检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Kulyabin, Mikhail Zhdanov, Aleksei Nikiforova, Anastasia Stepichev, Andrey Kuznetsova, Anna Ronkin, Mikhail Borisov, Vasilii Bogachev, Alexander Korotkich, Sergey Constable, Paul A. Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstr. 3 Erlangen91058 Germany Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University Mira 32 Yekaterinburg620078 Russia Ophthalmosurgery Clinic"Professorskaya Plus" Vostochnaya 30 Yekaterinburg620075 Russia Ural State Medical University Repina 3 Yekaterinburg620028 Russia Flinders University College of Nursing and Health Sciences Caring Futures Institute Adelaide Australia

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset. © 2023, CC BY.

关键词： Optical coherence tomography

来源：评论

学校读者我要写书评

暂无评论

Scene Recognition Model in Underground Mines Based on CNN-LSTM and Spatial-Temporal Attention Mechanism

Scene Recognition Model in Underground Mines Based on CNN-LS...

引用

International Symposium on computer, Consumer and control (IS3C)

作者： Tianwei Zheng Chi Liu Beizhan Liu Mei Wang Yuancheng Li Pai Wang Xuebin Qin Yuan Guo College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an China College of Computer Science Beijing Institute of Technology Beijing China SHCCIG Yubei Coal Industry Co Ltd. Yulin China College of Computer Science Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9781728193625

ISBN: (纸本)9781728193632

Based on the convolutional neural network (CNN) and long short-term memory neural network (LSTM), combined with data enhancement technology and spatial-temporal attention mechanism, a scene recognition model is established. In order to balance the difference in the amount of data between different samples, data enhancement technology based on video data samples is introduced. Aiming at improving the performance of the model, a spatial-temporal attention mechanism is used to improve the accuracy of scene recognition. The model sample scenes used in this paper include three types: fully mechanized coal face, coal mine roadway and mining machinery. The experimental verification shows that: compared with the traditional convolutional neural network, the accuracy of the CNN-LSTM model is improved by 2.136%, and the CNN-LSTM model with spatial-temporal attention mechanism is improved by 2.921%. The accuracy of the deep CNN-LSTM model with spatial-temporal attention mechanism is about 93.063%.

关键词： Computational modeling Face recognition Neural networks Data models Coal mining Convolutional neural networks Machinery

来源：评论

学校读者我要写书评

暂无评论

DeepContact: High throughput quantification of membrane contact site based on electron microscopy imaging

DeepContact: High throughput quantification of membrane cont...

引用

2022生物物理大会

作者： Liqing Liu Shuxin Yang Yang Liu Xixia Li Junjie Hu Li Xiao Tao Xu National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Center for Biological Imaging Institute of Biophysics Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences College of Life Science University of Chinese Academy of Sciences Ningbo HuaMei Hospital University of Chinese Academy of Sciences School of Biomedical Engineering Guangzhou Medical University

Membrane contact site (MCS)-mediated organelle interactions plays essential roles in the cell. Quantitative analysis of the MCS reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool is lacking. Here, we developed "DeepContact", a deep learning protocol for optimizing organelle segmentation and contact analysis based on label-free electron microscopy(EM).

关键词： DeepContact High throughput quantification of membrane contact site based on electron microscopy imaging

来源：评论

学校读者我要写书评

暂无评论

Multiple-image encryption algorithm based on a new 3D hyperchaotic map and Whac-A-Mole scrambling model

引用

Expert Systems with Applications 2025年 290卷

作者： Lu, Jun Xia, Xue Zhang, Xiaoqiang Zhao, Ruoyu Zhang, Yushu School of Cyber Security Gansu University of Political Science and Law Lanzhou730070 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China College of Computer and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang330013 China

Image privacy protection is crucial in daily life, particularly when securing multiple images. Existing multiple-image encryption algorithms suffer from size incompatibility and inefficiency, so this paper proposes a novel multiple-image encryption algorithm. Firstly, a three-dimensional hyperchaotic map is designed by combining the classical maps, i.e., Sine and Henon. Chaos performance tests demonstrate that the constructed map shows no periodicity, extreme sensitivity to initial values, etc., significant dynamic characteristics and high unpredictability. Secondly, multiple images are fused into a cube, and Whac-A-Mole scrambling model is designed. Finally, by integrating Cartesian products with chaotic index permutation, a disordered, non-repeating three-dimensional spatial coordinate combination is created, enabling spatial diffusion across planes. Experimental analyses show that the information entropy of ciphertext cube is 7.9999, and its correlation is close to 0. Meanwhile, the average encryption time for a 256 × 256 image is 0.1156 s. Compared with existing algorithms, the proposed algorithm significantly improves resistance to attacks, encryption efficiency, and multiple-image size compatibility. Its high efficiency enables the simultaneous encryption of multiple images, making it suitable for real-time applications. © 2025 Elsevier Ltd

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Deciphering circRNA-drug sensitivity associations via global-local heterogeneous matrix factorization and hypergraph contrastive learning

引用

Expert Systems with Applications 2025年 292卷

作者： Tiyao Liu Shudong Wang Yuanyuan Zhang Shanchen Pang Wenjing Yin Wenhao Wu Yingye Liu College of Computer Science and Technology Institute of Software China University of Petroleum Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China The School of Information and Control Engineering Qingdao University of Technology Qingdao 266525 China

Growing evidence highlights the critical role of circular RNAs (circRNAs) as regulators of cellular drug sensitivity, significantly influencing drug efficacy. While matrix factorization has proven feasible in uncovering circRNA-drug sensitivity associations, existing methods rely solely on decomposing the association matrix and fail to efficiently incorporate richer biological information. Moreover, current predictive models are limited in representing multi-perspective relationships and higher-order relationships in circRNA-drug sensitivity associations. To address these limitations, we propose a novel model based on global-local heterogeneous matrix factorization and hypergraph contrastive learning (HMFHCL). HMFHCL first calculates the global and local similarities of circRNAs and drugs, then constructs global and local circRNA-drug heterogeneous networks and performs matrix factorization of the adjacency matrices of these networks to extract information-rich feature representations. By utilizing multi-source information, HMFHCL effectively captures richer structural features and reveals potential connections in heterogeneous networks. Next, we constructed multiple circRNA and drug hypergraphs using global and local association matrices to capture higher-order interactions between nodes via hypergraph convolution. To enhance feature learning, comparative learning is applied to both global and local views of circRNA/drugs, effectively mining the similarities and differences between global structures and local details, improving the model’s ability to perceive underlying patterns and the consistency of representation learning. Finally, HMFHCL integrates circRNA and drug features from different perspectives to predict circRNA-drug associations effectively. Comprehensive experiments on three benchmark datasets demonstrate that HMFHCL outperforms state-of-the-art models, highlighting its superior ability in uncovering complex circRNA-drug sensitivity associations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications

arXiv

引用

arXiv 2020年

作者： Yang, Helin Xiong, Zehui Zhao, Jun Niyato, Dusit Yuen, Chau Deng, Ruilong School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore University of Technology and Design Singapore138682 College of Control Science and Engineering Zhejiang University Hangzhou310027 China

With the rapid deployment of the Internet of Things (IoT), fifth-generation (5G) and beyond 5G networks are required to support massive access of a huge number of devices over limited radio spectrum radio. In wireless networks, different devices have various quality-of-service (QoS) requirements, ranging from ultra-reliable low latency communications (URLLC) to high transmission data rates. In this context, we present a joint energy-efficient subchannel assignment and power control approach to manage massive access requests while maximizing network energy efficiency (EE) and guaranteeing different QoS requirements. The latency constraint is transformed into a data rate constraint which makes the optimization problem tractable before modelling it as a multi-agent reinforcement learning problem. A distributed cooperative massive access approach based on deep reinforcement learning (DRL) is proposed to address the problem while meeting both reliability and latency constraints on URLLC services in massive access scenario. In addition, transfer learning and cooperative learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. Simulation results clearly show that the proposed distributed cooperative learning approach outperforms other existing approaches in terms of meeting EE and improving the transmission success probability in massive access scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

DNS based measurement and analysis of IPv6 domain name support 3

DNS based measurement and analysis of IPv6 domain name suppo...

引用

2020 3rd International Symposium on Big Data and Applied Statistics, ISBDAS 2020

作者： Li, Feng Cao, Huaping Ma, Weijun Hu, Chao Wang, Zhanfeng Lu, Xiyu Nanjing NO.1 Military Representative Department of Equipment Development Ministry Nanjing210007 China Beijing100029 China Nanjing Goldenwater Shangyang Information Technology Co. Ltd Nanjing city210014 China College of Command and Control Engineering Army Engineering University of Pla Nanjing210007 China School of Computer Science and Engineering Southeast University Nanjing211189 China

At present, the IPv4 addresses has been fully allocated, and the worldwide deployment of IPv6 addresses is accelerating due to its advantages. In order to investigate current development of IPv6, the IPv6 addresses of domain names are firstly resolved by DNS servers, and the obtained IPv6 addresses data, mainly including the geographic location statistics of IPv6, the proportion of southeast coastal cities, the comprehensive statistics of top-level domain names, etc., can be used to analyze the domain name supported by IPv6. The analysis of the measurement results has great guiding significance for further understanding the future development and application of IPv6. © Published under licence by IOP Publishing Ltd.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A two-mode offspring generation selection mechanism with co-evolution for sparse large-scale multiobjective optimization

引用

Information sciences 2025年 718卷

作者： Huang, Xiaodong Wang, Jian Wang, Gaige Zhang, Yong Gong, Dunwei Jin, Yaochu Pal, Nikhil R. Qingdao266580 China Qingdao266580 China School of Computer Science and Technology Ocean University of China Shandong Qingdao266580 China School of Information and Control Engineering China University of Mining and Technology Jiangsu Xuzhou221000 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Shandong Qingdao266061 China College of Engineering Westlake University Zhejiang Hangzhou310024 China Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

Sparse large-scale multiobjective optimization problems (LSMOPs) have a wide range of practical applications. In recent years, numerous multiobjective evolutionary algorithms (MOEAs) have been developed to address the complexities of these problems. However, many existing MOEAs designed to solve sparse LSMOPs typically rely on fixed, experience-based vectors to guide offspring generation, which often makes it challenging to determine the optimal guiding vectors for different population states, leading to premature convergence and loss of population diversity. To some extent, this leads to a subjective selection of the vector used for offspring generation. To address this issue, we propose a two-mode offspring generation selection mechanism (TOGSM) that incorporates diversified sparse knowledge into the offspring generation process. The switching between these two modes is based on a designed offspring performance indicator. We also divide the population into two subpopulations by employing techniques of Pareto dominance relationship and fitness values. In each generation, the loser subpopulation generates offspring solutions during the reproduction process, under the guidance of the winner subpopulation. Experimental results confirm that TOGSM incorporating two-mode mechanism and co-evolution strategy can generate higher quality Pareto optimal solutions with faster convergence speed than the state-of-the-art (SOTA) comparative algorithms. © 2025 Elsevier Inc.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

arXiv

引用

arXiv 2022年

作者： Li, Lei Wu, Fuping Wang, Sihan Luo, Xinzhe Martín-Isla, Carlos Zhai, Shuwei Zhang, Jianpeng Liu, Yanfei Zhang, Zhen Ankenbrand, Markus J. Jiang, Haochuan Zhang, Xiaoran Wang, Linhong Arega, Tewodros Weldebirhan Altunok, Elif Zhao, Zhou Li, Feiyan Ma, Jun Yang, Xiaoping Puybareau, Elodie Oksuz, Ilkay Bricq, Stephanie Li, Weisheng Punithakumar, Kumaradevan Tsaftaris, Sotirios A. Schreiber, Laura M. Yang, Mingjing Liu, Guocai Xia, Yong Wang, Guotai Escalera, Sergio Zhuang, Xiahai School of Data Science Fudan University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Departament de Matemàtiques & Informàtica Universitat de Barcelona Barcelona Spain Computer Vision Center Universitat Autònoma de Barcelona Spain School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Northwestern Polytechnical University Xi'An China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China College of Physics and Information Engineering Fuzhou University Fuzhou China Comprehensive Heart Failure Center Wuerzburg University Hospitals Wuerzburg Germany School of Engineering University of Edinburgh Edinburgh United Kingdom School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Canada Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecomm-Unications Chongqing China ImViA Laboratory Université Bourgogne Franche-Comté Dijon France Computer Engineering Department Istanbul Technical University Istanbul Turkey Le Kremlin-Bicêtre France Department of Mathematics Nanjing University of Science and Technology Nanjing China Department of Mathematics Nanjing University Nanjing China

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (***/zhuangxiahai/0/myops20/). © 2022, CC BY-NC-SA.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Middle East and North African Health Informatics Association (MENAHIA): Technological initiatives for 'One Health'

引用

Yearbook of Medical Informatics 2023年第1期32卷 320-329页

作者： Abd-Alrazaq, Alaa Al-Shorbaji, Najeeb Lakhdari, Kheira Elbokl, Ahmed Farouk, Hani Wahba, Hoda Elgasser, Naema Mohell, Rania Emara, Tamer Ayatollahi, Haleh Kalhori, Sharareh R. Niakan Rabiei, Reza Tara, Mahmood Akhu-Zaheya, Laila Al-Qutob, Raeda Alrazak, Sadam Alabed Al-Jafar, Eiman Alhuwail, Dari Ghazal, Hassan Dehbi, Zineb El Otmani Al Idrissi, Najib Fihri, Ouafaa Fassi Belyamani, Lahcen Shahzad, Tariq Ahmed, Zakiuddin Ahmad, Arfan Shah, Zubair Hamra, Eman Abu Taweel, Adel Nashwan, Abdulqadir J. Househ, Mowafa Hamdi, Mounir Al-Thani, Dena Alam, Tanvir Alshammari, Abdulwahhab Alnafrani, Sana Alali, Haitham AI Center for Precision Health Weill Cornell Medicine-Qatarc Doha Qatar EHealth Development Association Amman Jordan Abou Bekr Belkaid University of Tlemcen Chetouane Algeria Ain Shams University Virtual Hospital Ain Shams University Cairo Egypt Ain Shams University Cairo Egypt World Health Organization Cairo Egypt Alexandria Regional Blood Transfusion Center Alexandria University Alexandria Egypt Health Management and Economics Research Center Health Management Research Institute Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran Peter L. Reichertz Institute for Medical Informatics (PLRI) Technical University of Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciencesc Tehran Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciencesc Mashhad Iran School of Nursing Jordan University of Science and Technology Jordan Jordan Centre for Disease Control and Prevention Amman Jordan Faculty of Applied Science and Engineering University of Toronto Toronto Canada Kuwait Health Informatics Association Kuwait Information Science Department College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Mohammed VI University of Health Sciences Casablanca Morocco National Center for Scientific and Technical Research (CNRST) Rabat Morocco Hassan II Agronomic and Veterinary Institute Rabat Morocco COMSATS University Islamabad Sahiwal Campus Islamabad Pakistan Digital Care Karachi Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Al Quds Unive

MENAHIA (Middle East and North African Health Informatics Association) is the International Medical Informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined digital health. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this. A number of countries in the region have already shown the depth and the work that they do to integrate the concept of 'One Health' in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends. © The Author(s) 2023.

关键词： 'One Health' 'One Health' informatics Algeria Artificial Intelligence Climate Change Digital Health Egypt Health Informatics Health Information Technologies International Medical Informatics Association (IMIA) Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco Pakistan Palestine Qatar Saudi Arabia United Arab Emirates Zoonitic Diseases

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：