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

7,090 篇 会议
47 篇 期刊文献
39 册 图书

馆藏范围

7,176 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,912 篇 工学
- 4,098 篇 计算机科学与技术...
- 2,520 篇 软件工程
- 1,048 篇 信息与通信工程
- 789 篇 控制科学与工程
- 663 篇 生物工程
- 642 篇 电气工程
- 424 篇 生物医学工程（可授...
- 285 篇 交通运输工程
- 281 篇 机械工程
- 226 篇 仪器科学与技术
- 226 篇 安全科学与工程
- 206 篇 建筑学
- 197 篇 动力工程及工程热...
- 185 篇 光学工程
- 166 篇 化学工程与技术
- 154 篇 土木工程
1,899 篇 理学
- 1,065 篇 数学
- 717 篇 生物学
- 442 篇 统计学（可授理学、...
- 285 篇 物理学
- 252 篇 系统科学
- 140 篇 化学
1,151 篇 管理学
- 655 篇 管理科学与工程(可...
- 582 篇 图书情报与档案管...
- 315 篇 工商管理
407 篇 医学
- 353 篇 临床医学
- 297 篇 基础医学(可授医学...
- 188 篇 药学(可授医学、理...
244 篇 教育学
- 243 篇 教育学
211 篇 法学
- 197 篇 社会学
119 篇 农学
111 篇 经济学
50 篇 文学
7 篇 艺术学
5 篇 军事学

主题

557 篇 machine learning
456 篇 deep learning
269 篇 training
253 篇 data mining
240 篇 data models
220 篇 feature extracti...
218 篇 accuracy
184 篇 support vector m...
178 篇 predictive model...
138 篇 neural networks
137 篇 machine learning...
133 篇 computational mo...
131 篇 reinforcement le...
108 篇 convolutional ne...
103 篇 real-time system...
92 篇 forecasting
90 篇 artificial intel...
89 篇 learning systems
88 篇 transfer learnin...
86 篇 big data

机构

69 篇 chitkara univers...
17 篇 school of electr...
15 篇 computer science...
14 篇 harbin engineeri...
13 篇 graphic era hill...
12 篇 building civil a...
12 篇 uttaranchal inst...
12 篇 computer science...
11 篇 university of ch...
10 篇 school of electr...
10 篇 school of comput...
10 篇 graphic era deem...
10 篇 school of electr...
10 篇 yonsei univ dept...
9 篇 wuhan university...
9 篇 univ chinese aca...
9 篇 school of inform...
8 篇 school of remote...
7 篇 university of sa...
7 篇 microsoft resear...

作者

17 篇 kukreja vinay
15 篇 vinay kukreja
13 篇 yin hujun
12 篇 liu yang
12 篇 corchado emilio
11 篇 mehta shiva
10 篇 shiva mehta
10 篇 hujun yin
10 篇 cho sung-bae
9 篇 notsu akira
9 篇 silva catarina
9 篇 alhajj reda
7 篇 ksieniewicz pawe...
7 篇 hammer barbara
7 篇 zhang byoung-tak
7 篇 wozniak michal
7 篇 hussain abir
7 篇 soubra hassan
7 篇 huang de-shuang
7 篇 camacho david

语言

6,194 篇 英文
971 篇 其他
31 篇 中文
1 篇 西班牙文
1 篇 俄文

检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7176 条记录，以下是1211-1220 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 Fake Detection for Preventing Audio and Video Frauds Using Advanced Deep learning Techniques 6

Deep Fake Detection for Preventing Audio and Video Frauds Us...

引用

6th IEEE international conference on Recent Advances in intelligent Computational Systems, RAICS 2024

作者： Vaidya, Anusha O. Dangore, Monika Borate, Vishal Kisan Raut, Nutan Mali, Yogesh Kisan Chaudhari, Ashvini Trinity Academy of Engineering E & T C Department Pune India G.H Raisoni Institute of Engineering and Management Aiml Department India Dr D Y Patil College of Engineering & Innovation Computer Engineering Department Pune India G.H Raisoni College of Engineering and Management Aiml Department India Nmims Center for Distance & Online Education Pune India

ISBN: (纸本)9798350381689

Deepfakes allow for the automated gen- eration of fake video content, often accomplished through the use of generative adversarial networks. To address the increasing issue of deepfakes, this study focuses on constructing a model that incorporates advanced techniques. the researchers combined the ResNeXt, Long Short-Term Memory (LSTM), and ResNet architectures, selecting them based on their effectiveness in handling complex visual data and capturing temporal dependencies. Prior to detection, the dataset underwent pre-processing using the Multi-Task Cascaded Convolutional Neural Network (MTCNN), which facilitated the accurate extraction of facial regions. Importantly, the study evaluated the model across three diverse and significant datasets: the Face Forensics++ dataset (FF-DF), the Celeb-DF dataset, and the Facebook Deepfake Detection Challenge (DFDC) dataset. this comprehensive evaluation en- sured the model's ability to generalize and its suitability for real-world scenarios, as demonstrated by its exceptional detection accuracy. the combination of models employed in this study yielded highly accurate results and remained robust in the face of evolving deepfake technology. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Exploring the Knowledge Distillation 11th

Exploring the Knowledge Distillation

引用

10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： Khaider, Yasssine Rahhali, Dounia Alami, Hamza En Nahnahi, Noureddine LISAC Laboratory Faculty of Sciences Dhar EL Mehraz Sidi Mohamed Ben Abdellah University Fez Morocco Department of Engineering Innov-Tech Laboratory High Technology School Rabat Morocco

ISBN: (纸本)9783031473654

Knowledge distillation is a well-known method of model compressing but it is largely under-used. Knowledge distillation has the ability to reduce the size of a model by transferring the knowledge from a large (pre-trained or not) model into a smaller one with minimum loss to the accuracy, for a better fit in edge devices and the devices with low computational power. Furthermore, it can be used also as a performance enhancer for the student model as we’ll see in the experiments since it doesn’t need any additional data to be effective. this paper argues that the knowledge distillation method or one of its variations should always be used on deep learning models. In our experiments we trained and tested our models on some well-known datasets (MNIST, CIFAR10 and CIFAR100) to prove that the knowledge distillation and two of its variations give positive results when applied on over-fitted or under-fitted teacher models. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Online Teaching Strategies to Motivate Students' Active Participation Based on Personalized SPOC Teaching Mode 11

Online Teaching Strategies to Motivate Students' Active Part...

引用

11th international conference on Information and Education Technology, ICIET 2023

作者： Dou, W.N. Beijing University of Civil Engineering and Architecture School of Human Department of Foreign Languages Beijing China

ISBN: (纸本)9781665465489

Compared with the traditional teaching mode, online distance teaching mode breaks the barriers of time and space so that more people have access to education, which makes Massive Open Online Courses (MOOCs) popular all over the world, especially in China. However, MOOCs bring more challenges to teaching in the meantime. Students' participation in online learning becomes one of the most prominent problems, which is hard to deal with compared with traditional face-To-face teaching mode. So with the integration of the advantages of MOOCs and traditional teaching mode, Small Private Online Course(SPOC) teaching mode is more and more favored by educators. Guided by the 5C Framework theory, this paper aims to advance effective online-Teaching strategies in order to motivate students' active participation in online learning process under SPOC mode. Based on the analyses of qualitative and quantitative data, the results show that teachers can improve students' participation in online courses by 'the Optimized Combination' from three dimensions: interactive teaching platforms, teaching content carriers and teaching task difficulty, which provide a new perspective for improving the effectiveness of online teaching. © 2023 IEEE.

关键词： Teaching

来源：评论

学校读者我要写书评

暂无评论

Improved Path Planning Algorithm Based on RRT Algorithm and Quintic B-spline Curve 11

Improved Path Planning Algorithm Based on RRT Algorithm and ...

引用

11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Zhao, Duo Huang, Wendong School of Electrical Engineering Southwest Jiaotong University Chengdu610097 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Aiming at the problems of strong randomness, unsmooth path and local optimum in the path planning of the Rapidly-exploring Random Trees (RRT) algorithm. the paper designs an improved RRT algorithm with variable step size and goal orientation (VO-RRT), which introduces variable step size, oriented expansion node, optimized initial path and smoothed path four parts. through several experiments in 2D and 3D space to prove that the VO-RRT algorithm has enhanced obstacle avoidance ability and improved path smoothness in different obstacle spaces. the path nodes generated by the VO-RRT algorithm in 3D space are tested in URsim simulation software and manipulator body to verify the executability of the optimized path. © 2022 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Classification and Grading for Power Sensitive data Based on Hybrid data Distribution learning 11

Classification and Grading for Power Sensitive Data Based on...

引用

11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Huang, Xiuli Zhai, Yujia Shi, Congcong Jiang, Yiwen Zhang, Shijun State Grid Smart Grid Research Institute Co. LTD. State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China Big Data Center of State Grid Corporation of China Beijing China State Grid Jibei Electric Power Co. LTD. Beijing China

ISBN: (纸本)9798350333664

Power industry data contain various sensitive information, and the leakage of such data will cause critical damage to both the company and the country. However, the unbalanced and various data types make the traditional data classification and grading method fail to achieve enough performance. To better classify and grade the power sensitive data, we propose a Hybrid data Distribution learning Network, which simultaneously learns the distribution of multiple data types, including Character-based, Text-based, and Numerical data. Furthermore, we use an optimized nonlinear Support Vector Machine model to detect the grade of power data based on the learned data distribution. the experimental results show that our method outperforms other baselines and achieves a precision of 0.994, a recall of 0.985, and an F1 of 0.989. the visualization results also show that our method effectively learns the distribution of multiple types of power data. © 2023 IEEE.

关键词： classification and grading hybrid data distribution learning network nonlinear SVM model sensitive power data

来源：评论

学校读者我要写书评

暂无评论

Exploring Machine learning in IoT Smart Home Automation 8

Exploring Machine Learning in IoT Smart Home Automation

引用

8th IEEE international conference on Software engineering and Computer Systems, ICSECS 2023

作者： Waseem, Quadri Wan Din, Wan Isni Sofiah Bin Ab Rahman, Azamuddin Nisar, Kashif Faculty of Computing Pekan Malaysia AnalytiCray Kuala Lumpur55100 Malaysia Swinburne University of Technology Sydney Professional Computing Data Science ParramattaNSW2150 Australia

ISBN: (纸本)9798350310931

the Internet of things (IoT) has evolved in these years. Various types of organizations, industries, research domains and almost all types of intelligent future applications are utilizing the advantages of IoT. these applications include smart homes, smart cities, smart infrastructure smart communities, smart healthcare, smart agriculture and many more. 'Smart Homes' has emerged as one the latest Internet of things (IoT) applications known to automate household equipment's using remote or automated functioning from remote locations to improve the quality of life for its inhabitants. For a smart home system to function effectively, the machine learning (ML) implementation must go beyond basic remote control and simple automation. To fully realize its potential and provide homeowners with tremendous and unexpected benefits, more research and development in the fields of machine intelligence and smart home automation are required. In this research work, we aim to traverse ML in IoT smart home automation by classifying the home automation applications. We propose a taxonomy of machine learning (ML) for smart homes based on its application. this research also includes related surveys and literature reviews along with open challenges and issues as well as future directions in detail. © 2023 IEEE.

关键词： Automation

来源：评论

学校读者我要写书评

暂无评论

A Novel Prescribed Performance Control Strategy for Uncertain Nonlinear Systems 11

A Novel Prescribed Performance Control Strategy for Uncertai...

引用

11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Ju, Jiaying Liu, Na Liu, Yunlei School of Information and Electrical Engineering Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China Architecture and Urban Planning Design Institute Shandong Jianzhu University Jinan250101 China Jinan Chengbo Information Technology Co. Ltd Jinan250101 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, a new finite-time prescribed performance control strategy is proposed for unknown nonlinear systems. Compared with previous research results, the controller is simple in design, with fewer parameters and greatly reduced computational complexity. In addition, the tracking error can relatively quick converge to a predefined performance boundary region in the transient process and remain in this region after reaching the steady state. Finally, the stability analysis and simulation results verify the effectiveness of the proposed controller. © 2022 IEEE.

关键词： Controllers

来源：评论

学校读者我要写书评

暂无评论

GFTLSTM: Dynamic Graph Neural Network Model Based on Graph Framelets Transform 14th

GFTLSTM: Dynamic Graph Neural Network Model Based on Graph ...

引用

14th EAI international conference on intelligent Technologies for Interactive Entertainment, INTETAIN 2023

作者： Yang, Shengpeng Zhou, Siwei Yang, Shasha Shi, Jiandong Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China Zhejiang Normal University Jinhua China

ISBN: (纸本)9783031557217

there is currently a surge of interest in graph representation learning, with researchers increasingly focusing on methods and applications involving graph neural networks (GNNs). However, traditional GNN models for static graph data analysis have limitations in their ability to extract evolutionary patterns from dynamic graphs, which are more commonly observed in real-world data. Additionally, existing dynamic GNN models tend to prioritize low-frequency information while neglecting high-frequency information. To address these limitations, we introduce a dynamic graph neural network model that leverages graph framelet transforms, capitalizing on the benefits of traditional wavelets in multi-resolution analysis. the initial step involves constructing the graph framelet transform based on graph wavelet research, subsequently implementing multi-resolution graph convolution with both low-pass and high-pass filtering. then, we incorporate the convolution operation into a long short-term memory (LSTM) network. As a result, we develop a dynamic GNN model founded on the graph framelet transform, which effectively uncovers the evolutionary information embedded within dynamic graphs. In our experiment evaluation, we compare our model with 11 widely used dynamic graph representation learning algorithms across three public datasets of discrete dynamic graph representation learning tasks (encompassing six groups of experimental data). Our model outperforms the alternatives in terms of accuracy on the majority of the datasets. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

High Performance Real-time Anomaly Detection for Agricultural Monitoring Systems 11

High Performance Real-time Anomaly Detection for Agricultura...

引用

11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Eryilmaz, Selim Eren Yucel, Meric Ustundag, Burak Berk Istanbul Technical University Computer Engineering Istanbul Turkey Istanbul Technical University National Software Certification Research Center Istanbul Turkey

ISBN: (纸本)9798350303513

Due to technological advancements, real-time monitoring of various agricultural parameters has become essential to modern farming practices. Monitoring agricultural parameters such as temperature, humidity, soil moisture, and plant growth helps farmers to optimize their farming practices to improve crop yields. However, near-real-time monitoring involves significant amounts of data, and analyzing this data in real time for anomalies is a challenging problem. In this paper, we propose a novel approach for anomaly detection in near real-time agriculture monitoring using a cortical coding method to address this issue. the cortical coding method utilizes a tree structure to cluster and learn patterns from time-series data. the proposed method has previously proven helpful in tasks such as vector quantization and anomaly detection. this paper explores the application of cortical coding-based anomaly detection in agriculture monitoring. Incremental and continuous learning is highly beneficial for early and accurate detection in systems with large data streams. the proposed method is evaluated on a dataset obtained from an agricultural monitoring station in Turkey. Experiments show that the proposed method outperforms widely used forecast-based anomaly detection methods. © 2023 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Research on Alarm Cleaning and Outlier Detection Algorithms of Network data Analysis 11th

Research on Alarm Cleaning and Outlier Detection Algorithms ...

引用

11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Wang, Jingyun Li, Deyi Jin, Yuchao Han, Yuhui Li, Yi Xu, Lexi Cheng, Xinzhou Zhou, Shiyu Zheng, Yuting Research Institute China United Network Communications Corporation Beijing China

ISBN: (纸本)9789819721238

With the rapid development of 5G network, network architecture is becoming increasingly complex, and the number of network devices are also increasing significantly. Complex and diverse network data is generated during the daily operation of network devices. In addition, network alarms seriously threaten the network operation, and network outliers clearly indicate the existence of hidden dangers in the network. In order to guarantee effective network operation, this paper investigates two algorithms about alarm cleaning and outlier detection. An alarm cleaning algorithm is proposed and based on network function association relationship, which can effectively filter out the redundant alarm and facilitate intelligent alarm analysis. In addition, an outlier detection algorithm is proposed and based on the standard interquartile range (IQR) method and the outlier coefficient of data temporal correlation, which is helpful in detecting network outliers more effectively and facilitate network performance analysis and automatic operation and maintenance. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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 << < 118 119 120 121 122 123 124 125 126 127 > >>

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：