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

2,264 篇 会议
18 篇 期刊文献
16 册 图书

馆藏范围

2,298 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,928 篇 工学
- 1,213 篇 计算机科学与技术...
- 961 篇 软件工程
- 390 篇 信息与通信工程
- 235 篇 控制科学与工程
- 218 篇 机械工程
- 167 篇 电子科学与技术（可...
- 164 篇 化学工程与技术
- 158 篇 材料科学与工程（可...
- 154 篇 电气工程
- 128 篇 仪器科学与技术
- 126 篇 环境科学与工程（可...
- 115 篇 生物医学工程（可授...
- 97 篇 生物工程
- 92 篇 动力工程及工程热...
- 90 篇 土木工程
- 78 篇 建筑学
- 70 篇 光学工程
- 69 篇 冶金工程
- 68 篇 安全科学与工程
822 篇 理学
- 448 篇 数学
- 157 篇 物理学
- 121 篇 统计学（可授理学、...
- 116 篇 生物学
- 114 篇 系统科学
- 112 篇 化学
- 102 篇 生态学
506 篇 管理学
- 326 篇 管理科学与工程(可...
- 198 篇 图书情报与档案管...
- 144 篇 工商管理
75 篇 医学
71 篇 经济学
- 71 篇 应用经济学
62 篇 教育学
58 篇 法学
51 篇 农学
15 篇 艺术学
11 篇 军事学
5 篇 文学
1 篇 哲学

主题

35 篇 data mining
30 篇 educational inst...
26 篇 mobile ad hoc ne...
25 篇 wireless sensor ...
24 篇 bioinformatics
24 篇 genetic algorith...
19 篇 image segmentati...
19 篇 authentication
19 篇 cloud computing
17 篇 feature extracti...
17 篇 web services
17 篇 genomics
16 篇 quality of servi...
16 篇 routing protocol...
15 篇 support vector m...
15 篇 computational mo...
15 篇 remote sensing
15 篇 cryptography
14 篇 students
12 篇 computers

机构

29 篇 college of compu...
12 篇 school of comput...
11 篇 chongqing normal...
11 篇 school of comput...
9 篇 college of physi...
9 篇 college of compu...
8 篇 school of comput...
8 篇 information and ...
7 篇 school of comput...
7 篇 department of co...
6 篇 department of co...
6 篇 school of comput...
6 篇 school of comput...
6 篇 department of co...
6 篇 school of mechan...
6 篇 college of compu...
6 篇 school of softwa...
5 篇 school of inform...
5 篇 college of elect...
5 篇 college of manag...

作者

12 篇 ma yan
8 篇 zhang kun
8 篇 dhavachelvan p.
7 篇 wang jing
6 篇 wang hai-feng
6 篇 song li
6 篇 maode ma
6 篇 yuhang yang
6 篇 li ming
6 篇 he guotian
6 篇 ramesh t.
6 篇 mannava vishnuva...
6 篇 wang jun
5 篇 jiapeng xiu
5 篇 xu jing
5 篇 sukumar nandi
5 篇 chen liu
5 篇 liu fuyan
5 篇 li ying
5 篇 xu lijun

语言

2,265 篇 英文
32 篇 中文
1 篇 其他

检索条件"任意字段=2012 2nd International Conference on Advances in Computer Science and Engineering, CES 2012"

共 2298 条记录，以下是11-20 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：

Offline Writer Identification using Deep Convolution Neural Network 2

Offline Writer Identification using Deep Convolution Neural ...

引用

2nd IEEE international Maghreb Meeting of the conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Durou, Amal M. Aref, Ibrahim A. Erateb, Suleiman El-Mihoub, Tarek A. Ghalut, Tarik Emhemmed, Adel Saad University of Tripoli Faculty of Information Technology Tripoli Libya Lancaster University School of Computing and Comms Lancaster United Kingdom Coventry University Engineering and Digital Technologies Coventry United Kingdom Jade University of Applied Sciences Department of Engineering Science Wilhelmshaven Germany University of Tripoli Electrical Engineering Department Tripoli Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Deep convolutional neural networks (DCNN) are efficient in solving different pattern recognition problems and have been applied to extract image features (IFs). This paper investigates using deep learning (DL) techniques to improve the performance of the writer identification (WI) process. This work presents a novel approach for WI tasks by combining a DL technique with machine learning (ML). A convolutional neural network (CNN) is employed as a feature extractor along with a ML algorithm to classify those features. The standard Alex-Net model is utilized to extract IFs that located in the fully connected layers (FCLs). The support vector machine (SVM) model is selected as the classifier due to its efficient capabilities to improve identification performance (IP). The proposed model is tested using various types of the datasets, namely the Islamic Heritage Project (IHP) and Clusius. Furthermore, IAM and ICFHR-2012 datasets have been employed for benchmarking the proposed model. The results demonstrate the model achieves superior performance. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Image Segmentation Algorithm Based on Attention Mechanism and Jump Connection 2

Image Segmentation Algorithm Based on Attention Mechanism an...

引用

2nd international conference on Machine Learning and Intelligent Systems engineering, MLISE 2022

作者： Cui, Zhaotong Wei, Yanjun Li, Tianping Li, Guanxing Shandong Normal University School Of Physics And Electronic Science Jinan Shandong China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

With the development of deep learning, convolutional neural networks have become the mainstream of computer vision algorithms. In recent years, the biggest problem of applying convolutional neural networks to image segmentation is that they cannot achieve accurate segmentation of the last layer, also cause resolution loss when extracting features, and cannot meet the demand of different pixels requiring different context dependencies. To address these issues, we add an attention mechanism and a jump feature fusion method to deeplabv3+ so that features are extracted without severe feature loss and a broader range of contextual information can be encoded into local features. The feature map is further enriched by adding a module combining bilinear upsampling and deconvolution in the process of feature restoration. Compared to previous algorithms, the results of this algorithm are superior. A performance of 85.73% is achieved on PASCAL VOC2012 using the proposed model. © 2022 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Analysis of Medical Image by using Machine Learning Applications of Convolutional Neural Networks 2

Analysis of Medical Image by using Machine Learning Applicat...

引用

2nd international conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Naveen, Kommu Parvathi, R.M.S. Anna University Department of Electronics and Communication Engineering Tamilnadu Chennai600 025 India Sri Ramakrishna Institute of Technology Department of Computer Science Engineering Tamilnadu Coimbatore641 010 India

ISBN: (纸本)9781665400527

Deep learning or machine learning using image input is a good field in medical image analysis that is rapidly evolving. Machine learning is expected to become the norm in the field of medical image analysis by the inclusion of images in the next few decades. The idea of machine learning (ML), also known as deep learning, has recently become very popular in many fields, including computer vision. The in-depth learning strategy based on the Convulsive Neural Network (CNN) was launched in late 2012 and quickly won the prestigious Global computer Vision competition, ImageNet ranking. Since then, academics from many fields, including medical film analysis, have begun to take an active part in the rapid progression of deep learning. This paper looks at in-depth learning techniques and how to use them in the analysis of medical images. This study is 1) managing customary machine learning strategies in the PC vision industry, 2) what changed in machine learning prior and then afterward the presentation of top to bottom learning, 3) dealing with in-depth learning patterns and 4) indepth practice applications for medical image analysis. Before and After Deep Learning Compare Deep Learning with Machine Learning Before Profound Learning ML With Feature Input (or Feature-Based ML) is normal and the essential and key contrast among pre-and post-profound learning ML is straightforwardly to the picture information object. Although model depth is an important asset, division or element extraction is the wellspring of top to bottom learning capacity. The graph of the Deep Learning test shows a long history of deep learning methods in the classroom for ML using inputs, with the exception of the new term 'deep learning'. The term 'deep learning' was used in ML class medical image analysis with image entry before it was coined to address issues such as pest and non-pest taxonomy, taxonomy, taxonomy and pest or organ identification. Pests. Deep learning using image input is a very powerf

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Digital Transformation in the Era of Industry 4.0 and Society 5.0: A perspective 2

Digital Transformation in the Era of Industry 4.0 and Societ...

引用

2nd international conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Menezes, Brenno Yaqot, Mohammed Hassaan, Sarah Franzoi, Robert Alqashouti, Noof Al-Banna, Adnan Hamad Bin Khalifa University Doha Division of Engineering Management and Decision Sciences College of Science and Engineering Doha Qatar

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

As an aftermath of the 4th Industrial Revolution at the beginning of century XXI, augmented information integrated into a cyber-physical system has changed the information age. From this point in mankind's history (circa 2012), emerging technologies to allow operations of everything in a smarter way have led both industry and society to re-think well-established processes or scratch new ones for (re) executions of anything as much as autonomous and intelligent. It permitted unforeseen improvements in the economy and well-being towards high-level standards of efficiency, sustainability, and safety, to name a few. Within the so-called Industry 4.0 (I4) age, advances in information and computing technologies (ICT) coupled with new capabilities in modeling and solving algorithms (MSA) and mechatronics (MEC) have enabled improvements in both industry and society. In industry, it represents more innovative design, operation, and control in several fields of manufacturing, logistics and supply chains, agricultural productions, etc. In society, besides the widespread information and communication capabilities that transformed the previous states of society into a more interconnected world, we observe the rise of crowdsourcing that aims to meet a reduced operational cost at the shared phase of new business models. Additionally, in the wake of the global 1st pandemic occurring within the era of high-tech communities, the push in the educational processes has modified how individuals share, connect, and communicate a collection of workloads, expertise, follow-ups, etc., where capabilities, readiness, and preparedness have changed in the named Society 5.0 (S5). In this work, we analyze the interplay of I4 pillars in industry and society to identify gaps and challenges in the research and development to be overcome towards an achievable deployment within a complete I4-S5 phase. © 2022 IEEE.

关键词： Industry 4.0

来源：评论

学校读者我要写书评

暂无评论

A lightweight attention-guided semantic segmentation network using a group convolution pyramid

A lightweight attention-guided semantic segmentation network...

引用

Computing, Communication, Perception and Quantum Technology (CCPQT), international conference on

作者： Siyu Yu Dandan Huang Guixian Wang Anni Yao School of Electronic Information Engineering Changchun University of Science and Technology Changchun China School of Electronic Information Engineering Name Of Changchun University of Science and Technology Changchun China

Semantic segmentation is a key part of computer vision tasks, but its application is greatly limited by memory capacity and computational cost. This paper proposes a lightweight attention-guided semantic segmentation network (LAGNet) that adopts a joint group convolution pyramid strategy. Specifically, we introduced a lightweight symmetric double attention module (LS-DAM) and an adaptive selection interaction module (ASIM). In the LS-DAM, the self-attention adopts adaptive maximum pooling and average pooling instead of its previous form, in which the pixel-by-pixel similarity is calculated. Moreover, The ASIM has the capability to seamlessly integrate both high-level semantic information and low-level geometric information, leading to a remarkable enhancement in the performance of semantic segmentation. We evaluate our proposed LAGNet model using mean intersection over union (mIoU) on PASCAL VOC 2012 and Cityscapes, two commonly used datasets in the field of semantic segmentation, and finally achieve state-of-the-art performance of 81.73% and 81.90%, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Airline Customer Value Analysis and Customer Churn Prediction Based on LRFMC Model and K-means Algorithm 2

Airline Customer Value Analysis and Customer Churn Predictio...

引用

2nd international conference on computer science and Management Technology, ICCSMT 2021

作者： Ran, Jingwen Cheng, Xingqi Hubei University of Economics Wuhan China University of Stuttgart Department of Computer Science Stuttgart Germany

ISBN: (纸本)9781665420631

Due to the increasingly significant competition inside and outside the aviation industry, airlines choose to conduct personalized sales to passengers for the purpose of increasing economic efficiency. In this paper, we select airlines customer information data during the period from 2012 to 2014, segment the value of air customers based on the LRFMC model and K-means algorithm. Then establish an airline customer churn prediction model, define churn customers, select characteristics, train SVM, Adaboost, RandomForest and Xgboost models, and then identify churn customers. Finally, the four models are compared and the optimal model is obtained. This article aims to classify airline customers so that airlines can adopt different marketing strategies for customers of different values to maximize profits. Improve the problem of customer churn, enable airlines to maintain their own markets, and bring high profits to airlines. © 2021 IEEE.

关键词： Value engineering

来源：评论

学校读者我要写书评

暂无评论

Explaining YOLOv9 Object Detection with Attribution-Based Heatmap Methods

Explaining YOLOv9 Object Detection with Attribution-Based He...

引用

Electrical, Automation and computer engineering (ICEACE), international conference on

作者： Siyuan Fan School of Information Science and Technology Fudan University China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This study investigates the problem of explainability for visual object detectors, focusing on the YOLOv9 object detection model. The model is fine-tuned on the VOC2012 dataset using a pre-trained YOLOv9 model. The study demonstrates various attribution-based heatmap methods into the YOLOv9 architecture and analyzes their effectiveness in explaining individual detections. The attribution methods explored include Grad-CAM++, Grad-CAM, XGrad-CAM, Eigen-CAM, HiResCAM, LayerCAM, RandomCAM, and Eigen Grad-CAM. The analysis reveals that different methods provide unique perspectives about the model’s functioning, suggesting that the choice of attribution-based method can significantly influence the interpretation of the model’s predictions. In the study, Layer-CAM and HiRes-CAM consistently demonstrate superior performance in visualizing the features learned by the YOLOv9 model. This study provides valuable insights into the explainability of the YOLOv9 object detection model, highlighting the importance of selecting the right attribution method for model interpretation. The findings suggest that improving the transparency of such models can enhance their trustworthiness and applicability in real-world tasks, particularly in domains where interpretability is crucial.

关键词： Heating systems Visualization Analytical models Computational modeling Noise Object detection computer architecture Predictive models Feature extraction Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Autoregressive Techniques for Forecasting Applications 2

Autoregressive Techniques for Forecasting Applications

引用

2nd international conference on Secure Cyber Computing and Communications, ICSCCC 2021

作者： Priyavrat Sharma, Nonita Sikka, Geeta Dr. B.R. Ambedkar National Institute of Technology Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9781665444156

The objective of this research paper is to comparatively analyze different autoregressive techniques to forecast the daily count of rental bikes. The data set utilized for forecasting contains the demand of bikes on lease from 2011 to 2012 of the Capital Bikeshare system located in Washington, DC with the corresponding climate and seasonal information. Forecasting results of three models are compared with the help of Root Mean Squared Error and Mean Absolute Percent Error. In this paper, it is demonstrated that the ARIMA model is best in terms of fitting and forecasting precision than the other two models used for forecasting. This research work will be helpful for the bike-sharing firm to know about the demand for bikes for upcoming days so that they can make arrangements accordingly. © 2021 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Stock Prices Prediction Using Machine Learning

Stock Prices Prediction Using Machine Learning

引用

Innovation in Technology (INOCON), IEEE international conference for

作者： Aditi Gupta Akansha Khushboo Joshi Madhu Patel Ms. Vibha Pratap Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women New Delhi India

Precise prediction of the stock market is incredibly difficult due to how volatile and non-linear it is. The growth of artificial intelligence and improvements in processing power have increased the accuracy of programmed methods of prediction in predicting stock values. In this paper, we used Multilayer Linear-Regression, Convolutional Neural Network (CNN), and long short-term memory (LSTM) algorithms to analyze the price trends over different time periods to predict the closing price of five companies, operating in different sectors. The final features we used were open, high, low, and close prices (OHLC), which were chosen using data pre-processing techniques. The dataset we used was made up of daily prices from 3 November 2012 to 3 November 2022. The number of previous days that would be required to predict the current day’s closing price is known as the sequence length. Then, using a different set of information, we adjusted this length and evaluated the accuracy. Our models performed the best for a sequence length of 5 and LSTM outperforms other models for each company’s dataset with different sequence lengths.

关键词： Technological innovation Machine learning Companies Prediction algorithms Nonhomogeneous media Market research Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Interpretable MOOC Dropout Prediction using Different Ensemble Methods and XAI

Interpretable MOOC Dropout Prediction using Different Ensemb...

引用

Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Labib Hasan Khan Mohammed Ashfaqul Haque Esaba Ahnaf Ibrahim Moin Mostakim Muhammad Iqbal Hossain Dept. of Computer Science & Engineering BRAC University Dhaka Bangladesh

Massive open online course (MOOC) has been around for several years, which started to gain traction in 2012 when Coursera was established. MOOCs use pre-recorded lectures and scheduled weekly tests to provide content and access to students over the internet. The dataset used in this study is taken from the KDDCUP 2015 dataset, a publicly available dataset. Here, 12 features are considered, they are browser access, navigate, average chapter delays, etc to comprehend the possibility of dropout. This research study intends to predict the dropout of a learner so that it can be prevented through manual interaction. Additionally, XAI is implemented to inter-pret the models to suggest MOOC platforms, which feature impact dropout the most. Further, different ensemble learning techniques, namely voting classifier and stacking are used. The proposed voting classifier uses five of the best-performing machine learning models. Then, the performance of the model is evaluated by using multiple metrics such as precision, recall, and F1-score. Finally, a recall of 97.636 % has been obtained with stacking and f1-score of 91.603 % has been obtained with the hard voting classifier.

关键词：

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230页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：