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

22,002 篇 会议
3,552 篇 期刊文献
208 册 图书

馆藏范围

25,762 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

15,810 篇 工学
- 11,148 篇 计算机科学与技术...
- 9,355 篇 软件工程
- 4,296 篇 信息与通信工程
- 2,504 篇 控制科学与工程
- 2,154 篇 电子科学与技术（可...
- 2,139 篇 电气工程
- 1,539 篇 生物工程
- 1,321 篇 生物医学工程（可授...
- 1,135 篇 光学工程
- 1,029 篇 机械工程
- 884 篇 仪器科学与技术
- 834 篇 动力工程及工程热...
- 744 篇 化学工程与技术
- 624 篇 安全科学与工程
- 524 篇 交通运输工程
- 467 篇 材料科学与工程（可...
- 445 篇 建筑学
8,214 篇 理学
- 4,524 篇 数学
- 2,415 篇 物理学
- 1,705 篇 生物学
- 1,374 篇 统计学（可授理学、...
- 916 篇 系统科学
- 760 篇 化学
3,990 篇 管理学
- 2,397 篇 管理科学与工程(可...
- 1,826 篇 图书情报与档案管...
- 1,258 篇 工商管理
1,071 篇 医学
- 948 篇 临床医学
- 882 篇 基础医学(可授医学...
- 574 篇 药学(可授医学、理...
713 篇 法学
- 561 篇 社会学
445 篇 经济学
346 篇 教育学
316 篇 农学
110 篇 文学
73 篇 军事学
26 篇 艺术学
4 篇 哲学
3 篇 历史学

主题

681 篇 accuracy
529 篇 feature extracti...
474 篇 deep learning
473 篇 computational mo...
376 篇 support vector m...
370 篇 training
365 篇 computer science
319 篇 internet of thin...
283 篇 machine learning
281 篇 real-time system...
278 篇 computer archite...
235 篇 predictive model...
221 篇 routing
220 篇 reliability
218 篇 optimization
212 篇 data mining
210 篇 neural networks
207 篇 convolutional ne...
199 篇 throughput
193 篇 image segmentati...

机构

85 篇 dept. of compute...
84 篇 institut univers...
83 篇 north-west unive...
83 篇 université de st...
82 篇 infn sezione di ...
82 篇 tno technical sc...
81 篇 national centre ...
79 篇 nikhef national ...
76 篇 infn sezione di ...
69 篇 university würzb...
66 篇 università di ca...
53 篇 leiden universit...
51 篇 dept. of compute...
50 篇 school of automa...
50 篇 university of jo...
48 篇 university moham...
44 篇 university moham...
43 篇 in2p3 lpc campus...
42 篇 infn lnf via enr...
42 篇 university of gr...

作者

95 篇 ardid m.
93 篇 ferrara g.
92 篇 kalekin o.
92 篇 díaz a.f.
91 篇 boumaaza j.
90 篇 coleiro a.
89 篇 calvo d.
83 篇 gal t.
82 篇 bozza c.
80 篇 chen a.
78 篇 gozzini s.r.
76 篇 huang f.
76 篇 dallier r.
72 篇 anguita m.
63 篇 enzenhöfer a.
60 篇 kulikovskiy v.
58 篇 boettcher m.
57 篇 celli s.
54 篇 aly z.
52 篇 dornic d.

语言

24,761 篇 英文
608 篇 其他
295 篇 中文
75 篇 日文
21 篇 朝鲜文
1 篇 阿拉伯文
1 篇 德文
1 篇 法文
1 篇 斯洛文尼亚文
1 篇 土耳其文

检索条件"机构=Dept. Computer Technology and Computer Graphics"

共 25762 条记录，以下是4781-4790 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：

Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques 18

Deciphering Faces: Enhancing Emotion Detection with Machine ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ishaq, Muhammad Haseeb Mustafa, Rafay Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim Habib, Abdullah Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Heart Attack Detection And Alerting System

Heart Attack Detection And Alerting System

引用

Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Aarthi Elaveini Pragadeesh E Bhavesh Aglecha Metha G P Dept of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Dept. of Electronics and Computer Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This research presents a cutting-edge IOT-based heart attack detection and alerting system. It combines a number of sensors to continuously track vital indicators in the present. The device identifies physiological parameter irregularities that may be signs of a probable heart attack through a carefully crafted algorithm. Instant notifications are sent via IOT-enabled systems to the patient's family and designated healthcare professionals upon identification. The goal of the technology is to drastically shorten response times during critical cardiac episodes, allowing for timely medical intervention. This technology attempts to improve early detection by utilizing sensor data and IoT connectivity, thereby reducing the severity of heart attacks and raising the chance of successful intervention and patient survival.

关键词： Cardiac arrest Medical services Sensor phenomena and characterization Sensor systems Real-time systems Sensors Telecommunication computing Time factors Biomedical monitoring Monitoring

来源：评论

学校读者我要写书评

暂无评论

Survey on Use of AI/ML Algorithms in Enhancing Filesystem Performance and Efficiency

Survey on Use of AI/ML Algorithms in Enhancing Filesystem Pe...

引用

International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Vijay Olekar Yash Patil Vishvam Pawar Shubham Pitale V R. Jaiswal Vishal Naranje Dept. of Information Technology Pune Institute of Computer Technology Pune Pune India School of Engineering Architecture and Interior Design Amity University Dubai

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The processing power of CPUs is increasing day by day, but systems often struggle to fully utilize this power due to the bottleneck of file access time from the disk. Over the years, various techniques have been devised to reduce I/O latency while considering various file parameters. Our survey paper aims to shed light on these techniques, efficiently comparing them and highlighting the benefits and drawbacks of each approach. For our evaluation, we have considered various file parameters, such as access time, file size, directory structure, and kernel execution flow. We have combined these parameters with a range of supervised and unsupervised machine learning algorithms, including Decision Tree, ABLE, RNN, GNN, CART, XGBoost, CNN, and more. This survey aims to highlight the use of ML/AI algorithms in the intelligent detection of file access and I/O patterns to optimize the performance of modern applications.

关键词： Surveys Machine learning algorithms Adaptive systems File systems System performance Neural networks Predictive models

来源：评论

学校读者我要写书评

暂无评论

Network Anomaly Detection with Stacked Sparse Shrink Autoencoders and Improved XGBoost

Network Anomaly Detection with Stacked Sparse Shrink Autoenc...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Guan, Ziyue Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms. © 2023 IEEE.

关键词： autoencoders feature extraction Network anomaly detection particle swarm optimization XGBoost

来源：评论

学校读者我要写书评

暂无评论

Joint Optimization of Cache-Assisted Offloading and Resource Allocation in Mobile Edge Computing

Joint Optimization of Cache-Assisted Offloading and Resource...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Sun, Zhe Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Edge computing is a new architectural model that aims to offer computing, storage, and networking resources to support Internet of Things. Its primary strategy involves transferring computational tasks to the edge of network, which is closer to end-users. This paradigm facilitates offloading of computation, resulting in reduced latency and improved system performance. However, nodes located at the network edge have restricted energy and resources. As a result, running tasks entirely at the edge leads to higher energy consumption. This work proposes a novel three-tier offloading framework comprising of multiple mobile vehicles (MVs), a base station (BS), and a cloud data center (CDC). It jointly optimizes offloading rates of tasks, CPU computation rates of MVs, BS, and CDC, and the allocation of wireless bandwidth resources at MVs during partial computation offloading of tasks. It also considers limits of maximum computational resources and maximum delay of task execution. To further reduce the total system energy consumption, this work actively caches execution codes of tasks in MEC servers to reduce data transmission energy of MVs, which minimizes the total system energy consumption. This work develops a mixed integer nonlinear program and designs a mixed meta-heuristic algorithm with a multi-strategy adaptive particle swarm optimizer. Simulation results demonstrate that it outperforms various state-of-the-art algorithms by achieving lower energy consumption in fewer iterations. © 2023 IEEE.

关键词： cloud computing Computation offloading particle swarm optimization vehicular edge computing

来源：评论

学校读者我要写书评

暂无评论

Investigating the Role of Block Chain to Secure Identity in IoT for Industrial Automation 2

Investigating the Role of Block Chain to Secure Identity in ...

引用

2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Veeraiah, Vivek Preetham, Anusha Karthikeyan, P. Kumari, P. Lalitha Mehta, Kamakshi Kumbhkar, Makhan Adichunchanagiri University Dept. of R & D Computer Science Karnataka Mandya India Dayananda Sagar Academy of Technology and Management Dept. of Ise Karnataka India School of Management Studies Kongu Engineering College Perundurai TN Erode India Visakha Institute of Engineering & Technology Dept. of CSE AP Narava Visakhapatnam India Amity College of Commerce Amity University Haryana India Christian Eminent College Dept. of Computer Science Madhya Pradesh Indore India

ISBN: (纸本)9781665437899

Everyone who has an app that can access the block chain can get their hands on the data. There are blocks for each new piece of data that come in. A chronological chain of data is formed when a block is filled with data and chained to the previous block. Smart things are protected from cyber-attacks thanks to the IoT's technology's transparency and virtual incorruptibility. The block chain may be used to enable secure connectivity between IoT devices by storing data in transactions and confirming those transactions with nodes. The Internet of Things (IOT) has helped industrial automation reach new heights. We can improve the security and privacy by adding block chain to it. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

引用

2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

High-Capacity, A Secret Video Sharing Using DM-Stego and Genetic Algorithm

High-Capacity, A Secret Video Sharing Using DM-Stego and Gen...

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Logeshwari, R. Velswamy, Rajasekar Subhashini, R. Karunakaran, V. Srm Institute of Technology Department of Network and Communication Engineering Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India Sri Eshwar College of Engineering Dept. of CSE-AI and Ml Coimbatore India

ISBN: (纸本)9798350300857

Electronic media knowledge is unprecedently increasing in recent years. In almost all security control areas, traffic control, weather monitoring, video conferences, social media etc., videos and multimedia data analysis practices are used. As a consequence, it is necessary to retain and transmit these data, by considering the security and privacy issues. IN this research study, a new Div-Mod Stego algorithm is combined with the Multi-Secret Sharing method along with temporary frame reordering and Genetic algorithm to implement high-end security in the process of video sharing. The qualitative and quantitative analysis has also been carried out to compare the performance of the proposed model with the other existing models. A computer analysis shows that the proposed solution would satisfy the requirements of the real-time application. © 2023 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

DViTPolyp: Small Polyp Detection in Colonoscopy Videos based on Deep Vision Transformer 4

DViTPolyp: Small Polyp Detection in Colonoscopy Videos based...

引用

4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Ma, Ming Xia, Likun Seo, Hyunseok Katz School of Science and Health Yeshiva University Dept. of Graduate Computer Science and Engineering New York United States College of Information Engineering Capital Normal University Beijing China Biomedical Research Institute Korea Institute of Science and Technology Seoul Korea Republic of

ISBN: (纸本)9798350395914

Early detection of colorectal polyps and removal play a crucial role in the prevention of colon cancer. computer-aided detection (CAD) methods can be employed to help endoscopists reduce the polyp miss-rates. Numerous automatic polyp detection methods have been explored. However, it is still challenging to automatically detect colon polyps in small size in colonoscopy videos. This paper presents a method termed DViTPolyp for detecting small colorectal polyp in colonoscopy videos. The proposed method leverages the power of the deep vision transformer as the detection head in the network to aggregate rich global features, and uses the re-attention mechanism to regenerate attention maps to capture the features from the backbone model and DViT block. Also, we propose to use a power regularized Intersection Over Union (IoU) as the loss function to further enhance the accuracy of detection. Experiments are conducted on a mix of several public polyp datasets. Comparative results show that our proposed method has a better performance than other methods. © 2024 IEEE.

关键词： colonoscopy videos loss function polyp detection vision transformer

来源：评论

学校读者我要写书评

暂无评论

Latency-Minimized Computation Offloading in Vehicle Fog Computing with Improved Whale Optimization Algorithm

Latency-Minimized Computation Offloading in Vehicle Fog Comp...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xue, Xiangdong Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms. © 2023 IEEE.

关键词： computation offloading fog computing Lévy flight Vehicular networks whale optimization algorithm

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 << < 475 476 477 478 479 480 481 482 483 484 > >>

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：