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

3,115 篇 会议
20 册 图书
18 篇 期刊文献

馆藏范围

3,153 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,115 篇 工学
- 1,873 篇 计算机科学与技术...
- 997 篇 软件工程
- 259 篇 控制科学与工程
- 254 篇 电气工程
- 235 篇 信息与通信工程
- 198 篇 生物工程
- 149 篇 交通运输工程
- 105 篇 生物医学工程（可授...
- 96 篇 机械工程
- 60 篇 光学工程
- 58 篇 电子科学与技术（可...
- 54 篇 安全科学与工程
- 50 篇 材料科学与工程（可...
- 47 篇 动力工程及工程热...
- 41 篇 建筑学
- 36 篇 土木工程
509 篇 理学
- 259 篇 数学
- 204 篇 生物学
- 120 篇 统计学（可授理学、...
- 91 篇 物理学
- 57 篇 系统科学
401 篇 管理学
- 264 篇 管理科学与工程(可...
- 161 篇 图书情报与档案管...
- 100 篇 工商管理
202 篇 教育学
- 199 篇 教育学
165 篇 医学
- 160 篇 临床医学
- 79 篇 基础医学(可授医学...
- 52 篇 药学(可授医学、理...
69 篇 法学
- 58 篇 社会学
32 篇 经济学
- 32 篇 应用经济学
17 篇 农学
7 篇 文学
1 篇 艺术学

主题

293 篇 machine learning
290 篇 deep learning
142 篇 training
125 篇 data models
120 篇 neural networks
114 篇 accuracy
105 篇 feature extracti...
101 篇 data mining
87 篇 predictive model...
83 篇 artificial intel...
82 篇 computational mo...
68 篇 machine learning...
62 篇 students
57 篇 reinforcement le...
57 篇 real-time system...
46 篇 convolutional ne...
45 篇 big data
42 篇 internet of thin...
42 篇 training data
42 篇 humans

机构

30 篇 school of comput...
11 篇 institute of inf...
9 篇 kazan natl res t...
9 篇 school of cyber ...
7 篇 school of comput...
6 篇 school of inform...
5 篇 inesc tec porto
5 篇 hefei univ techn...
5 篇 wroclaw univ sci...
4 篇 univ michigan an...
4 篇 school of automa...
4 篇 chinese acad sci...
4 篇 college of compu...
4 篇 univ minho algor...
4 篇 university of ba...
4 篇 school of mechan...
4 篇 xi an jiao tong ...
4 篇 nanyang technol ...
4 篇 singapore manage...
4 篇 university of mi...

作者

7 篇 yang yang
7 篇 santos matilde
6 篇 peng jun
6 篇 taghi m. khoshgo...
6 篇 wang fei
5 篇 li peng
5 篇 horvath tomas
5 篇 wozniak michal
5 篇 machado jose
5 篇 r.c. luo
4 篇 zhang tao
4 篇 lee seong-whan
4 篇 ksieniewicz pawe...
4 篇 zhang lei
4 篇 yang yi
4 篇 novais paulo
4 篇 li li
4 篇 trong-hop do
4 篇 liu qin
4 篇 shimizu jun

语言

3,084 篇 英文
61 篇 其他
13 篇 中文

检索条件"任意字段=21st International Conference on Intelligent Data Engineering and Automated Learning"

共 3153 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：

Pneumonia Detection using Chest X-Rays

Pneumonia Detection using Chest X-Rays

引用

international conference on ent Computing and Control Systems (ICICCS)

作者： Maheshwar Anandh M Hari Prasad S R Hemalatha Computer Science and Engineering St. Joseph’s College of Engineering Chennai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Pneumonia remains a significant global health concern caused by bacteria, viruses, and fungi, necessitating timely and accurate diagnosis for effective treatment. This study proposes a deep learning-based approach for pneumonia detection using chest radiographs. We employ convolutional neural networks (CNNs) with transfer learning to enhance diagnostic accuracy and reduce computational training time. The methodology involves preprocessing chest X-ray images, analyzing the distribution of lung diseases, and training multiple models: a custom-built CNN (Model 1) and transfer learning-based models (Model 2: VGG16, Model 3: ResNet50V2). Transfer learning enables our models to leverage pre-trained feature extraction capabilities, allowing them to recognize pneumonia-related patterns more efficiently with limited labeled data. The models are evaluated based on accuracy, loss, and validation scores. Experimental results demonstrate that ResNet50V2, fine-tuned for pneumonia classification, achieves the highest testing accuracy of 88.94%, making it the most effective among the evaluated architectures. The findings suggest that automated pneumonia detection can assist physicians in diagnosis and monitoring, potentially improving clinical outcomes and reducing the burden on radiologists.

关键词： Training Analytical models Pneumonia Accuracy Computational modeling Transfer learning Convolutional neural networks X-ray imaging Biomedical imaging Testing

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Cybersecurity and Functional Framework for Remote Laboratories: A Software engineering Perspective

AI-Driven Cybersecurity and Functional Framework for Remote ...

引用

Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Nesreen Otoum Enrique Arias Jesús Jover Alfaro Department of Software Engineering University of Petra Amman Jordan Department of Computer Systems University of Castilla-La Mancha Albacete Spain Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Remote laboratories have reshaped the way engineering courses provide hands-on experimentation by enabling students to control physical lab equipment via online interfaces. nevertheless, their ease, However, these platforms face challenges such as security vulnerabilities, performance instability, and limited user support. This paper introduces a layered framework that integrates artificial intelligence (AI) into a secure, modular design. Using conventional software engineering techniques, our approach improves cybersecurity through intelligent threat detection, automated resource balancing, and context-aware user interaction. We provide a comprehensive explanation of the framework's architecture, which methodically addresses critical requirements. This includes a proactive cybersecurity apparatus that adapts to emergent threats, as well as real-time device control and data acquisition. Ultimately, this work aims to assist educators and institutions build or modernize remote lab environments that offer robust protection and seamless learning outcomes, even under rapidly shifting demands.

关键词： Remote laboratories Computer architecture Threat assessment Real-time systems Resource management Reliability Computer security Protection standards Software engineering

来源：评论

学校读者我要写书评

暂无评论

Empirical study to Select a Place to stay in NCR Area Using data Mining and Machine learning

Empirical Study to Select a Place to Stay in NCR Area Using ...

引用

Automation and Computation (AUTOCOM), international conference on

作者： Nitin Jain Surendra Singh Chauhan Ajay Goel Ashish Kumar Rastogi Alok Raj Priyanka Agrawal Computer Science & Engineering-Apex Chandigarh University Mohali India SRM University Sonipat Haryana India Bhai Gurdas Institute of Engineering & Technology Sangrur Punjab India Computer Science & Engineering IILM University Greater Noida India Security Engineer XenonStack Pvt. Ltd. Mohali Punjab India DevOps Engineer XenonStack Pvt. Ltd. Mohali Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

From starting of the human civilization, the peoples are traveling to one place to another place for different kinds of works such as business, tourism etc. and they are need accommodation to stay there. Before the 21st century, there was a limited option to search the places for accommodation. In the digital world there are options are increasingly in comparison with the previous time. Now if someone going for a vacation for a short term, he or she can find a hotel or lodge for the same, but when students migrate one place to another or a person transfer to another city or place than he need to find a suitable place to stay for a long time. In this research paper, we try to find the best solution for this kind of problem. Here we discuss the result with some predetermined parameters. These parameters set and trained on the collected data from different type of people from different geolocations. After it, the collected data is fed to the K-Means algorithm. This algorithm is used for the making the clusters. After it by using REst API, the output of K-Means algorithms is shown on a Map.

关键词： Machine learning algorithms Automation Geology Urban areas Clustering algorithms Machine learning data mining Business

来源：评论

学校读者我要写书评

暂无评论

Fire Detection System Using Deep CNN 1st

Fire Detection System Using Deep CNN

引用

1st international conference on intelligent Systems in Computing and Communications, ISCComm 2023

作者： Vikkurty, Sireesha Nagaratna Hegde, P. Chinthakrinda, Vennela Preethi Hegde, G.P. Shetty, Sudheer Department of CSE Vasavi College of Engineering Telangana Hyderabad India SDM Institute of Technolog Ujire India Department of ISE Alvas Institute of Engineering and Technology Moodbidri Affiliated to Visvesvaraya Technological University Karnataka Belagavi India

ISBN: (纸本)9783031756047

The occurrence of fire should be reported by a fire alarm at the earliest. With the advent of convolutional neural networks, this has become more reliable and advanced. The detection of fire conditions as early as possible is a prerequisite of a Fire Detection System, and to give sufficient time for effective counter actions by automated Fire personnel. Fire detection and forecast by conventional fire detection systems was achieved by using the by-products of fire such as smoke, temperature and flame which take a considerable amount of time to produce a level of fire which will trigger the smoke sensors and heat sensors. In this project, the proposed system is building a deep learning solution using convolutional neural networks. The proposed system is fine-tuned to stabilize the efficiency and accuracy by keeping in mind the nature of the target problem and fire data. The effectiveness of the proposed system is revealed and validated by the experimental results on benchmark fire datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Small-Scale dataset for Machine learning Based Network Intrusion Detection

Small-Scale Dataset for Machine Learning Based Network Intru...

引用

international Colloquium on Signal Processing & Its Applications, CSPA

作者： Mohammad Azmi Ridwan Nurul Asyikin Mohamed Radzi Faris Syahmi Samidi Suhaila Sulaiman Kaiyisah Hanis Mohd Azmi Wan Siti Halimatul Munirah Wan Ahmad Institute of Power Engineering Universiti Tenaga Nasional Selangor Malaysia Department of Electronic Systems Engineering MJIIT-UTM Kuala Lumpur Malaysia Institute For Research Development & Innovation IMU Kuala Lumpur Malaysia

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

An intrusion detection system (IDS) was introduced to detect various intrusive attempts on the network. Several machine learning (ML) techniques have been incorporated to build robust and intelligent IDS. Researchers are maximizing public intrusion datasets, such as the KDD CUP 99, NSL-KDD and the CICIDS-2018 datasets, to train the ML algorithms. However, public intrusion datasets have large data sizes, which cause a high training time for the ML algorithms. Moreover, public intrusion datasets have high-dimensional input features, thus requiring tedious feature extraction methods. Furthermore, these datasets have an imbalance ratio between normal and attack traffic, which leads to less efficient training supplied to the ML algorithms, resulting in low accuracy. An intrusion dataset that can reduce the training and processing time whilst having a balanced dataset between normal and attack traffic must be proposed to overcome these issues. Hence, a small-scale and robust intrusion dataset is developed in this paper to train the ML classifier. Simulation results show that the proposed dataset improves the accuracy of the ML classifier by 19.15%, 99.26% and 40.48% considering accuracy, recall and F-measure, respectively, compared with the CICIDS-2018 public dataset.

关键词： Training Measurement Accuracy Machine learning algorithms Runtime Simulation Machine learning Feature extraction Denial-of-service attack Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

引用

intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Gotte Ranjith Kumar Shubham Deepak Banerjee School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns.

关键词： Hair Training Measurement Accuracy Dermatology Medical services Machine learning Predictive models Planning Reliability

来源：评论

学校读者我要写书评

暂无评论

Ensemble Deep learning Approach for Identification of DDOS Attack 1st

Ensemble Deep Learning Approach for Identification of DDOS A...

引用

1st international conference on Advances in Artificial Intelligence and Machine learning in Big data Processing, AAIMB 2023

作者： Balakrishnan, C. kumar, V. S. Prassana Department of Computer Science and Engineering S.A.Engineering College Chennai India

ISBN: (纸本)9783031730641

Numerous IoT web apps have been hindered by multiple safety hazards and network intrusions as a result of the home control dataset's on going enhancement. Automating your home has always been centred on safety and the recognition of DDOS Threats. It is essentially possible to make use of numerous IoT web-based resources through input of a set of data for the home's automation or by tapping on a hyperlink in the application itself. The cloud manages the aforementioned difficulties in the concept of the Edge of Things. An attacker can create various web strikes. By embedding executable instructions or injecting illicit software inside a DDOS threat, many simultaneous intricate models are employed to improve system reliability and the ease of maintaining data disclosure. By accurately recognising malicious automated homes information set, it is crucial to enhance the steadfast reliability and integrity of IOT web apps. The site of harmful home automation information set detection according to character-centered material categorization emphasises will be investigated using an artificial learning algorithm system in this work. The fact that hazardous buzz phrases are exceptional home automation information set, it is possible to divide the equipment in an ordinary intelligent home setting into four classes: Class 1 is very high traffic reliability, Class 2 is high traffic certainty, Class 3 is medium traffic accuracy, and Class 4 is low traffic stability. According on the test outcomes, our suggested neural network identification algorithm is actually appropriate for high-accuracy tasks such as categorization. When compared to different categorization designs, the model's accuracy rate is over %. Considerable speculative and empirical advantages for web safety studies can be derived from the use of deep learning to aggregate home automation information to identify Web visitor's goals, which opens up novel possibilities for clever safety studies. © The Author(s), under ex

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Video Content Analysis and Classification Based on Human Activity Recognition 1st

Video Content Analysis and Classification Based on Human Act...

引用

1st international conference on Computational Technologies and Electronics, ICCTE 2023

作者： Hari, K.C. Pokharel, Manish Shrestha, Sushil Department of Computer Science and Engineering School of Engineering Kathmandu University Dhulikhel Nepal

ISBN: (纸本)9783031819346

Video content analysis holds significant potential in computer vision tasks for the automated understanding and interpretation of human motion and activities. This paper aims to introduce an intelligent framework to recognize human motion and activities within video data. By leveraging advanced techniques in feature extraction, temporal modeling and deep learning, a comprehensive approach that aims to accurately categorize and interpret various human activities is presented in this study. The combination of Convolution layers and Bidirectional Long Short-Term Memory Layers is used to extract the spatial and temporal features of the video enabling the detection of motion patterns and activity sequences. Video classification and Activity Recognition (VCHAR) is developed and trained using the University of Central Florida 101 Classes (UCF101) dataset. This dataset consists of 101 classes of activities such as archery, surfing, typing, swing and so on. The model is tested on day-to-day human activities videos. The study provides the classification of videos based on the activities within the video. The result of this study is compared with previous benchmark results and it showed better accuracy of 87.67%. The finding of this study contributes to improving surveillance security, sports activity, intrusion detection and robotics. Sport media companies, automation Industry, education sector and researchers will be benefitted from this type of study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Workshop on Machine learning in Smart Mobility 21st

Workshop on Machine Learning in Smart Mobility

引用

21st international conference on intelligent data engineering and automated learning

作者： Ferreira, Sara Cardoso, Henrique Lopes Rossetti, Rosaldo J. F. Artificial Intelligence & Comp Sci Lab LIACC Porto Portugal Res Ctr Terr Transports & Environm CITTA Porto Portugal Univ Porto Fac Engn Porto Portugal

The workshop on Machine learning in Smart Mobility (MLSM) was co-located with the 21st international conference on intelligent data engineering and automated learning—IDEAL 2020, held online on November 4–6. © ... 详细信息

ISBN: (纸本)9783030623647;9783030623654

The workshop on Machine learning in Smart Mobility (MLSM) was co-located with the 21^st international conference on intelligent data engineering and automated learning—IDEAL 2020, held online on November 4–6. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Automatic and intelligent Recommendations to Support students' Self-Regulation 21

Automatic and Intelligent Recommendations to Support Student...

引用

21st IEEE international conference on Advanced learning Technologies (ICALT)

作者： Afzaal, Muhammad Nouri, Jalal Zia, Aayesha Papapetrou, Panagiotis Fors, Uno Wu, Yongchao Li, Xiu Weegar, Rebecka Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden

ISBN: (纸本)9781665441063

In this paper, we propose a counterfactual explanations-based approach to provide an automatic and intelligent recommendation that supports student's self-regulation of learning in a data-driven manner, aiming to improve their performance in courses. Existing work under the fields of learning analytics and AI in education predict students' performance and use the prediction outcome as feedback without explaining the reasons behind the prediction. Our proposed approach developed an algorithm that explains the root causes behind student's performance decline and generates data-driven recommendations for action. The effectiveness of the proposed predictive model that constitutes the intelligent recommendations is evaluated, with results demonstrating high accuracy.

关键词： learning analytics Counterfactual Explanations intelligent Recommendations Self-Regulation Artificial Intelligence

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316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：