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

3,134 篇 会议
20 册 图书
18 篇 期刊文献

馆藏范围

3,172 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,131 篇 工学
- 1,886 篇 计算机科学与技术...
- 1,008 篇 软件工程
- 265 篇 控制科学与工程
- 257 篇 电气工程
- 244 篇 信息与通信工程
- 197 篇 生物工程
- 155 篇 交通运输工程
- 106 篇 生物医学工程（可授...
- 96 篇 机械工程
- 61 篇 光学工程
- 58 篇 安全科学与工程
- 57 篇 电子科学与技术（可...
- 49 篇 材料科学与工程（可...
- 47 篇 动力工程及工程热...
- 41 篇 建筑学
- 36 篇 土木工程
517 篇 理学
- 266 篇 数学
- 203 篇 生物学
- 123 篇 统计学（可授理学、...
- 90 篇 物理学
- 58 篇 系统科学
409 篇 管理学
- 268 篇 管理科学与工程(可...
- 165 篇 图书情报与档案管...
- 100 篇 工商管理
203 篇 教育学
- 200 篇 教育学
166 篇 医学
- 161 篇 临床医学
- 80 篇 基础医学(可授医学...
- 52 篇 药学(可授医学、理...
- 33 篇 公共卫生与预防医...
69 篇 法学
- 58 篇 社会学
31 篇 经济学
18 篇 农学
7 篇 文学
1 篇 艺术学

主题

295 篇 machine learning
291 篇 deep learning
142 篇 training
125 篇 data models
120 篇 neural networks
114 篇 accuracy
105 篇 feature extracti...
101 篇 data mining
87 篇 predictive model...
83 篇 artificial intel...
82 篇 computational mo...
68 篇 machine learning...
62 篇 students
57 篇 reinforcement le...
57 篇 real-time system...
47 篇 convolutional ne...
45 篇 big data
43 篇 internet of thin...
42 篇 support vector m...
42 篇 training data

机构

30 篇 school of comput...
11 篇 institute of inf...
9 篇 kazan natl res t...
9 篇 school of cyber ...
7 篇 school of comput...
6 篇 school of inform...
5 篇 inesc tec porto
5 篇 hefei univ techn...
5 篇 wroclaw univ sci...
4 篇 univ michigan an...
4 篇 school of automa...
4 篇 chinese acad sci...
4 篇 college of compu...
4 篇 univ minho algor...
4 篇 university of ba...
4 篇 school of mechan...
4 篇 xi an jiao tong ...
4 篇 nanyang technol ...
4 篇 singapore manage...
4 篇 university of mi...

作者

7 篇 yang yang
7 篇 santos matilde
6 篇 peng jun
6 篇 taghi m. khoshgo...
6 篇 wang fei
5 篇 li peng
5 篇 horvath tomas
5 篇 wozniak michal
5 篇 machado jose
5 篇 r.c. luo
4 篇 zhang tao
4 篇 lee seong-whan
4 篇 ksieniewicz pawe...
4 篇 zhang lei
4 篇 yang yi
4 篇 novais paulo
4 篇 li li
4 篇 trong-hop do
4 篇 liu qin
4 篇 shimizu jun

语言

3,039 篇 英文
125 篇 其他
15 篇 中文

检索条件"任意字段=21st International Conference on Intelligent Data Engineering and Automated Learning"

共 3172 条记录，以下是701-710 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：

VLEO Satellite Radio Access Network Slicing: A Hierarchical Deep Reinforcement learning Approach 21

VLEO Satellite Radio Access Network Slicing: A Hierarchical ...

引用

21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Juan Xiao, Ruoyu Sun, Peng Qiao, Ying Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (纸本)9798350329223

Network slicing is a key technology of Radio Access Networks (RAN), enabling support for heterogeneous service with various quality of service (QoS) requirements. However, effective RAN slicing solution remains a significant challenge in the time-varying satellite networks. Therefore, in this paper, a satellite RAN slicing framework with two time-scales and resource granularity is designed. In a large time-scale, radio resources are coarsely allocated to Very Low Earth Orbit (VLEO) satellites in the software-defined networking (SDN) level. In a small time-scale, radio resources and transmit power are finely allocated to ground users. An optimization problem is formulated to maximize ground users' QoS satisfaction of slices and system Spectrum Efficiency (SE). So we propose a satellite network slicing algorithm based on a hierarchical deep reinforcement learning framework (SNS-HRL). In a large time-scale, the resource allocation problem in the SDN allocation level is modeled as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). QMIX algorithm is utilized to allocate radio resources to different VLEO satellites. In a small time-scale, the resource allocation problem among slices is modeled as a Markov Decision Process (MDP), and the Proximal Policy optimization (PPO) algorithm is employed to schedule radio resources in the satellite allocation level. Additionally, resource allocation among users is achieved through a combination of the Proportional Fairness (PF) algorithm and the Lagrange multiplier method. Furthermore, simulation results demonstrate the effectiveness of the proposed satellite RAN slicing algorithm. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Context-Based Decision-Making Trust Scheme for Malicious Detection in Connected and Autonomous Vehicles 5

A Context-Based Decision-Making Trust Scheme for Malicious D...

引用

5th international conference on Computing, Electronics and Communications engineering. iCCECE 2022

作者： Eze, Emmanuel O. Keates, Simeon Pedram, Kamran Esfahani, Alireza Odih, Uchenna University of Greenwich Faculty of Engineering and Science School of Engineering United Kingdom University of Chichester West Sussex Chichester United Kingdom

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

The fast-evolving intelligent Transportation Systems (ITS) are crucial in the 21st century, promising answers to congestion and accidents that bother people worldwide. ITS applications such as Connected and Autonomous Vehicle (CAVs) update and broadcasts road incident event messages, and this requires significant data to be transmitted between vehicles for a decision to be made in real-Time. However, broadcasting trusted incident messages such as accident alerts between vehicles pose a challenge for CAVs. Most of the existing-Trust solutions are based on the vehicle's direct interaction base reputation and the psychological approaches to evaluate the trustworthiness of the received messages. This paper provides a scheme for improving trust in the received incident alert messages for real-Time decision-making to detect malicious alerts between CAVs using direct and indirect interactions. This paper applies artificial intelligence and statistical data classification for decision-making on the received messages. The model is trained based on the US Department of Technology Safety Pilot Deployment Model (SPMD). An Autonomous Decision-making Trust Scheme (ADmTS) that incorporates a machine learning algorithm and a local trust manager for decision-making has been developed. The experiment showed that the trained model could make correct predictions such as 98% and 0.55% standard deviation accuracy in predicting false alerts on the 25% malicious data © 2022 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation 21st

I Can’t Believe It’s Not Better: In-air Movement for Alzh...

引用

The 21st international Graphonomics Society conference, IGS 2023

作者： Bensalah, Asma Parziale, Antonio De Gregorio, Giuseppe Marcelli, Angelo Fornés, Alicia Lladós, Josep Computer Vision Center Universitat Autònoma de Barcelona Bellaterra Spain Computer Science Department Universitat Autònoma de Barcelona Bellaterra Spain DIEM University of Salerno Via Giovanni Paolo II 132 SA Fisciano84084 Italy AI3S Unit CINI National Laboratory of Artificial Intelligence and Intelligent Systems University of Salerno SA Fisciano Italy

ISBN: (纸本)9783031454608

During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Investigating Backpropagation Neural Networks for Joint Torque Prediction During Walking: A Gait Analysis study 6th

Investigating Backpropagation Neural Networks for Joint Torq...

引用

6th international and 21st National conference on Machines and Mechanisms, iNaCoMM 2023

作者： Pandey, Madhav Narayan, Jyotindra Trivedi, Dinesh K. Dwivedy, Santosha K. Department of Mechanical Engineering Delhi Technological University Rohini Delhi New Delhi110042 India Department of Mechanical Engineering Indian Institute of Technology Guwahati Assam Guwahati781039 India

ISBN: (纸本)9789819754229

Assessing the overall health and condition of individuals across various age groups is enhanced by prior examination of gait analysis. Nonetheless, the conventional method of collecting gait data typically requires the physical presence of participants. In this study, a machine learning technique called the backpropagation neural network (BPNN) is employed to estimate lower-limb joint torques based on anthropometric and joint movements. The joint movements of 40 healthy participants (25 males, 15 females) aged 7–65 are collected using an existing motion capture setup. The input data consists of age, sex, total body mass, total height, thigh length, calf length, ankle-foot length, thigh mass, calf mass, ankle mass, hip joint, knee joint, and ankle joint angles of all participants. The output data contains the joint torques of the simplified leg model computed from the inverse dynamics formulation using the Euler-Lagrangian principle. The BPNN is trained using two optimization algorithms, Levenberg-Marquardt (LM) and resilient propagation (RP), and a comparative analysis reveals the notable capabilities of the LM-BPNN model in accurately estimating joint torques. The effectiveness of the BPNN models is further demonstrated through the estimation of joint torques for a female subject (60 years) from the testing dataset. The LM-BPNN model is found to be effective by 63.28%, 29.07%, and 4.23% for hip, knee, and ankle joint torque prediction, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Joints (anatomy)

来源：评论

学校读者我要写书评

暂无评论

Ensemble Deep learning Approaches for Myopic Maculopathy Plus Lesions Segmentation 1

引用

Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023

作者： Xiao, Fan Hou, Junlin Xu, Jilan Xu, Yiqian Zhang, Bo Zhang, Yuejie Zou, Haidong Feng, Rui Academy for Engineering and Technology Fudan University Shanghai China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopia is a leading cause of visual impairment and blindness in several countries. Effective diagnosis and intervention are crucial, typically relying on manual image analysis by ophthalmologists, which is time-consuming and experience-dependent. In this work, we introduce an ensemble of deep learning techniques for myopic maculopathy plus lesions segmentation. Specifically, we utilize UNet, UNet++, and DeeplabV3+ to segment three lesions with strong data augmentation. Our ensembled model has proved to be effective in the MICCAI 2023 Myopic Maculopathy Analysis Challenge (MMAC). This dataset covers classification, segmentation, and spherical equivalent prediction, fostering automated analysis research. Extensive experiments on the MMAC dataset reveal the superior performance of our proposed approach, which ranked 1st on the challenge leaderboard. This work addresses a critical need for accurate and efficient myopic maculopathy diagnosis and intervention. The code will be publicly available at https://***/FDU-VTS/MMAC. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

An Advanced Tool for Semi-automatic Annotation for Early Screening of Neurodevelopmental Disorders 21st

An Advanced Tool for Semi-automatic Annotation for Early Scr...

引用

21st international conference on Image Analysis and Processing (ICIAP)

作者： Bernava, Giuseppe Massimo Leo, Marco Carcagni, Pierluigi Distante, Cosimo Natl Res Council Italy Inst Chem Phys Proc IPCF Viale Ferdinando Stagno dAlcontres 37 I-98158 Messina Italy Natl Res Council Italy Inst Appl Sci & Intelligent Syst Via Monteroni Snc I-73100 Lecce Italy

ISBN: (纸本)9783031133244;9783031133237

Non-invasive solutions (no sensors nor markers) appear the most appealing for assessment of body movements and facial dynamics in order to predict Neurodevelopmental disorders (NDD) even in the first days of life. To this aim, recent advances in machine learning applied could be effectively exploited on visual data framing the children, but they suffer from the scarcity of annotated data for training the algorithms. In order to fill this gap, in this paper, a semi-automatic tool specifically designed for labelling videos of children in cribs is introduced. It consists of a Graphical User Interface allowing to select: 1) videos, or static images, to be processed and 2) the desired annotation goal achieved by state-of-the-art deep learning based neural architectures.

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Research on Spatio-Temporal data Processing Framework

Research on Spatio-Temporal Data Processing Framework

引用

international conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Liu He Ren Ao Li Jianping College of Computer Science Chongqing University Chongqing China Chongqing Academy of Educational Sciences Chongqing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Aiming at the challenges in time series data processing, this paper proposes a new deep learning model, which uses Spiking neurons with time series processing ability to organize, represent and transmit information instead of traditional methods. By combining the time representation characteristics of Spiking neurons with the hierarchical deep connection advantages of complex networks, this study explores a deep learning model combining time and space, which shows significant advantages in the representation and processing ability of time series data. Specifically, we design and implement a model that combines the Spiking mechanism with complex networks. This model can effectively capture the dynamic changes in time series data and improve processing efficiency. The experimental results show that the proposed model has achieved good performance on multiple time series data sets, which verifies its effectiveness and feasibility in time series data representation and processing. This study provides a new method for the field of time series data analysis, which is expected to be widely used in intelligent systems, financial forecasting, bioinformatics and other fields.

关键词： Deep learning Biological system modeling Time series analysis Neurons Complex networks Information processing Media data models Timing intelligent systems

来源：评论

学校读者我要写书评

暂无评论

A Federated learning Aggregation that Integrates Clustering and Momentum Correction Based on Historical High-Quality Gradients 21

A Federated Learning Aggregation that Integrates Clustering ...

引用

作者： Xu, Jian Guo, Bing Chen, Fei Shen, Yan Dai, Shengxin Dai, Cheng Sichuan University School of Computer Science Chengdu China Chengdu University of Information Technology School of Computer Science Chengdu China

ISBN: (纸本)9798350329223

Nowadays, federated learning emerges as a new technique to preserve data privacy in distributed machine learning by limiting local data to the client and only aggregating model parameters from multiple parties on the server side. However, it also faces consequent threats from malicious clients, and some methods have been proposed without sufficient defense capability and efficient aggregation. To address these problems, we propose a novel aggregation algorithm, which incorporates PCA operation and clustering to select the excellent client in each round;and brings in a momentum-accelerated historical quality gradient-based approach to increase the weight when the performances of the global model are consistently degraded, which can achieve faster convergence on the basis of ensuring that the update direction of the global model is not manipulated by malicious nodes. Experimental results on the MNIst datasets show that our algorithm is only 0.93% lower than FedAvg under a no-attack scenario, and exhibits faster convergence compared to Krum, Multi-Krum, and Trimmed Mean Aggregation, and the accuracy improves 10.68%,35.83%,19.65% under Black-Box Edge Attack, Sign Flipping Attack, and Same Value Attack. © 2023 IEEE.

关键词： Momentum

来源：评论

学校读者我要写书评

暂无评论

PaGAN: Generative Adversarial Network for Patent understanding 21

PaGAN: Generative Adversarial Network for Patent understandi...

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Guarino, Guillaume Samet, Ahmed Nafi, Amir Cavallucci, Denis INSA Strasbourg ICUBE UMR CNRS 7357 Strasbourg France

ISBN: (纸本)9781665423984

In recent years, Deep learning methods have become very popular in Natural Language Processing (NLP), especially transformer-based architecture. NLP domain requires a high volume of annotated data to work. Unfortunately, obtaining high-quality and voluminous labeled data is expensive and time-consuming. One promising method which has singled out for its performance in the context of data deficiency is semi-supervised learning with Generative Adversarial Networks (GAN). In this paper, we propose a new approach called PaGAN which is a combination of a document classifier and a sentence-level classifier inside a GAN for patent documents understanding. The idea is to mine the patent's motivating problem (aka contradiction in TRIZ domain) which is fundamentally important to understand the underlying invention and its originality. PaGAN is applied and evaluated on a real-world dataset. Experiments show outperforming results of PaGAN comparatively to baseline approaches.

关键词： Patent Deep learning Generative adversarial network TRIZ

来源：评论

学校读者我要写书评

暂无评论

learning, Un-learning, and Relearning in 4IR in Rural Environments

Learning, Un-Learning, and Relearning in 4IR in Rural Enviro...

引用

2022 international conference on Maintenance and intelligent Asset Management, ICMIAM 2022

作者： Kishore, N. Pretorius, J.H.C. Chattopadhyay, G. University of Johannesburg Postgraduate School of Engineering Management Johannesburg South Africa Federation University School of Science Engineering and Information Technology GippslandVIC Australia

ISBN: (纸本)9781665461795

The Fourth Industrial Revolution (4IR) requires constant learning by people and machines to belong. Every person and all devices have become a node (i.e., attached to a network) [1] to "exist."The relevance of possessing the most important literacies and skills has put pressure on people from all ages and walks of life. For humans to obtain knowledge (technology, data, and human literacy) in 4IR, there will be a new generation of knowledge acquisition in the different stages of the human cycle due to radical industrial change. The need for 21st-century children to incorporate 4IR in their learning is to improve stEM (Science, Technology, engineering, and Mathematics) performance and critical thinking ability. The learning phase for children will create more logical and analytical adults that can co-exist with robots, cobots (collaborative robots), and AI (Artificial Intelligence). This form of learning is achieved through teaching children coding and robotics at schools from early childhood development (ECD) up until adulthood [2]. However, the critical condition of acquiring the set of skills is having access to them frequently. In rural areas, the basics for humanity to exist is the first battle that is fought daily before the children can even think of education. Hence, there are two battles kids in South Africa (especially the rural area) must overcome. However, once the children have access to education, learning 4IR will be the only type of knowledge they know. The same first-time learning of 4IR cannot be applied to adults with skills acquired previously through formal qualifications and trade experience, as one needs to thrive in the digital economy and new trends [3]. One must become a lifelong learner. Hence, the significant challenges people who live in Rural areas must overcome are their daily challenges while competing to stay ahead of the latest industrial revolution. This gives birth to the new norm of unlearning the previous survival techniques and r

关键词： Industry 4.0

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318页 << < 67 68 69 70 71 72 73 74 75 76 > >>

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：