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

13,379 篇 会议
18 篇 期刊文献
13 册 图书

馆藏范围

13,410 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,114 篇 工学
- 6,410 篇 计算机科学与技术...
- 5,128 篇 软件工程
- 1,743 篇 信息与通信工程
- 1,025 篇 控制科学与工程
- 736 篇 生物工程
- 724 篇 电气工程
- 665 篇 生物医学工程（可授...
- 597 篇 网络空间安全
- 512 篇 光学工程
- 452 篇 安全科学与工程
- 404 篇 电子科学与技术（可...
- 347 篇 交通运输工程
- 329 篇 动力工程及工程热...
- 304 篇 机械工程
- 268 篇 仪器科学与技术
2,699 篇 理学
- 1,326 篇 数学
- 881 篇 生物学
- 641 篇 统计学（可授理学、...
- 540 篇 物理学
- 331 篇 系统科学
- 244 篇 化学
1,882 篇 管理学
- 1,305 篇 管理科学与工程(可...
- 792 篇 图书情报与档案管...
- 574 篇 工商管理
946 篇 医学
- 686 篇 临床医学
- 679 篇 基础医学(可授医学...
- 659 篇 公共卫生与预防医...
- 264 篇 药学(可授医学、理...
509 篇 法学
- 470 篇 社会学
265 篇 经济学
- 265 篇 应用经济学
226 篇 农学
207 篇 教育学
198 篇 文学
9 篇 军事学
1 篇 艺术学

主题

2,084 篇 accuracy
1,684 篇 deep learning
1,188 篇 computational mo...
1,185 篇 machine learning
1,044 篇 training
1,026 篇 data models
969 篇 predictive model...
725 篇 real-time system...
691 篇 convolutional ne...
684 篇 feature extracti...
676 篇 machine learning...
584 篇 support vector m...
534 篇 federated learni...
496 篇 adversarial mach...
338 篇 contrastive lear...
311 篇 medical services
295 篇 random forests
286 篇 analytical model...
278 篇 prediction algor...
270 篇 neural networks

机构

105 篇 lovely professio...
101 篇 chitkara univers...
57 篇 department of co...
47 篇 school of comput...
45 篇 school of comput...
45 篇 department of co...
45 篇 department of co...
42 篇 the islamic univ...
40 篇 shandong provinc...
37 篇 college of compu...
32 篇 university of ch...
28 篇 department of co...
27 篇 department of co...
26 篇 computer science...
26 篇 graphic era deem...
25 篇 department of co...
25 篇 amrita vishwa vi...
25 篇 the islamic univ...
23 篇 department of co...
23 篇 college of intel...

作者

23 篇 satvik vats
18 篇 shiva mehta
18 篇 kukreja vinay
18 篇 vats satvik
17 篇 anjali t.
17 篇 rishabh sharma
17 篇 deepak upadhyay
16 篇 senthil kumar th...
15 篇 singh jagendra
15 篇 venugopalan manj...
13 篇 manjunatha
13 篇 rakesh kumar
12 篇 ramya maranan
11 篇 selvanayaki kola...
11 篇 s. kaliappan
10 篇 andersson karl
10 篇 muthukumaravel a...
10 篇 yang yang
10 篇 ayodeji olalekan...
10 篇 surendran r.

语言

12,117 篇 英文
1,292 篇 其他
343 篇 中文
1 篇 西班牙文

检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是4921-4930 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 Investigation into the Practical Use of Internet of Things Technology in Smart City Applications 1

An Investigation into the Practical Use of Internet of Thing...

引用

1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Das, Rashmi Prava Muduli, Debendra Luhach, Ashish Kumar CV Raman Global University Computer Science and Engineering Department Bhubaneswar India Papua New Guinea University of Technology Department of Electrical and Communication Engineering Morobe Lae Papua New Guinea

ISBN: (纸本)9798350361780

This study presents a comprehensive method of mixing randomly generated simulated datasets to analyze several Internet of Things (IoT) applications in the context of smart cities Using matplotlib and numpy libraries, combined with Python programming language, analysis simulate real-time data on temperature, air quality and energy consumption. The learning methodology includes making pie charts, line plots, bar graphs etc. to present the findings graphically for understanding the application of Internet of Things in smart cities. Devery of smart city experiences, weather water system, traffic reduction, temperature over time, smart homes, smart parking areas. From temperature change to smart home to parking lot and city specific of car reduction experiences, each model provides insight into the dynamic features of a smart city implementation method of learning that combines visual techniques and simulated randomly produced information providing a strong foundation for analyzing potential technological impacts Enhances the discussion on smart and sustainable urban development towards the goal of creating resilient and adaptive smart cities. © 2024 IEEE.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

Evaluation of the Effects of Climate Change on Surface Water Resources at the Ain Kwachia Dam Using machine learning Between 2008 and 2021 4th

Evaluation of the Effects of Climate Change on Surface Water...

引用

4th international conference on GIRE3D Participatory and Integrated Management of Water Resources in Arid Zones

作者： Gramz, Mohamed Batchi, Mouhcine Azagane, Moulay Hicham El-Boukhari, Adnane Mettouchi, Mehdi El Bouziani, Jamal Ibn Tofail Univ Terr Environm & Dev Lab Kenitra Morocco Ibn Tofail Univ Literature Arts & Pedag Engn Lab Kenitra Morocco Hassan II Univ Space Planning & Environm Lab Casablanca Morocco Natl Dept Planning Terr Rabat Morocco

ISBN: (纸本)9783031630408;9783031630385;9783031630378

Surface water resources are subject to climate change, particularly in arid zones such as Morocco's central plateau, and to the massive demand placed on water by human activities, which calls for equitable management of water potential, In this sense, hydraulic engineering is a solution for better storage and management of surface water, as well as for mitigating and adapting to climate variability. This is the aim of the Ain Kwaachia dam on the Ykem river in the northwestern part of the central plateau of Morocco. While the use of remote sensing technology, based on the acquisition of satellite images, is a kind of machine learning to quantify spatio-temporal variations in water potential, so the processing and the analysis of these image data, based on a GIS geographic information system, and this, from the Land Use index for land cover, with an unsupervised classification from 2008, the year of commissioning of the dam subject of this research, and a supervised classification around the year 2021. The results obtained show that the variations affecting the study area have been noticeable in recent years, from 9.78 km(2) for 2016, to 4.23 km(2) for 2021, as a result of the repercussions of climate change, with falling rainfall on the one hand, and in the other hand rising temperatures.

关键词： Water resources machine learning GIS Land use Ain Kouachia dam

来源：评论

学校读者我要写书评

暂无评论

Vehicle Target Detection with an Integrated Attention Mechanism 3

Vehicle Target Detection with an Integrated Attention Mechan...

引用

3rd international conference on Cloud computing, Big data Application and Software Engineering, CBASE 2024

作者： Wang, Yuzhuo Wang, Haihui Hu, Po Hu, Yuanzhong School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331506582

To address the problems of traditional vehicle detection models regarding equipment requirements, detection accuracy, and overlapping target leakage rate, this study proposes an improved vehicle target detection algorithm for YOLOv8. Firstly, we introduce EMA, an attention mechanism for cross-space learning, to improve the attention to feature information. Secondly, it combines EMA with the C2f module in the Neck network to fuse more important feature information, obtaining more accurate vehicle feature information. Finally, it uses the MPDloU loss function to speed up the model's convergence and improve detection accuracy. Experiments conducted on Pascal VOC and MS COCO datasets show that the performance of the improved algorithm proposed in this paper exceeds that of the original model. The enhanced model's mAP reaches 85.71%, 3.27% higher than the original model, improving vehicle target detection accuracy and being highly practical. © 2024 IEEE.

关键词： attention mechanism MPDIoU vehicle detection YOLOv8

来源：评论

学校读者我要写书评

暂无评论

ECSeg: Edge-Cloud Switched Image Segmentation for Autonomous Vehicles 21

ECSeg: Edge-Cloud Switched Image Segmentation for Autonomous...

引用

21st Annual IEEE international conference on Sensing, Communication, and Networking, SECON 2024

作者： Zhou, Siyuan Van Le, Duc Tan, Rui College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798331519186

Existing autonomous vehicles have not utilized the cloud computing for execution of their deep learning-based driving tasks due to the long vehicle-to-cloud communication latency. Meanwhile, the vehicles are in general equipped with the resource-constrained edge computing devices which may be unable to execute the compute-intensive deep learning models in real time. The increasing data transmission speed of the commercial mobile networks sheds light upon the feasibility of using the cloud computing for autonomous driving. Our city-scale real-world measurements show that the vehicles can partially use the cloud computing via the fifth generation (5G) mobile network with the low data transmission latency. In this paper, we present the design and implementation of ECSeg, an edge-cloud switched image segmentation system that dynamically switches between the edge and cloud for executing the deep learning-based semantic segmentation models to understand the vehicle’s visual scenes in real time. The switching decision-making is challenging due to the intricate interdependencies among various factors including the dynamic wireless channel condition, vehicle’s movement and visual scene change. To this end, we employ deep reinforcement learning to learn an optimal switching policy. Extensive evaluation based on both real-world experiments and trace-driven simulations demonstrates that ECSeg achieves superior image segmentation accuracy for autonomous vehicles, compared with four baseline approaches. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Analyzing Deep learning Techniques in Natural Scene Image Classification

Analyzing Deep Learning Techniques in Natural Scene Image Cl...

引用

2024 IEEE international conference on Big data, Bigdata 2024

作者： Kanavos, Athanasios Papadimitriou, Orestis Al-Hussaeni, Khalil Karamitsos, Ioannis Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates Rochester Institute of Technology Graduate and Research Department Dubai United Arab Emirates Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350362480

Image classification is a fundamental task in computer vision, with wide applications including autonomous navigation, content recommendation, and environmental monitoring. This paper offers a comprehensive comparative analysis of deep learning techniques using the Intel Natural Scenes Image dataset, which features diverse scenes such as forests, mountains, seas, streets, buildings, and glaciers. Our study evaluates the performance of various Convolutional Neural Network (CNN) architectures, focusing on aspects such as network design, hyperparameters, data augmentation, and transfer learning strategies. We describe our experimental setup in detail, including the CNN models used, preprocessing techniques applied, and evaluation metrics employed. The results and discussions present key findings, highlighting the strengths and limitations of the approaches studied and providing guidance for future research and practical applications. Our systematic analysis yields valuable insights into effective strategies for recognizing natural scenes. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Single to Diverse Video Generation Using Conditional Diffusion Probabilistic Model 1

Single to Diverse Video Generation Using Conditional Diffusi...

引用

1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Verma, Ayushi Badal, Tapas Gupta, Siddharth Sinha, Saatvik Gupta, Indrajeet School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and AI S R University Warangal India

ISBN: (纸本)9798350361780

In the interdisciplinary field of computer vision and deep learning, the generation of video sequences stands as a critical task, with wide-ranging applications from entertainment to surveillance systems. We introduce a novel framework based on the Conditional Diffusion Probabilistic Model (CDPM), featuring significant modifications to the convolution block for handling complex patterns and temporal dynamics. Our model integrates Global Response Normalization (GRN) and Sigmoid Linear Unit (SiLU) activation functions, enhancing its ability to interpret high-dimensional video data. This integration reflects our commitment to advancing video generation technology. The model's effectiveness is validated by its performance on various datasets: achieving an NNFDIV score of 0.57 in the SinGAN-GIF dataset and 0.44 in the HP-VAE-GAN dataset, outperforming existing methods. It also excels in NIQE, RECO, and PSNR metrics, indicative of superior image quality and coherence. These results collectively highlight the model's robustness in diverse video generation, marking a notable advancement in computer vision and deep learning. © 2024 IEEE.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

Integrating MIMO-TCN to Predict Pollen Outbreak in Various Geographic Locations 1

Integrating MIMO-TCN to Predict Pollen Outbreak in Various G...

引用

1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Sugandha Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Computer Science and Engineering Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq National University of Science and Technology Dhi-Qar Iraq

ISBN: (纸本)9798350356816

This study examines how automated learning approaches can improve pollen allergy outbreak and prevention. It focuses on atmospheric information from Japan and how it relates to pollen counts. Predictive systems are created by utilizing complex computations to analyze a variety of criteria, including weather patterns and environmental conditions, in order to forecast pollen quantities. The work makes use of an extensive collection of data that includes measurements of pollen and atmospheric information from different parts of Japan, and it uses a strong training procedure to produce models for prediction that are efficient. After that, tests are conducted using data from India to evaluate the models' flexibility and efficacy in forecasting allergic reactions to pollen in various geographical areas. The findings show how ML-driven methods might advance our knowledge of pollen allergy dynamics and enable preventative allergy relief strategies. The effectiveness of the created models is evaluated using assessment indicators such as Accuracy, Bias, Recall, and Precision, which demonstrate their dependability and adaptability in forecasting allergic reactions to pollen in a variety of geographical situations. © 2024 IEEE.

关键词： Allergies

来源：评论

学校读者我要写书评

暂无评论

Enhancing Early Lung Cancer Detection using Deep learning Techniques 4

Enhancing Early Lung Cancer Detection using Deep Learning Te...

引用

4th international conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Badaya, Harsh Mishra, Shruti Satapathy, Sandeep Kumar Cho, Sung-Bae Mallick, Pradeep Kumar Vellore Institute of Technology Computer Science and Engg Tamil Nadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Yonsei University Department of Computer Science 50 Yonsei-ro Sudaemoon-gu Seoul03722 Korea Republic of Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798350349856

Lung cancer is one of the most deadly and persistent diseases in the world today, making early identification of this disease crucial. This research focuses on the integration of cutting-edge deep learning technologies, namely Convolutional Neural Networks (CNNs), with modern medical insights to improve lung tumour detection using plain chest X-rays (CXR). Using a novel technique to transfer learning, we optimise our model's effectiveness for better diagnostic results in the early identification of lung cancer. Our suggested model, which combines ResNet50, EfficientNetB0, and DenseNet121, achieves outstanding accuracy (97.41%) while minimising loss (0.0803), exceeding individual models. This unique model combines the characteristics of many architectures to provide improved feature extraction and sophisticated pattern identification in medical imaging. Beyond a literature review, our study adds a real answer that has the potential to revolutionise early detection, giving a powerful weapon for combatting this lethal illness. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Brain Tumour Classification Using Convolutional Neural Network 15

Brain Tumour Classification Using Convolutional Neural Netwo...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandiyarajan, M. Reddy, P. Anji Valarmathi, R.S. Manideep, N. Sai Mokshitha, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350370249

This study presents an investigation into the development of a brain tumor classification and treatment planning system leveraging Deep learning methods, particularly Convolutional Neural Networks (CNNs). The research aims to enhance accuracy and efficiency in this domain by leveraging the capabilities of deep learning. An extensive assessment of the brain tumor classification model is carried out, showcasing its effectiveness in accurately identifying various tumor types, including glioma, meningioma, pituitary tumors, and cases without tumors. The model achieves high precision, recall, and F1-score values across these tumor categories, demonstrating robust performance in clinical decision-making scenarios. Leveraging Magnetic Resonance Imaging (MRI) data, the CNNbased model achieves an impressive overall accuracy of 95%. Methodologically, the study involves preprocessing MRI images for contrast enhancement and noise reduction, followed by feature extraction using CNN architectures comprising convolutional and pooling layers. The model undergoes training on a sizable collection of annotated brain tumor images, with data augmentation techniques employed to enhance generalization and mitigate overfitting. © 2024 IEEE.

关键词： Cerebral tumor Classification Model Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Momentum for the Win: Collaborative Federated Reinforcement learning across Heterogeneous Environments 41

Momentum for the Win: Collaborative Federated Reinforcement ...

引用

41st international conference on machine learning, ICML 2024

作者： Wang, Han He, Sihong Zhang, Zhili Miao, Fei Anderson, James Department of Electrical Engineering Columbia University New York United States School of Computing University of Connecticut Storrs United States

We explore a Federated Reinforcement learning (FRL) problem where N agents collaboratively learn a common policy without sharing their trajectory data. To date, existing FRL work has primarily focused on agents operating in the same or "similar" environments. In contrast, our problem setup allows for arbitrarily large levels of environment heterogeneity. To obtain the optimal policy which maximizes the average performance across all potentially completely different environments, we propose two algorithms: FEDSVRPG-M and FEDHAPG-M. In contrast to existing results, we demonstrate that both FEDSVRPG-M and FEDHAPG-M, both of which leverage momentum mechanisms, can exactly converge to a stationary point of the average performance function, regardless of the magnitude of environment heterogeneity. Furthermore, by incorporating the benefits of variance-reduction techniques or Hessian approximation, both algorithms achieve state-of-the-art convergence results, characterized by a sample complexity of (equation presented). Notably, our algorithms enjoy linear convergence speedups with respect to the number of agents, highlighting the benefit of collaboration among agents in finding a common policy. Copyright 2024 by the author(s)

关键词： Federated learning

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 << < 489 490 491 492 493 494 495 496 497 498 > >>

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：