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41-50 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：

machine learning-Driven Distributed Intrusion Detection System for the Internet of Vehicles 13

Machine Learning-Driven Distributed Intrusion Detection Syst...

引用

13th international conference on Research in computing at Feminine, RIF 2024

作者： AlFarra, Joud Alam, Leena Marir, Naila Sarirete, Akila Computer Science Department Effat College of Engineering Effat University Jeddah Saudi Arabia LIRE Laboratory Constantine 2-Abdelhamid Mehri University Constantine Algeria

The Internet of Vehicles (IoV), integrating vehicles with EV charging stations, faces significant vulnerabilities to cyber threats. Designing an efficient Intrusion Detection System (IDS) to detect sophisticated attacks within EV charging stations is crucial for safeguarding this interconnected network. However, creating IDS models using centralized machine learning approaches requires overcoming challenges such as scalability, time-consuming analysis, and real-time processing requirements. In this context, we propose a novel distributed system to IDS design for the IoV, leveraging distributed learning techniques to address these challenges and enhance EV charging station security. This study utilizes different machine learning models to improve the effectiveness of abnormal behavior detection. Based on the robustness and adaptability of machine learning models, the detection and classification of intrusion data are significantly enhanced. Our presented system is validated using the CICEV2023 dataset. Simulation results demonstrate that our algorithm achieves higher efficiency and accuracy compared to existing centralized schemes. © 2024 Copyright for this paper by its authors.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Lightweight Adaptive learning Algorithm for Energy-Latency Tradeoff in IoMT-Enabled Edge computing 17

Lightweight Adaptive Learning Algorithm for Energy-Latency T...

引用

17th international conference on machine Vision, ICMV 2024

作者： Yadav, Rahul Shafiq, Muhammad Kumar, Mohit Wei, Li Elaziz, Mohamed Abd College of Computer Science and Technology Harbin Engineering University Harbin China Cyberspace Institute of Advance Technology Guangzhou University Guangzhou China Department of Information Technology National Institute of Technology Jalandhar India Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt

ISBN: (纸本)9781510688278

Smart Healthcare is witnessing widespread implementation of Internet-of-Medical-Things (IoMT) devices. These devices play a crucial role in collecting vast amounts of data from various smart healthcare applications, which are then processed to facilitate informed decision-making. Edge computing has emerged as a valuable platform offering computational resources to handle this data collection efficiently. However, a critical challenge arises from the inappropriate and inefficient classical approaches to fair resource allocation in these energy-intensive, short battery life and delay-intolerant portable devices. To address this issue, this paper presents a Lightweight Adaptive learning Offloading Algorithm designed to optimize energy and latency in an edge computing environment. The proposed algorithm formulates the problem as a combined minimization of latency and energy costs while satisfying constraints related to limited battery capacity and service latency deadlines. To achieve this, the algorithm employs the adaptation parameters (α, β, γ), and the tradeoff target (C′) is configured based on the specific task requirements. Experimental results demonstrate the advantages of the proposed scheme, including energy savings and minimized latency in an edge environment. To validate the effectiveness of the proposed scheme under a realistic scenario using the iFogSim2 simulator. © 2025 SPIE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Research on data-driven Virtual Experimental Framework 24

Research on Data-driven Virtual Experimental Framework

引用

7th international conference on Educational Technology Management, ICETM 2024

作者： Li, Miaomiao He, Xiangchun College of Educational Technology Northwest Normal University Gansu Lanzhou China

ISBN: (纸本)9798400717468

With the rapid development of computing technology, data-driven method has become an indispensable part of scientific research and engineering design. Aiming at the problems of high cost and long cycle of traditional experimental methods, a data-driven virtual experimental framework is proposed. The framework utilizes advanced data analysis techniques and machine learning algorithms to predict and simulate the behavior of complex systems without the need for physical experiments. Based on the research of virtual experiment literature, this paper understands the current status of virtual experiment research, summarizes the key elements of virtual experiment, and proposes a virtual experiment framework including data layer, service layer and user layer by using design-based research method. The framework includes knowledge graph construction of data layer and behavior data construction based on xAPI. Service layer data collection, analysis and application and other basic functions;The design of specific virtual experiments in the application layer and the feedback of experiment reports, and the data collection, analysis and application methods for the overall experiment design through the experiment link. In the part of result report, it not only gives the overall results of the data analysis of the whole experiment process, but also gives the personalized resources and intelligent assistant database resources to provide data feedback for the improvement of the virtual experiment. © 2024 Copyright held by the owner/author(s).

关键词： data driven

来源：评论

学校读者我要写书评

暂无评论

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

Risk Resolution of Wisdom Education in the Era of Digital Intelligence 24

Risk Resolution of Wisdom Education in the Era of Digital In...

引用

7th international conference on Educational Technology Management, ICETM 2024

作者： Peng, Shaodong School of Education Science Hunan Normal University Hunan Changsha China

ISBN: (纸本)9798400717468

Wisdom education in the era of digital intelligence emphasizes the use of intelligent technology to empower education. It can integrate data wisdom, educational wisdom, and cultural wisdom to achieve the precision, creation, and optimization of education, and cultivate learners’ wisdom literacy. Due to the potential problems in algorithm, calculation data, and computing power and other aspects of problems in intelligent technology, as well as due to human-related factors such as emotional barriers and management’s losing control, wisdom education in the era of digital intelligence faces many risks. This paper applies the methods of philosophical speculation, ethical analysis, emotional analysis, and teaching pathology analysis to study the four major risks in them. (alienation of educational nature, human-machine ethical anomie, lack of artificial emotion, and pathological teaching), and intending to find the resolutions of the corresponding risks in wisdom education. The supposed resolutions are empowering the human-like nature of educational artificial intelligence and combining it with moral education to obtain a complete educaitonal nature of wisdom education;strengthening the governance of human-machine ethics from the source of risk (participants) through the utilization of intelligent educational ethical norms;combining human emotions with artificial emotions to establish a soul that is inclined to goodness of intelligent education;inhibiting pathogenic factors and enhancing disease resistance factors treat teaching diseases and prevent effectively through various therapies. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

An Intrusion Detection System for Multiclass Classification Across Multiple datasets in Industrial IoT Using machine learning and Neural Networks Integrated with Edge computing 2

An Intrusion Detection System for Multiclass Classification ...

引用

2nd international conference on data, Information and computing science, CDICS 2024

作者： Zhukabayeva, Tamara Ahmad, Zulfiqar Karabayev, Nurdaulet Baumuratova, Dilaram Ali, Mushtaq Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Computer Science and Information Technology Hazara University Mansehra21300 Pakistan

ISBN: (纸本)9781643685762

With the rapid expansion of industrial IoT (IIoT), maintaining robust cybersecurity is essential for the smooth operation of industrial processes. Industrial environments require adaptive solutions to effectively mitigate evolving cyber threats and protect sensitive operations. This research aims to improve the cybersecurity of industrial IoT environments. The research intends to design and implement an adaptive and real-time intrusion detection system with edge computing integration that improves the reliability of the operations in industrial IoT. We incorporated machine learning approaches to classify cyber threats using XGBoost and Deep Neural Networks (DNN). A comparative analysis of results obtained from two datasets shows that the XGBoost model was slightly more accurate than the DNN model, with an accuracy of 79% for dataset D1 and approximately 99.42% for data set D2. This analysis also clearly demonstrates the usefulness of these machine learning approaches and the need to select a model depending on the requirements for detecting particular attacks. Confusion matrix analysis shows that both models have several advantages in terms of recognizing different types of cyber threats. © 2025 The Authors.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Partial Distributed Deep learning Inference Model for Image based Edge Device Cluster 24

Partial Distributed Deep Learning Inference Model for Image ...

引用

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

作者： Xiao, Dun Wang, Xiaogang Yang, Zhihe Huang, Chao School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In recent years, the development of AI technology has driven the development of network edge applications such as smart manufacturing, smart factories, and smart cities. Deep neural networks are increasingly being deployed in smart manufacturing edge application scenarios to carry out edge detection tasks. However, there is a gap between the expensive DNN computing network and the limited computing power of the edge devices. To deploy compute-intensive neural networks on resource-constrained edge devices, traditional approaches rely either on offloading workloads to a remote central cloud or on optimizing compute on local edge devices. However, the method of uploading tasks to the central cloud is significantly affected by the unreliability and delay of the WAN, and the sensitive information of users is easy to be leaked during transmission, and the local computing method is also limited by the computing power. The latest machine learning models have great demands on memory, computing, and energy, which poses challenges to distributed computing and data aggregation for heterogeneous and resource-constrained edge devices. In order to address these challenges, this paper proposes solutions for different image types in intelligent manufacturing scenarios. When images are processed in the production process of intelligent manufacturing, this paper adopts the method of horizontal partitioning of the DNN model to perform DNN-based inference tasks on the edge device cluster. In order to reduce the consumption of network bandwidth, a dynamic priority scheduling algorithm is used to optimize the distribution of inference tasks at the edge nodes to improve the inference speed. Finally, experiments show that the edge node optimization scheduling method proposed in this paper has a good effect on the performance of running time, communication cost and energy consumption of edge devices. © 2024 Copyright held by the owner/author(s).

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

A machine learning (ML)-Inspired Method for Intrusion Detection in IoT Devices Networks 5th

A Machine Learning (ML)-Inspired Method for Intrusion Detect...

引用

5th international conference on computing science, Communication and Security

作者： Venkatesh, Veeramuthu Raj, Pethuru Nedium, Roshitha Edara, Jahnavi Reddy, Kalluru Amarnath Anushiadevi, R. SASTRA Deemed Univ Sch Comp Thanjavur 613401 Tamilnadu India Reliance Jio Cloud Serv JCS Bangalore 560025 Karnataka India

ISBN: (纸本)9783031751691;9783031751707

The Internet of Things (IoT) and machine learning (ML) represent dynamic research fields with significant growth. IoT applications have gained popularity among technology researchers and developers, and the increasing deployment of IoT devices in critical infrastructures improves efficiency and reliability and raises concerns about cyber-attacks. Enterprises have effectively implemented IoT services, enabling automated production through remote and intelligent control. However, this adoption has concurrently introduced novel security vulnerabilities. Addressing the security and privacy challenges in IoT, especially considering energy limitations and scalability issues, remains a critical focus in computer security. This paper aims to prevent multiple attacks targeting sensor nodes' data manipulation. It encompasses a range of threats, such as sensing layer attacks, malfunctions, tamper attacks, false data injection, base node and clone attacks. The proposed approach involves a threat model and a pairing algorithm that utilizes machine learning to associate each sensor node with its corresponding node. To accomplish the goal, the performance of the proposed solution is compared with various machine learning models, including Decision Tree, Linear Regression, k-nearest neighbors (KNN), Random Forest, and AdaBoost. The evaluation utilized two openly accessible real-world datasets, with metrics such as accuracy in attack detection, training time, and testing time being considered. By incorporating machine learning algorithms to improve attack detection in IoT environments, the proposed approach represents a significant advancement in enhancing security and privacy. The results highlight the importance of adopting advanced techniques to safeguard IoT systems from potential threats. The proposed method achieved an impressive 96.75% accuracy rate in detecting attacks, surpassing existing solutions with nearly twice the speed in training and testing times. As the IoT landsc

关键词： Sensor nodes Security attacks Intrusion Detection System Critical infrastructure machine learning Internet of Things (IoT)

来源：评论

学校读者我要写书评

暂无评论

machine learning Algorithms for Artificial Intelligence Technology Development Online Communication Platform 14th

Machine Learning Algorithms for Artificial Intelligence Tech...

引用

14th international conference on Frontier computing, FC 2024

作者： Ma, Nancao Liaoning Institute of Science and Engineering Liaoning Jinzhou China

ISBN: (纸本)9789819627936

Artificial intelligence creates an English learning situation that connects virtual and real, guides students to enter deep learning, and is conducive to students’ exploration in practice. Major breakthroughs in artificial intelligence research and applications depend on massive data collection, improvements in computer speed and computing power, and advances in machine learning algorithms. Implementing the teaching concept in the era of artificial intelligence in specific forms of educational practice reflects interactivity and real-time, breaking the barriers of traditional offline education. Based on artificial intelligence technology, the development of an online communication platform for interactive English teaching will help the interactive teaching model to achieve results in English language teaching. This paper studies augmented reality technology and Agent technology, designs the online communication platform hierarchy, and provides solutions for platform development. In the process of practical application, give full play to the leadership of teachers and the subjectivity of students, and improve students’ language knowledge and English language skills. © 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

Privacy-Preserving Asynchronous Vertically Federated learning against Batch Level Label Inference Attacks 24

Privacy-Preserving Asynchronous Vertically Federated Learnin...

引用

2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Jin, Yu Liu, Ying College of Information Science & Electronic Engineering Zhejiang University Zhejiang Hangzhou China

ISBN: (纸本)9798400709869

Vertical Federated learning (VFL), which enables multiple parties that hold different features of the same data set to collaboratively train a global learning model without sharing their raw data, is an important paradigm in FL. However, VFL is vulnerable to privacy threats, particularly Label Inference Attacks (LIA), where passive participants try to deduce sensitive labels of the active party. Existing defense strategies, such as Homomorphic Encryption (HE) and Differential Privacy (DP), often face challenges related to high computational overhead or tradeoff between privacy and accuracy, especially in asynchronous settings. Considering this, in this paper, we propose a novel defense mechanism against batch-level label inference attacks (BLI) in vertical asynchronous federated learning (VAFL), where client-level differential noise is added to obscure batch-level gradients such that the passive participants cannot derive the label information from the active participant. Besides, to address party's asynchronicity, our approach incorporates a temporal weight decay strategy according to the staleness of each party. A series numerical simulations are then performed to verify the effectiveness and robustness in privacy protection for VAFL scenarios. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

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 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：