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

1,576 篇 会议
1 篇 期刊文献

馆藏范围

1,577 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

899 篇 工学
- 774 篇 计算机科学与技术...
- 533 篇 软件工程
- 253 篇 信息与通信工程
- 194 篇 控制科学与工程
- 130 篇 电气工程
- 84 篇 网络空间安全
- 78 篇 生物医学工程（可授...
- 75 篇 安全科学与工程
- 64 篇 交通运输工程
- 62 篇 生物工程
- 55 篇 电子科学与技术（可...
- 45 篇 动力工程及工程热...
- 43 篇 仪器科学与技术
- 42 篇 光学工程
- 42 篇 石油与天然气工程
306 篇 理学
- 164 篇 数学
- 83 篇 生物学
- 73 篇 统计学（可授理学、...
- 61 篇 物理学
- 37 篇 系统科学
- 33 篇 化学
267 篇 管理学
- 203 篇 管理科学与工程(可...
- 106 篇 图书情报与档案管...
- 84 篇 工商管理
- 34 篇 公共管理
114 篇 医学
- 80 篇 临床医学
- 77 篇 公共卫生与预防医...
- 64 篇 基础医学(可授医学...
51 篇 法学
- 48 篇 社会学
39 篇 经济学
- 39 篇 应用经济学
18 篇 文学
15 篇 教育学
11 篇 农学
2 篇 军事学
1 篇 艺术学

主题

193 篇 data models
157 篇 accuracy
153 篇 computational mo...
92 篇 training
86 篇 feature extracti...
86 篇 machine learning
77 篇 predictive model...
69 篇 data mining
68 篇 deep learning
63 篇 real-time system...
57 篇 big data
49 篇 adaptation model...
45 篇 security
44 篇 robustness
41 篇 decision making
40 篇 convolutional ne...
39 篇 cloud computing
36 篇 analytical model...
35 篇 neural networks
34 篇 optimization

机构

14 篇 lovely professio...
7 篇 shandong provinc...
7 篇 key laboratory o...
6 篇 college of mecha...
6 篇 institute of aer...
5 篇 lloyd institute ...
5 篇 college of intel...
5 篇 university of ch...
5 篇 national univers...
5 篇 department of co...
5 篇 shandong fundame...
5 篇 national univers...
5 篇 institute of aer...
4 篇 karnataka bangal...
4 篇 shanghai jiao to...
4 篇 school of comput...
4 篇 chitkara univers...
4 篇 new horizon coll...
4 篇 srm institute of...
4 篇 school of softwa...

作者

7 篇 nijhawan ginni
5 篇 jeanette miriam ...
5 篇 lu xiaobo
5 篇 singh navdeep
4 篇 nicola franco
4 篇 choudhry mani de...
4 篇 akhil karnam
4 篇 zhang rui
4 篇 yu zhang
4 篇 akshya j.
4 篇 lihui wang
4 篇 qu yuben
4 篇 wang lihui
4 篇 li lei
4 篇 dong chao
4 篇 dutt varun
4 篇 puri chetan
3 篇 yuangan wang
3 篇 junqi luo
3 篇 zhang lei

语言

1,273 篇 英文
303 篇 其他
4 篇 中文

检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1577 条记录，以下是891-900 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：

Learning Based Opportunistic data Transmission for cognitive IoT

Learning Based Opportunistic Data Transmission for Cognitive...

引用

Social computing and Networking (SocialCom), ieee international conference on

作者： Li Li Xiaoxiong Zhong School of Computer Science Shenzhen Institute of Information Technology Shenzhen China Tsinghua Shenzhen International Graduate School Shenzhen China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331521097

ISBN: (纸本)9798331521103

As the rapid development of communication technology, more and more smart devices will be connected in cognitive radio Internet of Things (CIoT) using an opportunistic manner. However, the dynamicity and heterogeneity of spectrum will make it more difficult in data transmission strategy design for unreliable IoT. Opportunistic routing exploits the broadcast property of wireless medium to improve network performance in an opportunistic communication manner. Inspired by this, in this paper, we propose a Nash-learning and Transfer Learning based Opportunistic data Transmission scheme, NTLODT, for differentiated services CIoT, which exploits Nash-learning for candidate set optimization and transfer learning for data transmission decision from a trust mechanism perspective. In addition, we prove that the convergence and complexity of the proposed algorithm. The simulation results demonstrate that the effectiveness of NTLODT in terms of average energy cost per bit, average delay, expected cost of routing and throughput.

关键词： Performance evaluation Social computing Costs Simulation Transfer learning Routing Throughput data communication Smart devices Optimization

来源：评论

学校读者我要写书评

暂无评论

Navigating the Digital Landscape: Enhancing Service Discovery and Portability in Digital Marketing Strategies 23rd

Navigating the Digital Landscape: Enhancing Service Discover...

引用

23rd international conference on Next Generation Wired/Wireless Networks and Systems (NEW2AN) / 16th conference on Internet of Things and Smart Spaces (RuSMART)

作者： Djasurovna, Ergashxodjaeva Shaxnoza Obitovich, Kurolov Maksud Akramovich, Axmedov Ikrom Tashkent State Univ Econ Dept Mkt I Karimov St 49 Tashkent 100066 Uzbekistan

ISBN: (纸本)9783031609961;9783031609978

In today's fast-paced digital landscape, businesses are compelled to embrace digital marketing strategies to reach and engage their target audiences effectively. The evolution of digital marketing presents both opportunities and challenges, as organizations strive to maintain brand consistency and user engagement across diverse online platforms and channels. This research delves into the dynamic realm of digital marketing, with a primary focus on service discovery and portability as crucial components of contemporary marketing strategies. The study addresses several central research questions, investigating the evolution of digital marketing, the critical factors influencing service discovery, and the strategies for ensuring seamless portability across platforms. It explores how emerging technologies such as artificial intelligence, data analytics, and cloud computing are transforming the digital marketing landscape. Additionally, the research seeks to uncover the potential advantages and disadvantages of integrating these technologies into marketing practices. The significance of this research lies in its potential to guide businesses in optimizing their digital marketing efforts. By enhancing service discovery and portability, organizations can augment customer engagement, brand visibility, and maintain a competitive edge in the ever-evolving digital ecosystem. To achieve these objectives, the research utilizes a comprehensive methodology, combining qualitative and quantitative approaches. Case studies, surveys, interviews, and data analysis provide valuable insights, offering practical recommendations for businesses to adapt their digital marketing strategies. This research thus serves as a valuable resource for organizations navigating the complex digital marketing landscape, equipping them with the knowledge and strategies needed to thrive in the digital age.

关键词： Digital Marketing Service Discovery Portability Emerging Technologies Mobile-Centric Strategies data Privacy User Engagement

来源：评论

学校读者我要写书评

暂无评论

Prediction of Energy Efficiency and Environmental Sustainability in Greenhouse Tomato Production: Insights and Analysis

Prediction of Energy Efficiency and Environmental Sustainabi...

引用

cognitive computing and complex data (ICCD), international conference on the

作者： Mahdi A. Mahdi Wafaa K. Mahmood Yongfeng Tang Yao Liu Hong Ki An Faculty of Electonic Information Engineering Huaiyin Institue of Technology Huai’an China Department of Production Engineering and Metallurgy University of Technology Baghdad Iraq Faculty of Mangement Engineering Huaiyin Institue of Technology Huai’an China Faculty of Civil Engineering & Technology Universiti Malaysia Unimap Perlis Malaysia

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Greenhouse tomato production is a vital sector in modern agriculture, providing a controlled environment to optimize yield and quality. However, the high energy consumption associated with greenhouse operations raises concerns about environmental sustainability, particularly regarding greenhouse gas (GHG) emissions. This study develops a comprehensive neural network model to predict both tomato yield and GHG emissions, integrating key inputs such as heating energy, cooling energy, lighting energy, fertilizers, and water usage. The model was trained and validated using data from greenhouse operations, demonstrating effective learning and rapid convergence, stopping at epoch 11 out of 1000 with a final performance metric (mean squared error) of 0.00374 and a gradient of 0.0936. The results show that the model can accurately predict yield and GHG emissions, with the best validation performance achieved at epoch 5 (MSE = 0.3523). The model's predictions closely follow the trends of actual data, as illustrated by a relatively strong training correlation (R=0.78262) and moderate overall correlation (R=0.56593). Error histograms indicate that most predictions are close to actual values, though there are areas for further refinement. This dual-focus approach to optimizing productivity and environmental sustainability offers significant potential for enhancing resource use efficiency in greenhouse agriculture.

关键词： Productivity Analytical models Correlation Cooling Green products Lighting Predictive models data models Greenhouse gases Fertilizers

来源：评论

学校读者我要写书评

暂无评论

Quantum Machine Learning in the cognitive Domain: Alzheimer's Disease Study

Quantum Machine Learning in the Cognitive Domain: Alzheimer'...

引用

ieee conference on High Performance Extreme computing (HPEC)

作者： Emine Akpinar Department of Physics Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, primarily affecting the elderly population and leading to significant cognitive decline. This decline manifests in various mental faculties such as attention, memory, and higher-order cognitive functions, severely impacting an individual's ability to comprehend information, acquire new knowledge, and communicate effectively. One of the tasks influenced by cognitive impairments is handwriting. By analyzing specific features of handwriting, including pressure, velocity, and spatial organization, researchers can detect subtle changes that may indicate early-stage cognitive impairments, particularly AD. Recent developments in classical artificial intelligence (AI) methods have shown promise in detecting AD through handwriting analysis. However, as the dataset size increases, these AI approaches demand greater computational resources, and diagnoses are often affected by limited classical vector spaces and feature correlations. Recent studies have shown that quantum computing technologies, developed by harnessing the unique properties of quantum particles such as superposition and entanglement, can not only address the aforementioned problems but also accelerate complex data analysis and enable more efficient processing of large datasets. In this study, we propose a variational quantum classifier with fewer circuit elements to facilitate early AD diagnosis based on handwriting data. Our model has demonstrated comparable classification performance to classical methods and underscores the potential of quantum computing models in addressing cognitive problems, paving the way for future research in this domain.

关键词： Computers Quantum computing Accuracy Quantum entanglement Computational modeling Machine learning Vectors Alzheimer's disease Integrated circuit modeling Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

BK-MSSNN: BI-Kernel Based Multi-Scale Subgraph Neural Networks 22

BK-MSSNN: BI-Kernel Based Multi-Scale Subgraph Neural Networ...

引用

22nd international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2024

作者： Jiang, Kang Peng, Jia-Wang Yuan, Xin Xu, Wei Tong, He Chen, Sheng-Bing Xu, Li-Xiang College of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia College of Aeronautical Engineering Beijing Polytechnic Beijing100176 China College of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Artificial Intelligence Hefei Institute of Technology Hefei238076 China

ISBN: (纸本)9798331528072

Graph Neural Networks (GNNs) have achieved remarkable success in handling graph-related tasks. Most GNN frameworks are based on Message Passing Neural Networks (MPNNs). However, subgraph structures are often complex and multi-scale, and the performance of MPNN in graph isomorphism discrimination tasks may have certain limitations. To address this issue, this paper investigates the semantic associations between subgraphs of different scales and proposes a novel Bi-Kernel Multi-Scale Subgraph Neural Network (BK-MSSNN) to represent more finegrained subgraph semantic information. Specifically, we sample global subgraphs and local subgraphs of different scales, and integrate two graph kernel methods, namely the RW kernel and the Weisfeiler-Lehman kernel, to construct multiple associative relationships between subgraphs for updating node embeddings. Experimental results on five public datasets demonstrate that our proposed model outperforms other benchmark models in graph classification tasks. © 2024 ieee.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Wind Power Prediction based on Multivariate and Convolutional Neural Network-Fast Deep Stacked Network Extreme Learning Machine

Wind Power Prediction based on Multivariate and Convolutiona...

引用

cognitive computing and complex data (ICCD), international conference on the

作者： Junyang Xu Qiwei Zhang Yuexin Liu Xuefeng Wang Chu Zhang Hydraulic Engineering Office Huadong Engineering Corporation Limited Hangzhou China Smart Energy Division Hangzhou Huachen Power Control Engineering Corporation Limited Hangzhou China School of Automation Huaiyin Institute of Technology Huai’an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Accurate wind power prediction can optimize the scheduling of electric power resources, reduce the scheduling pressure due to wind power fluctuations, and contribute to better planning of power grid operation to ensure the stability and security of the power grid. Traditional ELM is difficult to accurately characterize the complex relationship between environmental factors and wind power, and considering that the problem of poor solvability of multivariate data may affect the hardness of wind power prediction, this study proposes a hybrid wind power prediction model based on Random Forest (RF) feature selection, Convolutional Neural Networks (CNNs), and Fast Deep Stacked Network Extreme Learning Machine (FDSNELM). Firstly, RF is used to select features for multivariate wind power data to screen out the influencing factors with strong correlation with the wind power data; secondly, CNN is used to extract features for historical multivariate wind power data; then, the feature-extracted wind power data are used as inputs to the FDSNELM model to predict the future wind power. The wind power data of wind farms in Yunnan province in December 2020 is taken as the research object, and the RF-CNN-FDSNELM model proposed in this paper is compared with the BP, ELM, TCN, FDSNELM, and CNN-FDSNELM models. The results show that the RF-CNN-FDSNELM model established in this study has higher prediction accuracy and higher stability, and can be better applied to wind power prediction.

关键词： Radio frequency Accuracy Correlation Extreme learning machines Computational modeling Wind power generation Predictive models Feature extraction data models Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Garbage Image Classification Based on Improved ShuffleNet Fusion Algorithm

Garbage Image Classification Based on Improved ShuffleNet Fu...

引用

cognitive computing and complex data (ICCD), international conference on the

作者： Kaikai Shang Shaozhang Xiao Ken Lin Kangliang Xiao Yin Xia Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Aims to address the problems of low accuracy of existing garbage classification and low classification ac curacy of models, large model size and difficulty of deployment on portable devices. A lightweight garbage image classification model is proposed by merging the improved lightweight model ShuffleNet V2 and MobileViT. Firstly, the structure of the ShuffleNet V2 model is adjusted and improved to enhance the cross-channel information interaction ability of the model, which improves the feature information interaction ability between the channels of the ShuffleNet V 2 model; secondly, the improved ShuffleNet V2 model is fused with the MobileViT model, the advantage of the MobileViT module in global feature information extraction is used to overcome the shortcomings of the ShuffleNet V2 network, which is difficult to extract global feature information. Comparative experiments are conducted on public datasets to evaluate the performance of the model. The experimental results show that the accuracy of the proposed SNViT model is significantly better than the D enseNet-121 model, and also has a better classification performance compared to EfficientNet V2, MobileNet V2 and other models, while the precision rate, recall rate and F1 value are also improved. The improved SNViT model improves the accuracy of the image classification task and achieves high classification performance, while the number of parameters is small and easy to train and deploy.

关键词： Accuracy Convolution Computational modeling Merging Feature extraction Information retrieval data models data mining Image classification Periodic structures

来源：评论

学校读者我要写书评

暂无评论

Preliminary Investigation of SSL for complex Work Activity Recognition in Industrial Domain via MoIL

Preliminary Investigation of SSL for Complex Work Activity R...

引用

ieee Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Qingxin Xia Takuya Maekawa Jaime Morales Takahiro Hara Hirotomo Oshima Masamitsu Fukuda Yasuo Namioka Multimedia Engineering Osaka University JP Corporate Manufacturing Engineering Center Toshiba Corporation

In this study, we investigate a new self-supervised learning (SSL) approach for complex work activity recognition using wearable sensors. Owing to the cost of labeled sensor data collection, SSL methods for human activity recognition (HAR) that effectively use unlabeled data for pretraining have attracted attention. However, applying prior SSL to complex work activities such as packaging works is challenging because the observed data vary considerably depending on situations such as the number of items to pack and the size of the items in the case of packaging works. In this study, we focus on sensor data corresponding to characteristic and necessary actions (sensor data motifs) in a specific activity such as a stretching packing tape action in an assembling a box activity, and try to train a neural network in self-supervised learning so that it identifies occurrences of the characteristic actions, i.e., Motif Identification Learning (MoIL). The feature extractor in the network is used in the downstream task, i.e., work activity recognition, enabling precise activity recognition containing characteristic actions with limited labeled training data. The MoIL approach was evaluated on real-world work activity data and it achieved state-of-the-art performance under limited training labels.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Traffic Sign Small Object Detection Algorithm Combining Multi-feature and Accurate Location

Traffic Sign Small Object Detection Algorithm Combining Mult...

引用

cognitive computing and complex data (ICCD), international conference on the

作者： Zunwang Ke Shuaibo Chen Minghua Du Jisheng Zhou Yugui Zhang School of Software Xinjiang University Urumqi China The First Medical Center Chinese PLA General Hospital Beijing China Cardiovascular Department Peking University Third Hospital Beijing China Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

In the rapid development of autonomous driving technology, the accurate detection of traffic signs has become an indispensable part. Traffic signs are usually small in size, which leads to low detection accuracy, missed detections, and false detections with general object detection algorithms when capturing small, low-pixel traffic signs. To tackle these issues, this paper proposes a traffic sign small object detection algorithm combining multi-feature and accurate location. The algorithm utilizes a Triple Feature Fusion Module (TFF) to fully integrate features of different scales, providing rich shallow details for small-scale object detection; In addition to the original three detection layers, we have added a dedicated layer for detecting small-scale objects in YOLOv8, enhancing the algorithm's capability for small-sized traffic signs; By combining the normalized Gaussian distance (NWD) and CIoU, the loss function is optimized to tackle the problem that CIoU loss is overly sensitive to the position shifts of small-scale objects. Tested on the TT100K2021 and CCTSDB2021 datasets, the results showed that the mAP of our algorithm reached 82.8% and 81.5% respectively, which is an increase of 9.6% and 2.2% compared to the original YOLOv8. It is proved that the proposed algorithm has remarkable performance in traffic sign detection. https://***/ShuaiboChen/***

关键词： Accuracy Sensitivity Fuses data visualization Object detection Feature extraction Robustness cognitive systems Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Performance = Implementation + Hardware + Input data, with application to SpMV

Performance = Implementation + Hardware + Input Data, with a...

引用

ieee international conference on High Performance computing Workshops (HiPCW)

作者： Khushboo Chaudhari Shrirang Karandikar Sneha Thombre MKSSS’s Cummins College of Engineering for Women India AlgoAsylum India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

Algorithms are typically developed based on an implicit, abstract model of the hardware and inputs. Performance analysis of an implementation generally focuses on identifying bottlenecks utilizing methods such as Intel’s Roofline approach. However, actual performance is influenced by a complex interplay among the implementation, the hardware, and the input. In this paper, we present a methodology that considers a comprehensive view of this interaction and demonstrate how it can be applied to the sparse matrix-vector multiplication kernel, resulting in a 30.29% improvement in performance.

关键词： High performance computing conferences Hardware

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158页 << < 86 87 88 89 90 91 92 93 94 95 > >>

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：