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

4,129 篇 会议
38 册 图书
6 篇 期刊文献

馆藏范围

4,173 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,733 篇 工学
- 2,463 篇 计算机科学与技术...
- 2,042 篇 软件工程
- 616 篇 生物工程
- 586 篇 信息与通信工程
- 362 篇 生物医学工程（可授...
- 266 篇 控制科学与工程
- 214 篇 电气工程
- 211 篇 光学工程
- 147 篇 安全科学与工程
- 136 篇 机械工程
- 123 篇 电子科学与技术（可...
- 118 篇 化学工程与技术
- 105 篇 交通运输工程
- 91 篇 动力工程及工程热...
1,368 篇 理学
- 674 篇 数学
- 646 篇 生物学
- 332 篇 统计学（可授理学、...
- 306 篇 物理学
- 197 篇 系统科学
- 127 篇 化学
720 篇 管理学
- 393 篇 管理科学与工程(可...
- 388 篇 图书情报与档案管...
- 237 篇 工商管理
340 篇 医学
- 315 篇 临床医学
- 308 篇 基础医学(可授医学...
- 221 篇 药学(可授医学、理...
- 125 篇 公共卫生与预防医...
159 篇 法学
- 138 篇 社会学
156 篇 教育学
- 154 篇 教育学
141 篇 农学
- 140 篇 作物学
89 篇 经济学
9 篇 军事学
5 篇 文学

主题

522 篇 machine learning
240 篇 deep learning
117 篇 support vector m...
99 篇 adversarial mach...
92 篇 accuracy
92 篇 machine learning...
83 篇 forecasting
74 篇 convolutional ne...
70 篇 predictive model...
69 篇 feature extracti...
57 篇 artificial intel...
53 篇 students
51 篇 learning systems
48 篇 decision trees
48 篇 training
47 篇 reinforcement le...
47 篇 computational mo...
47 篇 computer vision
46 篇 internet of thin...
41 篇 sentiment analys...

机构

46 篇 chitkara univers...
15 篇 graphic era hill...
10 篇 college of manag...
9 篇 graphic era deem...
8 篇 computer science...
8 篇 beijing universi...
7 篇 school of comput...
7 篇 school of comput...
7 篇 school of comput...
7 篇 department of in...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 computer science...
6 篇 lovely professio...
6 篇 in2p3 lpc campus...
6 篇 kkr and ksr inst...
6 篇 institut univers...
6 篇 national centre ...
6 篇 greater bay area...

作者

18 篇 kukreja vinay
15 篇 mehta shiva
10 篇 william p.
8 篇 sharma vikrant
7 篇 mahamudul hasan
7 篇 shiva mehta
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.
6 篇 huang f.

语言

4,030 篇 英文
142 篇 其他
30 篇 中文

检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4173 条记录，以下是241-250 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：

ICSPC 2023 - 4th international conference on Signal Processing and Communication

ICSPC 2023 - 4th International Conference on Signal Processi...

引用

4th international conference on Signal Processing and Communication, ICSPC 2023

ISBN: (纸本)9798350300772

the proceedings contain 88 papers. the topics discussed include: design of a smart embedded vision system based on FPGA for medical applications;a novel deep learning approach for detection of sleep apnea from ECG signal;skin cancer prediction using deep learning techniques;hybrid power management using interleaved landsman converter implemented through machine learning;a novel framework for network intrusion detection in healthcare domain;online sales prediction in e-commerce market using machine learning;deep neural networks based detection and analysis of fake tweets;navigator for visually impaired person;plant-pathogen detection using different machine learning techniques;and enhancing public safety through license plate recognition for counter terrorism through deep learning technique.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predicting Loan Risk in Banking Sectors using machine learning 4

Predicting Loan Risk in Banking Sectors using Machine Learni...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Akalya, S. Veronica, J. Jenitha, Ms. J. Merlin Mary Gladence, Ms. L. Mary Angel, Ms. T. G. Ruby Sathyabama Institute of Science and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798331523923

In the world of banking, loan eligibility is essential for ensuring that institutions manage risk effectively while providing customers with the credit they need. this project focuses on optimizing and modernizing the loan eligibility process through the application of advanced machine learning algorithms, especially Artificial Neural Networks. Our objective is to create predictive models that evaluate an applicant's qualification based on their financial and behavioral data. By leveraging the power of ANN s, we will identify the most robust model to validate loan eligibility in real time. For applicants who do not meet the criteria for their desired loan, our system will recommend alternative loan options, increasing their chances of securing credit. this method enhances the applicant's experience while adhering to contemporary banking standards by offering personalized solutions. By automating loan risk analysis, we aim to minimize human bias, save time, and ensure data-driven, objective decision-making. Ultimately, this project will accelerate loan approvals, provide deeper insights into risk management, and enhance customer satisfaction within the financial sector. © 2025 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Endometriosis Labelling using machine learning 4

Endometriosis Labelling using Machine learning

引用

4th international conference on Communication, Computing and Industry 6.0, C216 2023

作者： Visalaxi, S. Sudalaimuthu, T. Sowmya, V.J. Department of Computer Science Hindustan Institute of Technology and Science Chennai India Department of Master of Computer Applications Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9798350327328

Endometriosis is a disease affecting the women's reproductive system. the lesion-like structure that exists in the women's reproductive organ creates an impact on their fertility. the diagnostic approach of endometriosis was performed by a radiologist using scanning procedures. those procedures predict the occurrence of endometriosis but not the severity of endometriosis. Amidst the radiologist, machine learning techniques play a predominant role to identify the severity of endometriosis. Among all machine learning techniques, the proposed approach uses a support vector machine. Support vector machine is a contemporary technique for predicting clinical-based data. Support vector machine analyzes the influencing factor that incorporates Adnexal mass, Tube blockage, Lesion size, and lesion color for predicting the severity of endometriosis as well as classifying the endometriosis as Ovarian and Deep Infiltrating endometriosis. the execution was performed and trained accuracy obtained was 85%, test accuracy was 84.5% for radius basis function (rbf) kernel and the cross-validation score was 82.5%. Also, the available data was trained using Random Forest and Linear Regression. Among all three models, the Support vector machine outperforms well with hyper parameter as rbf for the given data to classify the endometriosis and identify the severity of endometriosis. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study on Earth System Science using machine learning and Process Mining 4

A Comprehensive Study on Earth System Science using Machine ...

引用

4th international conference on Communication, Computing and Industry 6.0, C216 2023

作者： Vijayalakshmi, V. thiyagarajan, Gomathi Department of DSBS SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Applications CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350327328

machine learning has emerged as a powerful tool in Earth System Science offering new avenues for data analysis and predictive modeling. this paper deep dive into the application of machine learning techniques to understand and predict air quality a critical aspect of Earth System Science. Process mining techniques are incorporated to explore data patterns and relationships. Our work explores the use of machine learning and process mining techniques in Earth System Science, with a specific focus on air quality assessment. the study involves data collection, preprocessing and the development of machine learning models including Linear Regression, Decision Trees, Random Forest, XGBoost and CatBoost to predict Air Quality Index (AQI). Performance evaluation using Root Mean Squared Error (RMSE) and R-squared (R2) metrics reveals that XGBoost and CatBoost demonstrate superior predictive accuracy. Process mining techniques are applied to datasets containing city-specific NOx (nitrogen oxides) measurements, resulting in the creation of heuristic process models. this research serves as a foundational exploration of the integration of machine learning models and process mining techniques in Earth System Science with a focus on air quality. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Sound signal-based transformer operation status monitoring system 4

Sound signal-based transformer operation status monitoring s...

引用

4th international conference on computer Engineering and application, ICCEA 2023

作者： Xu, Haifeng Cai, Jidong Xu, Long Xu, Fei Shaoxing Power Supply Company State Grid Zhejiang Electric Power Co. Ltd Shaoxing China State Grid Zhejiang Electric Power Co. Ltd Hangzhou China

ISBN: (纸本)9798350347548

this paper proposes a system for monitoring the operational status of transformers based on sound signals. the system employs an adaptive noise reduction technique to preprocess the sound signals and a random forest algorithm to classify the signals into different operational states. the adaptive noise reduction technique effectively removes background noise and other irrelevant sound signals, allowing for more accurate monitoring of the transformers. the random forest algorithm is a popular machine learning technique that can handle complex, high-dimensional datasets, making it well-suited for analyzing the large number of features extracted from sound signals. Experimental results from field tests on four transformers demonstrate that the proposed algorithm is effective in identifying the operational states of the transformers and can provide timely alerts when faults occur. the system presented in this paper can serve as a useful tool for monitoring the health of transformers. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Vision-assisted applications based on deep learning 4

Vision-assisted applications based on deep learning

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Wang, Xingzheng Xi’an University of Electronic Science and Technology Xi’an710126 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

With the rapid development of technology and the widespread application of deep learning, computer vision technology has become an important tool to help visually impaired people. Traditional visual assistance systems are often limited by recognition accuracy and real-time performance, while deep learning technology provides new ideas for the development of visual assistance applications with its powerful feature extraction and learning capabilities. this application is based on existing deep learning models and deploys object detection algorithms to mobile devices for user convenience by testing the system in actual environments. the intelligent voice interaction function provided by this application enhances the user experience and interaction sense. the application features are relatively complete, not only allowing users to select images from the album but also providing camera photography as input, as well as real-time content detection and output. At the same time, the application provides voice input and output, greatly simplifying the difficulty of user operation, and also facilitating users to obtain output and make corresponding judgments faster. this application provides a new visual aid tool for visually impaired individuals, making their lives more convenient. © 2025 SPIE.

关键词： Vision aids

来源：评论

学校读者我要写书评

暂无评论

Research on Wireless Traffic Prediction Based on Federated learning and Differential Privacy 4

Research on Wireless Traffic Prediction Based on Federated L...

引用

4th international conference on Telecommunications, Optics, and computer Science, TOCS 2023

作者： Gao, Xingzhen Zhao, Yuhong School of Information Engineering Inner Mongolia University of Science and Technology Baotou014010 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

Wireless traffic prediction in wireless communication networks holds significant application value for optimizing resource allocation. However, precise prediction faces numerous challenges due to traffic complexity, diversity, and dynamics. Complex spatiotemporal dependencies exist between regional base stations, requiring accurate capture. Traditional centralized methods necessitate centralized storage and processing of substantial user data, posing high privacy risks and potential leaks. this paper proposes a wireless traffic prediction model combining federated learning and differential privacy to capture fused spatiotemporal features of base stations and enhance predictive performance. Concurrently, existing federated learning systems have been proven to harbor potential threats during the training phase, endangering data privacy. this paper tackles privacy threats by adaptively pruning to safeguard data privacy, achieving privacy protection. Experimental results demonstrate that this model outperforms mainstream algorithms in prediction accuracy and privacy protection, showcasing broad application prospects. © 2024 SPIE.

关键词： Wireless networks

来源：评论

学校读者我要写书评

暂无评论

Deep learning Approach to Sugarcane Disease Identification: From Image Analysis to Mobile application 4

Deep Learning Approach to Sugarcane Disease Identification: ...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Chakravarty, Ajay Jain, Arpit Saxena, Ashendra Kumar Teerthanker Mahaveer University Ccsit Moradabad India Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India College of Computing Sciences & Information Technology Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9798350387490

Sugarcane, a critical crop for sugar and biofuel production, is vulnerable to various diseases that can severely impact yields and profitability. Early and accurate disease detection is crucial for timely intervention and minimizing losses. this study presents a mobile application leveraging the power of Deep learning to accurately identify sugarcane diseases directly from leaf images. this paper proposed a smart phone based application that uses machine learning aspects to identify Disease in Sugarcane plant from image. A convolutional neural network model that was trained on a large dataset of photos of sugarcane leaves in both healthy and diseased stages is used by the application. the model's mobile deployment optimization ensures quick and accurate disease prediction on user devices. the program offers an easy-to-use interface for taking pictures of leaves, which the trained CNN model subsequently processes and examines. the user is presented with pertinent disease information and suggested management strategies in addition to the projected disease category and confidence level. Sugarcane growers, extension agents, and farmars may find this user-friendly and easily accessible mobile application to be a useful tool for on-the-spot disease diagnosis, allowing prompt intervention and supporting the sustainable production of this essential commodity. © 2024 IEEE.

关键词： Android Deep learning machine learning Mobile application Sugarcane Disease Identification

来源：评论

学校读者我要写书评

暂无评论

machine learning based Detection of Post Traumatic Stress Disorder of Mental Health 4

Machine Learning based Detection of Post Traumatic Stress Di...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Nagarajaiah, Kavyashree Krishnappa, Madhu Hanakere Asha, K.R. Sri Siddhartha Institute of Technology Department of Mca Karnataka Tumkur572105 India Bangalore Institute of Technology Department of Mca Bangalore India

ISBN: (纸本)9798350300093

Researchers have been driven to investigate the possible role that machine learning may play in the treatment of mental health illnesses as a result of the increasing prevalence of mental health issues and the need for more effective medical care. this article provides a comprehensive examination of the current state of the art regarding the use of a wide variety of machine learning algorithms to the process of diagnosing mental health problems. In addition to this, this study will discuss about the obstacles, restrictions, and opportunities that lie ahead in the field of machine learning when it comes to mental health. this study constructs a library of research articles and studies on machine learning approaches employed for the prediction of mental health concerns by searching credible sources. Moreover, this systematic review is conducted by using the PRISMA methodology. After the process of elimination and selection, a total of 30 unique research articles are reviewed. Schizophrenia, bipolar illness, anxiety, depression, PTSD, and other mental health issues are only some of the categories into which we've placed the collected research articles. this section will first analyse the findings and then reflect on the obstacles and restrictions that have been encountered by the community doing research on the application of machine learning to mental health. In addition, this study outlines the potential directions that future study and development efforts in the field of applying machine learning toward mental health may take in order to take advantage of the opportunities presented. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Filtering Ads based on User Browsing Data using Data Analytics and machine learning 4

Filtering Ads based on User Browsing Data using Data Analyti...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sharma, Rahul Srivastava, Sparsh Amaran, Sibi Srm Institute of Science and Technology Department of Computer Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

Digital Advertising is a data-driven strategy for reaching the target audience. It has gained popularity because of the revenue it generates for the advertising agencies and the companies. It serves as a precious resource as it creates data that could be processed to give insights on the usage and can be altered to receive more audience. On the contrary, for some users, due to the irrelevance of the advertisements being shown, this experience can leave a distaster for visiting sites. the current Contextual Advertisement model predicts and displays the advertisements based on the current page's surrounding content. Ergo, a dire need to incorporate behavioural targeting with this model to create a more enhanced user experience. Consequently, the advertisements will be a distraction to the user, which increases the probability of reaching the target pool for the companies. this study aims to build a solution prototype depicting Ads' ideal workflow in the Digital environment where the user experience is maintained and the advertising companies are benefited. © 2023 IEEE.

关键词： machine 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418页 << < 21 22 23 24 25 26 27 28 29 30 > >>

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：