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

294,361 篇 会议
838 篇 期刊文献
278 册 图书

馆藏范围

295,477 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

188,507 篇 工学
- 130,356 篇 计算机科学与技术...
- 110,106 篇 软件工程
- 45,902 篇 信息与通信工程
- 42,904 篇 控制科学与工程
- 27,467 篇 电气工程
- 21,636 篇 电子科学与技术（可...
- 20,462 篇 生物工程
- 19,683 篇 机械工程
- 16,726 篇 生物医学工程（可授...
- 13,434 篇 仪器科学与技术
- 12,542 篇 光学工程
- 11,829 篇 动力工程及工程热...
- 9,270 篇 化学工程与技术
- 9,223 篇 安全科学与工程
- 8,886 篇 交通运输工程
- 7,629 篇 网络空间安全
88,382 篇 理学
- 47,275 篇 数学
- 22,408 篇 物理学
- 21,781 篇 生物学
- 14,648 篇 统计学（可授理学、...
- 11,942 篇 系统科学
- 9,657 篇 化学
50,387 篇 管理学
- 33,549 篇 管理科学与工程(可...
- 20,142 篇 图书情报与档案管...
- 14,984 篇 工商管理
14,545 篇 医学
- 11,830 篇 临床医学
- 11,266 篇 基础医学(可授医学...
- 7,407 篇 药学(可授医学、理...
- 6,217 篇 公共卫生与预防医...
8,266 篇 法学
- 6,762 篇 社会学
6,125 篇 经济学
4,993 篇 农学
4,410 篇 教育学
2,460 篇 文学
830 篇 军事学
651 篇 艺术学
27 篇 哲学
24 篇 历史学

主题

12,413 篇 computer science
6,801 篇 feature extracti...
6,595 篇 computational mo...
5,650 篇 training
5,216 篇 accuracy
5,082 篇 automation
4,478 篇 computer archite...
4,277 篇 deep learning
4,243 篇 data mining
3,837 篇 machine learning
3,690 篇 control systems
3,638 篇 neural networks
3,539 篇 support vector m...
3,501 篇 predictive model...
3,440 篇 convolutional ne...
3,320 篇 robustness
3,186 篇 conferences
3,145 篇 optimization
3,145 篇 testing
3,136 篇 data models

机构

1,037 篇 chitkara univers...
956 篇 school of automa...
619 篇 school of comput...
530 篇 school of comput...
497 篇 school of comput...
419 篇 school of automa...
413 篇 school of comput...
336 篇 department of co...
316 篇 school of comput...
302 篇 college of compu...
288 篇 school of comput...
287 篇 maharishi school...
284 篇 school of comput...
281 篇 department of co...
264 篇 college of infor...
256 篇 school of comput...
255 篇 lovely professio...
252 篇 institute of inf...
248 篇 department of co...
245 篇 university of ch...

作者

209 篇 kukreja vinay
186 篇 china
176 篇 wang wei
136 篇 li wei
129 篇 liu yang
125 篇 lei wang
123 篇 yang yang
121 篇 yang liu
109 篇 zhang tao
108 篇 wei wang
103 篇 wei zhang
102 篇 zhang wei
95 篇 sharma neha
87 篇 chauhan rahul
84 篇 li li
81 篇 wei li
80 篇 rui wang
79 篇 liu wei
68 篇 li yang
68 篇 wang xin

语言

284,668 篇 英文
7,849 篇 其他
3,003 篇 中文
39 篇 西班牙文
31 篇 土耳其文
9 篇 法文
9 篇 葡萄牙文
3 篇 俄文
1 篇 德文

检索条件"任意字段=IEEE International Conference on Computer Science and Automation Engineering"

共 295477 条记录，以下是4721-4730 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：

If Human Can Learn from Few Samples, Why Can't AI? An Attempt On Similar Object Recognition With Few Training Data Using Meta-Learning 2

If Human Can Learn from Few Samples, Why Can't AI? An Attemp...

引用

2nd ieee North Karnataka Subsection Flagship international conference, NKCon 2023

作者： Dutta, Subhayu Goswami, Saptiva Debnath, Sonali Adhikary, Subhrangshu Majumder, Anandaprova Dr. B.C. Roy Engineering College Computer Science and Engineering Durgapur713206 India National Institute of Technology Computer Science and Engineering Durgapur713209 India

ISBN: (纸本)9798350314045

An age-old problem that persists with deep learning is that it requires thousands of training samples to learn. Transfer learning is an advancement to deep learning through which learned models can be adapted to recognize new data. But, learning new data with transfer learning depends on the properties of the previous data the model was trained on. In cases where the properties of the new data are different and all the classes of the new data have very few contrasting properties, transfer learning does not produce reliable results. Therefore, we require a method to learn properties from the dataset having low variation among classes using very few samples. To achieve this, we have proposed the usage of a Few-Shot Meta-Learning algorithm and tested this on two open-source datasets having similar-looking classes. The results from the experiment show that the proposed method can reach up to 100% classification accuracy with only 30 samples. Using the same number of samples, transfer learning obtained up to 41.9% accuracy, and general convolution neural network obtained up to 32.0% accuracy. The model can be implemented to reduce the requirement of a large number of annotated training samples to train deep learning models. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Software Platform of Reliability Analysis Using an Adaptive Kriging Model 28

Software Platform of Reliability Analysis Using an Adaptive ...

引用

28th international conference on automation and Computing, ICAC 2023

作者： Zhao, Yuqing Yu, Shui Li, Yun University of Glasgow Department of Electronic and Electrical Engineering Chengdu China Shenzhen Institute for Advanced Study Uestc Shenzhen518110 China

ISBN: (纸本)9798350335859

Addressing current challenges facing reliability analysis in CAD, this paper presents a software platform for predicting engineering system reliability through a Kriging surrogate model. To begin, it first compares the performance of six analysis methods across three case studies and then identifies the most accurate and cost-effective approach. It is seen that the Monte Carlo simulation method offers stability but requires massive function calls, while Kriging surrogate models achieve a balance between accuracy and computational costs. The iterative localization of Most Probability Point method produces reliable results at a lower time consumption. The study thus recommends a software architecture accordingly and highlights future improvements, so as to contribute to the creation of a reliable and efficient software platform for engineering system reliability prediction. © 2023 ieee.

关键词： Reliability analysis

来源：评论

学校读者我要写书评

暂无评论

D-Alarm: An Efficient Driver Drowsiness Detection and Alarming System 6

D-Alarm: An Efficient Driver Drowsiness Detection and Alarmi...

引用

6th ieee international conference on Advances in science and Technology, ICAST 2023

作者： Borah, Biju Mukherjee, Shyamapada National Institute of Technology Silchar Department of Electrical Engineering Assam 788010 India National Institute of Technology Rourkela Department of Computer Science and Engineering Odisha 769008 India

ISBN: (纸本)9798350359817

In the coming years, the demand for IoT devices connecting our homes, cities, and industries will grow significantly. We now inhabit a world where interconnected smart devices can be controlled through single commands, fostering an era of automation, particularly in smart homes. IoT is at the forefront of this transformation, with many of these devices relying on machine learning models to decode data and make accurate predictions. However, as the number of connected devices escalates, network congestion becomes a challenge. To address this, incorporating machine learning intelligence into edge devices is essential. Edge computing, which involves processing data closer to its source, reduces latency and enables faster analysis. Consequently, IoT data can be gathered and processed at the edge, reducing the reliance on cloud-based solutions. Yet, deploying machine learning models on small IoT microcontroller units presents memory and computation constraints. This paper introduces a deep learning model for driver engagement recognition in real-time video streams, alongside optimization techniques for deployment on edge devices like Nvidia Jetson. The goal is to facilitate the execution of machine learning models on compact, low-power integrated circuits, addressing this critical bottleneck. © 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

Securing the Industrial IoT: A Novel Network Intrusion Detection Models 3

Securing the Industrial IoT: A Novel Network Intrusion Detec...

引用

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

作者： Ramaiah, Mangayarkarasi Rahamathulla, Mohemmed Yousuf Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

The growth of automation in the industry makes securing IoT networks a critical priority. A resilient network intrusion detection system (NIDS) can reduce cyber threats. Detecting network traffic irregularities with deep learning (DL) and machine learning (ML) techniques can improve IoT network security and reduce cyber threats. This study analyzes the merits of LSTM and other ML models in mitigating anticipated security breaches in Industrial IoT (IIoT) networks. To design robust NIDS, including an appropriate dataset is essential. Therefore, to identify dynamic threats targeting Industrial Internet of Things (IIoT) networks, deep learning (DL) and machine learning (ML) models underwent training and evaluation utilizing the EdgeIIoT-2021 dataset. The experimental findings demonstrate that the proposed approach surpasses the performance of the current state-of-the-art Network Intrusion Detection Systems (NIDS). The ERT-based IIoT-NIDS achieved a cyber-attack detection accuracy of 99.93%, while the LSTM-based IIoT-NIDS achieved an accuracy of 99.85%. © 2024 ieee.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Sensitivity Analysis and Extended Evaluation of the Two-Level Stochastic Model for the Lateral Movement of Vehicles within their Lane 10

Sensitivity Analysis and Extended Evaluation of the Two-Leve...

引用

10th international conference on automation, Robotics, and Applications, ICARA 2024

作者： Neis, Nicole Beyerer, Juergen Porsche Engineering Group GmbH Weissach Germany Fraunhofer IOSB and Vision and Fusion Lab Karlsruhe Germany

ISBN: (纸本)9798350394245

The range of vision of vehicle sensors used by auto-mated driving functions is considerably influenced by the lateral movement of vehicles within their lane, both of the recording vehicle and of vehicles around it. To cover this phenomenon in simulations for virtual validation, a two-level stochastic model for the lateral movement of vehicles within their lane under homogeneous traffic conditions has recently been proposed by Neis et al. [1]. It consists of the superposition of a Markov model for the discrete and systematic coarse movement, and a noise model for the stochastically independent fine movement. The general importance of the parameter choice on the performance of the two-level model has already been noted when the model was introduced. More details are added by the present work performing a sensitivity analysis on the model's parameters. Moreover, the evaluation of the model is extended by assessing its capability to describe the lateral speed. The work deepens the understanding of the model's way of working and its sensitivity to the parameters, and motivates the parameter choice in previous work. Besides this, potential for improvement of the model beyond what has already been discussed in the introductory paper is identified based on the extended investigation. © 2024 ieee.

关键词： Stochastic models

来源：评论

学校读者我要写书评

暂无评论

Boosting Software Quality: A Machine Learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 ieee.

关键词： Data reliability

来源：评论

学校读者我要写书评

暂无评论

Zero-Shot Low-Light-Level Image Enhancement Algorithm Based on Atmospheric Scattering Model 5

Zero-Shot Low-Light-Level Image Enhancement Algorithm Based ...

引用

5th international conference on Machine Learning and computer Application, ICMLCA 2024

作者： Shi, Shijie Zhou, Dake Shao, Yiming College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798331530334

Low-light image enhancement remains a pivotal area of focus within the realm of image processing. While current learning-based enhancement techniques have shown advancements in managing noise or color distortion, they often fail to strike an optimal balance between these two aspects and are constrained by the availability and quality of training data. In this research, we introduce a novel zero-shot learning method for low-light image enhancement, named ZRASM, which leverages the Atmospheric Scattering Model (ASM) and integrates a denoising network grounded in the Noise2Noise theory. ZRASM is capable of noise reduction and color restoration without the need for external training data, operating solely from a single input image. Our experimental results indicate that ZRASM surpasses several state-of-the-art models in terms of image enhancement performance. © 2024 ieee.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Advancing Horizons: Exploring the Aerospace IoT Revolution - Analysis, Advancements, and Prospects for the Future 3

Advancing Horizons: Exploring the Aerospace IoT Revolution -...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Kumar, Gotte Ranjith Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India Chandigarh Engineering College Department of Computer Science & Engineering Mohali Jhanjeri India Sr University School of Computer Science & Artificial Intelligence Warangal India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

This study explores the profound influence of the Internet of Things (IoT) on the aerospace sector, examining the complex interplay between aircraft and IoT technology. This research examines the potential advantages and obstacles associated with the use of IoT technology in the aerospace industry. The primary areas of interest are improved operational effectiveness, heightened safety measures, and more scope for creativity. The use of IoT technology in the aviation industry has seen a notable rise. This has led to the widespread deployment of sensors that collect real-time data pertaining to diverse factors, including engine functionality and the in-flight experiences of passengers. The paper presents a thorough examination of Aerospace IoT, highlighting its significant impact on several areas within the aerospace industry. The objective of this project is to provide significant contributions to scholars, industry experts, policymakers, and technologists by offering important insights on the transformative capabilities of Aerospace IoT. © 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

Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning 8

Crime-Intent Sentiment Detection on Twitter Data Using Machi...

引用

8th international conference on automation, Control and Robotics engineering, CACRE 2023

作者： Bokolo, Biodoumoye George Ogegbene-Ise, Ebikela Chen, Lei Liu, Qingzhong Sam Houston State University Dept. of Computer Science HuntsvilleTX United States Kennesaw State University Dept. of Comp. and Software Eng. KennesawGA United States Georgia Southern University Dept. of Info. Tech. Statesboro Georgia

ISBN: (纸本)9798350302776

This research examines sentiment analysis in the context of crime intent using machine learning algorithms. A comparison is made between a crime intent dataset generated from a Twitter developer account and Kaggle's sentiment140 dataset for Twitter sentiment analysis. The algorithms employed include Support Vector Machine (SVM), Naïve Bayes, and Long Short-Term Memory (LSTM). The findings indicate that LSTM outperforms the other algorithms, achieving high accuracy (97%) and precision (99%) in detecting crime tweets. Thus, it is concluded that the crime tweets were accurately identified. © 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

Multimodal Depression Recognition Based on Self-Supervised Learning 4

Multimodal Depression Recognition Based on Self-Supervised L...

引用

4th international conference on computer science, Electronic Information engineering and Intelligent Control Technology, CEI 2024

作者： Wang, Guangkai Wang, Rui Yang, Qin Wu, Yong School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China Ordnance NCO Academy Army Engineering University PLA Wuhan China

ISBN: (纸本)9798350378542

Depression is a widespread and severe mental health disorder, characterized by a significant prevalence and a high incidence rate, which presents numerous challenges in diagnosis and treatment. We introduce an innovative multimodal approach for depression recognition utilizing self-supervised learning. The method analyzes audio and video data through three steps: extracting image features, extracting audio features, and performing multimodal temporal fusion, enabling an assessment of depression severity. Extensive experiments conducted on the AVEC2014 dataset demonstrate that the proposed model achieves a Mean Absolute Error (MAE) of 7.013 and a Root Mean Square Error (RMSE) of 8.616 in depression prediction tasks, showcasing strong performance. © 2024 ieee.

关键词： Self-supervised 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500页 << < 469 470 471 472 473 474 475 476 477 478 > >>

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：