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

5,634 篇 会议
90 篇 期刊文献
10 册 图书

馆藏范围

5,734 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,774 篇 工学
- 2,332 篇 计算机科学与技术...
- 1,329 篇 软件工程
- 567 篇 信息与通信工程
- 466 篇 电气工程
- 200 篇 生物工程
- 174 篇 控制科学与工程
- 174 篇 生物医学工程（可授...
- 109 篇 机械工程
- 84 篇 交通运输工程
- 83 篇 化学工程与技术
- 80 篇 电子科学与技术（可...
- 79 篇 光学工程
- 69 篇 仪器科学与技术
- 65 篇 动力工程及工程热...
- 55 篇 土木工程
- 55 篇 安全科学与工程
897 篇 理学
- 478 篇 数学
- 235 篇 生物学
- 231 篇 物理学
- 191 篇 统计学（可授理学、...
- 119 篇 系统科学
- 66 篇 化学
589 篇 管理学
- 327 篇 图书情报与档案管...
- 305 篇 管理科学与工程(可...
- 163 篇 工商管理
289 篇 医学
- 243 篇 临床医学
- 144 篇 基础医学(可授医学...
- 80 篇 药学(可授医学、理...
- 52 篇 公共卫生与预防医...
76 篇 法学
- 71 篇 社会学
54 篇 经济学
45 篇 教育学
39 篇 农学
26 篇 文学
2 篇 军事学
1 篇 历史学
1 篇 艺术学

主题

1,601 篇 data models
535 篇 predictive model...
531 篇 training
496 篇 machine learning
461 篇 data mining
416 篇 big data
405 篇 computational mo...
343 篇 feature extracti...
333 篇 task analysis
327 篇 prediction algor...
268 篇 deep learning
263 篇 machine learning...
243 篇 classification a...
238 篇 analytical model...
191 篇 clustering algor...
190 篇 support vector m...
183 篇 neural networks
158 篇 natural language...
142 篇 training data
139 篇 mathematical mod...

机构

19 篇 sun yat sen univ...
19 篇 daffodil interna...
12 篇 univ chinese aca...
11 篇 tsinghua univers...
11 篇 university of ch...
11 篇 school of data a...
10 篇 carnegie mellon ...
9 篇 renmin univ chin...
9 篇 lviv polytechnic...
8 篇 shenzhen researc...
7 篇 shanghai jiao to...
7 篇 college of compu...
7 篇 university of el...
7 篇 school of comput...
7 篇 national univers...
7 篇 harbin institute...
7 篇 beijing key lab ...
7 篇 renmin univ chin...
6 篇 university of sc...
6 篇 school of comput...

作者

7 篇 li li
7 篇 wang wei
7 篇 xiao yanghua
7 篇 syed akhter hoss...
6 篇 wen ji-rong
6 篇 cui bin
6 篇 zhao wayne xin
6 篇 ying liu
6 篇 sheikh abujar
5 篇 yang tong
5 篇 liu yang
5 篇 li zhang
5 篇 jia weijia
5 篇 chen liu
5 篇 yang yang
5 篇 xiao hongbo
5 篇 nguyen trong-the
5 篇 yu jie
5 篇 albert bifet
5 篇 wang bin

语言

5,596 篇 英文
130 篇 其他
14 篇 中文
2 篇 俄文
1 篇 乌克兰文

检索条件"任意字段=2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020"

共 5734 条记录，以下是601-610 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：

Deep Learning in Mining Business Intelligence

Deep Learning in Mining Business Intelligence

引用

2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Anisuzzaman, B M Siddique, Ashfaquer Rahat Al Mamun, Tareq Jalal Jamil, Md Shah Hossain Mukta, Md. Saddam United International University Dept.of CSE Bangladesh

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

Business Intelligence (BI) describes how people, businesses, and organizations get the most out of their data through various methods and procedures. BI technologies and tremendous amounts of data are conducted in a competitive business market for identifying new opportunities with long-term stability. Deep neural networks (DNN), which promise to exceed traditional machine learning models in prediction performance, are transforming the field. During our study, we discovered a paucity of studies on deep learning (DL) in BI. DL techniques may be applied in BI, and this article aims to offer an overview of various techniques. We also tried to identify the challenges of DL to be used in BI. Based on insights, it is essential to conduct suitable data management technology for the structured, semistructured, and unstructured data derived from the market. DL in mining BI can open the issues to extract the most significant value from massive amounts of data, processing power, and algorithms. © 2022 IEEE.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Mean Denoising Algorithm for Pepper and Salt Noised Image 14

An Adaptive Mean Denoising Algorithm for Pepper and Salt Noi...

引用

14th international conference on Graphics and Image processing (ICGIP)

作者： Qin, Fengqing Wei, Kanglin Yuan, Liang Zheng, Xiyi Cao, Lilan Zhu, Lihong Li, Chaorong Xue, Yilan Hu, Yujie Yibin Univ Fac Artificial Intelligence & Big Data Yibin 644000 Peoples R China Yibin Univ Fac Intelligent Mfg Yibin 644000 Peoples R China

ISBN: (纸本)9781510666313;9781510666320

To improve the quality of the pepper and salt noised image, an adaptive mean filter algorithm is proposed. Firstly, the appropriate filter window size is automatically selected according to the intensity of noise in the image. Secondly, whether each pixel is a pepper and salt noise point is detected. The noised point outputs the mean gray value of the unnoised pixels in the current window. The non-noised point outputs the original value. Thirdly, the denoising effect of the three algorithms was compared by simulation experiments, and the influence of different pepper and salt noise density on denoising effect was tested. From the objective and subjective judgement, the propose method achieves higher PSNR and better visual effect.

关键词： pepper and salt image denoise adaptive median filter mean filter

来源：评论

学校读者我要写书评

暂无评论

Spatio-Temporal Context Modeling for Road Obstacle Detection 1

引用

4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Multi-label Feature Selection Based on Fuzzy Neighborhood Mutual Discrimination Index 12

Multi-label Feature Selection Based on Fuzzy Neighborhood Mu...

引用

12th international conference on Information Technology in Medicine and Education, ITME 2022

作者： Wang, Chenxi Chen, E. Ren, Mengli Yu, Xiehua Lin, Yaojin Li, Shaozi Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China Minnan Science and Technology University School of Computer Science Lab of Data Science and Intelligence Application Quanzhou China Xiamen University School of Computer Science Lab of Data Science and Intelligence Application Xiamen China

ISBN: (纸本)9798350310153

Multi-label feature selection advances the performance of multi-label classification models by eliminating irrelevant and redundant features. Most multi-label feature selection algorithms assume that the training set containing logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances of labels. To solve the problem, we proposes a multi-label feature selection method based on fuzzy neighborhood mutual discriminant index. Firstly, logical labels are transformed into label distribution by label enhancement. Secondly, the information entropy is referenced to the label distribution learning, the label neighborhood similarity matrix is constructed to describe the ambiguity between labels. Finally, linking the fuzzy neighborhood discrimination index under the selected feature subset with label neighborhood similarity matrix is proposed, which is used for judging the distinguishing ability of the feature subset to label. Comprehensive experiments on 6 multi-label datasets show that the algorithm outperforms other algorithms in classification performance. © 2022 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Breaking Filter Bubble: A Reinforcement Learning Framework of Controllable Recommender System 23

Breaking Filter Bubble: A Reinforcement Learning Framework o...

引用

32nd ACM World Wide Web conference, WWW 2023

作者： Li, Zhenyang Dong, Yancheng Gao, Chen Zhao, Yizhou Li, Dong Hao, Jianye Zhang, Kai Li, Yong Wang, Zhi Tsinghua University China Huawei Noah's Ark Lab China Carnegie Mellon University United States Tsinghua Shenzhen International Graduate School Tsinghua University Research Institute of Tsinghua Pearl River Delta China Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Peng Cheng Laboratory China

ISBN: (纸本)9781450394161

In the information-overloaded era of the Web, recommender systems that provide personalized content filtering are now the mainstream portal for users to access Web information. Recommender systems deploy machine learning models to learn users' preferences from collected historical data, leading to more centralized recommendation results due to the feedback loop. As a result, it will harm the ranking of content outside the narrowed scope and limit the options seen by users. In this work, we first conduct data analysis from a graph view to observe that the users' feedback is restricted to limited items, verifying the phenomenon of centralized recommendation. We further develop a general simulation framework to derive the procedure of the recommender system, including data collection, model learning, and item exposure, which forms a loop. To address the filter bubble issue under the feedback loop, we then propose a general and easy-to-use reinforcement learning-based method, which can adaptively select few but effective connections between nodes from different communities as the exposure list. We conduct extensive experiments in the simulation framework based on large-scale real-world datasets. The results demonstrate that our proposed reinforcement learning-based control method can serve as an effective solution to alleviate the filter bubble and the separated communities induced by it. We believe the proposed framework of controllable recommendation in this work can inspire not only the researchers of recommender systems, but also a broader community concerned with artificial intelligence algorithms' impact on humanity, especially for those vulnerable populations on the Web. © 2023 Owner/Author.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Advancing Prediction of Rice Production in Sumatra: Utilizing eXtreme Gradient Boosting Algorithm for Accurate Forecasts

Advancing Prediction of Rice Production in Sumatra: Utilizin...

引用

international conference on Informatics and Computing (ICIC)

作者： Reviana Siti Mardiah Adang Suhendra Fitrianingsih Fitrianingsih Dian Kemala Putri Department of Information Technology Gunadarma University Depok Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Rice production in Sumatra is crucial to food security in Indonesia. However, accurately predicting rice production is challenging due to climate change and agricultural land conversion. To address this challenge, this research proposes utilizing XGBoost with feature engineering to improve the accuracy of rice production prediction on Sumatra Island. This research aims to develop a prediction model capable of understanding and anticipating fluctuations in rice production by analyzing historical data encompassing rice production, land area, and climate from 1993 to 2020. The performance of the proposed prediction model was assessed using R-squared ($\mathrm{R}^{2}$), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) as primary evaluation metrics. The results show that through feature engineering, the prediction model can achieve the highest accuracy, especially with a 90:10 data split resulting in an $R^{2}$ value of $\mathbf{0. 9 7 6}$, RMSE and MAE values of $\mathbf{0. 1 0 9}$ and 0.076. This result indicates that the application of feature engineering techniques and large training data considerably improves the accuracy of the prediction model.

关键词： Measurement Accuracy Computational modeling Training data Production Predictive models Prediction algorithms data models Resource management Meteorology

来源：评论

学校读者我要写书评

暂无评论

Optimizing Bayesian Belief Network Analysis for Autonomous Vehicles

Optimizing Bayesian Belief Network Analysis for Autonomous V...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Hulin Jin Zhiran Jin Yong-Guk Kim International Institute of Engineering Psychology Denver Colorado USA Foothill Preparatory School Temple City California USA Department of Computer Engineering Sejong University Seoul South Korea

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This study investigates the extension of Bayesian Belief Network (BBN) analysis tailored for autonomous vehicles (AVs), specifically targeting the optimization of lane change decision making in structured road environments. Structured roads, characterized by clear lane markings and predictable traffic patterns, provide a controlled environment for AV operations. However, efficient and safe lane changes remain critical to improving traffic flow and overall driving safety. To address this challenge, advanced BBN models are used that integrate real-time data and communication technologies to improve decision accuracy. The methodology involves constructing BBNs to model lane-changing behavior based on historical driving data, using rigorous statistical analysis to understand driver responses under different conditions. Results show significant improvements in lane change efficiency, highlighting implications for AV operations in complex urban environments.

关键词： Communication systems Roads Heuristic algorithms Decision making Urban areas data models Bayes methods Vehicle dynamics Autonomous vehicles Vehicles

来源：评论

学校读者我要写书评

暂无评论

Forecasting Air Passengers in Phuket, Thailand: An Application of Machine Learning algorithms

Forecasting Air Passengers in Phuket, Thailand: An Applicati...

引用

Big data Analytics and Practices (IBDAP), international conference on

作者： Aphinan Photun Thanakorn Thaminkaew Department of Mathematics Faculty of Science Naresuan University Phitsanulok Thailand Big Data Institute (Public Organization) Bangkok Thailand

The COVID-19 pandemic occurred in 2020 had a significant impact on the global economy, resulting in a significant decrease in air passengers. As a result, time series trends experienced significant fluctuations that were difficult to predict. The objective of this research was to forecast the number of air passengers in Phuket, Thailand, from September to December 2022. Additional features, such as exchange rates and time-based properties, were included to enhance the accuracy of our predictions. We then compared the performance of statistical models and machine learning models using evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). Our findings revealed that the SARIMAX model outperformed other models for air passenger forecasting. Nonetheless, machine learning models might be more appropriate for forecasting new data due to their stability with additional training data. This research emphasized the importance of selecting the appropriate model based on the available data and the particular needs of the forecasting problem.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Object Detection Method for Bank Operation and Maintenance Scenarios 5

A Lightweight Object Detection Method for Bank Operation and...

引用

5th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Xue, Junmin Rao, Yunbo Wu, Jiangang Gou, Haosong Liu, Yong Yang, Yang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Data Center of Postal Savings Bank of China Beijing China Chengdu Aerospace Science and Industry Big Data Research Institute Corporation Limited Chengdu China Sichuan Institute of Computer Sciences Chengdu China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Due to the complex background of bank operation and maintenance scenarios, it is extremely difficult to detect and analyze the object of operation and maintenance scenarios. The traditional target detection algorithms, such as F-RCNN, are not suitable for object detection. We propose a lightweight object detection method for bank operation and maintenance scenarios based on deep learning. The proposed method highlights two improvements based on Yolov5. The first adjustment of it is this method constructs a new convolution method and a new activation function. Also, we use the multi-scale output strategy of Feature Pyramid Networks (FPN) to develop a new lightweight feature extraction algorithm. These changes we made are based on the real-life situation, such as bank operation and maintenance scenarios. Experiments have been carried out on the same data set with multiple object detection algorithms, and the experimental results show that the proposed method reduces the complexity of the activation function. Meanwhile, the module is flexible, and can be easily applied to other similar business scenario detection models. © 2022 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Design of Brand Positioning and Market Analysis System Based on Visual Communication

Design of Brand Positioning and Market Analysis System Based...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Liao Sha College of Art and Design Xiangnan University Chenzhou China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

With the intensification of market competition and the increasing diversity of consumer demands, scientific brand positioning and visual image design are becoming increasingly important for enterprises' brand marketing strategies. This study aims to construct a brand positioning and marketing system integrating visual design, market analysis, and decision support based on visual communication theory and big data analysis technology. The system, implemented through a distributed microservices architecture, realizes advantages such as high performance and scalability. The visual design module utilizes deep learning technology to automatically generate personalized visual identification schemes, while the data analysis module integrates multiple data sources for mining and analysis. The decision engine integrates the outputs of both modules to form comprehensive brand visual images and marketing strategy recommendations. The system has been validated through multiple practical application cases, significantly enhancing enterprises' brand image and marketing performance. In the future, the system will continue to be optimized and upgraded, exploring new models of integration between visual and data analysis to bring innovative practices to the field of brand marketing.

关键词： Visualization data analysis Visual communication Scalability Soft sensors Education Microservice architectures Information processing Big data Engines

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500页 << < 57 58 59 60 61 62 63 64 65 66 > >>

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：