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

3,111 篇 会议
20 册 图书
18 篇 期刊文献

馆藏范围

3,149 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,113 篇 工学
- 1,871 篇 计算机科学与技术...
- 997 篇 软件工程
- 259 篇 控制科学与工程
- 252 篇 电气工程
- 234 篇 信息与通信工程
- 198 篇 生物工程
- 149 篇 交通运输工程
- 105 篇 生物医学工程（可授...
- 96 篇 机械工程
- 60 篇 光学工程
- 57 篇 电子科学与技术（可...
- 54 篇 安全科学与工程
- 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
506 篇 理学
- 257 篇 数学
- 204 篇 生物学
- 120 篇 统计学（可授理学、...
- 90 篇 物理学
- 57 篇 系统科学
400 篇 管理学
- 263 篇 管理科学与工程(可...
- 161 篇 图书情报与档案管...
- 99 篇 工商管理
201 篇 教育学
- 198 篇 教育学
165 篇 医学
- 160 篇 临床医学
- 79 篇 基础医学(可授医学...
- 52 篇 药学(可授医学、理...
69 篇 法学
- 58 篇 社会学
32 篇 经济学
- 32 篇 应用经济学
17 篇 农学
7 篇 文学
1 篇 艺术学

主题

293 篇 machine learning
290 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...
61 篇 students
57 篇 reinforcement le...
56 篇 real-time system...
46 篇 convolutional ne...
45 篇 big data
42 篇 internet of thin...
42 篇 training data
42 篇 humans

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

3,083 篇 英文
58 篇 其他
13 篇 中文

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

共 3149 条记录，以下是3051-3060 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：

FRD: Few-shot rumor detection for the novel coronavirus pneumonia epidemic

FRD: Few-shot rumor detection for the novel coronavirus pneu...

引用

IEEE international Symposium on Parallel and Distributed Processing with Applications and IEEE international conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Danroujing Chen Xiaoliang Chen Xuyang Wang Weicheng Wang Xiaoyu Dong School of Computer and Software Engineering Xihua University Chengdu China

With the full arrival of the digital era, fueled by both information technology and business marketing, rumors are produced and spread endlessly on social networks. During the recent novel coronavirus pneumonia epidemic, online rumors have continued to flourish. Most existing studies on traditional rumor detection rely on a large number of features in practical applications. However, the current severe epidemic scenarios have limited rumor information features, and it remains a challenging problem to detect epidemic rumors with high accuracy using only limited information. As a result, we propose a novel Few-shot Rumor Detection model (FRD) for the novel coronavirus pneumonia, which is combined with meta-learning to be able to accurately identify rumors as soon as possible in crises. Specifically, we started by using the BERT+BiLstM combination for rumor text feature extraction and representation to generate the historical rumor sample-wise vector and epidemic rumor sample-wise vector; secondly, the prototypical network was introduced to summarize the historical rumor data, and the feature vectors of samples belonging to the same category were averaged to obtain the prototype representation of historical rumor category; finally, we utilize the modified cosine similarity measure function to calculate the distance between the class-wise vector of historical rumor text and the sample-wise vector of epidemic rumor, and complete the rumor detection according to the nearest neighbor method. Our experimental results on English datasets show that the FRD rumor detection model proposed in this paper is superior to other baseline algorithms in terms of accuracy, precision, recall and macro F1 value. From the comparison of experimental results, the FRD model can effectively improve conventional rumor detection methods, and better realize the early detection of sudden epidemic rumors.

关键词： COVID-19 Epidemics Social networking (online) Pulmonary diseases Computational modeling Prototypes Nearest neighbor methods

来源：评论

学校读者我要写书评

暂无评论

Late Fusion of Text and Images for Predicting User Interests in Pinterest Social Network

Late Fusion of Text and Images for Predicting User Interests...

引用

international conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Areli Cabrera Oros Jonathán de Jesús Estrella Ramírez Juan Carlos Gómez Carranza División de Ingenierías Campus Irapuato-Salamanca Universidad de Guanajuato Salamanca Gto. México

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

Due to the large amount of information on social media, predicting user interest becomes crucial for various tasks including the improvement of the user experience in these platforms. This paper introduces a late fusion model designed to predict one of 29 predefined categories (considered as the user interests) established by Pinterest for any new pin uploaded by a user. A pin is an information unit with two components: an image and a short text comment. Given a new pin, the proposed model performs a multimodal classification by transforming and classifying each data modality (image and text) independently and then fusing the category probabilities with a weighted sum to predict the final categories for the pin. To test the validity of the fusion, a set of experiments is conducted analyzing and comparing the performance of different state-of-the-art deep learning transformations for each modality; and using a logistic regression model for classification. Additionally, the fusion is evaluated under different values for a weighting factor, in charge of assigning less or more importance to each data modality in the final decision. The experiments are carried out over a dataset composed of 29,000 pins collected directly from the Pinterest website. The results show that the late fusion of image and text representations increases the accuracy of predicting user interests when compared to the use of each single modality.

关键词： Deep learning Logistic regression Accuracy Social networking (online) Transforms Predictive models Vectors Pins Web sites Multimedia communication

来源：评论

学校读者我要写书评

暂无评论

YOLO-Based Detector for Enhancing Workplace Safety: Real-Time PPE Detection and Monitoring

YOLO-Based Detector for Enhancing Workplace Safety: Real-Tim...

引用

ACS/IEEE international conference on Computer Systems and Applications

作者： Souha Amri Laila Ouannes Anis Sahbani Najoua Essoukri Ben Amara Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Enova Robotics S.A. CNRS Institule for intelligent Systems and Robotics (ISIR) Sorbonne Université Paris France

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Ensuring the safety of workers in hazardous environments is crucial, given the potential severity of accidents. Indeed, Personal Protective Equipment (PPE), including safety helmets, vests, and boots, is vital in mitigating risks. However, adherence to PPE usage often falls short due to inadequate safety awareness among workers. To address this challenge, we aim to develop a robust safety monitoring system by leveraging advancements in deep learning and object detection technology. Our proposed method uses YOLOv7 and YOLOv8 algorithms for real-time detection and classification of safety equipment, including helmets, vests, and boots. For this aim, we carefully collect and annotate datasets from diverse sources like Roboflow and Kaggle. Then, we accurately evaluate performance metrics such as precision, recall, and speed through rigorous training and testing of various YOLOv7 and YOLOv8 configurations on annotated data batches. By conducting thorough comparative analyses, we aim to identify the most effective model for each PPE category, considering both detection accuracy and computational efficiency. Finally, the object detection system is deployed through a web interface to determine whether a person is protected or not.

关键词： Personal protective equipment Training Accuracy Computational modeling Employment streaming media Helmets Real-time systems Safety Monitoring

来源：评论

学校读者我要写书评

暂无评论

A smart fog-to-cloud system in airport: challenges and lessons learnt

A smart fog-to-cloud system in airport: challenges and lesso...

引用

international conference on Mobile data Management

作者： Antonio Salis Jens Jensen Telco & Media Division Engineering Sardegna Srl Cagliari Italy STFC RAL UK Research and Innovation Didcot United Kingdom

ISBN: (数字)9781728146638

ISBN: (纸本)9781728146645

Airports are one relevant environment in the smart cities implementation. Recent progresses in IoT have made it possible to develop advanced real-time services and assistance tools for travelers, making use of fog computing as an architectural model that places itself between the Cloud and the IoT, in the Cloud-to-Things Continuum, fulfilling requirements for strict real-time responses. The main tool exploits the concept of proximity marketing and covers the essential experience through the airport onto the flight, but also allows travelers to spend their waiting time by discovering the facilities of the airport, supported by a recommendation system using machine learning. The system provides also the airport planners an aggregated and anonymized view, like heat maps, of travelers behavior that highlight bottlenecks in the infrastructure, or highlight situations that require intervention, such as emergencies. The app in based on Android and makes use of the fog-to-cloud platform developed by the Horizon2020-funded mF2C project, giving evidence that the adoption of the fog-to-cloud approach brings significant benefits in terms of performance and optimization of resource usage, giving an objective evidence of the impact of the mF2C framework.

关键词： Airports Edge computing Cloud computing Computer architecture Security Real-time systems Androids

来源：评论

学校读者我要写书评

暂无评论

Experiential Virtual learning on the impacts of Covid-19 on Air Quality through Integration of Research in stEM Education

Experiential Virtual Learning on the impacts of Covid-19 on ...

引用

2023 ASEE Annual conference and Exposition - The Harbor of engineering: Education for 130 Years, ASEE 2023

作者： Mitra, Madhumi Nagchaudhuri, Abhijit Klein, Will University of Maryland Eastern Shore United States University of Maryland College Park United States

Poor air quality can be detrimental to human health, by causing or exacerbating health conditions. Sensitive groups such as children and the elderly with underlying conditions are at an increased risk for adverse health effects due to poor air quality. Due to the Covid-19 Pandemic, there is an observed change in air quality due to government restrictions and lockdown from 2020-2022 in many of the populated cities of the world. The University of Maryland Eastern Shore students (UMES) students in 3 undergrad courses (Introduction to Environmental Sciences;Biology for Honors students;and Ecology) and 1 graduate course (Teaching stEM at K-12 schools), and summer-exchange undergraduate engineering and high school interns had the opportunity to research the effects of Covid-19 pandemic on air quality for selected overpopulated cities in the world. The data collected were from March 2020 through summer of 2022. The objectives of integrating this research in stEM education are: a) to find a correlation among air quality parameters because of Covid-19;b) to analyze the effects of the pandemic on CO, NO2, O3, PM2.5, PM10, and SO2 in some of the selected populated cities (Kolkata, Milan, Los Angeles, São Paulo, Shanghai, and Sydney);c) to hone content knowledge in environmental science, chemistry, physics, and statistics;and d) to enhance data analytical skills, problem solving skills, as well as presenting research. By analyzing the effects of the Covid-19 pandemic on air quality, the scope for environmental restoration is addressed as more information is disseminated on what contributes to air quality, and how humans can manipulate it. In each of the courses mentioned above, two modules were dedicated to this topic. The first module introduced the topic and background information and the second one required relevant data analysis and presentations. Since the Environmental Science class had over 40 students, the students were broken down into groups for this activity. Various

关键词： students

来源：评论

学校读者我要写书评

暂无评论

Detecting Malignant Tumors Using a Network of Spiking Neurons Through a Population Based stochastic Temporal Encoding

Detecting Malignant Tumors Using a Network of Spiking Neuron...

引用

IEEE India conference (INDICON)

作者： Agnivo Ghosh Ayan Chakraborty Aniruddha Chandra Sumit Kundu Saswat Chakrabarti Department of Electronics and Communication Engineering National Institute of Technology Durgapur Durgapur India G S Sanyal School of Telecommunications Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Spiking neural network (SNN) has gained attention of researchers for its energy efficient and brain-like cognitive abilities. SNNs are now being explored for medical diagnosis to leverage their benefit of sparse, event driven and energy-efficient information processing. In this work an SNN has been designed to classify malignant and benign tumors from cell tissue data obtained from the Wisconsin breast cancer dataset. A novel population-based stochastic temporal encoding scheme has been adopted to convert the cell tissue features into equivalent neuronal spike trains. A supervised learning algorithm has been developed in synergy with the encoding scheme. Our method has resulted in an average accuracy of 90.8% and a best accuracy of 94% for the classification task. The proposed method performs reasonably well (within 2%) in comparison to popular machine learning algorithms such as Support Vector Machine (SVM) with linear kernel and Multi Layered Perceptron (MLP). The present method has a promising potential for further exploration in medical and healthcare applications.

关键词： Support vector machines Accuracy Machine learning algorithms Supervised learning Benign tumors Spiking neural networks Encoding Breast cancer Energy efficiency Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

DeepQMLP: A Scalable Quantum-Classical Hybrid Deep Neural Network Architecture for Classification

DeepQMLP: A Scalable Quantum-Classical Hybrid Deep Neural Ne...

引用

international conference on VLSI Design

作者： Mahabubul Alam Swaroop Ghosh Department of Electrical Engineering Penn State University

ISBN: (数字)9781665485050

ISBN: (纸本)9781665496612

Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks (e.g., classification/regression). The search for ideal QML models is an active research field. This includes identification of efficient classical-to-quantum data encoding scheme, construction of parametric quantum circuits (PQC) with optimal expressivity and entanglement capability, and efficient output decoding scheme to minimize the required number of measurements, to name a few. However, most of the empirical/numerical studies lack a clear path towards scalability. Any potential benefit observed in a simulated environment may diminish in practical applications due to the limitations of noisy quantum hardware (e.g., under decoherence, gate-errors, and crosstalk). We present a scalable quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired by classical deep neural network architectures. In DeepQMLP, stacked shallow Quantum Neural Network (QNN) models mimic the hidden layers of a classical feed-forward multi-layer perceptron network. Each QNN layer produces a new and potentially rich representation of the input data for the next layer. This new representation can be tuned by the parameters of the circuit. Shallow QNN models experience less decoherence, gate errors, etc. which make them (and the network) more resilient to quantum noise. We present numerical studies on a variety of classification problems to show the trainability of DeepQMLP. We also show that DeepQMLP performs reasonably well on unseen data and exhibits greater resilience to noise over QNN models that use a deep quantum circuit. DeepQMLP provided up to 25.3% lower loss and 7.92% higher accuracy during inference under noise than QMLP.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of Low Velocity Impact Performance of Carbon/Flax Bio-Hybrid Composites Using Experimental and Neural Network Approaches

Prediction of Low Velocity Impact Performance of Carbon/Flax...

引用

international Bhurban conference on Applied Sciences & Technology, IBCAst

作者： Manzar Masud Aamir Mubashar Computational Mechanics Group Department of Mechanical Engineering School of Mechanical and Manufacturing Engineering National University of Science and Technology (NUST) H-12 Islamabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

In this paper., the effect of fiber hybridization and stacking configurations on the low-velocity impact behavior of carbon/flax bio-hybrid composite laminates was investigated. The methodology was developed based on experimental testing and then utilizing an artificial neural network-based machine learning regression model. Five different stacking configurations of carbon/flax material were manufactured using the vacuum bagging method and then subjected to low-velocity impact with impact energies ranging from 15J to 90J. data in the form of peak impact force was recorded for each stacking configuration. A symmetric configuration with a uniform distribution of flax layers across the thickness exhibited enhanced performance. Two machine learning approaches—deep neural network (DNN) with Adam optimizer and DNN with stochastic gradient descent (SGD)—were applied. The performance metrics used to evaluate the performance were the coefficient of determination $(\mathrm{R}^learning)$ , mean square error (MSE)., and mean absolute error (MAE). The best-performing model for the prediction of peak impact force was DNN-SGD., with three hidden layers and 80 neurons in each hidden layer. The highest correlation and the smallest MSE and MAE values were attained using the DNN-SGD model. The developed methodology and the model serve as powerful tools for the prediction of the mechanical properties of bio-hybrid composite laminates., utilizing minimal resources and saving time as well.

关键词： Biological system modeling stacking Force Neurons Machine learning Predictive models Optical fiber networks Laminates Optical fiber testing Biological neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Auto Insurance Fraud Detection Using Convolutional Neural Networks

Enhancing Auto Insurance Fraud Detection Using Convolutional...

引用

international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Ratchanon Wongpanti Sirion Vittayakorn School of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

With the increasing number of vehicles in the global fleet, the size of the auto insurance market is projected to reach $1.3 billion USD by 2030. While this growth in the issuance of auto insurance policies brings prosperity to the industry, it also amplifies the risk of fraudulent activities. These fraudulent practices have a significant impact on the industry, resulting in the loss of billions of USD annually. Despite efforts to prevent such activities, the expertise available is often overwhelmed by the sheer volume of cases. In this paper, we propose an auto insurance fraud detection system that leverages a one-dimensional Convolution Neural Network (ID-CNN) model in combination with two data augmentation techniques, Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Networks (CTGAN), to address the class imbalance problem prevalent in fraud detection datasets. Furthermore, we also employ Focal Loss as the loss function in our deep learning model to effectively tackle the difficulty in classifying the minority class. By combining the ID-CNN model with these imbalance manipulation techniques and the Focal Loss function, we aim to enhance the system's ability to accurately identify fraudulent activities, even in the presence of highly imbalanced data. Our proposed approach seeks to mitigate the financial losses incurred by the auto insurance industry due to fraud and provide a more robust and efficient fraud detection system.

关键词： Industries Adaptation models Neural networks Insurance Computer architecture data models Fraud

来源：评论

学校读者我要写书评

暂无评论

A Dual-Port, and Single-Element Planar Inverted-F Antenna for Current 4G/5G Portable Applications

A Dual-Port, and Single-Element Planar Inverted-F Antenna fo...

引用

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

作者： Saqer S. Alja'afreh Amjaad T. Altakhaineh Mallak H. Al-shamaileh Aser M Matarneh Omar R. Daoud Mohammad Al-Khawaldah I Electrical and Computing Engineering School of Engineering and Computing American International University Al-Jahra Kuwait Data Science and Machine Learning Dept. Pioneers International Academy Amman Jordan Communications and Computer Engineering Al-Ahliyya Amman University Amman Jordan

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

A first-hand, low-profile, two-port planar inverted-F antenna is proposed for current 4G/5G sub-3 GHz hand-portable multiple-input and multiple-output (MIMO) applications. The design features a profile of about 5 mm and dual-coplanar feeds, which are isolated using only a modified top plate, while the shared ground plane is not perturbed as in previous designs. An open-edge slot on the top of the PIFA provides a 3λ/4 current path. The proposed antenna covers approximately a 15% impedance bandwidth fraction in the range of 2.45-2.85 GHz. The simulated results, including scattering parameters, far-field radiation patterns, and the envelope correlation coefficient, all exhibit excellent features.

关键词： Wireless communication Correlation coefficient Simulation Bandwidth Specific absorption rate Impedance MIMO communication

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315页 << < 302 303 304 305 306 307 308 309 310 311 > >>

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：