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
71 篇 期刊文献
44 册 图书

馆藏范围

6,957 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
1,720 篇 理学
- 1,022 篇 数学
- 494 篇 生物学
- 372 篇 统计学（可授理学、...
- 306 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
158 篇 cloud computing
139 篇 machine learning...
124 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...
20 篇 vivekananda glob...
18 篇 university of ch...
18 篇 vishwakarma inst...
17 篇 chitkara univers...
17 篇 lovely professio...
17 篇 department of sc...
16 篇 department of el...
15 篇 vishwakarma inst...
14 篇 key laboratory o...

作者

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

语言

4,160 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 6957 条记录，以下是751-760 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：

An Improved Apriori Algorithm Based on Transaction Sequence Counting 10

An Improved Apriori Algorithm Based on Transaction Sequence ...

引用

10th IEEE international conference on Cyber Security and Cloud computing and 9th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Ning, Zuoting Ouyang, Zihua Deng, Xiangcheng Hunan Police Academy Department of Information Technology China

ISBN: (纸本)9798350312461

Apriori algorithm has the advantages of simplicity and easy realization, which can realize the search for some association between data. However, it also has unavoidable flaws. The traditional serial Apriori algorithm has the disadvantages of frequent scanning database, generating large amount of candidate item-set data and consuming a lot of memory resources. This paper proposes some technologies based on association principle, and proposes an improved Apriori algorithm to overcome the shortcomings of Apriori algorithm. When dealing with transactions, the algorithm filters out transactions that cannot generate frequent item-set, which can greatly reduce the amount of data processing. Theoretical analysis and experimental results show that the proposed scheme has the best performance in efficiency and stability. © 2023 IEEE.

关键词： data handling

来源：评论

学校读者我要写书评

暂无评论

international Workshop on Edge-Cloud Intelligence (ECI@CIKM2023) 23

International Workshop on Edge-Cloud Intelligence (ECI@CIKM2...

引用

32nd ACM international conference on Information and Knowledge Management (CIKM)

作者： Qi, Lianyong Zhang, Xuyun Xu, Xiaolong Gong, Wenwen China Univ Petr East China Qingdao Peoples R China Macquarie Univ Sydney NSW Australia Nanjing Univ Informat Sci & Technol Nanjing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798400701245

With the exponential growth of mobile devices and the increasing demand for personalized and context-aware services, service-oriented applications play a crucial role in alleviating information overload and improving user experience. At the same time, Edge-Cloud Intelligence (ECI) provides a distributed computing paradigm that leverages the computational capabilities of edge servers to bring services closer to end-users, enabling low-latency and high-bandwidth data processing. The convergence of service applications and ECI has emerged as a promising way to enhance the efficiency and quality of service provisioning in today's dynamic and resource-constrained mobile environment. This call for papers invites researchers and practitioners to contribute to the exploration of the synergy between service applications and ECI infrastructure.

关键词： cloud computing edge computing intelligent computing

来源：评论

学校读者我要写书评

暂无评论

Node-Aware Adaptive data processing Method Based on Att-BiGRU-RNN 4

Node-Aware Adaptive Data Processing Method Based on Att-BiGR...

引用

4th international conference on Smart Grid and Energy Engineering, SGEE 2023

作者： Sun, Qiao Xu, Fang Chai, Chao Liu, Ke Beijing FibrLink Communications Co. Ltd. Beijing China Tianjin University of Science & Technology Tianjin China

ISBN: (纸本)9798350359619

In the realm of edge computing for the Internet of Things (IoT) within the power industry, the efficient processing, accurate classification, and rapid response to massive IoT data have become urgent challenges. Traditional centralized data processing methods based on cloud computing are inadequate to handle the current vast data volumes. To address these issues, this paper proposes an intelligent edge data processing framework based on the combination of Bidirectional Gated Recurrent Units- Recurrent Neural Network with Attention Mechanism (Att-BiGRU-RNN) and Bloom filters. This framework integrates an attention mechanism to capture temporal dependencies in data from edge nodes, enabling preliminary prediction and categorization of data types. It dynamically adjusts the parameter structure of Bloom filters, reducing collisions and minimizing data false positives. This framework dynamically and adaptively selects appropriate data for processing at the edge nodes, significantly reducing the amount of data transmitted to the cloud, thereby conserving communication bandwidth and reducing time delays. It effectively addresses real-time identification methods for terminal devices in access networks and facilitates fast access to the network. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Streamlining Information Capture and Retrieval with AI-Enabled Systems 3

Streamlining Information Capture and Retrieval with AI-Enabl...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Sudhamsu, Gadug Agarwal, Ankita Dhivrani, Rekha Bhanu, K.S. Jeyalaxmi, M. Nawadkar, Ashwini R. Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation. © 2023 IEEE.

关键词： information organizational predictive processing security

来源：评论

学校读者我要写书评

暂无评论

A data LAKE PROTOTYPE SYSTEM FOR MULTIWAVELENGTH (MWA) AND MULTIMESSENGER ASTRONOMY (MMA) 16

A DATA LAKE PROTOTYPE SYSTEM FOR MULTIWAVELENGTH (MWA) AND M...

引用

16th international conference on ICT, Society and Human Beings, ICT 2023, the 15th international conference on e-Health, EH 2023, the 9th international conference on Connected Smart Cities, CSC 2023 and 8th international conference on Big data Analytics, data Mining and Computational Intelligence, BigDaCI 2023

作者： Bento, Matheus Rosa, Reinaldo Roberto de Almeida, Ulisses Barres INPE-MCTI Av. dos Astronautas 1758 SP São José dos CamposCEP 12227-010 Brazil Lab for Computing and Applied Mathematics COPDT-INPE-MCTI Av. dos Astronautas 1758 SP São José dos CamposCEP 12227-010 Brazil R. Dr. Xavier Sigaud 150 - Urca RJ Rio de JaneiroCEP 22290-180 Brazil

ISBN: (纸本)9789898704504

At the beginning of the 21st century, large telescopes are coming into operation, making astronomy a Big data science like few others. Recently, given this scenario, applied computing to astronomy and space sciences has pointed out major challenges involving data science and data engineering. Faced with such a challenge, this article presents a first prototype of a data lake as a strategic solution for heterogeneous data astronomy such as multi-wavelength astronomy (MWA), also including multi-messenger astronomy (MMA). The proposed prototype presents a three-layer architecture, composed of storage, processing and application layers. The storage layer incorporates S3 for raw data storage, MariaDB for simple data storage (crud) and TimescaleDB for complex data storage. The processing layer includes Laravel for CRUD operations and Python for handling large volumes of data. The application layer uses ReactJS with the *** library for analytical data visualization. The solution proposes to deliver, when necessary, agility and concatenation between heterogeneous data (light curve, spectra and images) coming from different silos (instruments and telescopes). Thus, the solution, within the MWA and MMA paradigms, incorporates data cube technology and can be customized for applications other than astronomy, such as space weather monitoring and debriefing in the context of space medicine. © ICT *** rights reserved.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Leveraging Federated Learning and XAI for Privacy-Aware and Lightweight Edge Training in Network Traffic Classification

Leveraging Federated Learning and XAI for Privacy-Aware and ...

引用

2023 IEEE international conference on computing, ICOCO 2023

作者： Ariffin, Azizi Zaki, Faiz Anuar, Nor Badrul Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350302684

The exponential growth of internet traffic causes significant challenges for network traffic classification, such as maintaining data privacy and requiring more computing resources. To address such challenges, moving the processing to data instead of the opposite shows some promise. Related technologies, such as edge computing, present a potential solution as they reduce latency and bandwidth and consider data privacy. However, edge computing often runs on resource-constraint devices, thus making complex model training like deep learning challenging. As such, this paper proposes a distributed and lightweight edge training method for network traffic classification using federated learning and XAI, which are currently underexplored in the domain. In doing so, we managed to run deep learning on edge devices and preserve data privacy. We evaluate the proposed method using the ISCXVPN2016 dataset on three NVIDIA Jetson devices to emulate edge devices. The results show a 36.71% improvement in F1 score, training time, memory, and GPU utilization when benchmarked against the more conventional centralized training. © 2023 IEEE.

关键词： data privacy

来源：评论

学校读者我要写书评

暂无评论

Research on Image processing of Big data Based on Computer Vision

Research on Image Processing of Big Data Based on Computer V...

引用

2023 international conference on Internet of Things, Robotics and Distributed computing, ICIRDC 2023

作者： Wang, Cui Wang, Yuanyuan Weifang Engineering Vocational College Shandong Weifang262500 China

ISBN: (纸本)9798350375275

With the advent of the era of big data, the image processing of big data based on Computer Vision (CV) has become a research field of great concern. The purpose of this study is to explore how to improve image processing methods through deep learning technology to better adapt to large-scale and high-dimensional image data. We propose an innovative image processing framework, based on the improved ResNet50, and introduce multi-scale feature fusion, attention mechanism and width adaptation strategies. In the task of image classification, our method has achieved remarkable improvement in accuracy, precision, recall and Fl value. Through the effective fusion of multi-scale features, the model better captures the abstract information in the image and improves the classification accuracy. In the target detection task, our method shows excellent performance in mAP, false alarm rate and so on. The improved ResNet50, which introduces attention mechanism, is more reliable in identifying the target position and category. The experimental results further prove the innovation and practicability of our proposed method. Compared with the traditional model and the latest image processing methods, our method shows significant advantages in big data image processing. This provides new ideas and technical support for solving large-scale and diversified image data processing problems. The contribution of this study is to provide a new solution for big data image processing based on CV. The improved ResNet50 model has made remarkable progress in performance, which opens up a new research direction for deep learning research in the field of big data image processing. © 2023 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Parallel Algorithm Design and Optimization for Numerical Simulation Application of Ion Implantation in Silicon 29

Parallel Algorithm Design and Optimization for Numerical Sim...

引用

29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qi, Ruixuan Yang, Wangdong Yan, Xiuwen Li, Honglu Zhang, Yifei Li, Kenli Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China The 48th Research Institute of China Electronic Technology Corporation Hunan Changsha China

ISBN: (纸本)9798350330717

We design a computational framework for ion implantation into silicon that can simulate the reaction process of cascade collisions in silicon and the subsequent annealing process. Since simulating materials requires large-scale particle data and high-precision simulation requirements, which often take up a lot of computing time, the conventional computing process needs to be accelerated in parallel to solve current problems. However, the current field of ion implantation focuses on the development of algorithms, often neglecting the design of parallel algorithms, and has not tested them on large-scale supercomputing clusters. Based on Tianhe new generation high-performance computers, this paper proposes a parallel computing framework for numerical simulation of ion implanted silicon based on multi-core processor architecture. By optimizing the data structure, the local continuity of the data is enhanced to improve the overall communication efficiency. Divide three-dimensional data for processes and adapt to MPI parallel computing strategies. Finally, a multi-level parallel computing strategy based on OpenMP is added to improve computing efficiency. After being deployed on the Tianhe new generation high-performance computer platform, experimental results show that on a single node, the multi-level parallel computing framework can increase the computing speed by 20.81X. After conducting tests at different scales, we can see that the parallel efficiency can be maintained at a high level. © 2023 IEEE.

关键词： data structure optimization ion implantation multi-level parallel strategy

来源：评论

学校读者我要写书评

暂无评论

Utilizing Big data algorithms and Cloud computing to Establish Centralized Electronic Health Records 5th

Utilizing Big Data Algorithms and Cloud Computing to Establi...

引用

5th international conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Sharma, Khushboo Saxena, Kumud Bargavi, Manju Chaudhary, Chetan Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Computer Science Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper aims to leverage massive statistics algorithms and cloud computing to create centralized electronic fitness statistics (EHRs) that are quickly and easily handy for all authorized healthcare professionals. The paper will evaluate the critical principles and technological advances that might be essential to setting up a possible EHR gadget. It will start with a dialogue about what an EHR is and how it has grown to be an increasing number of critical these days. The discussion will also include an overview of the different styles of EHRs and their benefits and disadvantages. The paper will then study the benefits of big statistics algorithms and cloud computing for EHRs. It will discuss how cloud computing permits efficient records retrieval, storage, and analysis competencies, in addition to how big facts analytics may be used for meaningful evaluation of large datasets. Eventually, the paper will consider the demanding situations related to using significant facts algorithms and cloud computing and recommend a few possible solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Design of Massive data Management System Based on DBN Algorithm

Design of Massive Data Management System Based on DBN Algori...

引用

2023 international conference on Power, Electrical Engineering, Electronics and Control, PEEEC 2023

作者： Liu, Yijiang Nanjing University of Finance & Economics Jiangsu Nanjing210046 China Massey University Wellington999030 New Zealand

ISBN: (纸本)9798350329124

With the rapid development of the network, the World Wide Web has become the carrier of a large amount of information, which contains a lot of valuable information, and its great potential can not be underestimated. At present, both at home and abroad are paying attention to the potential business opportunities brought by big data, and all walks of life have their own potential business data to achieve the greatest commercial profits in the market. At present, data processing technology in storage system has its own advantages, but it also has its own shortcomings in large-scale data processing. This article takes a large-scale transaction processing system as the research platform, and designs and implements a massive database monitoring and management system to address the main problems in massive database monitoring and management. The system architecture, running model, module design, and key technologies involved are included. Then, using labeled data, the algorithm based on L-BFGS is used to fine tune the system model. At the same time, in order to avoid overfitting, the drop out method is introduced in the pre training and fine tuning process. The experimental results using five fold cross validation and different scale datasets show that the proposed classification method outperforms KNN, SVM, and BP algorithms, and the classification results are relatively stable. © 2023 IEEE.

关键词： Learning algorithms

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 << < 72 73 74 75 76 77 78 79 80 81 > >>

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：