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

13,377 篇 会议
17 篇 期刊文献
16 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

7,105 篇 工学
- 6,398 篇 计算机科学与技术...
- 5,095 篇 软件工程
- 1,730 篇 信息与通信工程
- 1,008 篇 控制科学与工程
- 735 篇 生物工程
- 721 篇 电气工程
- 667 篇 生物医学工程（可授...
- 582 篇 网络空间安全
- 511 篇 光学工程
- 451 篇 安全科学与工程
- 398 篇 电子科学与技术（可...
- 344 篇 交通运输工程
- 328 篇 动力工程及工程热...
- 288 篇 机械工程
- 267 篇 仪器科学与技术
2,667 篇 理学
- 1,298 篇 数学
- 879 篇 生物学
- 637 篇 统计学（可授理学、...
- 535 篇 物理学
- 328 篇 系统科学
- 242 篇 化学
1,877 篇 管理学
- 1,300 篇 管理科学与工程(可...
- 789 篇 图书情报与档案管...
- 573 篇 工商管理
947 篇 医学
- 687 篇 临床医学
- 681 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...
508 篇 法学
- 471 篇 社会学
262 篇 经济学
- 262 篇 应用经济学
223 篇 农学
210 篇 教育学
198 篇 文学
9 篇 军事学
1 篇 艺术学

主题

2,085 篇 accuracy
1,690 篇 deep learning
1,200 篇 machine learning
1,188 篇 computational mo...
1,044 篇 training
1,026 篇 data models
968 篇 predictive model...
724 篇 real-time system...
687 篇 convolutional ne...
684 篇 feature extracti...
676 篇 machine learning...
585 篇 support vector m...
531 篇 federated learni...
484 篇 adversarial mach...
330 篇 contrastive lear...
311 篇 medical services
295 篇 random forests
289 篇 analytical model...
278 篇 prediction algor...
271 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...
42 篇 the islamic univ...
42 篇 department of co...
40 篇 shandong provinc...
36 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

12,140 篇 英文
1,269 篇 其他
306 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4971-4980 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：

Experimental Analysis of learning Based Approach to Evaluate the Integration of Social Networking Platform with Internet of Things Technology

Experimental Analysis of Learning Based Approach to Evaluate...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Karunakaran, A. Venkatraman, C.B. Suganthi, P. Santhi, G.B. Alok, Narkhede Shalout, Imad S. A. Engineering College Department of Ece Chennai India Sona College of Technology Department of Eee Salem India Sri Sai Ram Institute of Technology Department of Cse Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa university Faculty of engineering Department of Civil Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Enterprising entrepreneurs are forging a new paradigm for consumer-business interactions by leveraging the convergence of social networking platforms-such as Facebook and Twitter-and the Internet of Things (IoT). This paper aims to assess an integration pipeline's performance using a learning based approach. Our aim is to understand the complex interplay between data that has been collected from IoT and how social media users act using advanced methods in data analytics. Our approach involves gathering data from both SNPs and the Internet of Things (IoT) and pre-processing it as needed before building a model that can be predicted and its performance evaluated. As opposed to the proposed method Elevated Social Networking Integration (ESNI) is a better strategy than the existing Social Networking Integration Analyzer (SNIA), which permits collecting less information through connected devices to enhance decision-making on consumer behavior and encounters. By offering a methodical approach to evaluating the value proposition of combining IoT with SNP, this study adds to the corpus of knowledge. The framework facilitates seamless interaction across diverse social media platforms, optimizing data flow and user experience. Empirical testing demonstrates ESNI's effectiveness in boosting engagement metrics and user satisfaction compared to conventional integration methods. This research highlights ESNI's potential to redefine social networking strategies, providing a robust foundation for future innovations in digital communication and marketing. © 2024 IEEE.

关键词： data assimilation

来源：评论

学校读者我要写书评

暂无评论

An Attention-Based, Dual-Domain Adaptive Convolutional Neural Network for Fast MRI Reconstruction 24

An Attention-Based, Dual-Domain Adaptive Convolutional Neura...

引用

10th international conference on computing and Artificial Intelligence, ICCAI 2024

作者： Hu, Rui Chen, Shufan Tao, Jinxu Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei23007 China Department of Automation University of Science and Technology of China Hefei23007 China

ISBN: (纸本)9798400717055

In recent years, deep learning has demonstrated great potential in accelerating Magnetic Resonance Imaging (MRI). However, most existing works simply consider MRI reconstruction as an image-to-image task, or typically use a single and identical network for both frequency and image domain reconstruction simultaneously, without taking into consideration the data characteristics and internal interactions of the two domains, which leads to ineffective learning of the image reconstruction models. In this study, we proposed an attention-based, dual-domain reconstruction network to facilitate fast and accurate MRI reconstruction by adaptively learning different data characteristics in the frequency domain and image domain. In response to the inhomogeneous energy distribution in K-space, we designed a densely connected multi-attention network for frequency domain reconstruction. For image aliasing at low sampling rates, we developed a residual encoder-decoder network with an attention module for the image domain reconstruction. The overall network contains two parallel and interactive branches that simultaneously perform on K-space and image data, with the data fusion modules to fuse dual domain information in each reconstruction layer, and the long skip connections to transfer inter-layer information. We have evaluated our method on two datasets. A series of comparative and ablation experiments demonstrate that our method effectively reconstructs images from undersampled K-space data, achieving better image reconstruction performance and restoring significant textures of brain slices. © 2024 ACM.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

IoT Solutions to Assist Patients With Diabetic Foot: A Systematic Mapping 7

IoT Solutions to Assist Patients With Diabetic Foot: A Syste...

引用

Joint of the 7th international conference on Applied Informatics Workshops, ICAIW 2024

作者： Moura, Karla Haryanna S. Lelli, Valéria Marinho, Fabiana G. Federal University of Ceará Fortaleza Brazil Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil

The use of Internet of Things (IoT) applications in the prevention and treatment of diabetic foot has stimulated increasing research interest. Researchers are actively developing strategies to create applications that enable early and personalized interventions for patients with diabetic foot. Additionally, there is a growing focus on leveraging these technologies to enhance patient monitoring effectiveness. This paper provides a systematic mapping of research studies addressing the use of IoT in diabetic foot prevention and treatment. The mapping identified 22 relevant studies proposing various approaches, including smart shoes, smart insoles, and smart socks for remote monitoring. These studies also explored IoT strategies that integrate, cloud computing, prediction algorithms and machine learning to store and analyze collected data. This paper discusses the challenges faced on developing IoT applications for diabetic patients, emphasizing issues related to application architecture, the scope of solutions, the technologies used, and data integration. © 2024 Copyright for this paper by its authors.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Empirical Analysis of Individual Differences Based on Sentiment Estimation Performance Toward Speaker Adaptation for Social Signal Processing 16th

Empirical Analysis of Individual Differences Based on Sentim...

引用

16th international conference on Social computing and Social Media (SCSM) Held as Part of the 26th international conference on Human-Computer Interaction (HCII)

作者： Li, Sixia Okada, Shogo Japan Adv Inst Sci & Technol Grad Sch Adv Sci & Technol Nomi Ishikawa Japan

ISBN: (纸本)9783031612800;9783031612817

Understanding user's internal state is indispensable for human-robot interaction in social signal processing. To mitigate the bias of sentiments observed by third-party annotators, the importance of self-reported by users themselves was pointed out recently. However, the self-reported internal state is not displayed as similar multimodal behaviors among different individuals, this leads to performance gap between self-reported and third-party sentiment estimations. Speaker adaptation for social signal processing (SASSP) is necessary to learn individual social signal characteristics to mitigate the individual differences. Towards effective adaptation for speakers with different characteristics, clarifying influence of individual differences in internal state estimation is necessary but has not been clarified. To address this problem, this study conducted empirical analysis by training and testing models on multimodal data of a group of speakers. Then, we analyze the relationships between the best model's performance and speaker's characteristics including age, gender, personalities, and speaker's expectation before human-robot interaction experiment. The results showed that these aspects all have influence on estimation performance in SASSP due to expression differences. This study provides suggestions and directions on setting SASSP policies for self-reported internal state estimation.

关键词： Sentiment estimation speaker adaptation for social signal processing machine learning

来源：评论

学校读者我要写书评

暂无评论

learning Multi-Granularity Task Primitives from Construction Videos for Human-Robot Collaboration

Learning Multi-Granularity Task Primitives from Construction...

引用

ASCE international conference on computing in Civil Engineering (I3CE)

作者： Pan, Zaolin Yu, Yantao Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Human-robot collaboration (HRC) is an emerging solution for the construction industry's productivity and safety challenges. The seamless HRC requires robots to understand the structure of construction tasks. Nevertheless, the implicit, dynamic construction task flow poses a nontrivial challenge. To address this challenge, a vision-based multi- granularity task's primitive learning method is proposed. This study seeks to enhance the mutual understanding between workers and robots by determining which granularity level is best for the tasks' understanding. Results show that the intermediate level has the best compromise between classification performance and embedded task knowledge. The outcomes will improve the smoothness of a HRC team.

关键词： Robots

来源：评论

学校读者我要写书评

暂无评论

Real-time Glass Recycling Quality Assurance and Contamination Reduction with IoT and Random Forest algorithm 7

Real-time Glass Recycling Quality Assurance and Contaminatio...

引用

7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Balasubadra, K. Shadaksharappa, B. Seeni, Senthil Kumar Sridevi, V. Thamizhamuthu, R. Srinivasan, C. R.M.D. Engineering College Department of Information Technology Chennai Tamil Nadu Kavaraipettai India Sri Sairam College of Engineering Department of Computer Science And Engineering Bengaluru Karnataka Anekal India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Academy of Maritime Education And Training Deemed To Be University Electrical And Electronics Engineering Tamil Nadu Chennai India Srm Institute of Science And Technology Department of Computing Technologies Kattankulathur Campus Chennai Tamil Nadu Chengalpattu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

The recycling industry is crucial to environmental protection and resource conservation. Glass recycling has great sustainability potential, but quality assurance and contaminant reduction are major challenges. Integrating the Internet of Things (IoT) with the Random Forest algorithm, this work offers a new solution to these difficulties. IoT devices are used in glass recycling collection, transportation, sorting, and processing. These devices measure glass quality, contaminants, temperature, humidity, and other parameters in real-time. data is sent to a central system for analysis and decision-making. Random Forest, a machine learning method that handles complicated, multidimensional datasets, powers the system. This algorithm learns from past data and updates with real-time data. It detects contaminants, foreign elements, and faults in recycled glass. This permits fast remedial action, enhancing recycled glass quality and decreasing contamination. Application of this mechanism yields two outputs. First, it improves glass recycling efficiency and produces high-quality glass for remanufacturing. Second, it significantly minimizes the environmental and economic implications of contamination and low-quality glass, which may limit recycling yields and increase waste disposal. In comparison to traditional approaches, the proposed approach that utilizes the IoT and the Random Forest algorithm reduces contamination and ensures the quality of glass recycling by 30%. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Ultra-high definition video quality assessment method based on deep learning 9

Ultra-high definition video quality assessment method based ...

引用

9th IEEE international conference on data science in Cyberspace, DSC 2024

作者： Li, Wei He, Can Zhou, MingLe Zhang, HengYu Liu, WenLong Hisense Video Technology Co. Ltd. Qing Dao China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

With the development of display technology, people's demand for high-definition video is increasing, and the requirements for video quality are also increasing. Nowadays, a large number of videos have emerged, and how to distinguish the quality of videos faces many challenges. Nowadays, deep learning is developing rapidly, and many video quality assessment (VQA) methods have emerged. These VQA methods can efficiently evaluate video quality and save a lot of manpower and material resources. To achieve the evaluation of UHD video quality, we propose an inter-frame downsampling UHD video quality assessment model (IFDU-VQA) based on deep learning. This model is a no-reference (NR) evaluation model that adopts a dual-branch structure: one branch uses a recursive network to deeply extract video inter-frame features, and combines inter-frame downsampling technology with a multi-information fusion (MIF) module to achieve different time and frequency information. Effective fusion;the other branch applies convolutional neural networks (CNN) to extract features from individual video frames. By fusing the outputs of the two branches, regression and pooling modules are used to calculate the final quality score of the video. Compared with multiple existing models, experimental results show that IFDU-VQA demonstrates superior performance on multiple standard data sets. © 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

Risk Analysis And Premium Calculation For Health Insurance 1

Risk Analysis And Premium Calculation For Health Insurance

引用

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

作者： Verma, Ritika Raj, Simran Tauqir, Zahra Tripathi, Subham Verma, Preeti Singh, Arjun Greater Noida Institute of Technology Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

The requirement for thorough risk analysis and precise premium calculation in health insurance grows more crucially as the healthcare industry changes. It is important to examine the evolving landscape of health insurance and highlight the growing need for careful risk analysis and accurate premium calculations as the healthcare industry undergoes significant changes. This research paper introduces an innovative project aimed at addressing the complexities of health insurance by bringing diverse perspectives together. The project initiates a systematic examination of the current state of health insurance, identifying significant barriers and opportunities. By conducting an extensive literature analysis, it sheds light on various risk factors affecting health insurance, encompassing demographic shifts, medical improvements, technological breakthroughs, and epidemiological patterns. This comprehensive understanding forms the basis for crafting a robust framework for risk analysis. Using advanced techniques such as machine learning (ML) and advanced data analytics, the project attempts to assess health risks both at the individual and population levels. The model aims to use datasets of previous claims, socio-economic metrics, and lifestyle indicators to improve prediction accuracy, ultimately contributing to more informed decision-making processes for insurers. Moreover, by aligning pricing strategies with risk profiles, investigation equivalences three regression model's performance: decision tree regression, multiple linear regression, and random forest (RF) regression, highlighting RF regression algorithm's superiority. While predicted insurance amount aids in understanding coverage needs, it's emphasized that accuracy varies and should not be the sole factor in selecting a plan. Nonetheless, estimating insurance costs provides valuable insights for optimizing coverage and maximizing benefits. © 2024 IEEE.

关键词： Risk analysis

来源：评论

学校读者我要写书评

暂无评论

Morphological Classification of Galaxies using Enhanced Deep learning Model 5

Morphological Classification of Galaxies using Enhanced Deep...

引用

5th IEEE international conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Nemade, Rohan Jha, Suraj Anand, Harsh Chandrasekaran, Saravanan Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379884

Galaxies in astrophysics are one of its fundamental, their morphological classification remains an essential key to unlock countless doors about the structure, formation and history of our Universe. For nearly a century, astronomers have assigned galaxies to perceptual categories such as ellipticals, spirals and irregulars. However, the extraordinary expansion of the galaxy database catalysed and instigated by sky surveys of ever-increasing scales rendered this classification more manual than practical. Because of the learning capability and its scaling nature, to learn hierarchical abstract features from the visual data Convolutional Neural Networks (CNN) are powerful tools to recognize complex patterns and structures in images, such as spiral arms or central bulges that contribute significantly to accurate morphological classification. That is why CNN are particularly well suited to the problem of galaxy classification. While traditional classification work used to solve the problem, it is not feasible for today's huge amounts of generated data. This attempt to build and train a CNN model using galaxy images as our domain. The Enhanced CNN model can achieve the high accuracy to morphological type of galaxies. The Enhanced CNN increase both efficiency and classification accuracy, particularly when processing large dataset. The Galaxy Zoo dataset indicated that our study can provide a tool for other astronomical research in future. The Enhanced CNN gain the overall accuracy rate are 95.29% with the performance indicator were calculated including average precision, recall, F1-score and Area under curve (AUC) is 0.9513, 0.9531, 0.9525 and 0.9853 respectively. The Enhanced CNN outperforms the various existing state-of-the models. © 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

Real-Time Energy Management in Buildings Using Reinforcement learning Techniques

Real-Time Energy Management in Buildings Using Reinforcement...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sri, T. Aruna Mallala, Balasubbareddy Bathrinath, S. Diana, I. Elsie Velmurugan, V. Aancy, H. Mickle Telangana Hyderabad500090 India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronic Engineering Telangana Hyderabad500075 India Kalasalingam Academy of Research and Education Department of Mechanical Engineering Tamil Nadu Krishnankoil626126 India Department of English Tamil Nadu Dindigul624622 India Vel Tech Rangarajan Dr.Sagunlatha R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Tamil Nadu Chennai600062 India Panimalar Engineering College Department of Master of Business Administration Tamil Nadu Chennai600123 India

ISBN: (纸本)9798331543617

While simultaneously maintaining the level of comfort and reducing the costs associated with functionality, the primary objective is to reduce the quantity of energy that is spent as much as possible. During the investigation, a framework that is anchored on RL is developed. By learning from literal and real-time data, reacting to varied environmental circumstances, and taking into account the actions of inhabitants, this system can make substantial alterations to the amount of energy that is currently being used. To anticipate and adjust to the requirements for energy, the system that has been shown makes use of complex algorithms. This ultimately leads to an increase in efficiency being achieved by the system. The results of the simulation indicate that there have been significant improvements in terms of both the amount of energy that has been saved and the functional performance that has been achieved in contrast to more conventional control strategies. Furthermore, the findings shed light on the probable role that RL will play in the accomplishment of sustainable energy operations in smart structures. This is in addition to the fact that they contribute to the broader pretensions of energy efficiency and environmental sustainability. © 2024 IEEE.

关键词： Reinforcement 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 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：