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561-570 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：

Integrating the Inception V3 Model in an End-to-End Driving Scenario 4

Integrating the Inception V3 Model in an End-to-End Driving ...

引用

4th international conference on computer Vision and Data Mining, ICCVDM 2023

作者： Ji, Guanhua University of California Los Angeles Los Angeles United States

ISBN: (纸本)9781510674448

the challenges of autonomous driving stem from its exposure to complex environments and stringent safety requirements. End-to-end autonomous driving entrusts the entire driving task to a single large model, allowing the model to directly derive control signals from sensor inputs. Although this approach has shown promising results, many end-to-end models require significant human effort for training through imitation learning. therefore, we aim to establish a reliable and cost-effective model by combining existing image recognition models through transfer learning. the selected model for this study is Inception V3. Based on this, experiments were conducted by adjusting the learning rate, freezing certain layers, and modifying the fully connected layer, resulting in improved model performance. this enabled better planning of driving paths. © 2024 SPIE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

the application and Challenges of Artificial Intelligence in Supporting Educational Innovation 4

The Application and Challenges of Artificial Intelligence in...

引用

4th international conference on Big Data Engineering and Education, BDEE 2024

作者： Cui, Rongyu Xie, Xin Fan, Long Yang, Xiaoli Higher Vocational College Chengdu Neusoft University Chengdu China

ISBN: (纸本)9798350388268

Amid the rapid development of information technology, artificial intelligence (AI) has brought profound changes to the field of education. By analyzing AI applications in China's educational innovation, this paper explores the potential and challenges of AI in educational reform and proposes strategies to promote the scientific, intelligent, and personalized development of the education system. Specific applications demonstrate AI's practical effects in enhancing teaching efficiency, enriching learning experiences, and improving intelligent education management. this study provides theoretical support and practical guidance for the comprehensive upgrading and optimization of the education system, summarizes the challenges of AI education technology and looks forward to the future development trend. © 2024 IEEE.

关键词： Artificial Intelligence Educational Innovation Educational System Optimization Intelligent Management Personalized learning

来源：评论

学校读者我要写书评

暂无评论

the application of large-scale MIMO detection algorithm in 5G and beyond 5G 4

The application of large-scale MIMO detection algorithm in 5...

引用

4th international conference on Signal Processing and machine learning, CONF-SPML 2024

作者： Tang, Qiteng Harbin Institute of Technology Shenzhen Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9781510674721

In recent years, the rapid advancement of 5G technology has brought to the forefront the pivotal role of Multiple-Input Multiple-Output (MIMO) system algorithms. this paper delves into a comprehensive exploration of two distinct algorithmic approaches within the context of 5G applications for massive MIMO systems. these two approaches are matrix transformation and machine learning, and the following paragraphs will shed light on their respective attributes and intricacies. Matrix transformation is a fundamental technique in MIMO systems, which aims to optimize the transmission of signals by manipulating the channel matrices. this method, while established and reliable, exhibits certain limitations in accommodating the dynamic and complex nature of 5G environments. On the other hand, machine learning algorithms, with their adaptability and capacity for self-improvement, have gained prominence in recent years. they offer a promising avenue for addressing the challenges presented by 5G MIMO systems, such as handling interference and optimizing resource allocation. In this paper, we provide concrete examples to analyze the strengths and weaknesses of both matrix transformation and machine learning in the context of 5G applications. Furthermore, we explore potential directions for the application of these algorithms and propose areas for improvement, with the ultimate goal of enhancing the efficiency and performance of massive MIMO systems in the evolving landscape of 5G technology. © 2024 SPIE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

An Efficient Decision Support System for Stock Market Prediction using Technical Indicators as Features by machine learning Approaches 4

An Efficient Decision Support System for Stock Market Predic...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Naga Pranava Shashank, V. Ramanan, Vimal S Altrin Lloyd Hudson, D. Sheik Abdullah, A. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350335750

In this Research, we have built machine learning Models to predict the next day price for the Nifty Index, Reliance and TCS Stocks. We will use R Project Library, quantmod, TTR for data acquisition and technical analysis. We have used the R Project machine learning Library caret along with the daily data for the last five years (June 2017 - June 2022) for the NIFTY Index and the Reliance and TCS Stocks. Our data will consist of Open, High, Low, Last (Close) and Volume. Our objective is to predict the price change for the next day using 6 technical indicators as features which include RSI, Williams%R, etc. For building machine learning Models, we have used the R Library caret. the primary methods which are used are KNN, Classification Regression Trees, Naive Bayes, SVM with Radial Basis Function and DNN. For k-nearest neighbors, we are able to achieve an accuracy of 72.78%, for DNN 72.533%, for CART 72.36%, for NB 70.08%, and for SVM 72.69% for the prediction accuracy of the next day. © 2023 IEEE.

关键词： Data acquisition

来源：评论

学校读者我要写书评

暂无评论

Research on Grab Algorithm Based on Deep learning 4

Research on Grab Algorithm Based on Deep learning

引用

4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Xue, Xinyi Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350342710

In the development of artificial intelligence, the use of object grabbing is the direction of future development, and the grabbing method based on data processing and computer vision mode is the focus of research. the data analysis of convolution network and neural network in the mode of detection algorithm can not only effectively improve the low recognition rate, but also further improve the efficiency of object grabbing recognition algorithm. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of machine learning Techniques for Concrete Compressive Strength Prediction 4

Comparative Analysis of Machine Learning Techniques for Conc...

引用

4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Musleh, Fuad Taha, Ranyah Musleh, Abdel Rahman University of Bahrain Civil Engineering Department Isa Town Bahrain Al Iman School Computer Department Isa Town Bahrain University of Bahrain Electrical Engineering Department Isa Town Bahrain

ISBN: (纸本)9798350369786

the compressive strength of concrete is of utmost importance in ensuring the structural stability and integrity of construction projects. With increasing importance placed on this aspect, there is a demand for intelligent and efficient techniques to accurately predict and evaluate compressive strength. machine learning methods (ML) present a promising approach to developing accurate models for assessing the compressive strength of concrete. By leveraging the power of data analysis and pattern recognition, these techniques enable the construction industry to obtain reliable and robust concrete for a wide range of applications. ML models can identify the key factors influencing compressive strength, allowing for more informed decision-making, and ensuring the construction of structurally sound and durable projects. this article explores the assessment and comparison of four machine learning (ML) methods, namely Gradient Boosting, Categorical Gradient Boosting CatBoost, Random Forest (RF), and extreme gradient boosting (XGBoost) regressors, for predicting concrete compressive strength. Among these techniques, the CatBoost Regressor demonstrated superior performance in terms of accuracy, R-squared coefficient of determination, and Root Mean Square Error RMSE, surpassing the other three methods. © 2023 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

machine learning Algorithms for Disease Diagnosis using Medical Records: A Comparative Analysis 4

Machine Learning Algorithms for Disease Diagnosis using Medi...

引用

4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Gupta, Ashulekha Chaithra, N. Jha, Janhvi Sayal, Anu Gupta, Veethika Memoria, Minakshi Dpeartment of Management Studies Dehradun India Bangalore India School of Accounting and Finance Taylor's Business School Taylor's University Malaysia Doon University Depatment of Economics Dehradun India UIT Uttaranchal University Department of CSE Dehradun India

ISBN: (纸本)9798350341126

the development of machine learning algorithms has revolutionized the medical data categorization industry through the introduction of artificial intelligence. the development of mathematical models employing statistical theory to draw conclusions from samples has proven to be a fruitful application of machine learning. Due to the enormous amounts of patient data, machine learning algorithms have been implemented into the medical industry to make crucial diagnostic decisions. Particularly for busy institutions, accurate diagnosis can be difficult and time-consuming. By analyzing medical imaging data and learning from labelled examples, machine learning algorithms have demonstrated tremendous potential for automatically classifying and identifying diseases. this study evaluates the performance of four prominent machine learning algorithms in various medical contexts, including cardiac care, trauma units, breast cancer diagnosis, etc. Using the Heart Disease UCI dataset as an example, this study evaluates the accuracy of various cardiovascular disease prediction algorithms using accuracy ratings and confusion matrices. It insinuates that machine learning algorithms, such as logistic regression, random forest, deep neural networks, and gradient boosting, have the potential to improve healthcare decision-making processes and demonstrates the importance of machine learning algorithms in this field. © 2023 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Stubble Burning Detection and Prevention using machine learning and Blockchain 10

Stubble Burning Detection and Prevention using Machine Learn...

引用

17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Faridi, Arman Rasool Hafeez, Aiman Masood, Faraz Salmony, Monir Yahya Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9789380544472

Stubble burning in India contributes significantly to air pollution by emitting harmful gases and particles into the atmosphere. this annual practice is responsible for the severe pollution that envelops the country every winter, particularly in the National Capital Region. Lower temperatures and steadier air in winter exacerbate the impact of rice stubble burning. However, research on using machine learning with blockchain to address this problem is limited. this study aims to provide a conceptual framework for using machine learning and blockchain to prevent and detect stubble burning. With the help of machine learning and satellite images, areas originating the pollution can be identified. this can be done with pre-processed images sent to pre-trained models to identify the place of origin of the pollution. If the pollution crosses a threshold limit, then the location will be stored in the blockchain. A decentralized application will be created from where these details can be accessed by concerned authorities. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

2024 IEEE 4th international conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

2024 IEEE 4th International Conference on Sustainable Energy...

引用

4th IEEE international conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

ISBN: (纸本)9798350383997

the proceedings contain 408 papers. the topics discussed include: a three-switch three-phase inverter with novel switching approach for grid-connected applications;MTPA based energy efficient strategy for scalar control of induction motor drives;enhancing power system reliability: leveraging machine learning for unsymmetrical fault detection and analysis;modified SRF theory based shunt active power filter for power quality improvement;power quality enhancement through virtual synchronous generator based bypass nonlinearity control;sensor-less duty modulated predictive current control of PMSM drives;enhancement of AGC for deregulated interconnected power system with integration of EV using a type-2 fuzzy controller;comparative analysis of material choices for enhanced efficiency in wireless power transfer;and modelling and closed loop control of SPS Dual active bridge for CC-CV charging.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Anxiety Prediction and Analysis- A machine learning Based Approach 4

Anxiety Prediction and Analysis- A Machine Learning Based Ap...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Farooq, Sheikh Afaan Konda, Ojasri Kunwar, Anshul Rajeev, Navmi Chandigarh University Department of Computer Science Punjab Mohali India Chandigarh University Bachelor of Engineering Punjab Mohali India

ISBN: (纸本)9798350335750

Anxiety is considered a psychological disorder that causes stress, emotional trauma, and so forth, which affects a person both mentally and physically. this condition causes a person to experience fear, helplessness, a genetic predisposition, homelessness and unemployment, alcohol and other drug usages, and traumatic life events. To avoid the risk factors, it's necessary to diagnose the illness earlier. there are several symptoms we can look for to determine if someone has an anxiety disorder or not. We converted all the symptoms into parameters, which are further used for training test data and model fitting. And to validate it further, a real-time dataset taken from Kashmir is being used. As machine learning has many models by which anyone can predict different outputs, in this study, we use a different machine learning model to predict the output (whether the person is suffering from anxiety or not). this study is based on a classification problem in which we compare different models and then make a comparison table of them. In short, our study uses the result that comes out after screening a person to predict anxiety. the model is fitted in a very simple way so that any patient or person can predict whether he or she is affected by anxiety or not with an accuracy of 95%. © 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 << < 53 54 55 56 57 58 59 60 61 62 > >>

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：