检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

2,236 篇 会议
13 册 图书
5 篇 期刊文献

馆藏范围

2,254 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,606 篇 工学
- 1,382 篇 计算机科学与技术...
- 966 篇 软件工程
- 321 篇 控制科学与工程
- 241 篇 信息与通信工程
- 218 篇 电气工程
- 177 篇 生物工程
- 129 篇 生物医学工程（可授...
- 91 篇 机械工程
- 76 篇 交通运输工程
- 57 篇 光学工程
- 50 篇 电子科学与技术（可...
- 43 篇 化学工程与技术
- 42 篇 安全科学与工程
- 35 篇 仪器科学与技术
- 32 篇 土木工程
466 篇 理学
- 237 篇 数学
- 182 篇 生物学
- 101 篇 统计学（可授理学、...
- 81 篇 物理学
- 47 篇 系统科学
- 41 篇 化学
434 篇 管理学
- 287 篇 管理科学与工程(可...
- 162 篇 图书情报与档案管...
- 79 篇 工商管理
170 篇 医学
- 163 篇 临床医学
- 81 篇 基础医学(可授医学...
- 54 篇 药学(可授医学、理...
- 34 篇 公共卫生与预防医...
76 篇 教育学
- 74 篇 教育学
49 篇 法学
- 44 篇 社会学
30 篇 经济学
18 篇 农学
2 篇 军事学

主题

173 篇 machine learning
160 篇 deep learning
135 篇 training
100 篇 data models
78 篇 feature extracti...
76 篇 accuracy
70 篇 data mining
67 篇 neural networks
66 篇 predictive model...
61 篇 reinforcement le...
57 篇 computational mo...
52 篇 support vector m...
46 篇 real-time system...
42 篇 machine learning...
39 篇 training data
39 篇 artificial intel...
36 篇 visualization
35 篇 learning systems
35 篇 adaptation model...
31 篇 big data

机构

18 篇 kazan natl res t...
12 篇 king mongkuts un...
10 篇 institute of inf...
9 篇 school of cyber ...
7 篇 chitkara univers...
7 篇 college of compu...
6 篇 king mongkuts un...
6 篇 department of co...
6 篇 the key laborato...
5 篇 chinese acad sci...
5 篇 department of co...
5 篇 department of co...
4 篇 peng cheng lab
4 篇 univ chinese aca...
4 篇 department of ci...
4 篇 school of electr...
4 篇 department of el...
4 篇 school of inform...
4 篇 univ chinese aca...
4 篇 school of comput...

作者

7 篇 yang qiang
7 篇 piriyasurawong p...
6 篇 ouya samuel
6 篇 akatimagool soms...
5 篇 mendy gervais
5 篇 cortez paulo
5 篇 kraysman natalia...
4 篇 peng shaoliang
4 篇 liu sheng
4 篇 you linlin
4 篇 li han
4 篇 barabanova svetl...
4 篇 wang wei
4 篇 huang kaiqi
4 篇 lei baiying
4 篇 wang jinyan
4 篇 khatsrinova olga...
4 篇 shen dinggang
4 篇 li wei
4 篇 li qianmu

语言

2,155 篇 英文
93 篇 其他
27 篇 中文
1 篇 葡萄牙文

检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning"

共 2254 条记录，以下是561-570 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

2nd international conference on Electronic Systems and intelligent Computing, ESIC 2021

2nd International Conference on Electronic Systems and Intel...

引用

2nd international conference on Electronic Systems and intelligent Computing, ESIC 2021

ISBN: (纸本)9789811694875

The proceedings contain 73 papers. The special focus in this conference is on Electronic Systems and intelligent Computing. The topics include: Fake News Predictor: A Random Forest-Based Web Application for the Prediction of Fake News on Social Media;a New Single-Phase Asymmetrical Multilevel Inverter Topology with Reduced Switching Devices for Different Pulse Width Modulation Techniques;study of Filters for Improving the Output of Cascaded Seven-Level Inverter;a Design of Fork-Shaped Wearable Printed Antenna for Wireless Applications;performance Improvement of S-shaped for Wireless Communication;component-Wise Scrutiny of Existing Rule-Based Punjabi Grammar System and Implication for Accuracy Determination;role of Innovation in Education and Development;validation of Adaptive Neuro-Fuzzy Technique for Performance Analysis of Induction Machine;detection of Stress with Deep learning and Health Parameters Monitoring Using Raspberry Pi;energy-Efficient automated Guided Vehicle for Warehouse Management System;a Crow Search Algorithm-Based Machine learning Model for Heart Disease and Cervical Cancer Diagnosis;Design of an automated Stethoscope Using AI, IoT and Signal Processing;maximum Likelihood Estimation for Bangla–Odia Word Alignment;performance Analysis of Video Streaming Methods;intelligent Blockchain Services for Smart City Economy Using Emotion Analysis;decision-Making in Fuzzy Environment;dealing with Imbalanced data;physical Design of a Multibit D FlipFlop-Based Linear Feedback Shift Register;queuing Theory-Based Analysis of Berth Allocation and Management in Paradip Port for Container Ships;A Deep learning Model for Classification of COVID-19 Related Fake News;a Relative Study on Machine learning-Based Algorithm Used to Develop Recommender Systems;secure Watermarking to Protect Colour Images on Social Media from Misuse.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Driving Style Recognition of Leading Vehicles based on Semi-supervised Gaussian Mixture Model 2

Driving Style Recognition of Leading Vehicles based on Semi-...

引用

2nd international conference on Robotics, Automation and intelligent Control, ICRAIC 2022

作者： Song, Qingjin Li, Wenyun Pan, Yirong Leng, Aocheng Zhu, Ren School of Computer Science and Engineering Central South University Hunan Changsha China School of Education and Arts Hunan College of Foreign Studies Hunan Changsha China

Driving style recognition of leading vehicles is important to ensure driving safety and improve the efficiency of road traffic. Current supervised learning based methods achieve high recognition accuracy, but their annotations are labor-intensive and time-consuming in practical scenarios. Although unsupervised learning based methods do not require manual labeling, their recognition accuracy is still low. In order to reduce the labeling cost and improve the accuracy of driving style recognition simultaneously, we propose a driving style recognition method based on semi-supervised Gaussian mixture model. Firstly, the driving data is collected and pre-processed by OBD-II vehicle recorder, and 22-dimensional driving style feature parameters are selected and constructed. Secondly, the kernel principal component analysis method is used to reduce the dimensionality and obtain 6-dimensional discriminative principal components. Finally, the Gaussian mixture model is trained by utilizing both the labeled data and unlabeled data. Concretely, the model parameters are measured by the expectation maximization algorithm and the likelihood function. The simulation experimental results demonstrate that the proposed method can effectively improve the driving style recognition accuracy, as well as reduce the reliance on labeled data. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Research on Generational Evolution of Autonomous Transportation System in Autonomous Driving Scenario 22

Research on Generational Evolution of Autonomous Transportat...

引用

22nd COTA international conference of Transportation Professionals (CICTP) - Intelligence, Connectivity, and Mobility

作者： Wang, Ping Bai, Siyuan Wang, Yu Liu, Wenchao Li, Meng'en Changan Univ Sch Elect & Control Engn Xian Peoples R China Minist Nat Resources Technol Innovat Ctr Land Spatial Data Engn Informat Ctr Beijing Peoples R China

ISBN: (数字)9780784484265

ISBN: (纸本)9780784484265

As a new trend in the development of intelligent transportation systems, autonomous transportation systems (ATS) need to carry out relevant theoretical research in advance. In this article, ATS is divided into three generations, and the reference design set is decoupled and reconstructed according to the basic components of ATS, which is used to express reference schemes of fixed scenes. Based on the changing trend of the penetration rate of autonomous vehicles and the changes in communication technology, the scene changes of autonomous driving are divided. According to the construction method of the reference design set, based on the operation logic of "perception-learning-decision-response," analyze the main changes of autonomous driving in ATS, and explain the evolution process of ATS between generations. Finally, the change of traffic patterns at road intersections in autonomous driving scenarios is taken as an example to demonstrate the rationality and effectiveness of theoretical research.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

An intelligent Decision Support System for Production Planning in Garments Industry 22nd

An Intelligent Decision Support System for Production Planni...

引用

22nd international conference on intelligent data engineering and automated learning, IDEAL 2021

作者： Ribeiro, Rui Pilastri, André Carvalho, Hugo Matta, Arthur Pereira, Pedro José Rocha, Pedro Alves, Marcelo Cortez, Paulo EPMQ - IT Engineering Maturity and Quality Lab CCG ZGDV Institute Guimarães Portugal ALGORITMI Centre Department of Information Systems University of Minho Guimarães Portugal INFOS - Informática e Serviços Leça da Palmeira Portugal

ISBN: (纸本)9783030916077

In this paper, we propose an intelligent Decision Support System (IDSS) that combines prediction and optimization for production planning. We worked with a company that provides software for the garments Industry and that had access to real-world data related with a client that works with subcontractors. Using an automated Machine learning (AutoML) approach, we firstly target four predictive tasks that are crucial to estimate production planning indicators. Then, we use historical data and one of the predicted indicators to search for the best subcontractor allocation plan, which minimize both the cost and production time via an Evolutionary Multiobjective Optimization (EMO) algorithm (NSGA-II), achieving interesting results. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

automated Integration of BIM and 3D GIS for Sustainable Infrastructure Management

Automated Integration of BIM and 3D GIS for Sustainable Infr...

引用

learning and Technology conference (L&T)

作者： Yassmine Zada Imane Sebari Victor Morel Amanda Pierrot Department of Photogrammetry and Cartography School of Geomatics and Surveying Engineering IAV Hassan II Rabat Morocco Building Information Modeling Hub Setec International Paris France Geographic Information System Hub Setec International Paris France

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

In this paper, we present an automated integration approach for BIM and 3D GIS data about infrastructure projects to improve human-machine interaction with respect to sustainable project management. Based on the step-by-step process that starts with the preparation of data in BIM about buildings and complex infrastructures, CAD for the buried networks and terrain models, and BIM itself for buildings and complex infrastructures by generating IFC models. This is followed by a cleansing and optimisation step to find geometric conflicts. ETL processes automate the development of a unified spatial database integrating BIM and GIS data, without loss to geometric or attribute data. Automation tools are used to transfer data between BIM and GIS environments. This process enables the design of a 3D GIS application for 3D visualization and advanced spatial queries, such as efficient monitoring and decision making. From a case study in preliminary infrastructure projects, such as underground networks and building structure mapping, significant improvements in workflow efficiency were observed, improving by 30%, and data errors were reduced by 20% compared to manual methods. It is an essential gain for infrastructure integration and management, in particular within the complex environments involving smart cities. This approach contributes to sustainable infrastructure management through the use of global real-time views about projects.

关键词： Solid modeling Three-dimensional displays Smart cities Buildings Project management Spatial databases data models Real-time systems Optimization Monitoring

来源：评论

学校读者我要写书评

暂无评论

EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection 22

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

引用

22nd IEEE international Symposium on Biomedical Imaging, ISBI 2025

作者： Yang, Fan Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei 230026 China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou 215123 China Key Laboratory of Precision and Intelligent Chemistry Ustc Anhui Hefei 230026 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Cas Beijing 100190 China

ISBN: (纸本)9798331520526

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment. © 2025 IEEE.

关键词： Anatomical Landmark Detection Unsupervised Domain Adaptation

来源：评论

学校读者我要写书评

暂无评论

DT-FLDS: DT-enabled Federated learning data Sharing Model 19

DT-FLDS: DT-enabled Federated Learning Data Sharing Model

引用

2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Cheng, Yage Sun, Wen Zhang, Haibin Zhang, Rui Zhou, Xiangnan Xidian University The School of Cyber Engineering Xi'an China Northwestern Polytechnical University School of Cybersecurity Xi'an China

ISBN: (纸本)9798350346558

data sharing is crucial to promoting the process of informatization development. How to ensure the security of data and privacy is a major problem faced by data sharing. In this paper, we consider a DT-enabled federated learning data sharing model, named DTFLDS, where DTs capture the characteristics of shared devices to assist federal training. Federated learning is utilized to keep the data training locally and only share the aggregation model instead of the source data, which ensuring data and privacy security. Noting the reverse attack and privacy leakage in model transmission, a security aggregation algorithm based on secret sharing is proposed to ensure the security and efficiency of model aggregation. Numerical results show that the proposed is superior to the benchmark in sharing efficiency. © 2022 IEEE.

关键词： learning systems

来源：评论

学校读者我要写书评

暂无评论

22nd international conference on Web engineering, ICWE 2022

22nd International Conference on Web Engineering, ICWE 2022

引用

22nd international conference on Web engineering, ICWE 2022

ISBN: (纸本)9783031099168

The proceedings contain 45 papers. The special focus in this conference is on Web engineering. The topics include: SPARQL Endpoints and Web API (SWApi);about Lightweight Code Generation;web Push Notifications from Solid Pods;solid Web Monetization;Social Events Analyzer (SEA): A Toolkit for Mining Social Workflows by Means of Federated Process Mining;compaz: Exploring the Potentials of Shared Dictionary Compression on the Web;towards Differentially Private Machine learning Models and Their Robustness to Adversaries;FAIRification of Citizen Science data;enhance Web-Components in Order to Increase Security and Maintainability;Blockchain and AI to Build an Alzheimer’s Risk Calculator;applying a Healthcare Web of Things Framework for Infertility Treatments;achieving Corruption-Transparency in Service Governance Processes with Blockchain-Technology Based e-Participation;We Don’t Need No Real Users?! Surveying the Adoption of User-less Automation Tools by UI Design Practitioners;user Acceptance of Modified Web Page Loading Based on Progressive Streaming;supporting Natural Language Interaction with the Web;a Web Crowdsourcing Platform for Territorial Control in Smart Cities;MEMTD: Encrypted Malware Traffic Detection Using Multimodal Deep learning;Effective Malicious URL Detection by Using Generative Adversarial Networks;Configurable Per-Query data Minimization for Privacy-Compliant Web APIs;towards Bridging the Gap Between Knowledge Graphs and Chatbots;an Ontological Approach for Recommending a Feature Selection Algorithm;a Model for Meteorological Knowledge Graphs: Application to Météo-France data;explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers;dual-Attention Based Joint Aspect Sentiment Classification Model;lunatory: A Real-Time Distributed Trajectory Clustering Framework for Web Big data;engineering Annotations to Support Analytical Provenance in Visual Exploration Processes;dynamic Networ

关键词：

来源：评论

学校读者我要写书评

暂无评论

learning Representations from Local to Global for Fine-grained Patient Similarity Measuring in Intensive Care Unit 22

Learning Representations from Local to Global for Fine-grain...

引用

22nd IEEE international conference on data Mining (ICDM)

作者： Zhang, Xianli Qian, Buyue Li, Yang Gao, Zeyu Guan, Chong Wang, Renzhen Zheng, Yefeng Zheng, Hansen Li, Chen Xi An Jiao Tong Univ Sch Comp Sci & Technol Shaanxi Prov Key Lab Big Data Knowledge Engn Xian Peoples R China Capital Med Univ Beijing Chaoyang Hosp Beijing Peoples R China Sch Math & Stat Xian Shaanxi Peoples R China Tencent Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665450997

Patient similarity measurement is an essential step in discovering clinically meaningful subgroups and building case retrieval systems. Most existing studies implement this procedure using similarity measurement algorithms on the multivariate clinical time-series (input space) or the low-dimensional patient representation (representation space) learned by a representation learning model. However, they either suffer from the adverse effects of irrelevant variables in the data or fail to assess the fine-grained similarity underneath the disease progress. In this paper, we propose a method to measure more fine-grained patient similarity in the state space, where each patient is represented by a series of state representations that reveal the dynamic health status. We discuss three desiderata, including stability, personality, and interpretability, for the state representations, and on this basis, develop a supervised predictive model that learns good state representations for identifying similar patients and predicting patient outcomes. Experimental results on the publicly available dataset MIMIC-III show that our method offers a promising direction for precisely identifying similar patients at the state trajectory level, as well as accurately predicting outcomes.

关键词： patient similarity representation learning deep learning healthcare

来源：评论

学校读者我要写书评

暂无评论

Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement learning 22

Automatic Noise Filtering with Dynamic Sparse Training in De...

引用

22nd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Grooten, Bram Sokar, Ghada Dohare, Shibhansh Mocanu, Elena Taylor, Matthew E. Pechenizkiy, Mykola Mocanu, Decebal Constantin Eindhoven University of Technology Netherlands University of Alberta Canada University of Twente Netherlands Canada University of Luxembourg Luxembourg

Tomorrow's robots will need to distinguish useful information from noise when performing different tasks. A household robot for instance may continuously receive a plethora of information about the home, but needs to focus on just a small subset to successfully execute its current chore. Filtering distracting inputs that contain irrelevant data has received little attention in the reinforcement learning literature. To start resolving this, we formulate a problem setting in reinforcement learning called the extremely noisy environment (ENE), where up to 99% of the input features are pure noise. Agents need to detect which features provide task-relevant information about the state of the environment. Consequently, we propose a new method termed Automatic Noise Filtering (ANF), which uses the principles of dynamic sparse training in synergy with various deep reinforcement learning algorithms. The sparse input layer learns to focus its connectivity on task-relevant features, such that ANF-SAC and ANF-TD3 outperform standard SAC and TD3 by a large margin, while using up to 95% fewer weights. Furthermore, we devise a transfer learning setting for ENEs, by permuting all features of the environment after 1M timesteps to simulate the fact that other information sources can become relevant as the world evolves. Again, ANF surpasses the baselines in final performance and sample complexity. Our code is available online. © 2023 international Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共226页 << < 53 54 55 56 57 58 59 60 61 62 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：