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61-70 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：

Knowledge-Defined Internet of Things Architecture for Cloud-Edge Collaborative Industrial Monitoring 24

Knowledge-Defined Internet of Things Architecture for Cloud-...

引用

2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Zhou, Hongyu Xing, Fangyuan Tong, Fei Wang, Mufeng Zhang, Yan Yang, Shuaifeng Southeast University Nanjing China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400710681

The Industrial Internet of Things (IIoT) has become a crucial technology driving the transformation of manufacturing towards intelligence. Traditional industrial networks primarily focus on data collection, whereas emerging IIoT emphasizes intelligent management and optimized control utilizing advanced technologies such as big data and artificial intelligence. With the more complex application scenarios, sole reliance on cloud computing fails to meet requirements for low latency and high security, thus leading to a trend towards edge-cloud collaborative computing. Knowledge-Defined Networking (KDN) introduces a knowledge plane above the control and data planes, which utilizes machine learning and deep learning methods to extract and analyze data and can guide the control plane in updating and optimizing network management and control decisions. This architecture enables the system to understand domain knowledge, supports knowledge-based reasoning decisions, and facilitates higher-level intelligent applications. This paper proposes a Knowledge-Defined Edge-Cloud Collaborative IoT architecture tailored for industrial monitoring scenarios. Therein, the edge is responsible for initial real-time data processing, while the cloud handles large-scale data storage and complex analytics. Knowledge definition are introduced into the entire framework and enables a comprehensive understanding of equipment status through historical data analysis and real-time feedback, which facilitates predictive issue identification and optimization of production plans. The enabling technologies that can implement the functions of the knowledge-based cloud, knowledge-based edge, knowledge-based end, and interfaces are summarized, which are expected to provide guidance for future IIoT. © 2024 Copyright held by the owner/author(s).

关键词： Cloud computing architecture

来源：评论

学校读者我要写书评

暂无评论

data Orchestration Platform for AI Workflows Execution Across computing Continuum 15

Data Orchestration Platform for AI Workflows Execution Acros...

引用

15th international conference on Cloud computing and Services science, CLOSER 2025

作者： Arcas, Gabriel Ioan Cioara, Tudor Engineering and Data Solutions Department Bosch Engineering Center Cluj-Napoca Romania Computer Science Department Technical University of Cluj Napoca Romania

ISBN: (纸本)9789897587474

Cloud AI technologies have emerged to exploit the vast amount of data produced by digitized activities. However, despite these advancements, they still face challenges in several areas, including data processing, achieving fast response times, and reducing latency. This paper proposes a data orchestration platform for AI workflows, considering the computing continuum setup. The edge layer of the platform focuses on immediate data collection, the fog layer provides intermediate processing, and the cloud layer manages long-term storage and complex data analysis. The orchestration platform incorporates the Lambda Architecture principles for flexibility in managing batch processing and real-time data streams, enabling effective management of large data volumes for AI workflows. The platform was used to manage an AI workflow dealing with the prediction of household energy consumption, showcasing how each layer supports different stages of the machine learning pipeline. The results are promising the models are being trained, validated, and deployed effectively, with reduced latency and use of computational resources. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： data streams

来源：评论

学校读者我要写书评

暂无评论

Use neural Networks to Recognize Students' Handwritten Letters and Incorrect Symbols

Use neural Networks to Recognize Students' Handwritten Lette...

引用

2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and the correct option is one of the four, some students may write incorrect symbols or options that do not exist. In this paper, five classifications were set up - four for possible correct options and one for other incorrect writing. This approach takes into account the possibility of non-standard writing options. © 2025 SPIE.

关键词： Education and training machine learning data modeling Neural networks Image classification Visual process modeling Computer vision technology Deep learning Performance modeling Transformers

来源：评论

学校读者我要写书评

暂无评论

Monitoring the Concentration of Air Pollutants and Its Health Hazards Using machine learning Models 5th

Monitoring the Concentration of Air Pollutants and Its Healt...

引用

5th international conference on computing science, Communication and Security

作者： Jain, Aditi Shenoy, Aditya Adiga, Ananya Anekal, Anirudha Prajwal, Saritha PES Univ Bengaluru 560085 Karnataka India

ISBN: (纸本)9783031751691;9783031751707

With the world moving rapidly towards industrialization driven by economic growth and technological advancements, there is an alarming surge of air pollution leading to significant health concerns. In response, this work introduces a research-driven approach for a continuous air quality monitoring system, designed to continuously track air quality in real-time in the proximity and proactively predict potential health hazards for the user. Central to the system's efficacy is a state-of-the-art hybrid machine learning model, seamlessly amalgamating the strengths of Adaptive Long Short-Term Memory (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA) models, renowned for their ability in handling intricate time series data. This model is securely deployed on a cloud platform, ensuring not only accessibility but also scalability to meet current and future technological standards. The system primarily concentrates on the monitoring of air pollutants, such as PM2.5, PM10, and CO, ensuring that users have access to immediate and up-to-date insights into the air quality in their surroundings. Beyond this, the system goes a step further by employing this data to assess users' potential risk of developing lung cancer. Through the use of Internet of Things (IoT) sensors, the system can issue timely and potentially life-saving insights, providing users with valuable information for decision-making improving their well-being. In a world, where the link between air quality and health is increasingly evident, our research-based initiative serves as a beacon for a healthier future, while also fostering environmental consciousness and public well-being.

关键词： Lung Cancer IOT Adaptive LSTM ARIMA AQI Air Pollutants

来源：评论

学校读者我要写书评

暂无评论

Anomaly detection using different types of machine learning models in the context of "smart maintenance technologies in the manufacturing industry" 6

Anomaly detection using different types of machine learning ...

引用

6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Giliyana, San Mälardalen University Universitetsplan 1 Västeras Sweden Mälardalen Industrial Technology Center John Engellaus Gata 1 Eskilstuna633 61 Sweden

Industry 4.0 presents nine technologies, including the Industrial Internet of Things (IIoT), Big data and Analytics, Cloud computing, etc. The progress of Industry 4.0 places a new demand for maintenance. Some of the nine technologies of Industry 4.0, such as IIoT, Big data and Analytics, Cloud computing and Augmented Reality (AR), as well as Cyber-Physical System (CPS) and machine learning, play an important in the development of smart maintenance technologies. In smart maintenance research, it is presented how IIoT can be used for machine connection and maintenance data collection, machine learning models for maintenance data analysis and failure prediction, AR for maintenance instructions, etc. Although previous smart maintenance research presents many technologies for smart maintenance, the manufacturing companies still face many implementation challenges when implementing and using to add benefits to maintenance organizations in line with companies main goals. Many manufacturing companies still utilize reactive maintenance and are experiencing too much downtime. In this paper, I have shown how two machine learning models, Isolation Forest and Regression Learner, and the statistical technique, Interquartile range (IQR), can be applied in order to detect anomalies in an unsupervised dataset, consisting of travel time for a linear guide, and temperature, in a drill station, which is part of a Cyber-Physical production system, located at a smart production laboratory in Sweden. © 2025 The Authors.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Three-Factors Authentication and Key Agreement for Federated learning 6th

Privacy-Preserving Three-Factors Authentication and Key Agr...

引用

6th international conference on machine learning for Cyber Security, ML4CS 2024

作者： Wang, Guojun Jiang, Guixin Zhao, Yushuai Ji, Yinglin Yancheng Polytechnic College Yancheng224005 China School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

With the development of big data and distributed computing, federated learning has received widespread attention for protecting data privacy. Federated learning is a distributed machine learning framework that allows multiple participants to collaboratively train shared models with locally retained data. However, the issues of communication security and authentication between participants have emerged as key challenges affecting the security of federal learning systems. To address this challenge, we propose an authentication and key agreement protocol for federated learning environment. The proposed protocol enables authentication between edge devices and server, which establishes a secure session key to secure communication between participants. Additionally, we verify the security of the session key by the verification tool. Finally, experimental evaluation results show that the protocol performs well in terms of computational overhead and communication latency and is suitable for large-scale federated learning systems with resource-constrained devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Structural Health Monitoring data Analysis Using Deep learning Techniques 24

Structural Health Monitoring Data Analysis Using Deep Learni...

引用

8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Yang, Linfei Huang, Enzo Faculty of Science and Technology University of Macao China Faculty of Business Administration University of Macao China

ISBN: (纸本)9798400710094

With the rapid development of deep learning technology, its application in the field of Structural Health Monitoring (SHM) represents a significant leap from traditional detection methods. This paper aims to explore how deep learning algorithms enhance the assessment of civil infrastructure integrity by analyzing large datasets from sensors. We first introduce the background of SHM, highlighting the shortcomings of traditional methods in terms of accuracy and efficiency. Next, we employ deep learning models for data analysis, investigating their applications in structural damage detection and prediction. Through a comprehensive analysis of existing literature, we find that deep learning not only overcomes the limitations of manual inspections but also significantly improves the overall performance of SHM by enhancing real-time monitoring capabilities. Furthermore, this paper discusses hybrid systems that combine data-driven models with physical understanding, as well as the distributed intelligence brought about by edge computing. These factors collectively provide more reliable and flexible solutions for future infrastructure management. Our key findings indicate that the application of deep learning can significantly improve the accuracy and efficiency of SHM, enabling a quicker response to the needs of dynamic communities. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Residual-INR: Communication Efficient On-Device learning Using Implicit Neural Representation 24

Residual-INR: Communication Efficient On-Device Learning Usi...

引用

43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Chen, Hanqiu Yao, Xuebin Subedi, Pradeep Hao, Cong Georgia Institute of Technology United States Samsung Semiconductor Inc. United States

ISBN: (纸本)9798400710773

Edge computing is a distributed computing paradigm that collects and processes data at or near the source of data generation. The on-device learning at edge relies on device-to-device wireless communication to facilitate real-time data sharing and collaborative decision-making among multiple devices. This significantly improves the adaptability of the edge computing system to the changing environments. However, as the scale of the edge computing system is getting larger, communication among devices is becoming the bottleneck because of the limited bandwidth of wireless communication leads to large data transfer latency. To reduce the amount of device-to-device data transmission and accelerate on-device learning, in this paper, we propose Residual-INR, a fog computing-based communication-efficient on-device learning framework by utilizing implicit neural representation (INR) to compress images/videos into neural network weights. Residual-INR enhances data transfer efficiency by collecting JPEG images from edge devices, compressing them into INR format at the fog node, and redistributing them for on-device learning. By using a smaller INR for full image encoding and a separate object INR for high-quality object region reconstruction through residual encoding, our technique can reduce the encoding redundancy while maintaining the object quality. Residual-INR is a promising solution for edge on-device learning because it reduces data transmission by up to 5.16 × across a network of 10 edge devices. It also facilitates CPU-free accelerated on-device learning, achieving up to 2.9 × speedup without sacrificing accuracy. Our code is available at: https://***/sharclab/Residual-INR. © 2024 Copyright is held by the owner/author(s).

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Emotion detection: Facial emotion detection using ML 4th

Emotion detection: Facial emotion detection using ML

引用

4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Sharad Chauhan, Ritik Vajpayee, Pooja School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Cognitive perception of images is an intense task, like guessing the truth of a thought or a mystery. In this process, we use different methods to solve the need to know the job. In recent years, emotional intelligence has received widespread attention due to its important role in improving human-computer interaction and emotional analysis. Because of its processes for extracting visual features, deep neural networks-particularly convolutional neural network (CNN)-are frequently employed in FER. A lot of research has been done on CNN, but only a few areas have solved the facial recognition (FER) problem. After many reports and studies, the shortcoming of most existing models is that they often consider the front profile and ignore the side profile of the current image, although different angular views are also important for the perspective model. This helps you make informed decisions about personal needs, providing support, or security threats. However, this proved to be too complex for computers, and feature extraction needed a wide range of image processing methods. Numerous machine learning techniques are appropriate for this particular purpose. To use machine learning for any kind of detection, an algorithm must first be trained and then tested on relevant data. This article examines some machine learning algorithms and strategies for extraction that aid in the analysis of human emotion. This review contains a comprehensive review of a variety of materials, methods, and measures commonly used in psychology research. It also highlights the challenges and limitations of current models, including dealing with uncertainties and cultural differences. Face recognition in large datasets poses additional challenges due to the diversity of images as well as the differences between side and front views. © 2025 the Author(s).

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Information Content of machine learning Under Robo-Advisory Inference 14th

Information Content of Machine Learning Under Robo-Advisory ...

引用

14th international conference on Frontier computing, FC 2024

作者： Yeh, Chih-Chuan Department of Finance National Taichung University of Science and Technology No.129 Sec. 3 Sanmin Rd. North Dist Taichung City40401 Taiwan

ISBN: (纸本)9789819623945

This article examines the transformative impact of Robo-Advisors on wealth management through the integration of big data and artificial intelligence (AI) in algorithmic trading strategies. We explore how AI technologies, including machine learning and natural language processing, can analyze large datasets, identify market trends, and optimize investment portfolios. This study compares the performance of various forecasting algorithms within the robo-advisory framework, taking the efficient frontier into consideration as a key metric. The research highlights the potential of AI-driven innovations to improve investment decision-making and shape the future of financial services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine 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 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：