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

724 篇 会议
3 篇 期刊文献
2 册 图书

馆藏范围

729 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

474 篇 工学
- 379 篇 计算机科学与技术...
- 273 篇 软件工程
- 175 篇 信息与通信工程
- 106 篇 电气工程
- 81 篇 生物工程
- 76 篇 生物医学工程（可授...
- 47 篇 控制科学与工程
- 33 篇 电子科学与技术（可...
- 29 篇 机械工程
- 27 篇 光学工程
- 26 篇 化学工程与技术
- 18 篇 仪器科学与技术
- 14 篇 安全科学与工程
- 13 篇 交通运输工程
- 13 篇 农业工程
214 篇 理学
- 114 篇 数学
- 89 篇 生物学
- 67 篇 物理学
- 41 篇 系统科学
- 38 篇 统计学（可授理学、...
- 24 篇 化学
95 篇 医学
- 91 篇 临床医学
- 56 篇 基础医学(可授医学...
- 46 篇 药学(可授医学、理...
- 13 篇 公共卫生与预防医...
95 篇 管理学
- 57 篇 图书情报与档案管...
- 41 篇 管理科学与工程(可...
- 16 篇 工商管理
17 篇 农学
- 17 篇 作物学
11 篇 法学
- 11 篇 社会学
7 篇 教育学
6 篇 经济学
5 篇 军事学

主题

168 篇 machine learning
50 篇 deep learning
46 篇 support vector m...
46 篇 feature extracti...
41 篇 signal processin...
24 篇 signal processin...
23 篇 machine learning...
18 篇 image processing
18 篇 training
16 篇 artificial intel...
16 篇 accuracy
15 篇 electroencephalo...
14 篇 decision trees
14 篇 computer vision
13 篇 natural language...
13 篇 neural networks
13 篇 classification a...
13 篇 convolutional ne...
12 篇 data mining
12 篇 classification

机构

6 篇 xinjiang key lab...
6 篇 school of inform...
6 篇 fujian provincia...
6 篇 xinjiang key lab...
5 篇 beijing universi...
3 篇 fujian provincia...
2 篇 college of engin...
2 篇 university of jo...
2 篇 school of electr...
2 篇 china electric p...
2 篇 department of ar...
2 篇 department of it...
2 篇 valencian resear...
2 篇 ugam solutions c...
2 篇 department of co...
2 篇 chongqing univ s...
2 篇 guangzhou xinhua...
2 篇 college of elect...
2 篇 school of comput...
2 篇 guangzhou power ...

作者

10 篇 hamdulla askar
6 篇 ablimit mijit
6 篇 li zuoyong
3 篇 vysotska victori...
3 篇 teng shenghua
2 篇 veeresh
2 篇 xue jingfeng
2 篇 mohanty mihir na...
2 篇 agarwal harsh
2 篇 jin lingmin
2 篇 kalinina irina
2 篇 jia cairang
2 篇 renjith p.n.
2 篇 gozhyj aleksandr
2 篇 wen zhang
2 篇 li zheng
2 篇 selvi g. thamara...
2 篇 pandey trilok na...
2 篇 islam md. adnanu...
2 篇 kama mohd nazri

语言

700 篇 英文
28 篇 其他
4 篇 中文

检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, SPML 2020"

共 729 条记录，以下是161-170 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：

Detecting and Classifying PQD's occur in Microgrid by Scaling Basis Chirplet Transform and SVM classifier based Technique 3

Detecting and Classifying PQD's occur in Microgrid by Scalin...

引用

3rd IEEE international Power and Renewable Energy conference (IPRECON)

作者： Joga, S. Ramana Kumar Saiprakash, Chidurala Sinha, Pampa Ray, Lipsa Jena, Chitralekha Krishna, Teki Vamsee KIIT Univ Sch Elect Engn Bhubaneswar India Sasi Inst Technol & Engn Dept EEE Kadakatla India

ISBN: (纸本)9781665491754

In recent days, with the increase of industrial loads and residential loads need of local sources is increased. Renewable energy sources became alternative energy local sources and its integration to grid give rise to Microgrid concept. constant increase of loads give rise to Power Quality issues such as Power Quality Disturbances (PQD's). These PQD's causes interruption in power supply and leads to damage the grid. This made researchers to do research on PQD detectors or power quality analysers. Researchers still facing hurdles to detect these Power Quality issues due to type of grid, addition of loads and mode of operation. In this Paper PQD's are detected and classified through a novel combination technique based on scaling basis chirplet transform signal processing technique and support vector machine classifier. All the simulation and code work carried out in MATLAB software.

关键词： Power Quality Power Quality Monitoring machine learning signal processing Wavelet and Power Quality monitoring

来源：评论

学校读者我要写书评

暂无评论

Research on Text Recognition of Natural Scenes for Complex Situations 3

Research on Text Recognition of Natural Scenes for Complex S...

引用

3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Yu, Wenhua Ibrayim, Mayire Hamdulla, Askar Xinjiang University School of Information Science and Engineering Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

As a great invention that affects human development and progress, writing plays an essential role in human life and learning and the inheritance and development of culture. For thousands of years, the inheritance of Chinese culture has been inseparable from the writing records. Papermaking and printing have played a crucial role in the development of the times. Text extraction and recognition technology in natural scenes, that is, text recognition from photos or videos, has become a popular research direction in computer vision in recent years. Scene text recognition is a fundamental task in computer vision. It is often a critical step in many text-related tasks, such as document retrieval, card recognition, license plate recognition, and other tasks. The vital difference between the text recognition process and text detection is that the text in the photo or video needs to be recognized. Detection and recognition are mainly divided into two steps: locating text areas in photos or videos, that is, finding words or text lines of the bounding box;then the positioned text is recognized. Combining these two steps results in End-to-end Recognition of text. The continuous development of scene text detection and recognition algorithm system will lay the foundation for the research of recognition problems such as multilingual recognition of scene text and formula recognition. © 2022 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

machine learning Model Based on Deep Neural Networks for Emotion Detection Using Audio-Visual Modalities 3rd

Machine Learning Model Based on Deep Neural Networks for Emo...

引用

3rd international conference on signal and Data processing, ICSDP 2023

作者： Jeevakumari, S. A. Amutha Mohanty, Gyanadipta Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819795772

Emotion identification is a challenging endeavor since emotions can manifest in so many different ways. Recent advances have been made in the field, particularly with the development of deep neural networks, which have shown to be astonishingly good at recognizing emotional states. This work set out to investigate the difficulties of model-level fusion in order to develop a sophisticated multimodal model capable of simultaneously assessing both video and audio inputs for Emotion Recognition. To rigorously evaluate the performance of this novel approach, the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset was chosen as the ideal testing ground. This diverse dataset encompasses a wide range of emotional expressions, making it an excellent benchmark for assessing the proposed model’s accuracy and robustness. By combining cutting-edge deep learning techniques with a multimodal approach, this study aims to contribute to the evolving landscape of Emotion Recognition, with the potential to enhance applications in fields such as affective computing, human–computer interaction, and emotional well-being assessment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Rumor Detection Method Incorporating Correlation Features 3

A Rumor Detection Method Incorporating Correlation Features

引用

3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Cai, Xiangsheng Tohti, Turdi Hamdulla, Askar Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi830017 China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Most of the rumors on social media (e.g., Twitter) revolve around a certain domain or topic, which means that they want to express roughly the same meaning, only the way of expression or the words used are different. Most of the existing rumor detection work focuses on text semantic features, user information features, comment features, and communication structure features, but rarely utilizes the rumor features associated with the original text. Therefore, a GCN-BERT -based rumor detection method is proposed in this paper, which not only utilizes the rumor features of a message itself but also utilizes the rumor features of all the texts and words associated with it when performing rumor detection. Finally, three Twitter datasets are used for comparison experiments, and the results fully demonstrate that the method is more effective than existing rumor detection methods. © 2022 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

An Algorithm to Train Unrestricted Sequential Discrete Morphological Neural Networks 3rd

An Algorithm to Train Unrestricted Sequential Discrete Morph...

引用

3rd international Joint conference on Discrete Geometry and Mathematical Morphology (DGM)

作者： Marcondes, Diego Feldman, Mariana Barrera, Junior Univ Sao Paulo Inst Math & Stat Dept Comp Sci Sao Paulo Brazil Texas A&M Univ Dept Elect & Comp Engn College Stn TX USA

ISBN: (纸本)9783031577925;9783031577932

There have been attempts to insert mathematical morphology (MM) operators into convolutional neural networks (CNN), and the most successful endeavor to date has been the morphological neural networks (MNN). Although MNN have performed better than CNN in solving some problems, they inherit their black-box nature. Furthermore, in the case of binary images, they are approximations that loose the Boolean lattice structure of MM operators and, thus, it is not possible to represent a specific class of W-operators with desired properties. In a recent work, we proposed the Discrete Morphological Neural Networks (DMNN) for binary image transformation to represent specific classes of W-operators and estimate them via machine learning. We also proposed a stochastic lattice descent algorithm (SLDA) to learn the parameters of Canonical Discrete Morphological Neural Networks (CDMNN), whose architecture is composed only of operators that can be decomposed as the supremum, infimum, and complement of erosions and dilations. In this paper, we propose an algorithm to learn unrestricted sequential DMNN, whose architecture is given by the composition of general W-operators. We illustrate the algorithm in a practical example.

关键词： discrete morphological neural networks image processing mathematical morphology U-curve algorithms stochastic lattice descent algorithm

来源：评论

学校读者我要写书评

暂无评论

3rd international conference on 3D Imaging Technologies- Multidimensional signal processing and Deep learning, 3DIT-MSP and DL 2021

3rd International Conference on 3D Imaging Technologies- Mul...

引用

3rd international conference on 3D Imaging Technologies- Multidimensional signal processing and Deep learning, 3DIT-MSP and DL 2021

ISBN: (纸本)9789811924477

The proceedings contain 24 papers. The special focus in this conference is on 3D Imaging Technologies- Multidimensional signal processing and Deep learning. The topics include: Grid False Data Intrusion Detection Method Based on Edge Computing and Federated learning;comparative Analysis of Automatic Poetry Generation Systems Based on Different Recurrent Neural Networks;citrus Positioning Method Based on Camera and Lidar Data Fusion;network Intrusion Detection Based on Apriori-Kmeans Algorithm;research on the Adaptive Matching Mechanism of Graphic Design Elements Based on Visual Communication Technology;research on Graphic Design of Digital Media Art Based on Computer Aided Algorithm;water Environmental Quality Assessment and Effect Prediction Based on Artificial Neural Network;full-Focus Imaging Detection of Ship Ultrasonic-Phased Array Based on Directivity Function;research on Visual Communication of Graphic Design Based on machine Vision;dynamic Grey Wolf Optimization Algorithm Based on Quasi-Opposition learning;innovative Design of Traditional Arts and Crafts Based on 3D Digital Technology;Insulator Detection Study Based on Improved Faster-RCNN;image-Based Physics Rendering for 3D Surface Reconstruction: A Survey;Global Analysis of Discrete SIR and SIS Systems;a Fast Heuristic k-means Algorithm Based on Nearest Neighbor Information;Color Restoration of RGB-NIR Images in Low-Light Environment Using CycleGAN;preface;longitudinal Structure Analysis and Segmentation Algorithm of Dongba Document;development of Mobile Food Recognition System Based on Deep Convolutional Network;a Novel Space Division Rough Set Model for Feature Selection;Overview of SAR Image Change Detection Based on Segmentation;image Recognition Methods Based on Deep learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing Stroke Prediction in machine learning by Addressing Data Imbalance

Optimizing Stroke Prediction in Machine Learning by Addressi...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Jiacheng Wang Hanwen Gu Hong Gu School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China Department of Bioengineering University of Washington Seattle USA

Brain strokes, responsible for about 11% of global deaths annually according to the World Health Organization, pose significant health challenges. machine learning aids in early stroke risk prediction and personalized assessment, optimizing medical resource allocation. However, the low frequency of stroke cases in datasets poses challenges for accurate stroke prediction by these models. Addressing this data imbalance is key for effective prediction. This article focuses on: (i) analyzing machine learning outcomes on imbalanced stroke dataset; (ii) comparing methods to address data imbalances; and (iii) using Automated machine learning (AutoML) techniques to achieve high accuracy while realistically identifying stroke incidents. The results demonstrate that the application of Random Under Sampling (RUS) was instrumental in identifying the top-performing model, which showed superior performance in stroke prediction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Extracting User Behavioural Control Styles based on Process Mining 3

Extracting User Behavioural Control Styles based on Process ...

引用

3rd international conference on signal processing and Communication (ICPSC)

作者： Kumar, Manne Vinay Sriram, Pulime Satya Sai Selvi, A. Thamarai Amarnadh, Devarampati Venkata Sai Hindustan Inst Technol & Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9781665428644

The Internet Technology implies that bullying is not restricted to schools, colleges, universities or road places. Internet means Cyber bullying can take place anytime, anywhere, even at playground, home etc, via smart devices, emails, SMS, WhatsApp messages, and social media, 24 * 7 * 365. Due to Information and Communication Technologies, cyber bullies can happen to annoy, endanger, or embarrass anyone from any part of the world. Cyber bullying Instances has dangerous concerns such as mentally disorder;suicide is now regularly stated in most of the media houses. Social media like Twitter, YouTube, Facebook, and Instagram and Messaging system like WhatsApp, Kik etc have been listed as the top groups with the maximum proportion of consumers registering problems of cyber bullying. In our proposed framework, this work is to extract the cyber bullying from Twitter Tweets based on machine learning.

关键词： Corpus Cyber bullying Social Media Twitter machine learning Support Vector machine Precision Score

来源：评论

学校读者我要写书评

暂无评论

High-Dimensional signal processing using Classical-Quantum machine learning Pipelines with the TensorFlow Stack, Cirq-NISQ, and Vertica 3

High-Dimensional Signal Processing using Classical-Quantum M...

引用

3rd IEEE international conference on Quantum Computing and Engineering (QCE)

作者： Melvin, Theresa Northcentral Univ Sch Technol Adv Data Sci & Maching Learning Lab Vertica Scottsdale AZ USA

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

Modeling High Dimensional signal Data (HDSD) is challenging due to statistical complexities and an imbalance of noise to signal data, which creates ground truth problems. Likewise, enabling recognition and detection analytics on the information-bearing portion of the noise-obscured HDSD is computationally cost-prohibitive due to the unprecedented size, speed, and scale of HDSD. Consequently, human experts often out-perform Deep learning (DL) algorithms at HDSD recognition. This is problematic due to the spectrum of applications reliant on accurate HDSD detection. These include multi-sensor and biologic signal detection, virus outbreak surveillance, genetic association identification, image, audio, and sonar processing, source separation for speech identification, astrophysical source detection, financial time series, and satellite payload processing. To solve this problem and facilitate favorable DL outcomes, synthetic data possessing enough positive samples is needed to balance the HDSD dataset. Research reveals an entangled Quantum Generative Adversarial Network (QGAN) utilizing parameterized quantum circuits for the QGAN Generator (QGEN) and Discriminator (QDIS) exhibit an exponential advantage over a classical-GAN when generating highly accurate and cost-effective synthetic data. Using this method, an unsupervised TensorFlow-Quantum QGAN is prepared from Cirq Noisy Intermediate-Scale Quantum (NISQ) circuits. The QGEN and QDIS create new statistically consistent synthetic HDSD from real data;thereby solving the ground truth problem. Next, TensorFlow-GPU facilitates classical-DL to train the new synthetic HDSD dataset. Once validated, the TensorFlow model is exported to the production Vertica analytics tier for fast and accurate ML inference, where the HDSD is instantly identified and classified at ingest.

关键词： TensorFlow Quantum Cirq Entangled Quantum Generative Adversarial Network Vertica machine learning High-Dimensional signal Data processing

来源：评论

学校读者我要写书评

暂无评论

Real-time Sign Language Recognition using Computer Vision 3

Real-time Sign Language Recognition using Computer Vision

引用

3rd international conference on signal processing and Communication (ICPSC)

作者： Raval, Jinalee Jayeshkumar Gajjar, Ruchi Nirma Univ Inst Technol Elect & Commun Dept Ahmadabad Gujarat India

ISBN: (纸本)9781665428644

Speech impairment is a disability that affects an individual's ability to verbal communication. To overcome this issue sign language is used which is one of the most organised languages. There is definitely a need for a method or an application that can recognize sign language gestures so that communication is possible even if someone does not understand sign language. My paper is an effort towards filling the gap between differently-abled people like deaf and dumb and the other people. Image processing combined with machine learning helped in forming a real-time system. Image processing is used for pre-processing the images and extracting different hand from the background. These images obtained after extracting background were used for forming data that contained 24 alphabets of the English language. The Convolutional Neural Network proposed here is tested on both a custom-made dataset and also with real-time hand gestures performed by people of different skin tones. The accuracy obtained by the proposed algorithm is 83%.

关键词： machine learning Image processing Convolutional Neural Network

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73页 << < 13 14 15 16 17 18 19 20 21 22 > >>

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：