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

89,028 篇 会议
600 册 图书
349 篇 期刊文献
4 篇 学位论文
1 篇 科技报告

馆藏范围

89,981 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

38,470 篇 工学
- 27,649 篇 计算机科学与技术...
- 22,648 篇 软件工程
- 9,288 篇 信息与通信工程
- 8,455 篇 控制科学与工程
- 6,053 篇 电气工程
- 4,316 篇 电子科学与技术（可...
- 3,754 篇 生物工程
- 3,703 篇 机械工程
- 2,738 篇 生物医学工程（可授...
- 2,595 篇 动力工程及工程热...
- 2,175 篇 仪器科学与技术
- 1,884 篇 光学工程
- 1,855 篇 交通运输工程
- 1,639 篇 安全科学与工程
- 1,379 篇 化学工程与技术
- 1,040 篇 网络空间安全
19,403 篇 理学
- 12,563 篇 数学
- 4,028 篇 生物学
- 3,744 篇 系统科学
- 3,693 篇 物理学
- 3,461 篇 统计学（可授理学、...
- 1,386 篇 化学
10,642 篇 管理学
- 7,045 篇 管理科学与工程(可...
- 4,273 篇 图书情报与档案管...
- 3,420 篇 工商管理
2,331 篇 医学
- 2,038 篇 临床医学
- 1,985 篇 基础医学(可授医学...
- 1,266 篇 药学(可授医学、理...
1,358 篇 法学
- 1,189 篇 社会学
1,203 篇 经济学
- 1,200 篇 应用经济学
825 篇 农学
328 篇 教育学
220 篇 文学
194 篇 军事学
48 篇 艺术学
1 篇 哲学

主题

6,020 篇 genetic algorith...
5,137 篇 heuristic algori...
4,506 篇 real-time system...
4,367 篇 accuracy
4,274 篇 algorithm design...
3,866 篇 clustering algor...
3,496 篇 machine learning...
3,424 篇 prediction algor...
3,194 篇 optimization
3,047 篇 feature extracti...
2,939 篇 signal processin...
2,816 篇 computational mo...
2,667 篇 training
2,549 篇 control systems
2,494 篇 classification a...
2,479 篇 predictive model...
2,323 篇 machine learning
2,263 篇 neural networks
2,196 篇 support vector m...
2,124 篇 hardware

机构

193 篇 chitkara univers...
104 篇 school of automa...
98 篇 school of electr...
87 篇 lovely professio...
74 篇 centre of resear...
71 篇 school of comput...
67 篇 school of comput...
47 篇 national mobile ...
46 篇 department of co...
40 篇 chitkara univers...
39 篇 department of co...
38 篇 department of la...
37 篇 school of comput...
35 篇 karnataka bangal...
34 篇 college of compu...
33 篇 school of comput...
33 篇 school of electr...
32 篇 harbin engineeri...
32 篇 school of inform...
32 篇 department of el...

作者

36 篇 lei wang
36 篇 jun wang
34 篇 jian wang
30 篇 hisao ishibuchi
29 篇 li yang
26 篇 yu wang
26 篇 hui wang
25 篇 khalgui mohamed
24 篇 li peng
23 篇 wang jing
22 篇 jun zhang
21 篇 zhang tao
20 篇 rui wang
20 篇 wang lei
19 篇 bin wang
18 篇 zhang lei
17 篇 j. relin francis...
17 篇 amandeep nagpal
16 篇 xi chen
16 篇 vikram kumar

语言

87,355 篇 英文
2,189 篇 其他
509 篇 中文
7 篇 俄文
2 篇 德文
2 篇 法文
1 篇 西班牙文
1 篇 土耳其文
1 篇 乌克兰文

检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89982 条记录，以下是821-830 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：

Lung Cancer Detection: A Machine Learning Approach for Early Diagnosis 8

Lung Cancer Detection: A Machine Learning Approach for Early...

引用

8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Joshi, Praveen Kumar Prakash, Rahul Singh, Devendra Dhanik, Saurabh Tamta, Abhishek Sharma, Vaibhav Kumar Graphic Era Hill University School of Computing Department Bhimtal India National Institutes of Technology Department of Electronics Engineering Srinagar India Graphic Era Hill University Department of Computer Science and Engineering Bhimtal India

ISBN: (纸本)9798350367904

Lung cancer poses a significant health challenge, with early detection being crucial for better outcomes. Traditional diagnostic methods have limitations, leading to missed diagnoses. Machine learning offers promise in revolutionizing disease detection by leveraging data and advanced algorithms. This paper explores using machine learning for early lung cancer detection. Sophisticated algorithms enable accurate diagnostic systems, improving efficiency and scalability. Challenges like data quality and interpretability also need to be addressed. This study evaluates machine learning algorithms on real datasets to assess their effectiveness in clinical practice, contributing to the field of healthcare applications. We have found highest accuracy (96.77%), precision (98.33%), recall (98.33%) and F1-score (98.33%) in the Random Forest classifier. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Flood Susceptibility Mapping of the Chennai Floods of 2023

Machine Learning for Flood Susceptibility Mapping of the Che...

引用

2024 IEEE international conference on Signal Processing, Informatics, Communication and Energy systems, SPICES 2024

作者： Shah, Aditya Goutam Nakhuda, Mohammed Sahil Kumar, Spandan Vaibhav Vipin, M. Vincent, Shweta Kumar, Om Prakash Manipal Academy of Higher Education Manipal Institute of Technology Department of Information and Communication Technology Manipal576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Mechatronics Manipal576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Electronics and Communication Engineering Manipal576104 India

ISBN: (纸本)9798350376135

Rapid and precise urban flood forecasting is crucial for promptly implementing preventive actions. This article presents a review of the literature focusing on soft computing techniques and algorithms for predicting urban floods. We also present two famous machine learning algorithms viz., Random Forest (RF) and XGBoost to create a Flood Susceptibility Map (FSM) for the city of Chennai in the state of Tamil Nadu, India based on the historical data of the 2023 floods of Chennai. A decent classification accuracy of 65% and 70% has been achieved using our approach for both RF and XGBoost algorithms respectively. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Enhancing Trust-Based Attacker Detection in 5G Social Networks Through Advanced Artificial Intelligence Control

Enhancing Trust-Based Attacker Detection in 5G Social Networ...

引用

2024 international conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics Pune India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the evolving landscape of 5G social networks, the surge in connectivity and data exchange has amplified the susceptibility to malicious attackers, undermining the trust and security integral to these networks. This paper introduces an innovative approach to bolster trust-based attacker detection mechanisms through the integration of advanced artificial intelligence (AI) control techniques. Leveraging the prowess of machine learning (ML) algorithms and AI-driven analytics, our proposed framework enhances the accuracy and efficiency of identifying potential threats within 5G social networks. By implementing a hybrid model that combines anomaly detection algorithms with trust scoring systems, we aim to establish a robust security posture that dynamically adapts to emerging threats. The core of our work lies in the development of a predictive model that utilizes deep learning techniques to analyze network behaviors and user interactions, facilitating the early detection of anomalous activities indicative of malicious intent. Through extensive simulation and real-world application, our results demonstrate a significant improvement in detection rates and a reduction in false positives, thereby fortifying the trust and reliability of 5 G social networks. This study not only contributes to the advancement of secure network architectures but also sets a new precedent for the application of AI in cybersecurity, offering a scalable and efficient solution to safeguard against sophisticated attackers. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Autonomous Floating Garbage Collection Device using Computer Vision and Robotics 24

Autonomous Floating Garbage Collection Device using Computer...

引用

8th international conference on algorithms, Computing and systems, ICACS 2024

作者： Tamgadge, Dhammashree C. Nagpure, Bhanu R. Mandal, Riddhi S. Electronics Engineering G H Raisoni College of Engineering Nagpur India Computer Science and Engineering G H Raisoni College of Engineering Nagpur Nagpur India Hong Kong Metroplotian University Kowloon Hong Kong

ISBN: (纸本)9798400718304

This paper presents an autonomous garbage collection device, which uses computer vision and robotics to clean the floating waste in the local water bodies. The tedious and increasingly repetitive task of garbage collection is becoming a major problem worldwide, our device uses ESP32-CAM and Arduino UNO to automate this task, images of water bodies are streamed through the ESP32-CAM which is then analysed by python program on the local server created by the ESP32-CAM. Arduino UNO processes the algorithm for autonomous motion of the device, currently the algorithm used is very basic, just enough to be able to make the device move autonomously, there is scope to improve the autonomous movement algorithm. we have also added an ultrasonic sensor for obstacle detection so that devices motion is not interrupted by obstacles, for garbage detection we have used YOLO-V3 algorithm which is chosen for it fast performance [5], overall we the result of our testing showed that the device is very capable of collecting floating water waste and can be deployed in local water bodies for garbage collection although there are various areas where the device can be improved one of them being use of advanced microcontroller board for local processing of images. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ultrasonic sensors

来源：评论

学校读者我要写书评

暂无评论

Transition-Aware Human Activity Recognition using Ensemble Learning and Neural Networks

Transition-Aware Human Activity Recognition using Ensemble L...

引用

2024 international Research conference on Smart Computing and systems engineering, SCSE 2024

作者： Wijewardhana, Thamali Goonetilleke, Sahani University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka University of Moratuwa Department of Electronic and Telecommunication Engineering Moratuwa Sri Lanka

ISBN: (纸本)9798350375688

Ubiquitous computing has captivated a significant interest due to its usefulness in various domains. Human activity recognition (HAR) is an important area in ubiquitous computing which identifies human activities using sensor data. In HAR, there is a noticeable lack of focus towards transition aware HAR and most systems have focused only on identifying basic activities. However, for effective HAR, it is necessary to identify transitions accurately. In this paper, we present a novel HAR method which focuses on recognizing both basic activities and transitional activities using accelerometer and gyroscope data. We have implemented a stacked-ensemble model for activity classification using both traditional machine learning algorithms and neural networks. Our method was evaluated using a public dataset and our stacked ensemble model has achieved an overall classification accuracy of 99.74 % with oversampling. Our model accurately identifies both basic and transitional activities. © 2024 IEEE.

关键词： Ubiquitous computing

来源：评论

学校读者我要写书评

暂无评论

Analysis of the relationship between operating parameters and health state of power batteries 9

Analysis of the relationship between operating parameters an...

引用

9th international conference on Energy Materials and Electrical engineering, ICEMEE 2023

作者： Pi, Xingwang Liu, Qian School of Vehicle Applications Hunan Automobile Engineering Vocational College No. 79 Wisdom Road Shifeng District Hunan Province Zhuzhou City China

ISBN: (纸本)9781510672765

The health state of power batteries is influenced by various operating parameters, including charge-discharge rate, state of charge, and operating temperature. In this study, experiments were conducted to test the variations in the health state of power batteries under different charge-discharge rates, state of charge, and operating temperatures. The impact of these operating parameters on the health state of power batteries was analyzed. This research not only contributes to a more accurate assessment of the health state of power batteries but also provides labeled values for different operating parameters, which can be utilized in machine learning algorithms for determining the health state of power batteries. This, in turn, promotes the development of algorithms for assessing the health state of power batteries. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Batteries Chemical reactions Temperature metrology Safety Astatine Machine learning Resistance Control systems Corrosion Environmental management

来源：评论

学校读者我要写书评

暂无评论

Decentralized Cooperative Control for Multi-reconfigurable Manipulator Based on Adaptive Dynamic Programing

Decentralized Cooperative Control for Multi-Reconfigurable M...

引用

Complex systems and Intelligent Science (CSIS-IAC), international Annual conference on

作者： Bo Dong Yu Cao Tianjiao An Xinyi He Hongbo Dong Bing Ma Department of Control Science and Engineering Changchun University of Technology Changchun China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

This paper proposes a decentralized cooperative control for multi-reconfigurable manipulator (MRM) based on Adaptive Dynamic Programing (ADP). The control method can achieve both motion path tracking and control the force between the manipulator and the controlled object. The dynamic model of reconfigurable manipulator and controlled object is established using the Newton-Euler algorithm and load distribution. When facing unknown terms in the model, radial basis function neural network (RBFNN) is used for approximation. By taking advantage of the ADP algorithm to obtain the optimal control strategy. The closed-loop MRM system is proved to be stable by using the Lyapunov theory. Finally, the effectiveness of the control method is verified through numerical simulation.

关键词： Adaptation models Tracking Heuristic algorithms Dynamics Optimal control Radial basis function networks Approximation algorithms Numerical models Manipulator dynamics Load modeling

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Secure Cloud Storage for IoT Healthcare Using BiGRU and BiLSTM Models 3

Blockchain-Based Secure Cloud Storage for IoT Healthcare Usi...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Chougule, Prachi Arihant Sarumathi, S. Kant, Ravi Gupta, Raj Kumar Latha, U. Pushpa Perada, Anvesh Dkte Society's Tetile and Engineering Institute Department of Electrical Engineering Ichalkaranji India Hkbk College of Engineering Department of Computer Science and Engineering Bangalore India Ysaicds Shoolini University Department of Computer and Data Sciences Solan India Sardar Vallabhbhai Patel College Bhabua Vksu Department of Physics Ara India Department of English Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States

ISBN: (纸本)9798331508456

Machine Learning (ML) algorithms have experienced a significant increase in popularity owing to the digitisation of analogue processes and other technological advancements, like the Internet of Things (IoT). Dependable datasets are crucial for the precise forecasting and resolution abilities of these algorithms, which are indispensable in healthcare, cloud computing, engineering, and finance. Conversely, training datasets are vulnerable to manipulation, thereby skewing the results. To address this issue, blockchain-based approaches have been proposed to enhance the reliability and security of cloud-stored E-Health data generated by the Internet of Things. To improve the accuracy and efficacy of healthcare prediction models, our study focusses on an advanced strategy that integrates feature selection with model training. Optimal input data is refined by feature selection employing entropy and correlation coefficient methodologies. The proposed model, employing BiGRU and BiLSTM, surpasses conventional CNNs and BiRNNs, with an average accuracy of 94.57%. When integrated with the Internet of Things (IoT), blockchain technology ensures the secure storage and transmission of electronic health records (EHRs). This strategy enhances predictive accuracy and safeguards sensitive health data in cloud infrastructures by amalgamating feature selection techniques with robust deep learning models. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

A study of conventional and modern algorithms employed for MPPT in wind energy conversion systems: A review 3

A study of conventional and modern algorithms employed for M...

引用

3rd international conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Alhamdawee, Raheem Hussain, M. Manzoor Jawaharlal Nehru Technological University Hyderabad Dept. of Mechanical Engineering Hyderabad India

ISBN: (纸本)9798350309409

The production of electrical energy from renewable sources has become the most sought-after technique internationally as a result of growing concerns about environmental issues such as global warming and the depletion of fossil fuel reserves. To operate the wind turbine (WT) at maximum power, it is essential to utilize the full potential of the wind. Obviously, in this regard, maximum power point tracking (MPPT) algorithms are a pioneer and leaders in this field. Since there are a lot of research articles in this area, selecting a suitable technique requires careful examination. This paper reviews various recent studies that track maximum power points and capture output power obtained from wind energy conversion systems to the fullest extent possible. In order to get the most energy out of the wind, studies that focus on finding the point of maximum energy will be reviewed in this paper. These studies will be categorized according to measuring direct and indirect, also hybrid algorithms, and smart algorithms, and then they will be compared to see how well they can track the maximum point and identify problems. © 2024 IEEE.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Approach for the Identification of Photovoltaic and Electric Vehicle Profiles in a Smart Local Energy System 59

A Machine Learning Approach for the Identification of Photov...

引用

59th international Universities Power engineering conference, UPEC 2024

作者： Peiris, Vihanga Awagan, Goyal Jiang, Jing Northumbria University Department of Mathematics Physics and Electrical Engineering Newcastle United Kingdom

ISBN: (纸本)9798350379730

Smart local energy systems (SLESs) focus on in-tegrating more renewable energy sources to the electrical distribution network. Digitalization of SLESs can be achieved through digital twins (DTs). The DT is a virtual replica of the physical energy system. The accurate recognition of the availability, capacity, and quantity of the distributed energy resources (DERs) connected to the electrical distribution network enables the development of a comprehensive DT of the local energy system. This research study proposes to use Machine Learning (ML) algorithms to identify the availability of DERs in the local area and it contributes to the field by providing a comparative analysis of the results of different classification based ML algorithms when recognising availability of solar energy generation and electric vehicles (EVs) in aggregated grid data using a comprehensive labelled dataset. The results presented in this paper show that with the application of sliding window (SW) method for photovoltaic (PV) and EV identification from aggregated data, the accuracy, precision, recall and F1 score metrics of some ML algorithms can reach an approximate value of 98 % reflecting the effectiveness of the SW method and the respective ML algorithms. © 2024 IEEE.

关键词： Photovoltaics

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 << < 79 80 81 82 83 84 85 86 87 88 > >>

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：