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

576,909 篇 会议
7,448 篇 期刊文献
4,818 册 图书
9 篇 学位论文

馆藏范围

589,176 篇 电子文献
22 种 纸本馆藏

日期分布

学科分类号

339,378 篇 工学
- 243,230 篇 计算机科学与技术...
- 198,220 篇 软件工程
- 81,295 篇 信息与通信工程
- 52,766 篇 控制科学与工程
- 37,715 篇 电气工程
- 34,507 篇 生物工程
- 32,917 篇 电子科学与技术（可...
- 30,257 篇 机械工程
- 28,754 篇 生物医学工程（可授...
- 21,923 篇 光学工程
- 19,754 篇 仪器科学与技术
- 16,836 篇 动力工程及工程热...
- 16,254 篇 化学工程与技术
- 14,006 篇 安全科学与工程
- 12,737 篇 交通运输工程
- 11,317 篇 网络空间安全
- 10,688 篇 材料科学与工程（可...
154,102 篇 理学
- 85,330 篇 数学
- 38,116 篇 物理学
- 36,947 篇 生物学
- 26,028 篇 统计学（可授理学、...
- 18,353 篇 系统科学
- 16,426 篇 化学
92,517 篇 管理学
- 57,692 篇 管理科学与工程(可...
- 39,764 篇 图书情报与档案管...
- 27,424 篇 工商管理
24,125 篇 医学
- 20,173 篇 临床医学
- 18,863 篇 基础医学(可授医学...
- 12,344 篇 药学(可授医学、理...
14,784 篇 法学
- 12,191 篇 社会学
10,932 篇 教育学
10,736 篇 经济学
8,075 篇 农学
4,179 篇 文学
1,591 篇 军事学
1,317 篇 艺术学
51 篇 哲学
33 篇 历史学

主题

26,443 篇 computer science
17,227 篇 feature extracti...
17,159 篇 computational mo...
16,898 篇 accuracy
13,947 篇 training
11,666 篇 deep learning
11,042 篇 computer archite...
9,530 篇 data mining
9,418 篇 machine learning
9,293 篇 predictive model...
8,853 篇 support vector m...
8,524 篇 neural networks
8,250 篇 data models
8,244 篇 real-time system...
7,577 篇 optimization
7,396 篇 convolutional ne...
7,335 篇 software enginee...
7,197 篇 educational inst...
7,051 篇 artificial intel...
6,721 篇 testing

机构

1,989 篇 chitkara univers...
1,406 篇 school of comput...
1,113 篇 school of comput...
1,092 篇 school of comput...
894 篇 school of comput...
760 篇 department of co...
709 篇 department of co...
690 篇 school of comput...
682 篇 school of comput...
610 篇 school of comput...
592 篇 college of compu...
589 篇 lovely professio...
579 篇 department of co...
548 篇 school of comput...
510 篇 maharishi school...
488 篇 college of compu...
466 篇 college of compu...
458 篇 department of co...
451 篇 college of compu...
443 篇 school of comput...

作者

327 篇 wang wei
279 篇 yang liu
270 篇 wei wang
268 篇 yang yang
268 篇 li wei
262 篇 liu yang
245 篇 vinay kukreja
233 篇 kukreja vinay
224 篇 china
203 篇 wei li
201 篇 wei zhang
199 篇 li li
195 篇 zhang yu
174 篇 wang lei
174 篇 zhang lei
165 篇 li yang
154 篇 zhang wei
142 篇 liu wei
133 篇 chen chen
133 篇 li bo

语言

513,512 篇 英文
71,283 篇 其他
7,753 篇 中文
34 篇 法文
27 篇 西班牙文
24 篇 土耳其文
16 篇 俄文
8 篇 葡萄牙文
5 篇 德文
2 篇 阿拉伯文
2 篇 朝鲜文
2 篇 乌克兰文
1 篇 捷克文
1 篇 日文

检索条件"任意字段=International Multi-Conference in Computer Science and Computer Engineering"

共 589185 条记录，以下是4481-4490 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：

Motion Control Using Brain computer Interface Utilizing GCNs-Net with BILstms 4

Motion Control Using Brain Computer Interface Utilizing GCNs...

引用

4th IEEE international conference on Data engineering and Communication Systems, ICDECS 2024

作者： Gokulnath, S. Ponraj, Anitha Krishna, Bhargav Shivaganesh Sandeep Kalasalingam Academy of Research & Education Department of Computer Science and Engineering Tamilnadu Krishnan Kovil India Kalasalingam Academy of Research & Education Department of Computer Science and Engineering Tamilnadu Krishnan Kovil India

ISBN: (纸本)9798350393354

This study focuses on harnessing EEG (electroencephalogram) brain signals for motion control through a Brain-computer Interface (BCI). The research integrates advanced deep learning techniques, such as transfer learning with ResNet, for predicting and interpreting these brain signals. Additionally, the study utilizes LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network) for processing time-series EEG data, enhancing the system's ability to decode the user's intended movements.A key element of the research is the application of an attention-based BiLSTM-GCN (Bidirectional Long Short-Term Memory-Graph Convolutional Network) model. This model enables deep feature extraction and mining in the context of human motor imagery recognition. It empowers the system to effectively understand and respond to the user's intentions based on their EEG *** ultimate goal of this research is to create a more efficient and accurate BCI system that can recognize and interpret the user's brain signals, allowing for precise and intuitive motion control. This has the potential to revolutionize various fields, including assistive technology, neurorehabilitation, and human-computer interaction. The combination of EEG-based control and deep learning methods holds promise for enhancing the quality of life for individuals with disabilities and expanding the horizons of human-machine communication. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Detection of Kidney Cysts of Kidney Ultrasound Image using Hybrid Method: KNN, GLCM, and ANN Backpropagation

Detection of Kidney Cysts of Kidney Ultrasound Image using H...

引用

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

作者： Mardison Yuhandri Universitas Putra Indonesia YPTK Computer System Computer Science Faculty Padang Indonesia Universitas Putra Indonesia YPTK Information Technology Computer Science Faculty Padang Indonesia

ISBN: (纸本)9798350320954

This research is aim to detect kidney cysts from human kidney Ultrasound (USG) 2D Images. This research uses data from Hospital patients as many as 25 Ultrasound images of the human kidney in the format ***. This research uses the K-Nearest Neighbor (KNN) method for image classification of ultrasound images then using Gray Level Co-Occurrence Matrix (GLCM) method for image extraction to detect cyst and non-cyst regions from the result of classification after that using Artificial Neural Network (ANN) method type Backpropagation for image detection to find cysts from human kidney Ultrasound (USG) 2D Image from the result of image extraction. The result of this research is producing the algorithm to implement the method and the tool software application to detect kidney cysts from ultrasound 2D images. The accuracy of this tool is 84% which can detect with accurate 21 kidney cysts from 25 kidney ultrasound 2D images that validate of a Urology Specialist Doctor. © 2023 IEEE.

关键词： Backpropagation

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Enabled Intensity Level Classification of Corn Rust Disease 4

Deep Learning-Enabled Intensity Level Classification of Corn...

引用

4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Arshleen Kukreja, Vinay Gopal, Lisa Verma, Garima Sharma, Rishabh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350343670

Classifying plant diseases quickly and accurately is crucial to reducing their negative effect on agricultural output. In this research, we introduce a deep learning-based strategy for disease severity classification in maize using a multi-layer perceptron (MLP) model. Corn rust, which is brought on by Puccinia fungal infections, is a significant danger to harvest success. The proposed model makes use of a 25,000-image dataset that was painstakingly curated to capture various infection phases and then preprocessed to improve consistency and quality. The MLP framework is used to analyze the complex patterns that characterize fluctuating disease severity. Accuracy, precision, recall, and F1-score are used to assess the model's efficacy across a range of intensities. Understanding the subtleties of classification requires conducting a thorough confusion matrix study. The results of the model are also compared graphically to those of state-of-the-art methods. The results suggest that the model is capable of accurately classifying 96.74% of all intensity levels. Both the confusion matrix and the precision-recall metrics shed light on the strengths of individual classes. The suggested model's competitive position in the landscape of disease categorization algorithms is highlighted by the visual comparison. The ramifications of this study go beyond maize rust and contribute to the development of automated disease identification in agriculture. The combination of deep learning and agricultural knowledge provides a promising path toward better disease management. This study highlights the potential to strengthen food security through data-driven interventions as technology continues to reshape the agricultural landscape. © 2024 IEEE.

关键词： Corn disease Deep Learning Disease Classification multi-layer Perceptron

来源：评论

学校读者我要写书评

暂无评论

Underwater Image Enhancement Method Based on CGAN and Parallel Attention Mechanism 3

Underwater Image Enhancement Method Based on CGAN and Parall...

引用

3rd international conference on Neural Networks, Information and Communication engineering, NNICE 2023

作者： Ma, Chi Yang, Le Hu, Hui Chen, Yuenai Bu, Anxu The School of Computer Science and Engineering Huizhou University Huizhou516007 China The School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9798350335972

Due to the absorption and scattering of light in water, the underwater image has some problems such as colour distortion, serious colour difference and ambiguity, which seriously affects the detection of underwater resources and object recognition. At present, the latest underwater image enhancement methods are all based on deep learning, but they do not take into account the diversity of channel information and pixel features. In order to achieve better enhancement effect, a parallel attention mechanism combining Channel Attention and Pixel Attention is introduced based on Conditional Generative Adversarial Network consisting of multi-scale generator and double discriminator. Compared with other latest methods, the enhancement effect of the proposed method is closer to the real image, and the necessity of each module of the proposed model is proved by ablation study. © 2023 IEEE.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Effects of Initialization Methods on the Performance of multi-Objective Evolutionary Algorithms

Effects of Initialization Methods on the Performance of Mult...

引用

2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gong, Cheng Pang, Lie Meng Nan, Yang Ishibuchi, Hisao Zhang, Qingfu City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350337020

Population initialization is always needed in evolutionary multi-objective optimization (EMO) algorithms. Intuitively, a well-designed initialization method can help facilitate the evolutionary process and improve the performance of EMO algorithms. However, very few studies have investigated the effects of initialization methods on the performance of EMO algorithms. Many existing EMO algorithms randomly generate an initial population to start the evolutionary process. To fill this research gap and attract more attention from EMO researchers to this important yet under-explored issue, in this paper, we examine the effects of various initialization methods that may become promising alternatives to the commonly-used random initialization method. Each initialization method is evaluated through computational experiments on test problems of various sizes with 5-1000 decision variables. Experimental results clearly demonstrate the advantage of well-designed initialization methods over the random initialization method. This study provides useful insights into EMO algorithm design and motivates further research on population initialization. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Accelerating Surrogate-Assisted multi-Objective Evolutionary Optimization via a Dynamic Surrogate Model with Archive Updating 17

Accelerating Surrogate-Assisted Multi-Objective Evolutionary...

引用

17th international conference on Knowledge and Smart Technology, KST 2025

作者： Wang, Zi Ding, Yu Zou, Yawen Zhang, Chao Yu, Jun Graduate School of Science and Technology Niigata University Niigata-city Japan Graduate School of Science and Engineering University of Toyama Toyama Japan Faculty of Engineering University of Toyama Toyama Japan Institute of Science and Technology Niigata University Niigata-city Japan

ISBN: (纸本)9798331520403

In surrogate-assisted evolutionary computation, a common method for constructing surrogate models is the Gaussian Process (GP), which is widely used because of its flexibility in handling complex objective functions. However, GP can lead to an increased computation time when the sample size is large or when more samples are added to update the surrogate model. Furthermore, limited metrics are available for selecting the optimal surrogate model for a given problem, necessitating experimentation with each method to determine the best choice. In this paper, we propose a multi-objective evolutionary algorithm that addresses these issues by introducing an archive updating strategy and a dynamic surrogate model. In the experiment, by controlling the learning data with archive updating and dynamically selecting appropriate models from multiple surrogate models based on evaluation metrics, the computation time can be reduced without compromising the accuracy. © 2025 IEEE.

关键词： archive updating dynamic surrogate model multi-objective evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Spectrum Sensing Methodology Using Artificial Intelligence for Cognitive Radio Network 1

Spectrum Sensing Methodology Using Artificial Intelligence f...

引用

1st international conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Bhavani, M. Monica Balamurugan, P. Sankaran, Vikram Nattamai Majumder, Sohom Rakshitha, L.P. Nandagopal, K. SRM University Data Science & Business Systems Department Tamil Nadu Chennai India Institute: Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mathematics Tamil Nadu Chennai India Department of Industry Experts AtlantaGA United States JIS College of Engineering Department of Business Administration West Bengal Kalyani Kolkata India Brindavan Group of Institutions Department of Mathematics Karnataka Bangalore India Mohan Babu University Department of Mathematics Andhrapradesh Tirupati India

ISBN: (纸本)9798331518578

Spectrum sensing is an essential part of cognitive radio networks (CRNs) that enables efficient utilisation of the radio frequency spectrum and dynamic spectrum access. Conventional spectrum sensing technologies, such as energy detection, matched filtering, and cyclostationary features recognition, are often computationally complex, noise-sensitive, and unable to detect signals at low signal-to-noise ratio (SNR) levels. Artificial intelligence (AI)-based approaches have become a viable substitute to deal with these issues. CRNs can provide more precise, flexible, and effective spectrum sensing owing to AI approaches. In intricate and dynamic situations, these algorithms can identify primary users, predict spectrum usage, and categorise spectrum accessibility. Major AI-driven techniques for spectrum sensing are reviewed in this work, with an emphasis on supervised, unsupervised, and reinforcement learning techniques. The suggested approach supports the creation of intelligent, independent CRNs for next-generation wireless networks by improving spectrum efficacy, cutting down on sensing time, and facilitating real-time decision-making. © 2025 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Inspection of Automobile Components Using Mask R-CNN and Minimal Setup Vision 9

Inspection of Automobile Components Using Mask R-CNN and Min...

引用

9th international conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Ramachandran, Sharad Aishwarya, B. Pushpalatha, M. Poornima, S. Venkatesh, K. Srm Institute of Science And Technology Department of Networking And Communications kattankulathur India Srm Institute of Science And Technology Department of Computing Technologies kattankulathur India

ISBN: (纸本)9798350365092

Detecting surface defects in anodized automobile components on a serial production line is a formidable task that demands precision, given theseparts' critical role in ensuring vehicle safety and performance. This research proposes a ground-breaking system that tackles these challenges by minimizing hardware and leveraging software processing, rendering it automated, versatile, affordable, and precise. The proposed method adopts the Mask R-CNN algorithm for defect detection, enabling pixel-level instance segmentation and bounding the defects with boxes. Moreover, a multi-label classifier is deployed to distinguish between defective and non-defective components and predict the type of defect. The system's design also accounts for the computational time needed to ensure the system is suitable for high-speed conveyor belts. By accurately detecting defects and preventing defective components from causing catastrophic consequences, the proposed approach has the potential to revolutionize defect detection in serial production lines, making the process faster, more reliable, and safer © 2024 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Efficient Identification of DeepFake Images using CNN 2

Efficient Identification of DeepFake Images using CNN

引用

2nd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gowda, Naveen Chandra Veena, H.N. Ramani, D Roja School of Computer Science and Engineering REVA University Bengaluru India SJB Institute of Technology Department of Computer Science and Engineering Bengaluru India Newhorizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

The development of Deepfakes has become more prevalent with the rise of Generative Adversarial Networks (GANs). Deepfakes are synthetically created, modified images that have been made to look real;they pose severe social concerns with privacy an d security issues. To solve these issues, this paper offers a novel method to detect Deepfakes in photographs using a Convolutional Neural Network (CNN) architecture. To detect whether a image has been altered or not, the suggested method examines many aspects of the image. The proposed methods are tested using a sizable benchmark dataset of Deepfakes and non-Deepfakes, and were able to identify modified images with high precision. The strategy is resistant to a variety of manipulations, including those intended to trick cutting-edge detection techniques. It is found that proposed strategy will improve digital media's security and credibility while helping mitigate any possible problems of Deepfake images. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) DeepFake Deeplearning Generative Adversarial Networks (GANs)

来源：评论

学校读者我要写书评

暂无评论

Enhancing Network Function Virtualization and Service Function Chaining: Innovative Optimization Strategies and Their Impact 1

Enhancing Network Function Virtualization and Service Functi...

引用

1st international conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Upadhyay, Deepak Gupta, Mridul Sharma, Kunj Bihari Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350365016

Throughout this paper, the reader was able to better understand how innovative optimization techniques through NFV and SFC frameworks could and should be applied to address the developing and shifting focus of contemporary network infrastructures. Focusing on performance metrics performance via latency, throughput, resource utilization, and operational cost, the presented algorithms and machine learning models developed and executed due to this research showed innovative and dynamic optimization of network functions. Through further extensive simulations equalling 125 seconds, our findings reported creative results of improving performance in several significant areas: latency was reduced, throughput was enhanced, resource utilization was more efficient, and operational cost was reduced. These results indicated that our optimization strategies can pay off and bring a number of benefits to NFV and SFC that address a clear need for change in the current type of network management models. Therefore, this paper is valuable and serves as a testament of our strategies' power along with opening various doors for following network optimization research. Therefore, one can see how important it is to implement innovation in the face of change. © 2024 IEEE.

关键词： Network function virtualization

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 << < 445 446 447 448 449 450 451 452 453 454 > >>

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：