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

2,097 篇 会议
12 册 图书
8 篇 期刊文献

馆藏范围

2,117 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,575 篇 工学
- 1,411 篇 计算机科学与技术...
- 1,110 篇 软件工程
- 374 篇 信息与通信工程
- 289 篇 生物工程
- 190 篇 生物医学工程（可授...
- 158 篇 控制科学与工程
- 107 篇 电气工程
- 95 篇 光学工程
- 78 篇 化学工程与技术
- 59 篇 电子科学与技术（可...
- 59 篇 安全科学与工程
- 58 篇 机械工程
- 53 篇 仪器科学与技术
- 51 篇 动力工程及工程热...
806 篇 理学
- 462 篇 数学
- 312 篇 生物学
- 207 篇 统计学（可授理学、...
- 163 篇 系统科学
- 136 篇 物理学
- 81 篇 化学
491 篇 管理学
- 296 篇 图书情报与档案管...
- 226 篇 管理科学与工程(可...
- 133 篇 工商管理
186 篇 医学
- 168 篇 临床医学
- 162 篇 基础医学(可授医学...
- 110 篇 药学(可授医学、理...
- 86 篇 公共卫生与预防医...
77 篇 法学
- 62 篇 社会学
66 篇 教育学
- 65 篇 教育学
59 篇 经济学
- 59 篇 应用经济学
51 篇 农学
4 篇 军事学
2 篇 文学

主题

312 篇 machine learning
138 篇 deep learning
90 篇 machine learning...
88 篇 support vector m...
59 篇 big data
59 篇 feature extracti...
54 篇 data models
53 篇 data mining
52 篇 predictive model...
51 篇 forecasting
51 篇 training
49 篇 accuracy
47 篇 decision trees
47 篇 convolutional ne...
45 篇 internet of thin...
35 篇 artificial intel...
34 篇 sentiment analys...
31 篇 neural networks
31 篇 real-time system...
30 篇 adversarial mach...

机构

15 篇 maharishi school...
13 篇 centre of interd...
10 篇 chitkara univers...
8 篇 uttaranchal inst...
7 篇 college of techn...
7 篇 maharishi school...
7 篇 department of co...
6 篇 karnataka bangal...
6 篇 university of el...
6 篇 department of ar...
6 篇 uttaranchal univ...
6 篇 school of comput...
6 篇 department of co...
6 篇 school of comput...
5 篇 xinjiang key lab...
5 篇 school of inform...
5 篇 school of comput...
5 篇 xinjiang key lab...
5 篇 school of comput...
5 篇 vivekananda glob...

作者

7 篇 hamdulla askar
6 篇 ablimit mijit
5 篇 babu tina
5 篇 johri prashant
4 篇 lima jose
4 篇 sattar abdus
4 篇 santosh kumar
4 篇 preeti naval
4 篇 kumar santosh
3 篇 mebawondu jacob ...
3 篇 kumar purushotta...
3 篇 kumar ashwani
3 篇 alfurhood badria...
3 篇 siddhant
3 篇 lai dakun
3 篇 nataraj k.r.
3 篇 agrawal akshat
3 篇 pippal sanjeev
3 篇 ankit kumar
3 篇 liu ling

语言

1,904 篇 英文
212 篇 其他
25 篇 中文

检索条件"任意字段=3rd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2021"

共 2117 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：

Real-Time Tweets Streaming and Comparison Using Naïve Bayes Classifier 1

引用

3rd international conference on data science, machine learning and applications, icdsmla 2021

作者： Gowda, S. R. Shankara King, Rose Kumar, M. R. Pavan Department of Information Science and Engineering Don Bosco Institute of Technology Kumbalgodu Bengaluru560074 India

ISBN: (数字)9789811959363

ISBN: (纸本)9789811959356

The rapid growth of social media sites in recent times has introduced a special environment for researching human actions. Micro blogging website (Twitter) which also allows users to access and communicate their opinions on various of topics, occurrences, brands, and service providers. Tweets have been classified into different subgroups relating to the subject of the research. Various machine learning algorithms, such as baseline, Naive Bayes classifier, support vector machine (SVM), and many others, are presently used this to categorize posts on Twitter into favourable and unfavourable groups based on their views and opinions. This paper describes a solution to improve comparison between Twitter accounts besides trying to implement Naive Bayes using perception vibrant data for training from of the Twitter database. SentiWordNet, in combined effect with Naïve Bayes classifier, can help increase tweet accuracy rate by providing hope and optimism, hatefulness, and integrity ratings for phrases in Twitter messages. Tweepy, which is a Python package and Python-Twitter APIs are used in the actual introduction of the new system. The main work of this paper is to perform real-time streaming, collect the data set while performing real time and make comparison between them using Naïve Bayes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Cross-Site Request Forgery as an Example of machine learning for Web Vulnerability Detection 3

Cross-Site Request Forgery as an Example of Machine Learning...

引用

3rd international conference on Smart data Intelligence, ICSMDI 2023

作者： Rao, Mekala Srinivasa Kalyani, Birudugadda Vathsalya, Baswani Dhanunjay, Karri Narayana, Alasandalapalli Lakshmi Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9781665464871

This paper presents a strategy for discovering flaws in web applications through machine learning (ML). Web-based applications are especially troublesome to examine attributed to their variety and extensive usage of custom development methodologies. As little more than a basis, machine learning is extremely useful in website safety: It just might combine cognitive knowledge of web app terminology with automated software approaches based on verbally reported information. Mitch tool is the foremost machine learning strategy towards black-box investigation for Cross-Site Request Forgery (C.S.R.F) problems, was built using these principles. Mitch-helped us find Thirty-five recently developed cross-site request forgeries (C.S.R. Fs) in twenty wide fields, together with 3 main C.S.R. Fs in industry applications. © 2023 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based Plant Species Identification using VGG16 and Transfer learning: Enhancing Accuracy in Biodiversity and Agricultural applications 3

Deep Learning-based Plant Species Identification using VGG16...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kiran, K. Kaur, Arpanpreet School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants is important in matters concerning biological diversity, agriculture as well as management and utilization of resources but the conventional methods used in identification are time consuming and sometimes inaccurate. machine learning specifically deep learning is a more suitable replacement by offering a fast and accurate identity recognition. The research work works with a Kaggle dataset comprising of 24881 images of four plant species categorized into twenty-two with the objectives of increasing efficiency of the classification. We used transfer learning with weights from the ImageNet data set and obtained accuracy greater than 90% on the test data set, which shows the model’s ability to identify plant species based on leaf structure. To counter overfitting and enhance model generality, data augmentation methods were employed where the input images were rotated by 30, 45 and 60 degrees, and flipped vertically. The performances suggest that deep learning models, such as VGG 16, yield improved recognition for plant classification than conventional methods provide higher reliability. This research makes a valuable input toward a number of applications related to automated systems for agriculture and conservation, discussing possible uses of AI technique for better penetration of plant species, enhancing conservation goals, and future advancements in the agrarian field. Future research will leverage on these discoveries to make improvements on classification frameworks. © 2025 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey of IoT applications in Remote Patient Monitoring, Chronic Disease Management, and Smart Healthcare Infrastructure 3

A Comprehensive Survey of IoT Applications in Remote Patient...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Kavitha, V.P. Theivanathan, G. Magesh, V. Jayandhi, G. SRM Institute of Science and Technology Department of ECE Chennai India Velammal Engineering College Department of ECE Chennai India Saveetha School of Engineering Saveetha University Department of ECE Chennai India

ISBN: (纸本)9798350396157

This extensive study explores the wide range of Internet of Things (IoT) applications in the areas of remote patient monitoring, chronic disease management, and smart healthcare infrastructure. The review examines the changing role of IoT technology in revolutionizing healthcare paradigms and enhancing patient outcomes. The remote patient monitoring area explores the incorporation of wearable devices and sensors to track health parameters in real-time, promoting continuous and proactive healthcare management. The study investigates the utilization of Internet of Things (IoT) solutions, such as intelligent drug dispensers and interconnected inhalers, to improve adherence to treatment and maximize therapeutic interventions in the management of chronic diseases. The research delves into the advancement of intelligent healthcare infrastructure by integrating hospital equipment and leveraging IoT technology to optimize healthcare workflows. The focus is on the integration of data analytics, machine learning, and artificial intelligence to extract practical insights from the data produced by IoT-enabled healthcare systems. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods 3rd

Fault Classification of Wind Turbine: A Comparison of Hyperp...

引用

3rd international conference on Optimization, learning Algorithms and applications (OL2A)

作者： Pinna, Danielle Toso, Rodrigo Semaan, Gustavo de Sa, Fernando Pereira, Ana I. Ferreira, Angela Soares, Jorge Brandao, Diego Ctr Fed Educ Tecnol Celso Suckow Fonseca Rio De Janeiro Brazil Microsoft Redmond WA USA Univ Fed Fluminense Rio De Janeiro Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

The last few years have been marked by the insertion of renewable technologies in the global energy matrix, such as wind and solar energy, which are considered clean energies with low environmental impact. Wind turbines, responsible for the energy conversion process, are complex equipment that are expensive and susceptible to numerous failures. Monitoring turbine components can help detect failures before they occur, reducing equipment maintenance costs. This work compares the training time of different techniques for tuning hyperparameters in supervised machine-learning models for fault detection in wind turbines. Results show the importance of data optimization during model training.

关键词： Wind turbine machine learning Fault classification

来源：评论

学校读者我要写书评

暂无评论

Enhancing Android Malware Detection Through machine learning: Insights From Permission and Metadata Analysis 3

Enhancing Android Malware Detection Through Machine Learning...

引用

3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Shankar, G. Sridhar, S. Srivatsan, G. Kishan, R. Pranav Vikraman, S.S. R.M.D Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

The spread of the Android ecosystem has increased potentially harmful applications, which present a serious cybersecurity threat. According to reports, a significant percentage of applications housed on the Play Store (10-24%) may behave maliciously, hiding their evil intentions behind a facade of legitimacy. The present methods for detecting malware, which are frequently resource-intensive and slow to adjust to new threats, have limits when it comes to solving this growing problem. By carefully examining data on verified dangerous applications and developing a predictive model that can reliably identify malicious applications based on the permissions they request, this study seeks to address these issues. The paper also presents a novel method for creating a machine learning-based detection model using publicly accessible metadata. With an emphasis on Android malware detection, the main goal of this study is to create a reliable model that can classify applications as malicious (1) or benign (0) depending on the permissions they request. The main activities involve a thorough examination of Android permissions and metadata as critical markers for detecting any malware, as well as the development and evaluation of a machine-learning model using publicly available metadata. In the end, initiative aims to strategically apply machine learning techniques to lessen the growing threat of Android malware. This research aims to offer a strong and proactive solution to preventing the spread of harmful programs through careful analysis and the creation of an efficient model. © 2024 IEEE.

关键词： Android malware

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Content Moderation System using Adversarial machine learning 3

A Multimodal Content Moderation System using Adversarial Mac...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Barveen, A. Mohamed Faizal, M.K. Geetha, S. M.I.E.T. Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350375190

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. It is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LSTM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLSTM and CNN have been used to extract contextual features. This study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. The proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. The proposed system uses both text and image data. To improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training. © 2024 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Different numerical dimensions data flows: machine learning instead of correlation methods 3

Different numerical dimensions data flows: Machine learning ...

引用

3rd international conference on Optics, Computer applications, and Materials science, CMSD 2023

作者： Slavutskii, Leonid A. Lazareva, Nadezhda M. Portnov, Mikhail S. Slavutskaya, Elena V. Chuvash State University Moskovskii pr. 15 Cheboksary428015 Russia Chuvash State Pedagogical University K.Marx st. 38 Cheboksary428000 Russia

ISBN: (纸本)9781510674486

System analysis of multidimensional random data is one of the most important tasks of modern information technologies. Traditional statistical methods for analyzing such data do not always allow one to obtain adequate results. In this article*the joint use of the decision tree method and the artificial neural networks is proposed for data classification. It is shown that this approach can be an alternative to multiple factorization of multidimensional data and has a number of significant advantages over traditional statistical methods. Even when the data has limited and different digital scales, the use of machine learning makes it possible to conduct a hierarchical classification of intra-system data relationships, to reduce the number of parameters significant for analysis, and significantly reduce the size of the learning sample required. For the technical applications, this allows for real-Time data analysis using a microprocessor implementation of neural network algorithms. © 2024 SPIE. All rights reserved.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Prediction of gender from structural MRI images using Multiscale ShuffleNet Extreme learning machine 3

Prediction of gender from structural MRI images using Multis...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Stanley, Berakhah.F. Jeen Retna Kumar, R. Gnanaprakash, V. Bini Palas, P. Patturose, J Gold Beaulah Joel Devadass Daniel, D.J. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRM Easwari Engineering College Department of Electronics and Communication Engineering Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350372120

In recent years, the application of machine learning techniques to medical imaging data has shown promising results in various clinical tasks. One such task is the prediction of gender from structural MRI (magnetic resonance imaging) images, which holds potential implications for personalized medicine and understanding neurobiological differences between genders. In this study, we propose a novel approach utilizing Multiscale ShuffleNet Extreme learning machine (MSEL), a fusion of Multiscale feature extraction and ShuffleNet architecture with Extreme learning machine classifier. We demonstrate the effectiveness of our method on a large dataset of structural MRI images, employing state-of-the-art preprocessing techniques and feature extraction methods. Our results indicate significant accuracy and robustness in gender prediction, outperforming existing methodologies. Furthermore, we conduct comprehensive analyses to investigate the contribution of different components in our proposed framework, shedding light on the underlying mechanisms of gender-related brain structural differences. Overall, our study presents a promising avenue for utilizing advanced machine learning techniques in neuroimaging research, with potential applications in clinical diagnostics and personalized healthcare. © 2024 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Advanced Parkinson’s Disease Classification using Hybrid Model: SVM and Random Forest 3

Advanced Parkinson’s Disease Classification using Hybrid Mo...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, S.H. Rajeshkannan, M. Anish, M.J. Sandeep, Pasupuleti Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331527549

Parkinson’s disease (PD) is a progressive and chronic neurodegenerative condition that significantly impairs motor and cognitive functions, posing a considerable burden on global healthcare systems. Early and precise diagnosis is vital to improving patient outcomes, yet traditional diagnostic techniques often encounter limitations due to overlapping symptoms with other neurological disorders and the subtle nature of early PD indicators. This research introduces a machine learning-driven approach that employs a hybrid ensemble model integrating Support Vector machine (SVM) and Random Forest classifiers to enhance diagnostic accuracy by analyzing vocal and acoustic features. To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied, enabling the model to identify meaningful patterns within underrepresented data. Principal Component Analysis (PCA) is utilized to reduce dimensionality, preserving critical data variance while minimizing computational complexity and mitigating overfitting. Hyperparameter tuning further optimizes each classifier, bolstering the model’s accuracy and robustness. Comparative analyses demonstrate that this hybrid ensemble model effectively distinguishes PD patients from healthy individuals, showcasing its potential as a reliable predictive tool in clinical applications. This study highlights the promise of advanced machine learning methodologies in supporting early PD detection, offering clinicians a valuable resource for timely intervention and improved patient care. © 2025 IEEE.

关键词： hybrid classification machine learning models Parkinson’s Disease principal component analysis (PCA) random forest algorithm support vector machine (SVM)

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212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：