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

40,508 篇 会议
106 篇 期刊文献
97 册 图书

馆藏范围

40,711 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

23,083 篇 工学
- 19,888 篇 计算机科学与技术...
- 18,013 篇 软件工程
- 7,861 篇 信息与通信工程
- 7,818 篇 生物工程
- 4,829 篇 控制科学与工程
- 3,903 篇 生物医学工程（可授...
- 2,978 篇 电气工程
- 2,745 篇 光学工程
- 1,924 篇 网络空间安全
- 1,859 篇 电子科学与技术（可...
- 1,633 篇 安全科学与工程
- 1,440 篇 机械工程
- 1,428 篇 交通运输工程
- 1,347 篇 动力工程及工程热...
- 1,255 篇 化学工程与技术
14,261 篇 理学
- 8,024 篇 生物学
- 6,080 篇 数学
- 3,070 篇 物理学
- 2,811 篇 统计学（可授理学、...
- 1,467 篇 系统科学
- 1,463 篇 化学
7,498 篇 管理学
- 4,225 篇 图书情报与档案管...
- 3,690 篇 管理科学与工程(可...
- 2,021 篇 工商管理
3,604 篇 医学
- 3,122 篇 临床医学
- 3,078 篇 基础医学(可授医学...
- 2,390 篇 药学(可授医学、理...
- 1,392 篇 公共卫生与预防医...
1,563 篇 法学
- 1,390 篇 社会学
1,288 篇 教育学
- 1,284 篇 教育学
1,126 篇 农学
807 篇 经济学
283 篇 文学
53 篇 军事学
2 篇 艺术学
1 篇 哲学
1 篇 历史学

主题

2,376 篇 deep learning
1,939 篇 machine learning
823 篇 support vector m...
642 篇 convolutional ne...
566 篇 training
546 篇 feature extracti...
536 篇 forecasting
478 篇 computational mo...
467 篇 reinforcement le...
460 篇 machine learning...
456 篇 object detection
421 篇 computer vision
413 篇 neural networks
409 篇 internet of thin...
394 篇 predictive model...
355 篇 covid-19
332 篇 long short-term ...
309 篇 transfer learnin...
260 篇 image segmentati...
258 篇 convolution

机构

471 篇 chitkara univers...
135 篇 computer science...
126 篇 school of comput...
88 篇 karnataka bangal...
85 篇 college of compu...
83 篇 centre of interd...
67 篇 school of comput...
61 篇 school of comput...
58 篇 school of comput...
58 篇 graphic era deem...
53 篇 department of co...
50 篇 medical technica...
49 篇 computer science...
48 篇 school of comput...
47 篇 maharishi school...
46 篇 department of co...
43 篇 computer science...
43 篇 the islamic univ...
42 篇 department of co...
41 篇 school of comput...

作者

61 篇 shiva mehta
49 篇 sharma vikrant
40 篇 anjali t.
37 篇 vinay kukreja
34 篇 rakesh kumar
30 篇 deepak gerard
29 篇 sharma neha
27 篇 gill kanwarparta...
25 篇 vishal jain
25 篇 sheshang degadwa...
25 篇 li li
25 篇 nagaraj p.
24 篇 rishabh sharma
24 篇 kukreja vinay
22 篇 pareek piyush ku...
22 篇 shanmugasundaram...
21 篇 yu wang
21 篇 dhairya vyas
20 篇 kumar manish
20 篇 p nagaraj

语言

39,098 篇 英文
1,599 篇 其他
376 篇 中文
4 篇 葡萄牙文
3 篇 西班牙文
1 篇 德文
1 篇 土耳其文

检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40711 条记录，以下是4551-4560 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：

Deep learning-Based Apple Classification by Color 2

Deep Learning-Based Apple Classification by Color

引用

2nd international conference on Converging Technology in Electrical and Information engineering, ICCTEIE 2023

作者： Nasution, Feldiansyah Bin Bakri Nasution, Nurliana Hasan, Mhd Arief Lancang Kuning University Department of Informatic Engineering Pekan Baru Indonesia

ISBN: (纸本)9798350370645

Apple cultivation in Indonesia dates back to 1934, with the fruit originating from West Asia. These plants thrive in highland regions but are vulnerable to diseases and pests. To protect their apple crops, farmers often resort to regular pesticide spraying. However, early identification and targeted interventions for diseases and pests are crucial for effective crop management. This study introduces a cutting-edge digital solution that leverages deep learning, specifically the Convolutional Neural Network (CNN), for the purpose of classifying apples based on their skin color. Deep learning is a rapidly evolving field within machine learning and offers exceptional capabilities in various applications, including computer vision and image classification. The CNN-based method employed in this study exhibits remarkable accuracy in classifying apple types by color. Through extensive testing, the application achieved an impressive accuracy rate of 97.1%. This outcome underscores the potential of deep learning, particularly CNN, as a valuable tool for the rapid and precise identification of different apple varieties based on their skin color. Implementing such technology not only aids in apple classification but also supports timely interventions, ultimately leading to improved crop management practices. By providing farmers with an efficient means of monitoring and addressing apple crop health, this digital approach contributes significantly to the agricultural sector and enhances overall productivity. © 2023 IEEE.

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Optimal Radio Labeling for Strong Product Graph of Star Graph and Path 6

Optimal Radio Labeling for Strong Product Graph of Star Grap...

引用

6th IEEE international conference on Electronics and Communication engineering, ICECE 2023

作者： Hong, Jiaojiao Li, Feng Qinghai Normal University Computer College Xi'ning China

ISBN: (纸本)9798350328721

For the increasingly scarce frequency resources in wireless communication networks, topology diagrams are usually used to model the communication network, and the radio labelings of vertices in the graph are studied through "frequency -distance"constraints to simulate the allocation of frequency resources in the wireless communication network. This paper mainly studies the strong product graph of 4-order star graph and n-order path ( n≥3 ), labels its vertices based on relevant constraints, and provides the lower bound of radio labelings for this network structure, as well as the optimal radio labeling allocation scheme. © 2023 IEEE.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

Prediction of Flank Wear in Turning of Monel K500 by Using Machine learning Model in Comparison With Experimental Analysis

Prediction of Flank Wear in Turning of Monel K500 by Using M...

引用

2023 international conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Dilli Ganesh, V. Thangaraj, S. John Justin Saveetha Institute of Medical and Technical Science-SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Science-SIMATS Saveetha School of Engineering Department of Computer Science Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350348910

This study uses machine learning algorithms to estimate flank wear in Monel K500, a high-performance nickel-copper alloy, turning. Experimental findings are compared. Tool life, machining quality, and process efficiency depend on flank wear. Traditional flank wear predictions use empirical models based on experimental data, which may be inaccurate and unreliable. This work investigates an alternate flank wear prediction method utilizing machine learning algorithms. Support vector machines, random forests, and neural networks are used to create prediction models using cutting parameters, tool attributes, and workpiece qualities. Experimental data from Monel K500 turning experiments trains and validates the models. Machine learning models are tested against experimental analysis findings to determine accuracy and dependability. The study evaluates machine learning's flank wear prediction accuracy and finds the best model for Monel K500 turning operations. The models' study also reveals Monel K500 machining flank wear variables. This research improves flank wear prediction in Monel K500 turning operations and sheds light on machine learning in machining optimization. The findings can help manufacturers and researchers improve Monel K500 and comparable alloy machining techniques, reduce tool wear, and boost productivity. © 2023 IEEE.

关键词： Nickel alloys

来源：评论

学校读者我要写书评

暂无评论

HOW TO EXPLOIT HYPERSPHERICAL EMBEDDINGS FOR OUT-OF-DISTRIBUTION DETECTION? 11

HOW TO EXPLOIT HYPERSPHERICAL EMBEDDINGS FOR OUT-OF-DISTRIBU...

引用

11th international conference on learning Representations, ICLR 2023

作者： Ming, Yifei Sun, Yiyou Dia, Ousmane Li, Yixuan Department of Computer Sciences University of Wisconsin-Madison United States Meta United States

Out-of-distribution (OOD) detection is a critical task for reliable machine learning. Recent advances in representation learning give rise to distance-based OOD detection, where testing samples are detected as OOD if they are relatively far away from the centroids or prototypes of in-distribution (ID) classes. However, prior methods directly take off-the-shelf contrastive losses that suffice for classifying ID samples, but are not optimally designed when test inputs contain OOD samples. In this work, we propose CIDER, a novel representation learning framework that exploits hyperspherical embeddings for OOD detection. CIDER jointly optimizes two losses to promote strong ID-OOD separability: a dispersion loss that promotes large angular distances among different class prototypes, and a compactness loss that encourages samples to be close to their class prototypes. We analyze and establish the unexplored relationship between OOD detection performance and the embedding properties in the hyperspherical space, and demonstrate the importance of dispersion and compactness. CIDER establishes superior performance, outperforming the latest rival by 13.33% in FPR95. Code is available at https://***/deeplearning-wisc/cider. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Robust Autoencoders for Collective Corruption Removal 48

Robust Autoencoders for Collective Corruption Removal

引用

48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Taihui Wang, Hengkang Peng, Le Tang, Xian'e Sun, Ju University of Minnesota Computer Science and Engineering Minneapolis United States University of Minnesota Duluth Computer Science Duluth United States

ISBN: (纸本)9781728163277

Robust PCA is a standard tool for learning a linear subspace in the presence of sparse corruption or rare outliers. What about robustly learning manifolds that are more realistic models for natural data, such as images? There have been several recent attempts to generalize robust PCA to manifold settings. In this paper, we propose 1- and scaling-invariant 1/2-robust autoencoders based on a surprisingly compact formulation built on the intuition that deep autoencoders perform manifold learning. We demonstrate on several standard image datasets that the proposed formulation significantly outperforms all previous methods in collectively removing sparse corruption, without clean images for training. Moreover, we also show that the learned manifold structures can be generalized to unseen data samples effectively. © 2023 IEEE.

关键词： Denoising Image Reconstruction Manifold learning Robust Autoencoders Sparse Corruption

来源：评论

学校读者我要写书评

暂无评论

learning to Predict Shortest Path distance 9th

Learning to Predict Shortest Path Distance

引用

19th international conference on Advanced Data Mining and Applications, ADMA 2023

作者： Qu, Zhixin Zong, Zixiao Zhang, Jianfang Shenyang Aerospace University Liaoning Shenyang110136 China University of California IrvineCA92697 United States China National Institute of Standardization Beijing450047 China

ISBN: (纸本)9783031466700

As graph data emerging in various application e.g., biology, social network, scalable graph methods are required to analyze such data. However, traditional graph algorithms cannot meet the need due to their high complexity of both time and space. In this paper, a machine learning based method is proposed to predict shortest path under the case where complete accuracy is not required. A feed-forward neural network classification model and a regression model are constructed to deal with graphs with discrete path distance and continuous path distance, respectively. In addition, we further improve the above models to adapt the dynamic changes of graph data in the real world. The results on real-world datasets show that the proposed method can approach the shortest distance with lower error rate than the comparison methods. We also evaluated different graph embedding methods and training set construction methods on the experimental results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Regional Classification of Indonesian Folk Songs Based on K-NN and PCC-LDA Model

Regional Classification of Indonesian Folk Songs Based on K-...

引用

2023 international conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Mahardhika, Ferdinand Warnars, Harco Leslie Hendric Spits Nugroho, Anto Satriyo Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Organisasi Riset Elektronika Dan Informatika Jakarta10340 Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

One of the biggest challenges in Music Information Retrieval (MIR) is classification. MIR is a subset of Machine learning (ML), where the methods found in computer science can be used to solve problems in the music domain area. Indonesia is an archipelago country inhabited by around 1,340 ethnic groups spread across 38 provinces. This ethnic group has a variety of cultures, including folk songs, and almost every area in Indonesia has folk songs. In this study, we conducted a regional classification of 500 Indonesian folk songs from 10 regions in Indonesia. We build a model for regional classification tasks using the MIR approach. This model combines feature selection and dimension reduction capabilities. By using hyperparameter tunning and a cross- validation of 5 folds, the result of our proposed model for regional classification of Indonesian folk songs using K-NN with PCC-LDA can obtain an accuracy rate of 84.7%. © 2023 IEEE.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

Interfacing Kera's deep learning technique for Real-Time Age and Gender Prediction

Interfacing Kera's deep learning technique for Real-Time Age...

引用

2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Vankayalapati, Sahiti Shekar, R. Sindhu, Y. Rupa Reddy, M. Manvith Reddy, P. Sasidhar Mohammad, Ali Baig Koneru Lakshmaiah Educational Foundation Dept. of ECE Guntur India Presidency University School of Engineering Department of ECE Bangalore India REVA University School of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350348217

The 21st century has seen the humans achieved many unimaginable achievements in the world of technology. This technology can be used in a different way as individual desire. It can be used to identify a person's age and also identify the gender of a particular person by detecting face of the person with help of a camera image or even with a real time video. In this research paper, a complete procedure, what are the different methodologies and algorithms that can be used, and which one is more accurate method to be used will be shown. The aim of this work is designing as well developing model which can detect faces thereby predict the age and gender from real time images. This model can be used in police surveillance, policing to dating applications and much more. Deep learning techniques are used in designing this model. Convolution neural network and computer vision is also being used in this paper to adapt to real time processing. Convolutional neural network along with the computer vision and keras is used in training the model. After extensive testing and training, accuracy of 85 percent is achieved for the developed model. This model can be broadly used in airports, age restricted areas or buildings and can be used in restricting the alcohol access to the under aged people. © 2023 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

A Machine learning Approach in Predictive Maintenance in the IoT Enabled Industry 4.0 4

A Machine Learning Approach in Predictive Maintenance in the...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Madasamy, Sathiyakeerthi Shankar, B. Prabhu Yadav, Rakesh Kumar Jayalakshmi, K.P. Solution Architect Pilvi Systems Inc LewisvilleTX75057 United States Alliance University Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka562106 India Srm Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India St Joseph Engineering College Department of Electronics and Communication Engineering Karnataka Mangaluru India

ISBN: (纸本)9798350300888

Predictive maintenance using machine learning is a powerful technique for industries seeking to enhance their operations with minimize downtime. In an IoT-enabled Industry 4.0 environment, this approach can be taken to a new level by leveraging the vast amounts of data generated by connected devices. To implement a machine learning methodology to projecting conservation in an Industry 4.0 environment, several key steps need to be taken. First, data from IoT devices across the industrial ecosystem should be collected and centralized in a data lake or similar storage system. This data should include information on equipment health, sensor readings, and other relevant metrics. Next, the data should be preprocessed and transformed to ensure its quality and consistency. This may involve cleaning, normalization, and feature engineering to create relevant variables for use in machine learning models. Once the data has been preprocessed, a range of machine learning models can be trained on it to predict equipment failures or other maintenance issues. This may involve ongoing tuning and optimization of model hyperparameters or retraining the models on new data as it becomes available. Finally, the predictions generated by the machine learning models should be integrated into a broader maintenance management system to enable timely action. This may include triggering maintenance requests, generating work orders, or even automating maintenance tasks through the use of robots or other industrial automation technologies. By implementing a machine learning method to projecting preservation in an IoT-enabled Industry 4.0 environment, industries can optimize their operations, minimize downtime, and improve overall equipment effectiveness. © 2023 IEEE.

关键词： Industry 4.0

来源：评论

学校读者我要写书评

暂无评论

IoT Network Intrusion Detection Using Ensemble learning Approach 14

IoT Network Intrusion Detection Using Ensemble Learning Appr...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Hasan, Md Firoz Moon, Md. Hasan Raza, Dewan Mamun Daffodil International University Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350335095

The Internet of Things (IoT) is a sophisticated paradigm in which a vast number of items are networked together. These interconnected gadgets create a network of intelligent systems that exchange data without the need for computer or human-to-computer communication. These devices gather important data that has the potential to significantly alter society, industry, and the entire planet. Still, in the extremely hostile terrain of the internet, the reality of IoT is vulnerable to innumerous cyber-attacks. Because IoT bias have a low processing power and storehouse capacity, traditional high- end security results aren't suitable for securing an IoT system. The IoT bias are also connected for longer ages of time without *** highlights the need to produce smart security results that are movable , dispersed, and have a long service life. It's further practical to install security results for network data as opposed to per- device security for numerous IoT bias. When dealing with miscellaneous data of colorful sizes, artificial intelligence propositions like Machine learning and Deep learning have formerly demonstrated their significance. In order to support this, we delved the ensemble literacy approach in this ***, we've suggested the optimum network design suitable for the IoT Intrusion Detection System grounded on the estimated criteria. This innovative study beat the performance issues of conventional ways with an delicacy rate of97.68. © 2023 IEEE.

关键词： Internet of things

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 << < 452 453 454 455 456 457 458 459 460 461 > >>

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：