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561-570 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 Efficient Approach for Cross-Silo Federated Learning to Rank 37

An Efficient Approach for Cross-Silo Federated Learning to R...

引用

37th IEEE international conference on data Engineering (IEEE ICDE)

作者： Wang, Yansheng Tong, Yongxin Shi, Dingyuan Xu, Ke Beihang Univ BDBC SKLSDE Lab Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ IRI Beijing Peoples R China

ISBN: (纸本)9781728191843

Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data. However, with the increasing awareness of data privacy, it is harder to collect data from multiple owners as before, and the resultant data isolation problem makes the performance of learned LTR models severely compromised. Inspired by the recent progress in federated learning, we propose a novel framework named Cross-Silo Federated Learning-to-Rank (CS-F-LTR), where the efficiency issue becomes the major bottleneck. To deal with the challenge, we first devise a privacy-preserving cross-party term frequency querying scheme based on sketching algorithms and differential privacy. To further improve the overall efficiency, we propose a new structure named reverse top-K sketch (RTK-Sketch) which significantly accelerates the feature generation process while holding theoretical guarantees on accuracy loss. Extensive experiments conducted on public datasets verify the effectiveness and efficiency of the proposed approach.

关键词： Differential privacy Privacy conferences Collaborative work data engineering data models

来源：评论

学校读者我要写书评

暂无评论

AI data Analytics for Air Quality Modeling

AI Data Analytics for Air Quality Modeling

引用

international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Annandip Barua Md Abu Ahammed Babu Department of Computer Science & Engineering Cox's Bazar International University Bangladesh Volvo Car Corporation Sweden

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Air quality is a critical concern globally, with implications for public health and environmental sustainability. While improvements have been made since the implementation of the Clean Air Act of 1970, the adverse effects of air pollution persist, leading to significant health burdens and mortality rates worldwide. In this context, this study focuses on analyzing air quality in Leeds, UK, during the year 2020, leveraging data from monitoring stations administered by the Department for Environment, Food, and Rural Affairs (DEFRA). Given the unique circumstances of 2020, which included lockdowns due to the COVID-19 pandemic, this period offers valuable insights into the interplay between human activity and air pollution. The research employs a multi-phase approach, encompassing descriptive, inferential, machine learning, and deep learning analyses to comprehensively understand air quality patterns and predict pollutant concentrations. While existing literature predominantly focuses on other regions, particularly China and India, this study addresses the scarcity of research specific to the UK context. Findings reveal variations in pollutant levels, with particulate matter (PM10 and PM2.5), nitrogen oxides (NO2 and NO) and carbon monoxide (CO) being the primary pollutants of concern. Inferential analyses highlight significant differences in pollutant levels and correlations, potentially influenced by lock-down measures. Machine learning algorithms, notably random forests, demonstrate efficacy in predicting PM2.5 concentrations, mirroring findings from deep learning approaches. However, the study underscores the need for larger and more diverse datasets to enhance predictive accuracy and generalize findings. Future research directions include exploring hybrid deep learning algorithms, refining machine learning models, and expanding data collection efforts across various locations and time periods to better understand the dynamics of air quality. Overall, this research

关键词： Deep learning Machine learning algorithms Atmospheric modeling Heuristic algorithms Prediction algorithms Air pollution Market research

来源：评论

学校读者我要写书评

暂无评论

Adaptive Alternating Stochastic Gradient Descent algorithms for Large-Scale Latent Factor Analysis

Adaptive Alternating Stochastic Gradient Descent Algorithms ...

引用

IEEE international conference on Services Computing (SCC)

作者： Qin, Wen Luo, Xin Zhou, MengChu Chinese Acad Sci Chongqing Sch Univ Chinese Acad SciSch Comp Sci & Technol Chongqing Inst Green & Intelligent TechnolChongq Chongqing 400065 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Hengrui Chongqing Artificial Intelligence Res Ctr Dept Big Data Anal Tech Chongqing 400714 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9781665416832

Latent factor analysis (LFA) is highly efficient in knowledge discovery from high-dimensional and sparse (HiDS) matrices frequently encountered in big data and web service related applications. A stochastic gradient descent (SGD) algorithm is commonly adopted as a learning algorithm for LFA owing to its high efficiency. However, its sequential nature makes it less scalable when processing large-scale data. Although an alternating SGD algorithm decouples an LFA process to achieve parallelization, its performance relies on its hyper-parameter selection that is highly expensive to tune. To address it, this paper presents three adaptive alternating SGD algorithms, thus leading to three Parallel Adaptive LFA (PAL) models for LFA on large-scale HiDS matrices. Experimental studies on HiDS matrices from industrial service applications show that the proposed PAL models perform significantly better than existing ones in terms of both convergence rate and computational efficiency, as well as achieve competitive prediction accuracy for missing data.

关键词： Latent Factor Analysis High-Dimensional and Sparse Matrix Quality of Service Stochastic gradient descent

来源：评论

学校读者我要写书评

暂无评论

Towards First Urban data Space in Bulgaria 8

Towards First Urban Data Space in Bulgaria

引用

8th IEEE international Smart Cities conference, ISC2 2022

作者： Vassilev, Vassil Petrova-Antonova, Dessislava Bali, Tarun Radu, Sorin Sowinski-Mydlarz, Viktor Mariyanayagam, Dion Marinov, Evgeny Hristov, Petar O. Nakarmi, Sabin Rabka, Monika London Metropolitan University United Kingdom Gate Institute Sofia University Bg Bulgaria

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Smart city is no longer fiction, which targets the future - it is now around us and requires immediate action in different directions: environment, transport, energy, social and cultural life, healthcare, etc. This article presents the current efforts of GATE Institute at Sofia University for building an urban data space based on data from a variety of sources in the Bulgarian capital. It has an open architecture, based on a private cloud, which allows the integration of diverse data and provides different data processing capabilities and services necessary to build integral data spaces. The pilot implementation currently performs monitoring and analysis of the environmental factors in Sofia using a set of bespoke components, which implement data management and data analysis algorithms from simple filtering and correlation to data analytics and prediction using historical data, static modelled data and dynamic data from environmental sensors in real-time. It serves as a basis for data enrichment based on different sources and cross-domain analysis using a variety of methods. This new opportunity has a huge potential and will have a significant impact on urban life - from planning the infrastructure and managing the communal services to the personalization of social services for the citizens. © 2022 IEEE.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

A Review on the Construction of Business Intelligence System Based on Unstructured Image data 8

A Review on the Construction of Business Intelligence System...

引用

8th international conference on Information Technology and Quantitative Management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Zhang, Linzi Qi, Zhiquan Meng, Fan Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Peking Univ Dept Infomat Management Beijing 100871 Peoples R China

Business Intelligence (BI) has been an emerging significant issue in modern government and corporate functions, which attracts the attention of both researchers and merchants. Using the raw data from customers, BI provides effective decision-making knowledge for various cooperate governance operations with the method of data Warehouse and data Mining. As the explosive growth of data volume, it has evolved into using unstructured data including image and text in business intelligence system, from the initial descriptive analysis of computational statements. This paper will review the methods of storing and analyzing the unstructured image data used in the construction of BI system, including traditional image processing and novel deep learning methods. In each method, the specific algorithms and application scenarios will be summarized. Together, we will discuss the possible innovative progress of BI system construction and its future trends. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Business intelligence Unstructured image data Big data fusion

来源：评论

学校读者我要写书评

暂无评论

AI based Technologies for international Space Station and Space data 11

AI based Technologies for International Space Station and Sp...

引用

11th international conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Pant, Piyush Rajawat, Anand Singh Goyal, S.B. Potgantwar, Amol Bedi, Pradeep Raboaca, Maria Simona Constantin, Neagu Bogdan Verma, Chaman School of Computer Sciences and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Sandip Institute of Technology and Research Center Sandip University Nashik India Galgotias University Deapartment of Cse Greater Noida India National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Iasi Department of Power Engineering Gheorghe Asachi' Technical Iasi700050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

Billions of galaxies, stars, solar systems, planets, and other undiscovered mysterious objects are present in the continuously expanding space. Humans took a giant step forward when the international Space Station was deployed into Earth's lower orbit for a better understanding of space. This research focuses of the Artificial Intelligence based technologies that are deployed in the international Space Station and potential models. The study proposes some Machine Learning models and technologies that could be deployed in the international Space Station to increase its efficiency and provide security to the crew. Powerful and trending Machine Learning/Deep Learning algorithms like ANN and Clustering algorithms are suggested by the paper to get insights from the data gathered from the space and to promote the Industry Automation. A detailed explanation of the requirement, operation, and construction of NASA's 'Robonauts' designed for the international Space Station is discussed in the research. The paper also put light on the ATLAS, an asteroid detecting system. The methods for providing medical aid to the crew, debris and its influence, analyzing data and extracting insight from space research data using machine learning are also highlighted. Investigation on how technologies used in space exploration could be used in the ISS to improve its performance and an overview of some of the existing AI-based technologies deployed in the international Space Station (ISS) is discussed. © 2022 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning 8

Improving Portuguese Semantic Role Labeling with Transformer...

引用

8th IEEE international conference on data Science and Advanced Analytics (DSAA)

作者： Oliveira, Sofia Loureiro, Daniel Jorge, Alipio Univ Porto FCUP Dept Comp Sci LIAAD INESC TEC Porto Portugal

ISBN: (纸本)9781665420990

The Natural Language processing task of determining "Who did what to whom" is called Semantic Role Labeling. For English, recent methods based on Transformer models have allowed for major improvements in this task over the previous state of the art. However, for low resource languages, like Portuguese, currently available semantic role labeling models are hindered by scarce training data. In this paper, we explore a model architecture with only a pre-trained Transformer-based model, a linear layer, softmax and Viterbi decoding. We substantially improve the state-of-the-art performance in Portuguese by over 15 F1. Additionally, we improve semantic role labeling results in Portuguese corpora by exploiting cross-lingual transfer learning using multilingual pre-trained models, and transfer learning from dependency parsing in Portuguese, evaluating the various proposed approaches empirically.

关键词： Viterbi algorithm Transfer learning Semantics Training data Transformers data models Natural language processing

来源：评论

学校读者我要写书评

暂无评论

Multi-Granularity Feature Distillation Learning Network for Fine-Grained Visual Classification

Multi-Granularity Feature Distillation Learning Network for ...

引用

international conference on Image processing, Computer Vision and Machine Learning (ICICML)

作者： Cai, Yuhang Ke, Xiao Fuzhou Univ Coll Comp & Data Sci Fujian Key Lab Network Comp & Intelligent Informa Fuzhou Fujian Peoples R China

ISBN: (纸本)9781665464680

Fine-grained visual classification (FGVC) aims to identify objects belonging to multiple sub-categories of the same super-category (such as species of birds, models of cars and aircraft). The key to solving fine-grained classification problems is to learn discriminative visual feature representation with only subtle differences. Although previous work based on refined feature learning has made great progress, however, high-level semantic features often lack key information for fine-grained visual object nuances. How to efficiently integrate semantic information of different granularities from classification networks is a Critical. In this paper, we propose Multi-Granularity Feature Distillation Learning Network (MGFDL-Net). Our solution integrates multi-granularity hierarchical information through a multi-granularity fusion learning strategy to enhance feature representation. In view of the inherent challenges of large intra-class differences in FGVC, a cross-layer self-distillation regularization is proposed to strengthen the connection between high-level semantics and low-level semantics for robust multi-granularity feature learning. Comprehensive experiments show that our method achieves state-of-the-art performance on three challenging fine-grained visual classification FGVC datasets (CUB-200-2011, Stanford Cars and FGVC- Aircraft).

关键词： Fine-grained visual classification Multi-granularity feature learning Knowledge distillation

来源：评论

学校读者我要写书评

暂无评论

LSTM-based Pedestrian Trajectory Prediction Model under the General Direction Mechanism: DIR-LSTM 3

LSTM-based Pedestrian Trajectory Prediction Model under the ...

引用

3rd international conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Luo, Junyuan Fu, Qiongfang Xiao, Jianhua Xiao, Hongbo Liao, Jiyong School of Computer Science and Engineering Huaihua University Hunan Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Application in Hunan Province Universities Hunan Huaihua China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9798400716775

Accurate prediction of future traffic flow trends is essential to solve urban transportation problems. However, traffic flow prediction faces great challenges due to the multimodal nature of pedestrian behavior and the complexity of the traffic environment. Although a large number of studies have been conducted to investigate these issues in depth, there are still some limitations. In order to address these challenges more effectively, we propose a pedestrian trajectory prediction model based on long-short-term memory networks (LSTMs): the DIR-LSTM. The model introduces an innovative generalized direction mechanism and a self-attention mechanism, which captures pedestrian movement patterns more comprehensively and accurately by predicting overall directional movements first and then gradually subdividing them into individual directions of movement. The DIR-LSTM is designed to address the challenges posed by the diversity of pedestrian behaviors and the complexity of urban environments. To validate the state-of-the-art of the model, we conducted experiments using the publicly available ETH [10] and UCY [9] datasets. The experiments demonstrate that DIR-LSTM performs better in terms of accuracy compared to other models, providing a more reliable prediction tool for future urban traffic management. © 2023 ACM.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

3D track reconstruction of low-energy electrons in the MIGDAL low pressure optical time projection chamber

3D track reconstruction of low-energy electrons in the MIGDA...

引用

7th international conference on Micro Pattern Gaseous Detectors

作者： Tilly, E. Handley, M. Univ New Mexico Dept Phys & Astron 210 Yale Blvd NE Albuquerque NM 87106 USA Univ Cambridge Cavendish Lab JJ Thomson Ave Cambridge CB3 0HE England STFC Rutherford Appleton Lab Particle Phys Dept Didcot OX11 0QX Oxon England

We demonstrate three-dimensional track reconstruction of electrons in a low pressure (50 Torr) optical TPC consisting of two glass GEMs with an ITO strip readout in CF4 and CF4/Ar mixtures. The reconstructed tracks show a variety of event topologies, including short tracks from photoelectrons induced by Fe-55 5.9 keV X-rays and long tracks from gamma ray interactions and beta decays. algorithms for event identification and track ridge detection are discussed as well as multiple methods for integrating information from the camera image and ITO waveforms with the goal of full 3D reconstruction of the track.

关键词： data processing methods Gaseous imaging and tracking detectors Optical detector readout concepts Time projection chambers

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 << < 53 54 55 56 57 58 59 60 61 62 > >>

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：