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

6,842 篇 会议
70 篇 期刊文献
44 册 图书

馆藏范围

6,956 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,947 篇 工学
- 3,648 篇 计算机科学与技术...
- 2,754 篇 软件工程
- 1,193 篇 信息与通信工程
- 701 篇 控制科学与工程
- 510 篇 电气工程
- 474 篇 生物工程
- 331 篇 网络空间安全
- 313 篇 生物医学工程（可授...
- 274 篇 电子科学与技术（可...
- 259 篇 动力工程及工程热...
- 215 篇 交通运输工程
- 215 篇 安全科学与工程
- 213 篇 机械工程
- 208 篇 仪器科学与技术
- 179 篇 光学工程
- 165 篇 化学工程与技术
- 165 篇 石油与天然气工程
1,718 篇 理学
- 1,022 篇 数学
- 494 篇 生物学
- 372 篇 统计学（可授理学、...
- 305 篇 物理学
- 252 篇 化学
- 195 篇 系统科学
1,362 篇 管理学
- 778 篇 图书情报与档案管...
- 744 篇 管理科学与工程(可...
- 352 篇 工商管理
338 篇 医学
- 269 篇 临床医学
- 260 篇 基础医学(可授医学...
- 168 篇 公共卫生与预防医...
- 164 篇 药学(可授医学、理...
195 篇 法学
129 篇 经济学
87 篇 农学
71 篇 教育学
19 篇 文学
8 篇 军事学
2 篇 艺术学

主题

311 篇 big data
294 篇 machine learning
211 篇 deep learning
210 篇 data mining
202 篇 data models
157 篇 cloud computing
138 篇 machine learning...
123 篇 training
119 篇 computational mo...
116 篇 feature extracti...
108 篇 signal processin...
102 篇 predictive model...
97 篇 internet of thin...
96 篇 signal processin...
94 篇 prediction algor...
89 篇 support vector m...
83 篇 data privacy
80 篇 artificial intel...
74 篇 sentiment analys...
72 篇 edge computing

机构

88 篇 maharishi school...
67 篇 centre of interd...
31 篇 department of ar...
28 篇 tianjin universi...
28 篇 college of compu...
27 篇 vishwakarma inst...
26 篇 department of co...
26 篇 department of el...
25 篇 chitkara univers...
25 篇 department of co...
22 篇 department of in...
18 篇 university of ch...
17 篇 chitkara univers...
17 篇 lovely professio...
17 篇 department of sc...
17 篇 vishwakarma inst...
17 篇 vivekananda glob...
16 篇 department of el...
15 篇 vishwakarma inst...
14 篇 key laboratory o...

作者

11 篇 dipannita mondal
11 篇 ramakrishnan ram...
10 篇 wang jing
10 篇 nitin n. sakhare
10 篇 rajeswari d.
9 篇 mondal dipannita
8 篇 kumar ritesh
8 篇 akhilendra prata...
8 篇 li zuoyong
8 篇 kodipalli ashwin...
8 篇 jambi ratna raja...
7 篇 chen chen
7 篇 liu ying
7 篇 xu xiaolong
7 篇 vaishali singh
7 篇 zhang jing
6 篇 zheng-guang wu
6 篇 bin wang
6 篇 ye jun
6 篇 yang yang

语言

4,159 篇 英文
2,790 篇 其他
112 篇 中文

检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6956 条记录，以下是951-960 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：

Dynamic Representative data Management of Multiple Time Series for Edge computing

Dynamic Representative Data Management of Multiple Time Seri...

引用

IEEE international conference on Communications (IEEE ICC)

作者： Oikawa, Hiroki Ge, Hangli Koshizuka, Noboru Univ Tokyo Grad Sch Interdisciplinary Informat Studies Tokyo Japan

ISBN: (纸本)9781538674628

With applications demanding increased amount of data, edge computing, where data are processed on the edge, is gaining popularity. Edge devices can store a limited amount of data and need to extract important data from the collected data. On-line interpolation-based data management (O-IDM) is an efficient method for managing time series data on an edge. O-IDM extracts and stores data to represent its overall shape. However, using this method becomes computationally expensive when the amount of the retained data is large. In this paper, we propose fast-O-IDM (FO-IDM), which can perform the same data extraction as O-IDM at a lower computational cost. Furthermore, we propose dynamic representative data management (D-RDM), which is a multiple time series management method that applies FO-IDM. D-RDM can efficiently hold the characteristics of multiple time series by dynamically controlling the capacity based on the data features. The extraction accuracy and computational costs of D-RDM are evaluated via comparison with other management methods. The experimental results confirmed that D-RDM could extract the data more accurately than the other methods, reducing the data amount required to convey the feature of the data by up to 25.0 percentage points compared to naive O-IDM. In addition, when the number of time series and the amount of the retained data were 3 and 30,000 respectively, the processing time of D-RDM was reduced by 91.2 percentage point compared to O-IDM, indicating that the computational cost of D-RDM is sufficiently small for use in edge devices.

关键词： edge computing data management

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Archives: Revolutionizing Information Access for the Future

AI-Powered Archives: Revolutionizing Information Access for ...

引用

2023 IEEE international conference on Big data, Bigdata 2023

作者： Trehan, Vasuda University at Albany Suny Albany United States

ISBN: (纸本)9798350324457

Constant advancement in computing technology has led to an exponential increase in the volume of data stored over the past two decades. As Artificial Intelligence (AI), big data, and machine learning become a prominent part of society, these technologies are highly dependent on data as a gratuity. The development of AI technology depends on large volumes of data for learning algorithms to process and draw multiple entities, relationships, and clusters. This demand for enormous data has increased the need for efficient data storage and retrieval methods. The archival process is a long-standing tradition starting from libraries. data archival is a process of gathering, storing, and managing historical records. Artificial Intelligence advancements have developed insight into integrating AI into the data archival process. This research explores the use of AI in data archival processes. Across multiple fields, data archival is required, such as in academic journals, institutional archival, and many more. AI can be used in activities such as progression, description, search, and record-keeping. This research explores the multifaceted integration of AI into each step of the archival process, aiming to advance the field by enhancing data accessibility, efficiency, and overall effectiveness by reviewing the current literature and models to explain the trends related to AI services used for data archival. © 2023 IEEE.

关键词： ai integration archival challenges artificial intelligence data archival data discoverability historical records machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Fragment Analysis and Weighting Coefficients for Intelligent Text Monitoring 16

Fragment Analysis and Weighting Coefficients for Intelligent...

引用

16th international conference Management of Large-Scale System Development, MLSD 2023

作者： Rusyaeva, Elena Akhobadze, Gurami Poltavsky, Aleksandr V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Economic Dynamics and Innovation Management Moscow Russia V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Network Systems Control Moscow Russia

We propose a data processing and computing system based on intelligent text monitoring using fragment analysis and weights. We describe a computation technique to account for functional tokens in materials under study... 详细信息

ISBN: (纸本)9798350337907

关键词： data handling

来源：评论

学校读者我要写书评

暂无评论

Empowering Scientific Discovery Through computing at the Advanced Photon Source

Empowering Scientific Discovery Through Computing at the Adv...

引用

2023 international conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Parraga, Hannah Hammonds, John Henke, Steven Veseli, Siniša Allcock, William Côté, Benoit Chard, Ryan Narayanan, Suresh Schwarz, Nicholas X-ray Science Division Argonne National Laboratory LemontIL United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9798400707858

This paper explores the challenges and solutions for managing and processing the vast amount of data generated by the Advanced Photon Source (APS), a synchrotron light source facility producing ultra-bright x-rays for diverse scientific domains. With 68 experimental beamlines covering materials research, biology, and more, the APS serves a wide user base across academia, government, and industry. The ongoing upgrade of the APS storage ring and installation of new instruments will amplify data generation and processing demands. This paper discusses the approach to address these demands through automated data processing using standardized workflows that produce faster scientific insights. The APS data Management System coordinates various data related tasks to manage storage, data transfer, metadata cataloging, data processing, and interfaces with tools provided by Globus. Through integration with the Argonne Leadership computing Facility (ALCF), APS users can efficiently access high-performance computing resources. Standardized workflows have led to reduced computational burdens on scientists and greater accessibility of high performance computing resources. We demonstrate how standardization and collaboration enable scientists to rapidly convert raw data into meaningful scientific results, establishing a streamlined path from data collection to analysis and ultimately to publication. © 2023 ACM.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Transaction data Management Optimization Based on Multi-Partitioning in Blockchain Systems 21

Transaction Data Management Optimization Based on Multi-Part...

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： Zhang, Jingyu Hou, Xingxing Teng, Yilong Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. First, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods. © 2023 IEEE.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change 11

Ensemble Learning to Assess Dynamics of Affective Experience...

引用

11th international conference on Affective computing and Intelligent Interaction (ACIIW)

作者： Dollack, Felix Kiyokawa, Kiyoshi Liu, Huakun Perusquia-Hernandez, Monica Raman, Chirag Uchiyama, Hideaki Wei, Xin Nara Inst Sci & Technol Nara Japan Delft Univ Technol Delft Netherlands

ISBN: (纸本)9798350327458

The congruence between affective experiences and physiological changes has been a debated topic for centuries. Recent technological advances in measurement and data analysis provide hope to solve this epic challenge. Open science and open data practices, together with data analysis challenges open to the academic community, are also promising tools for solving this problem. In this entry to the Emotion Physiology and Experience Collaboration (EPiC) challenge, we propose a data analysis solution that combines theoretical assumptions with data-driven methodologies. We used feature engineering and ensemble selection. Each predictor was trained on subsets of the training data that would maximize the information available for training. Late fusion was used with an averaging step. We chose to average considering a "wisdom of crowds" strategy. This strategy yielded an overall RMSE of 1.19 in the test set. Future work should carefully explore if our assumptions are correct and the potential of weighted fusion.

关键词： affective computing continuous ratings biosignal processing machine learning data analysis challenge

来源：评论

学校读者我要写书评

暂无评论

Enterprise Financial Information Management System Based on data Analysis Algorithm 2

Enterprise Financial Information Management System Based on ...

引用

2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Zhou, Xiaohua Gingko College of Hospitality Management Department of Financial Management Sichuan Chengdu China

ISBN: (纸本)9798350347456

How financial management and accounting can be more closely integrated with all aspects of enterprise production and operation, and how to use modern information management tools to make financial information more accurate, fast and smoothly delivered to the relevant decision-making level and to avoid financial risks, have become new issues that need to be addressed. The purpose of this paper is to study the enterprise financial information management system based on data analysis algorithms. SQL server 2008 and efficient and simple JSP technology were used to implement the design of the functional modules. During the design of this paper, the BP neural network algorithm is analysed to evaluate and analyse the asset management in the financial management process, which effectively realises the intelligence of the financial management system. © 2023 IEEE.

关键词： Informatization

来源：评论

学校读者我要写书评

暂无评论

Probabilistic data Association Performance during Launch Vehicle Tracking 3

Probabilistic Data Association Performance during Launch Veh...

引用

3rd international conference on Range Technology, ICORT 2023

作者： Ravindra, Challa Rajkumar, S. Vaidhyanathan, A. SDSC-SHAR ISRO Sriharikota India

ISBN: (纸本)9781665493314

Probabilistic data Association (PDA) is Bayesian approach of deriving likelihood function for all measurements reported by radar. Tracking of Launch Vehicle with skin mode radar involves processing of multiple returns from rocket, separated stages and rocket plumes, which makes association of track measurements more uncertain. Using PDA we bring down the uncertainty of measurements by computing posterior probability for all measurements reported by radar during real time. Measurement with highest posterior probability is selected and given as input to processing filter. In this paper we analyze Probabilistic data Association (PDA) performance during tracking of Launch Vehicle by Multi Object Tracking Radar. © 2023 IEEE.

关键词： Launch vehicles

来源：评论

学校读者我要写书评

暂无评论

An Approach to Mine data for Predicting Forest Fires Using Support Vector Machines and Gini Index for Feature Selection

An Approach to Mine Data for Predicting Forest Fires Using S...

引用

2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Prathimesh Kunal Shingari, Groresh Sharma, Deepika Computer Science Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350333244

Increasing temperatures regularly cause dry vegetation in wooded regions to burn due to sun radiation, which in turn sparks forest fires. Predicting and mapping fire susceptibility in fire-prone forests worldwide is a critical research focus. This paper presents a data mining approach for forest fire prediction. The maps that arise from evaluating the Gini index's performance in identifying significant features serve as a basis for firefighting tactics and regulations that are more successful in promoting sustainable forest resource management. The proposed model utilizes Support Vector Machine for regression and achieves an improved accuracy of approximately 87% compared to previous research. This study emphasizes the importance of enhancing traditional algorithms by prioritizing key features to ensure consistent results and optimize predictive models. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Convertible certificateless proxy re-signcryption for data access control in cloud computing

Convertible certificateless proxy re-signcryption for data a...

引用

2023 international conference on Computer Application and Information Security, ICCAIS 2023

作者： Wu, Chenhuang Yang, Linghui Yuan, Beibei Li, Huimin Huang, Hui Fujian Key Laboratory of Financial Information Processing Putian University Fujian Putian351100 China Fujian Zhongxin Network Security Information Technology Co. Ltd. Fujian Fuzhou350011 China School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China

ISBN: (纸本)9781510675032

Cloud storage service enables users not only to save their local data storage costs but also to access their outsourced data at any time and anywhere. Traditionally, a data owner outsources the encrypted data to the cloud server and then checks its integrity with the help of cloud auditing strategies. Obviously, in such a manner, only confidentiality and integrity are considered following the above two independent steps. Furthermore, it becomes an issue that the data owner may want to share their outsourced data with some authorized data receivers and convince them that the data are from and the same as the data owner's. To solve this issue, absorbing the advantage of the certificateless cryptosystem, we propose an efficient certificateless proxy re-signcryption (CLPRSC) for data access control in cloud computing. Moreover, the CLPRSC possesses ciphertext verifiable, convertible, and designated re-signcryptable properties with the highest efficiency among the achievable signcryption with proxy re-encryption schemes, which may be of independent interest. © 2024 SPIE.

关键词： Access control

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 << < 92 93 94 95 96 97 98 99 100 101 > >>

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：