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

30,328 篇 会议
15,066 篇 期刊文献
430 册 图书

馆藏范围

45,824 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

27,068 篇 工学
- 19,575 篇 计算机科学与技术...
- 15,822 篇 软件工程
- 6,517 篇 信息与通信工程
- 4,764 篇 控制科学与工程
- 3,143 篇 电气工程
- 3,070 篇 生物工程
- 2,844 篇 电子科学与技术（可...
- 2,527 篇 生物医学工程（可授...
- 2,104 篇 机械工程
- 1,586 篇 仪器科学与技术
- 1,538 篇 光学工程
- 1,419 篇 动力工程及工程热...
- 1,268 篇 安全科学与工程
- 1,167 篇 化学工程与技术
- 1,117 篇 网络空间安全
- 1,084 篇 交通运输工程
14,735 篇 理学
- 7,087 篇 数学
- 4,613 篇 物理学
- 3,519 篇 生物学
- 2,317 篇 统计学（可授理学、...
- 2,002 篇 系统科学
- 1,293 篇 化学
8,389 篇 管理学
- 5,227 篇 管理科学与工程(可...
- 3,593 篇 图书情报与档案管...
- 2,641 篇 工商管理
2,828 篇 医学
- 2,411 篇 临床医学
- 2,113 篇 基础医学(可授医学...
- 1,270 篇 公共卫生与预防医...
- 1,221 篇 药学(可授医学、理...
1,436 篇 法学
- 1,152 篇 社会学
946 篇 经济学
716 篇 农学
714 篇 教育学
224 篇 文学
117 篇 军事学
87 篇 艺术学
8 篇 哲学
5 篇 历史学

主题

1,375 篇 computer science
1,104 篇 deep learning
1,085 篇 accuracy
911 篇 machine learning
907 篇 feature extracti...
849 篇 internet of thin...
810 篇 computational mo...
682 篇 training
567 篇 information tech...
561 篇 real-time system...
561 篇 artificial intel...
543 篇 support vector m...
505 篇 data mining
487 篇 security
454 篇 predictive model...
406 篇 monitoring
388 篇 cloud computing
359 篇 optimization
354 篇 reliability
350 篇 data models

机构

297 篇 faculty of scien...
290 篇 department for p...
281 篇 department of ph...
280 篇 departamento de ...
276 篇 department of ph...
274 篇 department of ph...
272 篇 department of ph...
270 篇 department of ph...
253 篇 faculté des scie...
235 篇 department of ph...
232 篇 department of co...
230 篇 department of ph...
218 篇 physics division...
195 篇 cavendish labora...
172 篇 yerevan physics ...
169 篇 institute of phy...
169 篇 school of physic...
168 篇 institute of phy...
162 篇 department of ph...
162 篇 kirchhoff-instit...

作者

276 篇 c. alexa
274 篇 c. amelung
273 篇 j. m. izen
272 篇 g. bella
272 篇 j. strandberg
272 篇 d. calvet
270 篇 g. spigo
268 篇 f. siegert
262 篇 m. klein
261 篇 r. ströhmer
258 篇 m. rijssenbeek
256 篇 s. jin
255 篇 g. gaudio
253 篇 h. a. gordon
251 篇 a. c. könig
248 篇 y. tayalati
245 篇 s. mohapatra
243 篇 h. sakamoto
235 篇 d. guest
217 篇 y. nagasaka

语言

40,466 篇 英文
4,887 篇 其他
471 篇 中文
62 篇 日文
19 篇 德文
17 篇 法文
14 篇 斯洛文尼亚文
7 篇 俄文
2 篇 朝鲜文
2 篇 荷兰文
1 篇 阿拉伯文
1 篇 西班牙文

检索条件"机构=Faculty of Science Computer & Information Technology Department of Computer System and Technology"

共 45824 条记录，以下是4711-4720 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：

Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation 41

Understanding Server-Assisted Federated Learning in the Pres...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Yang, Haibo Qiu, Peiwen Khanduri, Prashant Fang, Minghong Liu, Jia Department of Computing and Information Sciences Ph.D. Rochester Institute of Technology RochesterNY United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH United States Department of Computer Science Wayne State University DetroitMI United States Department of Computer Science and Engineering University of Louisville LouisvilleKY United States

Existing works in federated learning (FL) often assume either full client or uniformly distributed client participation. However, in reality, some clients may never participate in FL training (aka incomplete client participation) due to various system heterogeneity factors. A popular solution is the server-assisted federated learning (SA-FL) framework, where the server uses an auxiliary dataset. Despite empirical evidence of SA-FL's effectiveness in addressing incomplete client participation, theoretical understanding of SA-FL is lacking. Furthermore, the effects of incomplete client participation in conventional FL are poorly understood. This motivates us to rigorously investigate SA-FL. Toward this end, we first show that conventional FL is not PAC-learnable under incomplete client participation in the worst case. Then, we show that the PAC-learnability of FL with incomplete client participation can indeed be revived by SA-FL, which theoretically justifies the use of SA-FL for the first time. Lastly, to provide practical guidance for SA-FL training under incomplete client participation, we propose the SAFARI (server-assisted federated averaging) algorithm that enjoys the same linear convergence speedup guarantees as classic FL with ideal client participation assumptions, offering the first SA-FL algorithm with convergence guarantee. Extensive experiments on different datasets show SAFARI significantly improves the performance under incomplete client participation. 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

Enhancing Cybersecurity with Transformers: Preventing Phishing Emails and Social Media Scams

Enhancing Cybersecurity with Transformers: Preventing Phishi...

引用

IEEE Conference on Dependable and Secure Computing

作者： Essam Al Daoud Laith Al Daoud Mahmoud Asassfeh Ala'a Al-Shaikh Ala'a Saeb Al-Sherideh Suha Afaneh Department of Cybersecurity Zarqa University Zarqa Jordan Computer Information System Jordan University of Science and Technology Jordan

ISBN: (数字)9798331540289

ISBN: (纸本)9798331540296

In this study, the enhancement of cybersecurity through the use of transformer models is explored, specifically focusing on the prevention of phishing emails and social media scams. Two modern datasets were utilized: the Phishing_Pot dataset, which provided phishing emails across 19 languages, and a collection of legitimate emails. Additionally, a dataset from Baaz, an Arabic social media platform, was compiled. Four approaches to phishing detection were compared: zero-shot learning with large language models (LLMs), transformers for feature extraction followed by random forest classification, fine-tuning of small language models (SLMs) on newly generated datasets, and an ensemble method combining the top models from the previous approaches. The experiments demonstrated that LLMs such as GPT-4o, Gemini 1.5, and Claude 3 achieved high accuracy, while SLMs showed improved performance post fine-tuning. The ensemble methods can be used to enhance the accuracy of fine-tuned SLM for phishing detection and scam prevention, reaching an accuracy of 98.50% for the first dataset and 95.25% for the second.

关键词： Adaptation models Accuracy Social networking (online) Phishing Prevention and mitigation Zero shot learning Transformers Data models Electronic mail Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Transfer Learning and Deep Learning for Cloud Intrusion Detection Using Bio-Inspired Optimization

Hybrid Transfer Learning and Deep Learning for Cloud Intrusi...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Waleed Awwad Hamzah Alqudah Mohammed Aladaileh Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (JU) Amman Jordan Department of Computer Science College of Science Northern Border University (NBU) Arar KSA Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba Amman Jordan Department of Computer Science Faculty of Information Technology University of Petra (UoP) Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper proposes a novel hybrid model for cloud intrusion detection, integrating transfer learning with bio-inspired optimization techniques to enhance detection accuracy and efficiency. Cloud environments present unique challenges due to their dynamic and shared-resource nature, making traditional Intrusion Detection systems (IDS) inadequate. The proposed model leverages Convolutional Neural Networks (CNNs) for feature extraction, utilizing pre-trained models through transfer learning to reduce training time while maintaining high detection performance. In addition, bio-inspired optimization algorithms, specifically Cuckoo Search Algorithm (CSA) and Particle Swarm Optimization (PSO), are employed to fine-tune the CNN's hyperparameters, such as learning rate and batch size, ensuring optimal performance. The hybrid approach addresses the limitations of manual tuning and improves the model's ability to generalize across different cloud environments. Using the CICIDS 2017 dataset, the proposed system demonstrates superior performance in detecting both known and unknown threats, including zero-day attacks. The experimental results show significant improvements in accuracy of 99.3%, precision of 98.7%, recall of 98.5%, and an F1-score of 98.6% compared to state-of-the-art models.

关键词： Training Accuracy Biological system modeling Heuristic algorithms Transfer learning Intrusion detection Convolutional neural networks Particle swarm optimization Optimization Tuning

来源：评论

学校读者我要写书评

暂无评论

Analysis of Privacy, Security, and Trust in the Internet of Things (IoT) Ecosystem

Analysis of Privacy, Security, and Trust in the Internet of ...

引用

International Symposium on Networks, computers and Communications (ISNCC)

作者： Anteneh Girma Antione Searcy Adine Barrett Department of Computer Science and Information Technology University of the District of Columbia

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

In the wake of rapidly increasing connectivity and social acceptance of IoT devices, understanding the privacy, secu-rity, and trust dimensions of IoT is instrumental in shaping how connected technologies are applied. IoT is steadily revolutionizing different aspects of society. However, relying on a centralized third-party sharing platform inevitably leads to higher fees, lack of trust, and governance issues for both users and service providers, and creates an inherent bias and fraud in the system (Islam & Kundu, 2020). Despite the prevalence of challenges linked to the continuous leasing of IoT architectures, existing studies have not explored the privacy, security, and trust issues that such architectures present to users. In a sharing economy, privacy is secondary liberty because stakeholders are utilizing connected devices to conduct surveillance and violate the privacy rights of individuals. As such, the current research seeks to demonstrate the extent of security, trust, and privacy concerns in IoT, including demonstrating how these threats can be mitigated. It also revealed that a combination of security and privacy challenges as well as the user's perception of connected devices contribute to trust issues when using IoT. The research looks at How the application of IoT in different domains (domestic and industrial contexts) increased security, trust, and privacy concerns among users?

关键词： Privacy Data privacy Surveillance Instruments computer architecture Sharing economy Internet of Things Security Stakeholders information technology

来源：评论

学校读者我要写书评

暂无评论

Climate Change Analysis Using Machine Learning 3

Climate Change Analysis Using Machine Learning

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sharma, Dolly Gupta, Pawan Rawat, Gourav Kumar, Sharad Malik, Suraj Bansal, Nidhi School of Computing Department Graphic Era Hill University Haldwani India Biet Jhansi Dr. A. P. J. Abdul Kalam Technical University Faculty of Information Technology Lucknow India School of Engineering and Technology Sharda University Department of Computer Science & Engineering Greater Noida India Biet Jhansi Dr. A. P. J. Abdul Kalam Technical University Faculty of Computer Science & Engineering Lucknow India Iimt University Department of Computer Science and Engineering Meerut India Nagpur Campus Pune India

ISBN: (纸本)9798331518592

More analysis is required day to day due to the frequent changes in the weather. Every second the subject needs accurate analysis. This research paper is suggesting a useful technique for analysis using machine learning classification. Dynamic change occurs frequently in this scenario as the real-time application needs to get the process within the machine for accurate output. Accuracy is also a constraint for the situation. The dataset designed based on the selected location is divided by the number of zones, only then the analysis is done for processing to show the result. Sometimes an area is also restricted due to heavy disasters. Due to this care, the problem is divided into the sample area and classification is applied. The results generated by this research paper can show the optimal solution for the determined scenario. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Heart Disease Monitoring and Prediction Using Machine Learning Algorithm 8th

A Novel Heart Disease Monitoring and Prediction Using Machin...

引用

8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Senbagavalli, M. Karpagalakshmi, R.C. Sumathi, D. Lenin, J. Prasad, G.R.K. Manikandan, A. Department of Computer Science and Information Technology Alliance College of Engineering and Design Alliance University Bangalore India Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Kattankulathur Chennai603203 India

ISBN: (纸本)9789819757855

Background: Continuous monitoring of patient health statistics becomes a difficult task in hospitals. Manually, it is difficult to monitor the health of the patients in the hospital continuously. Older and unconscious older people in particular need to be monitored regularly, and their relatives want to be informed about their health at every time. We, therefore, propose a revolutionary system that easily computerizes this task. Our device provides an intelligent device for monitoring patient status, which uses sensors to track hospital patients’ health status and informs their relatives in the event of a problem via the Internet. Our system uses temperature, glucose levels and heart rate detection to track health. This task has been proposed to improve the monitoring system by using the Internet of Things (IoT) for hospital applications. Result: The suggested system was developed by MAX30100, LM35, ultrasonic sensor and nodeMCU connected to the Internet. BLYNK IoT Android app has already been used to send the notification via Android Application. The remote healthcare monitoring system is also proposed with cloud service and data analytics as to the aiding features. The readings are captured by mobile phone, which acts as the graphical user interface to get the status of the patient’s health. The implemented hardware results illustrate that the suggested model can continuously observe the physiologic parameters and save lives promptly. Conclusion: ML classification algorithms like Regression tree, SVM, and RF analysis for prediction accuracy of patient health data. The proposed RF algorithm’s simulation results provide minimum prediction error and high accuracy compared with the existing Regression tree and SVM algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ultrasonic sensors

来源：评论

学校读者我要写书评

暂无评论

Temperature Sensor Circuit for a Wearable Perspiration Monitoring system

Temperature Sensor Circuit for a Wearable Perspiration Monit...

引用

2023 International Symposium on Devices, Circuits and systems, ISDCS 2023

作者： Ichimura, Yuichiro Goto, Kenta Johguchi, Koh Graduate School of Science and Technology Shinshu University Electrical and Computer Engineering Division Department of Engineering Nagano Japan Shinshu University Faculty of Engineering Department of Electrical and Computer Engineering Nagano Japan

ISBN: (纸本)9798350315042

In this work, a prototype system has been designed with a 0.18-μm CMOS technology to capture perspiration rate in daily life. To calculate an amount of perspiration, a temperature sensor is necessary concurrently with a relative humidity sensing. The designed temperature sensor consists of 8-bit binary digital-to-analog converter and an analog front-end circuit for thermistors. The designed circuits for temperature and humidity sensors have successfully verified. © 2023 IEEE.

关键词： Humidity sensors

来源：评论

学校读者我要写书评

暂无评论

Automation 4.0 for Water Level Monitoring system 7

Automation 4.0 for Water Level Monitoring System

引用

7th International Conference on information technology, InCIT 2023

作者： Kimpan, Warangkhana Palananda, Attapon Kruekaew, Boonhatai School of Science King Mongkut's Institute of Technology Ladkrabang Department of Computer Science Bangkok Thailand Nakhon Pathom Rajabhat University Faculty of Science and Technology Department of Intelligent Control Innovation Nakhon Pathom Thailand

ISBN: (纸本)9798350358698

This paper proposed the concept of using automation 4.0 for monitoring the water level. The water level warning system specifications are to measure the water level using ultrasonic sensors and measure the amount of rainfall using a weighing rain gauge. The system automatically controls the measurement of the level of the flood using a Programmable Logic Controller (PLC) via PROFINET. Then the water level is monitored, and the results will be displayed through HMI technology via Web panel trainer, Node-Red dashboard, and transfer data via PROFICLOUD. Moreover, the warning information will be sent via LINE notification on mobile to people who live near water sources or staff in charge of preventing disasters. © 2023 IEEE.

关键词： Water levels

来源：评论

学校读者我要写书评

暂无评论

A Microservice Architecture with Load Balancing Mechanism in Cloud Environment 1

引用

International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2022

作者： Mummana, Satyanarayana Sambana, Bosubabu Ramanababu, Budimure Gandreti, Preethi Rani, P. Pratima Mishra, Priyanka Chandrasekhar, A. Narasimha Raju, K. Andhra Pradesh Vizianagaram India Jawaharlal Nehru Technological University Andhra Pradesh Kakinada India Department of Computer Science and Engineering Indian Institute of Information Technology Kota Rajasthan Jaipur India Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Information Technology Visakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Avanthi Institute of Engineering and Technology Vizianagaram Andhra Pradesh India Visakhapatnam Andhra University Andhra Pradesh India

ISBN: (数字)9783031151750

ISBN: (纸本)9783031151743

To solve the rising delay in responding to requests in Microservices Architecture (MSA), a load-balancing algorithm for requests is proposed based on microservice chain awareness. To formalize the request delay and microservice environment, the algorithm takes the average request delay and the host loads as the evaluation indicators. On this basis, the competition for shared microservices in microservice chain calling is studied. The simulation results show that the proposed rules can substantially reduce the put-off in requests in a complex microservice chain environment compared to the RR algorithm set of rules. In addition, it keeps excellent loading performance in an environment with unevenly distributed instances and balances loads of different hosts. In the more realistic tests of high-frequency requests, the proposed set of rules can drastically reduce the available reaction time of the system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

Integrating Digital Twins with AI for Real-Time Intrusion Detection in Smart Infrastructure Networks

Integrating Digital Twins with AI for Real-Time Intrusion De...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Sasikala, M. Mahaboob John, Y.M. Jothi, B. Nandhini, S. Senthil Kumar, S. Sona College of Technology Department of Information Technology Salem India Mahendra College of Engineering Ece Department Salem India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Karpagam Academy of Higher Education Computer Science and Engineering Coimbatore India R.M.K. College of Engineering and Technology Department of Mechanical Engineering Thiruvallur India

ISBN: (纸本)9798350360660

The integration of Digital Twins with Artificial Intelligence (AI) represents a revolutionary approach to enhancing real-time intrusion detection in smart infrastructure networks. Digital Twins, as virtual replicas of physical assets, enable continuous monitoring and predictive analysis, while AI provides advanced analytical capabilities for identifying and mitigating potential security threats. This paper presents a comprehensive framework that leverages Digital Twins in conjunction with AI algorithms to detect and respond to intrusions in smart infrastructure networks. By simulating network behavior and employing machine learning models to analyze data patterns, the proposed system ensures robust security measures. One of the proposed techniques involves the use of a hybrid anomaly detection algorithm combining Autoencoders and Recurrent Neural Networks (RNN). The Autoencoder is utilized to learn a compact representation of the normal network behavior by reducing dimensionality and reconstructing inputs, while the RNN captures temporal dependencies and sequences within the network data. This hybrid model enhances the detection of both known and unknown intrusion patterns by focusing on deviations from the learned normal behavior. The integration of these AI components with Digital Twins enables a more nuanced and accurate understanding of network anomalies, thereby improving the system's ability to detect and respond to intrusions promptly. Experimental results demonstrate the efficacy of this approach in real-time threat detection, showcasing significant improvements in response times and accuracy. This integration not only enhances the resilience of smart infrastructure but also paves the way for future advancements in secure and intelligent network management. © 2024 IEEE.

关键词： Network intrusion

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 << < 468 469 470 471 472 473 474 475 476 477 > >>

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：