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

1,313 篇 会议
11 篇 期刊文献
9 册 图书

馆藏范围

1,333 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

901 篇 工学
- 745 篇 计算机科学与技术...
- 487 篇 软件工程
- 302 篇 信息与通信工程
- 204 篇 电气工程
- 175 篇 生物工程
- 126 篇 生物医学工程（可授...
- 105 篇 光学工程
- 89 篇 控制科学与工程
- 80 篇 电子科学与技术（可...
- 58 篇 仪器科学与技术
- 46 篇 机械工程
- 35 篇 化学工程与技术
- 27 篇 安全科学与工程
- 26 篇 交通运输工程
- 17 篇 动力工程及工程热...
423 篇 理学
- 189 篇 生物学
- 186 篇 数学
- 149 篇 物理学
- 76 篇 统计学（可授理学、...
- 54 篇 系统科学
- 40 篇 化学
165 篇 管理学
- 104 篇 图书情报与档案管...
- 69 篇 管理科学与工程(可...
- 37 篇 工商管理
145 篇 医学
- 129 篇 临床医学
- 87 篇 基础医学(可授医学...
- 65 篇 药学(可授医学、理...
- 23 篇 公共卫生与预防医...
28 篇 教育学
- 28 篇 教育学
27 篇 法学
- 21 篇 社会学
16 篇 经济学
16 篇 农学
5 篇 军事学
1 篇 文学
1 篇 历史学

主题

211 篇 machine learning
132 篇 deep learning
78 篇 signal processin...
72 篇 accuracy
71 篇 feature extracti...
68 篇 signal processin...
61 篇 support vector m...
57 篇 machine learning...
46 篇 predictive model...
33 篇 neural networks
32 篇 convolutional ne...
30 篇 random forests
29 篇 computational mo...
29 篇 training
26 篇 natural language...
26 篇 prediction algor...
21 篇 technological in...
20 篇 real-time system...
19 篇 adversarial mach...
18 篇 image processing

机构

16 篇 chitkara centre ...
14 篇 centre of resear...
12 篇 shenyang ligong ...
6 篇 ai & ds vishwaka...
6 篇 school of inform...
5 篇 department of me...
5 篇 khulna universit...
5 篇 department of co...
4 篇 delhi technologi...
4 篇 heilongjiang uni...
4 篇 department of s&...
4 篇 beijing univ pos...
4 篇 it vishwakarma i...
4 篇 department of co...
4 篇 sri lanka instit...
4 篇 advanced telecom...
4 篇 shenyang ligong ...
4 篇 graduate school ...
3 篇 chitkara univers...
3 篇 karunya institut...

作者

6 篇 mehta shiva
5 篇 rasmita panigrah...
4 篇 neelamadhab padh...
4 篇 wang xin
3 篇 m. vigenesh
3 篇 baydeti nagaraju
3 篇 sinambela marzuk...
3 篇 santosh kumar sh...
3 篇 ganapati panda
3 篇 debendra muduli
3 篇 abujar sheikh
3 篇 a. sivanantham
3 篇 li yajie
3 篇 li aihua
3 篇 zhou feng
3 篇 islam md. sanzid...
2 篇 aira kharvel par...
2 篇 nokshangthemba
2 篇 maheswara rao v ...
2 篇 lee tzong-ru

语言

1,292 篇 英文
32 篇 其他
11 篇 中文

检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1333 条记录，以下是81-90 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：

A Mean Model Based Incremental learning Technique for Extreme learning machine 2nd

A Mean Model Based Incremental Learning Technique for Extrem...

引用

2nd international conference on Recent Trends in Advanced Computing, Disruptive Innovation (ICRTAC-DTI)

作者： Vidhya, M. Aji, S. Univ Kerala Thiruvananthapuram Kerala India

Development of efficient methods for processing the online data is interesting as well as challenging in the computing scenario of data intensive applications, especially in streaming. Because of the inherent computational nature of Extreme learning machine, the non-iterative learning mechanism, it has undergone serous studies and exploitation especially in the domain of online data analysis. This paper gives a simple and effective strategy for updating the learned parameters, the knowledge of a machine learning system, in a computationally better perspective comparing with the existing state-of-the-art learning techniques. The performance of different measures for the central tendency is analyzed in this work. It is found that the results are comparable in respect of normal models. The overall performance of the proposed method with LIBSVM data sets gives an interesting direction for the improvement of learning machines for online data processing. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Extreme learning machine Online Data Analysis Real-time Applications Central Tendency ELM learning Knowledge Updating

来源：评论

学校读者我要写书评

暂无评论

Classification of ECG signal Using machine learning Techniques 2

Classification of ECG Signal Using Machine Learning Techniqu...

引用

2nd international conference on Power and Embedded Drive Control, ICPEDC 2019

作者： Syama, S. Sweta, G. Sai Kavyasree, P.I.K. Reddy, K.Jagan Mohan Bengaluru Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amrita School of Engineering India

ISBN: (纸本)9781728124148

Electrocardiogram (ECG) signals are the impulses generated by the heart which are used to analyze the proper functioning of heart. Our work deals with the efficient analysis of Electrocardiogram (ECG) signals imported from MIT-BIH database into MATLAB platform, generation of the imported ECG signal, pre-processing the generated signal to remove the noises mainly the baseline wandering and power line interference from which features are extracted. For adequate study of the ECG signal Daubechies and Haar wavelet techniques are compared. Proper decomposition of the signal is achieved using Db4 and Db5 Daubechies wavelets as their scaling functions are analogous to ECG signal. PAN TOMPKINSONS algorithm is considered in our study as it serves best for precise identification of most prominent features namely QRS complexes, RR interval's as they constitute the major data required for clinical analysis and research. After feature extraction ECG signals are trained using machine learning techniques for detecting the presence of Arrhythmia using different classifiers adopting Weka software. © 2019 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Semantic Segmentation using K-means Clustering and Deep learning in Satellite Image 2

Semantic Segmentation using K-means Clustering and Deep Lear...

引用

2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Barthakur, Manami Sarma, Kandarpa Kumar Gauhati University Department of Electronics and Communication Engineering GuwahatiAssam781014 India

ISBN: (纸本)9781728107448

In this paper, a deep learning based method, aided by certain clustering algorithm for use in semantic segmentation of satellite images in complex background is proposed. The work considers the formation and training of SegNet in which the output of K-means clustering algorithm is used as input and the label of the particular region of interest (ROI) in the image are used as target. The method does not require any feature extraction, region growing or splitting methods to configure and train the SegNet, which is a deep convolutional Encoder-Decoder architecture trained with (error) Back Propagation learning. The method is tested with different satellite images. The method is also compared with the results obtained when trained with SegNet without selecting the ROI using K-means algorithm and evaluated using metrics such as accuracy, mean IoU and weighted IoU. The normalized confusion matrix is also plotted. The experimental results show that the method is reliable and suitable for real world situations. © 2019 IEEE.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

Automatic Recognition of Radar signal Types Based on Convolutional Neural Network 2

Automatic Recognition of Radar Signal Types Based on Convolu...

引用

2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Ruan, Guoqing Wu, Wei CETC Res Inst 28 Sci & Technol Informat Syst Engn Lab Nanjing 210007 Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In the field of cognitive electronic warfare, automatic feature learning and recognition of radar signal is an important technology to ensure intelligence reconnaissance. This paper analyses the basic structure of convolutional neural network (CNN) and proposes an automatic recognition algorithm for radar signal. Firstly, the radar signal is transformed into time-frequency image, and the principal component information of the image is extracted by image processing method. Then, the designed network CNN-LeNet-5 is used to realize self-learning and recognition of features. The simulation results show that the algorithm can effectively identify eight kinds of radar signals in low signal-to-noise ratio.

关键词： Cognitive electronic warfare Time-frequency analysis Convolutional neural network Automatic recognition

来源：评论

学校读者我要写书评

暂无评论

Automatic Feature Extraction for Vietnamese Sign Language Recognition using Support Vector machine 2

Automatic Feature Extraction for Vietnamese Sign Language Re...

引用

2nd international conference on Recent Advances in signal processing, Telecommunications and Computing (SigTelCom)

作者： Pham The Hai Huynh Chau Thinh Bui Van Phuc Ha Hoang Kha Ho Chi Minh City Univ Technol VNU HCM Fac Elect & Elect Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781538629765

This paper aims at finding an automatic approach for extracting features of the Vietnamese sign language to classify both static Vietnamese alphabet letters and their combing diacritic marks as dynamic hand gestures. A Vietnamese sign language recognition system (VSLRS) collects all images including depth images, RGB images, and skeletal join maps to extract the desired features of each hand gesture and their own movements. These characteristics are normalized and converted to build a full Vietnamese sign language combing diacritic marks. The primary features of this system are automatically extracting the hand gestures of the observed person before the Kinect device version 1, and both dynamic and static diacritic marks are able to be recognized because of the movement detection method. Multiclass support vector machines (SVMs) and the One-Against-All approach are employed to find two suitable SVMs for static and dynamic hand gesture recognition. During the recognition phase, all hand gestures are extracted, normalized, and then filtered out based on the Euclidean distance difference of hand positions in captured frames to go through the exact SVMs. The recognized letter or diacritic is the positive label of all the SVM classes. The experimental results demonstrate the proposed VSLRS recognized the Vietnamese sign language (VSL) in realtime with the high accuracy.

关键词： Support Vector machine one-against-all hand gesture extraction machine learning

来源：评论

学校读者我要写书评

暂无评论

Frequency-Hopping signal Sorting Based on Deep learning 2

Frequency-Hopping Signal Sorting Based on Deep Learning

引用

IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Qu, Yi Pei, Yuhao Song, Depeng Engn Univ PAP Coll Informat Engn Xian Peoples R China

ISBN: (纸本)9781538692981

In order to solve the problem that the sorting threshold of traditional frequency-hopping signal needs to be manually adjusted, Faster-RCNN and clustering algorithm is proposed. In this paper, the Faster-RCNN is firstly used to identify and locate all frequency-hopping points in the time-frequency spectrum diagram, and then AlexNet is used to obtain the number of frequency-hopping signal. Experimental results show that the Faster-RCNN can be effectively used for automatic signal sorting when the number of frequencyhopping signal is small.

关键词： Faster-RCNN AlexNet clustering algorithm Time-frequency diagram Frequency hopping signal

来源：评论

学校读者我要写书评

暂无评论

Research on Semantic Segmentation Algorithm of Multispectral Remote Sensing Image Based on Deep learning 2

Research on Semantic Segmentation Algorithm of Multispectral...

引用

2nd international conference on signal Image processing and Communication, ICSIPC 2022

作者： Li, Zhang Yibei, Chen Yiyong, Lin Bingxi, Dong Xinglong, Ran Xichang Satellite Launch Center Sichuan Xichang615000 China

ISBN: (纸本)9781510657694

The semantic segmentation technology of remote sensing image refers to labeling the semantic information of pixel-level of the image to complete the classification, namely, terrain classification. It is widely used in intelligent maps, smart cities and other aspects. With the increase of satellite image resolution and the development video communication moonlet, its application and scenes are greatly broadened. The traditional remote sensing image semantic segmentation method mainly uses statistical machine learning methods, which cannot take into account the spectral features and the context semantic relationship of pixels, and has a bottleneck in improving the accuracy of classification. In deep learning method, Using the convolutional neural networks to extract features can achieve classification results of remote sensing images with higher classification accuracy. Aiming at the problems of existing deep learning methods in multi-spectral image semantic segmentation, to make full use of the information of multi-spectral images, this paper proposes a semantic segmentation algorithm for multispectral remote sensing images based on deep learning and verified the method on open data sets. © 2022 SPIE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Research on Heartbeat Temporal signal Recognition Based on machine learning and Deep learning 2

Research on Heartbeat Temporal Signal Recognition Based on M...

引用

2nd IEEE international conference on Image processing and Computer Applications, ICIPCA 2024

作者： Chen, Meng Xidian University Shaanxi Xi'an710000 China

ISBN: (纸本)9798350360240

The rapid development of AI technologies, such as machine learning and deep learning, has provided new directions for medical research. With extensive data mining and learning, AI has demonstrated strong potential in tasks like heartbeat signal recognition and image diagnosis. This study utilizes real heartbeat signal samples from hundreds of thousands of instances to research heartbeat signal prediction using various machine learning and deep learning algorithms, including LightGBM, XGBoost, and CNNs. The results indicate that two CNN models, CNN-1 and CNN-2, achieved accuracies of 99.10% and 99.21% on the test set, significantly outperforming XGBoost's 98.79% and LightGBM's 98.97%. Through further data optimization and model fusion, under a weighted average fusion strategy, the evaluation metric for the CNN models was improved to a maximum of 99.53%. The experimental results of this paper provide data support and a feasible solution path for the auxiliary diagnosis of medical big data. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Generation of Netlist from a Hand drawn Circuit through Image processing and machine learning 2

Generation of Netlist from a Hand drawn Circuit through Imag...

引用

2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Mohan, Akshatha Mohan, Athulya Indushree, B. Malavikaa, M. Narendra, C.P. Electronics and Communication Bangalore Institute of Technology Bangalore India

ISBN: (纸本)9781665442909

Circuit diagrams are used to depict electronic or electrical circuits graphically. It is simple for everyone to put their thoughts on paper. However, in order to conduct simulations in the different available tools, the circuit model needs be in digital form. This project presents several image processing and machine learning approaches for the conversion of hand-drawn circuits to netlists. Rather than training the dataset for all components, a technique based on the length ratios of a few of lines was employed to identify elements such as a voltage source, ground, and capacitor. Various image processing techniques are used to eliminate noise and prepare pictures for further processing. HOG feature extraction is utilized throughout the training and segmentation stages to detect resistor, diode, and inductor components. The final stage is to construct a netlist from the detected elements, wires, and their locations, as well as the identified nodes. © 2022 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Credit Card Fraud Detection Using machine learning Techniques 2

Credit Card Fraud Detection Using Machine Learning Technique...

引用

2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Vejalla, Indrani Battula, Sai Preethi Kalluri, Kartheek Kalluri, Hemantha Kumar VFSTR University India RVR&JC College of Engineering India SRM University AP India

ISBN: (纸本)9798350310719

There are many types of fraud in our daily life. One of the frauds occurring these days is credit card fraud. When people around the globe make credit card transactions, there will also be fraudulent transactions. To avoid credit card fraud, we must know the patterns and how the fraud values differ. This paper proposed credit card fraud detection using machine learning based on the labeled data and differentiating the fraudulent and legitimate transactions. The experiment was conducted using supervised machine-learning techniques. © 2023 IEEE.

关键词： Crime

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134页 << < 5 6 7 8 9 10 11 12 13 14 > >>

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：