we propose to overcome this challenge by designing a TC accelerator utilizing the emerging processing-in-MRAM (PIM) architecture. The true innovation behind our approach is a novel method to perform TC with bitwise logic operations (such as AND), instead of the traditional approaches such as matrix computations. This enables the efficient in-memory implementations of TC computation, which we demonstrate in this paper with computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) arrays. Furthermore, we develop customized graph slicing and mapping techniques to speed up the computation and reduce the energy consumption. We use a device-to-architecture co-simulation framework to validate our proposed TC accelerator. The results show that our data mapping strategy could reduce 99.99% of the computation and 72% of the memory WRITE operations. Compared with the existing GPU or FPGA accelerators, our in-memory accelerator achieves speedups of 9× and 23.4×, respectively, and a 20.6× energy efficiency improvement over the FPGA accelerator.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Blockchain and IoT data Analytics for Fine-Grained Transportation Insurance 24

Blockchain and IoT Data Analytics for Fine-Grained Transport...

引用

24th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2018

作者： Li, Zengxiang Xiao, Zhe Xu, Quanqing Sotthiwat, Ekanut Mong Goh, Rick Siow Liang, Xueping Institute of High Performance Computing ASTAR Singapore Big Data Experience Center Computer Engineering Department King Mongkut's University of Technology Thonburi Thailand Old Dominion University NorfolkVA United States

ISBN: (纸本)9781538673089

Innovations such as the Cloud, Internet of Things (IoT), and data analytics have already dramatically altered the customer experience in many, if not all, industries. Blockchain, as another emerging technology, is expected to be the next generation infrastructure to established trusted multiparty collaborations. In this paper, we investigated the convergence of aforementioned technologies, by presenting a prototype of fine-grained transportation insurance. Insurance premium were assessed based on vehicle's usage and driver's behavior, which were deduced from streaming IoT data collected from mobile sensors. This incentive mechanism promotes fairness among drivers and encourages safer driving style. The prototype takes advantage of both private blockchain (e.g., high transaction rate in Hyperledger) and public blockchain (e.g., inbuilt cryptocurrency). Besides system architecture and implementation details, preliminary performance evaluations are presented and discussed. © 2018 IEEE.

关键词： Insurance

来源：评论

学校读者我要写书评

暂无评论

Constructing a Comprehensive Clinical database Integrating Patients' data from Intensive Care Units and General Wards 12

Constructing a Comprehensive Clinical Database Integrating P...

引用

12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Liu, Tongbo Liu, Xiaoli Fan, Yong Xu, Haoran Ng, Yeuk Lam Li, Peiyao Xue, Wanguo Zhang, Zhengbo Chinese PLA General Hospital Department of Computer Management and Application Beijing China School of Biological Science and Medical Engineering Beihang University Beijing China Chinese PLA General Hospital Department of Biomedical Engineering Beijing China University of Toronto Faculty of Arts Science Toronto Canada Medical Big Data Center Chinese PLA General Hospital Beijing China

ISBN: (纸本)9781728148526

Collection and analysis of large volumes of ICU data are invaluable to the advancement of clinical knowledge, and large-scale ICU databases have been effective resources to understand risk factors and perform predictive analysis by using machine learning. This paper introduces the construction of a comprehensive clinical database: PLAGH-ICU database, which integrated data from nine intensive care units, emergency department and general wards. data from several sources were extracted and integrated in the database, including patient demographics, hospital administrative data, physiological data, medications, lab test, fluid balance data, notes and reports etc. Detailed information about the database, such as patient characteristics, disease distribution, category of data, data records were illustrated. As far as we know, this is the first comprehensive ICU database developed and used as a research database in datathon event in China. Such kind of database will promote the research progress in critical care medicine, as well as the development of modern hospital information system in China. © 2019 IEEE.

关键词： Intensive care units

来源：评论

学校读者我要写书评

暂无评论

Multiview feature selection combining latent space and similarity structure learning

引用

Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Regularization-adaptive Non-negative Latent Factor Analysis-based Model For Recommender Systems

A Regularization-adaptive Non-negative Latent Factor Analysi...

引用

IEEE International Conference on Human-Machine Systems

作者： Jiufang Chen Xin Luo MengChu Zhou School of Computer Science China West Normal University Nanchong Sichuan China Chinese Academy of Sciences Chongqing Inst. of Green and Intelligent Tech Chongqing China Department of Big Data Analyses Techniques Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk Chongqing China Department of Electrical and Computer Engineering New Jersey Inst. of Tech Newark NJ USA

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158723

Non-negative latent factor analysis (NLFA) can high-efficiently extract useful information from high dimensional and sparse (HiDS) matrices often encountered in recommender systems (RSs). However, an NLFA-based model requires careful tuning of regularization coefficients, which is highly expensive in both time and computation. To address this issue, this study proposes an adaptive NLFA-based model whose regularization coefficients become self-adaptive via particle swarm optimization. Experimental results on two HiDS matrices indicate that owing to such self-adaptation, it outperforms an NLFA model in terms of both convergence rate and prediction accuracy for missing data estimation.

关键词： Computational modeling Adaptation models Sparse matrices Analytical models Tuning Particle swarm optimization big data

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：