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781-790 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：

Towards Multi-Label Open Set Diagnosis for Medical Images

Towards Multi-Label Open Set Diagnosis for Medical Images

引用

IEEE international Symposium on Biomedical Imaging

作者： Shijie Pang Yuze Zhao Rui-Wei Zhao Rui Feng Academy for Engineering and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In real clinical scenarios, diagnosis algorithms may face the complex concurrent presence of multiple common, rare, or even unknown disease conditions. In particular, rare disease categories may be underrepresented or even absent from the class-imbalanced training set, leaving safe and trustworthy diagnostic decision-making extremely challenging. In this work, we first formalize the medical image diagnosis as a multi-label open set recognition under class-imbalanced training data and propose a novel Multi-label Open set Diagnosis (MOSD) solution to address this problem. Our MOSD first employs a Balanced Multi-label data Augmentation (BMDA) module to augment multi-label image data to alleviate the class imbalance in model training more effectively. Then it also boasts a Multi-label Evidential Deep learning (MEDL) module with tailored learning objectives to better estimate the uncertainty for multi-label open set prediction. Experimental results on the ODIR-5K benchmark successfully demonstrate its superiority in both close set known class classification and open set unknown class detection.

关键词： Training Uncertainty Image recognition Face recognition Training data Multi label classification Robustness Medical diagnosis Medical diagnostic imaging Diseases

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

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

引用

IEEE international Symposium on Biomedical Imaging

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

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

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.

关键词： Training Measurement Adaptation models Three-dimensional displays Smoothing methods Head Kernel Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

A Deep learning and Transfer learning-Based Application for Skin Cancer Classification

A Deep Learning and Transfer Learning-Based Application for ...

引用

IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Sabrina Karboua Fouzi Harrag Sarrah Karboua Amal Booutadjine Mohamed Deriche Department of Computer Science Setif 1 University-Ferhat ABBAS Abdelhamid MEHRI - Constantine 2 University Algeria College of Engineering and Information Technology AIRC Ajman University UAE

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Skin cancer is the most prevalent type of cancer worldwide, accounting for approximately one-third of all cancer cases, with its incidence steadily rising in recent years. This is why early and precise identification of skin cancer is crucial for effective treatment and enhancing survival rates. Leveraging advancements in deep learning and transfer learning, we developed a comprehensive system to address this challenge. In this study, we employed the HAM10000 dataset, a benchmark resource for skin cancer research, to train and evaluate deep learning models. Two architectures, DenseNet201 and ResNet50, were fine- tuned and modified to enhance their accuracy in classifying skin lesions as benign or malignant. Rigorous preprocessing, data augmentation, and model fine-tuning were utilized to address challenges such as dataset imbalance and improve classification accuracy. Our experiments revealed that the modified fine-tuned DenseNet201 model achieved superior performance compared to fined-tuned ResNet50 and other leading models reviewed in the literature with an accuracy of 95.04%. This model was then integrated into a mobile application, “SkinSafe,” to provide an accessible and user-friendly tool for real-time skin cancer classification.

关键词： Deep learning Accuracy Transfer learning Skin Real-time systems data models Mobile applications Lesions Skin cancer Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Q-learning and Neural Networks in automated Driving Systems

Q-learning and Neural Networks in Automated Driving Systems

引用

data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Zeyu Chen Mechanical Engineering UCL London UK

With the development of artificial intelligence, machine learning, and other disciplines, automation in various fields is becoming popular. Therefore, autonomous driving systems in the automotive field represent a major trend in the future of intelligent transportation systems. This paper focuses on the application of Q-learning and deep neural networks in autonomous driving systems. The simulation in Python is used to simulate the condition of the car driving alone on the road. Firstly, the normal car driving situation was analyzed and the actions and states in Q-learning were set. The action set-up ensures the distance sensors’ number and direction. Then, this paper draws the neural network and simulates it to get the data. Secondly, it analyzes the resulting reward scores and iteration times and identifies uncertainties through line graphs. In addition, it observes the simulation video and derives the reasons why uncertainty appears. In the simulation, the Best Score, Average generation time and Genetic distance were obtained from the simulation against the number of Gens. The data for genetic distance was found to increase with the number of times it was generated, indicating that the Q-learning component was successfully run in the simulation. And it was found that the uncertainty was due to the birth point of the vehicle. According to the uncertainty, further development of the simulation could include more road environments and different obstacles.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predicting Participants’ Performance in Programming Contests Using Deep learning Techniques 22nd

Predicting Participants’ Performance in Programming Contest...

引用

22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Mahbubur Rahman, Md. Chandra Das, Badhan Biswas, Al Amin Musfique Anwar, Md. Iowa State University Ames Iowa United States Jahangirnagar University Dhaka Bangladesh Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031274084

In recent days, the number of technology enthusiasts is increasing day by day with the prevalence of technological products and easy access to the internet. Similarly, the amount of people working behind this rapid development is rising tremendously. Computer programmers consist of a large portion of those tech-savvy people. Codeforces, an online programming and contest hosting platform used by many competitive programmers worldwide. It is regarded as one of the most standardized platforms for practicing programming problems and participate in programming contests. In this research, we propose a framework that predicts the performance of any particular contestant in the upcoming competitions as well as predicts the rating after that contest based on their practice and the performance of their previous contests. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

25th International Conference on Internet Computing and IoT,...

引用

25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859229

The proceedings contain 33 papers. The special focus in this conference is on Internet Computing and IoT. The topics include: Malware Detection in the IoT Home Network;IoT-Based Analysis of Environmental and Motion data for Comfort and Energy Conservation in Optimizing HVAC Systems;improving Critical Controls Using IoT and Computer Vision;A data-Driven Driving Under the Influence (DUI) Detection, Notification and Prevention System Using Artificial Intelligence and Internet-Of-Things (IoT);understanding User Interactions with IoT Process Models: A Demographic Perspective;Advancing IoT Process Modeling: A Comparative Evaluation of BPMNE4IoT and Traditional BPMN on User-Friendliness, Effectiveness, and Workload;Threat Detection Using MLP for IoT Network;harnessing Social Robotics and the Internet of Things to Reduce the Risk of Older Adults Developing Hypothermia and Dehydration;re/Imagining Smart Home Automation Framework in the Era of 6G-Enabled Smart Cities;smart Roadway Monitoring: Pothole Detection and Mapping via Google Street View;Energy-Efficiency Modeling for AI Applications on Edge Computing;optimizing Wireless Sensor Network Node Placement Using Bacterial Foraging Optimization;The Vital Role of Small and Marginal Farmer in Future of Our Climate: Democratization of Machine learning, Artificial Intelligence, and Dairy Cow Necklace Sensors in Achieving the UN Climate Change Goals (COP21) and the Paris Agreement;autonomous Driving Prototype with Raspberry Pi by Using Image Processing Technology;towards Implementation of Privacy-Preserving Federated learning Aggregation Using Multi-key Homomorphic Encryption;advancing Nursing Education Through Virtual Reality Training: A Revolutionary Approach to Ensuring Patient Safety;multiDrone Simulator An Open Source Multi-plataform Tool to Use in Tests of Optimized Flight of Group of Drones;Revolutionizing Multiplayer Gaming: A Deep Dive into VisionXO, a 3D Multiplayer Tic-Tac-Toe Game.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Safety Boundary in Virtual Reality: An Approach Based on User Motion Analysis and Prediction 19

Safety Boundary in Virtual Reality: An Approach Based on Use...

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： Qu, Chenxin Che, Xiaoping Cai, Zimo Li, Haohang Wang, Siyuan Beijing Jiaotong University School of Software Engineering Beijing China

ISBN: (纸本)9798350346558

In 2021, with the popularity of the concept of the Metaverse, Virtual Reality (VR) technology has gradually developed, which will play an indispensable role in promoting the construction of the Merse. However, due to the immature technology, the safety of users cannot be fully guaranteed. While users have a deeper sense of immersion in the virtual world, they are likely to lose their perception of the surrounding physical environment. The purpose of this study is to solve the pain point that users still cause body collisions within the safety boundary in the VR scene. We recruited 62 participants to play VR games and trigger particular events in the virtual reality scene. Three basic motion event categories were defined according to the common motions of users. We collected participants' characteristic data through questionnaires and recorded the user's motion information using the headset and controllers. After acquiring the participants' motion data, we segmented the three-dimensional coordinates, extracted the motion range features, and constructed the relational data set. Furthermore, we used statistical methods to mine the correlation information between user characteristics and user motion range categories and explore and analyze the possibility of optimizing the dataset. Finally, we used the machine learning methods to train the multi-classification prediction model and deployed our safety boundary systems in the virtual reality environment. © 2022 IEEE.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Analysis and Prediction of COVID-19 Cases Using Machine learning Algorithms

Analysis and Prediction of COVID-19 Cases Using Machine Lear...

引用

international conference on Computing, Communication, Electrical and Biomedical Systems, ICCCEBS 2021

作者： Balasubramani, S. Sudhakar, A. Kalyan, Ch. Satwik, M. sai Department of Computer Science & Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9783030861643

The new respiratory disease named COVID-19 was first confirmed in Wuhan, China, in December 2019. From December, corona have been spread across the world. In order to decrease that effect, the government of India had taken the decision to implement the lockdown in our country. On 22nd of March, our Prime Minister announced Janata Curfew. The first lockdown was started on 25th of March 2020. After the cases had decreased, the government split the lockdowns into three phases which are named as lockdown 2.0, 3.0, and 4.0 by giving some relaxations. In our paper we have studied the data of people affected by COVID-19 consisting confirmed cases, deaths, and recoveries during lockdown and before lockdown and after lockdowns. We have used two supervised machine learning models such as linear regression and polynomial regression in order to predict the future cases in India. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

learning Association Between learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions 22nd

Learning Association Between Learning Objectives and Key Con...

引用

22nd international conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion

作者： Shimmei, Machi Matsuda, Noboru North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9783030782702;9783030782696

It has been shown that answering questions contributes to students learning effectively. However, generating questions is an expensive task and requires a lot of effort. Although there has been research reported on the automation of question generation in the literature of Natural Language Processing, these technologies do not necessarily generate questions that are useful for educational purposes. To fill this gap, we propose QUADL, a method for generating questions that are aligned with a given learning objective. The learning objective reflects the skill or concept that students need to learn. The QUADL method first identifies a key concept, if any, in a given sentence that has a strong connection with the given learning objective. It then converts the given sentence into a question for which the predicted key concept becomes the answer. The results from the survey using Amazon Mechanical Turk suggest that the QUADL method can be a step towards generating questions that effectively contribute to students' learning.

关键词： Question generation MOOCS learning engineering

来源：评论

学校读者我要写书评

暂无评论

Relationship between Trends, Job Profiles, Skills and Training Programs in the Factory of the Future 22

Relationship between Trends, Job Profiles, Skills and Traini...

引用

22nd IEEE international conference on Industrial Technology (ICIT)

作者： Pontes, Joseane Geraldes, Carla A. S. Fernandes, Florbela P. Sakurada, Lucas Rasmussen, Ann Lilith Christiansen, Lasse Hafner-Zimmermann, Sabine Delaney, Kieran Leitao, Paulo Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana UTFPR Campus Ponta Grossa Curitiba Parana Brazil Univ Coll Northern Denmark Selma Lagerlofs Vej 2 DK-9000 Aalborg Denmark Steinbeis 2I GmbH Kienestr 35 D-70174 Stuttgart Germany Cork Inst Technol Rossa Ave Bishopstown Cork Ireland

ISBN: (纸本)9781728157306

Industry 4.0 is promoting the digitisation of manufacturing sectors towards smart products, machines, processes and factories. The adoption of disruptive technologies associated to this industrial revolution is re-shaping the manufacturing environment, decreasing low-skilled activities and increasing highskill activities. These technological trends are affecting the job profiles and the skills required by the workforce, which demand proper training programs to address upskilling and reskilling needs. Having this in mind, this work proposes a model that contributes to understand how technological trends may impact the new job profiles and relevant skills, as well as how these skills may be upskilled by the workforce through available training programs according to their gaps and impact. The applicability of the proposed model was illustrated by considering two trends, the connectivity and the value of the data, and a catalogue of compiled new job profiles and training programs.

关键词： Training learning systems conferences data visualization Tools Market research Production facilities

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 << < 75 76 77 78 79 80 81 82 83 84 > >>

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：