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

分类表

所选分类

>> <<

限定检索结果

标题

标题
作者
主题词
出版物名称
出版社
机构
学科分类号
摘要
ISBN
ISSN
基金资助
索书号

作者

作者
标题
主题词
出版物名称
出版社
机构
学科分类号
摘要
ISBN
ISSN
基金资助
索书号

文献类型

92 篇 会议
1 册 图书
1 篇 期刊文献

馆藏范围

94 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

74 篇 工学
- 68 篇 计算机科学与技术...
- 57 篇 软件工程
- 31 篇 信息与通信工程
- 21 篇 生物医学工程（可授...
- 21 篇 生物工程
- 14 篇 电气工程
- 10 篇 电子科学与技术（可...
- 8 篇 机械工程
- 8 篇 控制科学与工程
- 6 篇 仪器科学与技术
- 5 篇 光学工程
- 4 篇 化学工程与技术
- 4 篇 交通运输工程
- 3 篇 安全科学与工程
52 篇 理学
- 29 篇 数学
- 23 篇 生物学
- 12 篇 统计学（可授理学、...
- 11 篇 物理学
- 10 篇 系统科学
- 6 篇 化学
20 篇 医学
- 20 篇 临床医学
- 19 篇 基础医学(可授医学...
- 19 篇 药学(可授医学、理...
- 3 篇 公共卫生与预防医...
20 篇 管理学
- 13 篇 图书情报与档案管...
- 8 篇 管理科学与工程(可...
- 6 篇 工商管理
4 篇 法学
- 2 篇 法学
- 2 篇 社会学
3 篇 经济学
- 3 篇 应用经济学
2 篇 教育学
1 篇 农学

主题

13 篇 machine learning
7 篇 support vector m...
7 篇 neural networks
6 篇 signal processin...
5 篇 signal processin...
4 篇 learning algorit...
4 篇 predictive model...
3 篇 covid-19
3 篇 analytical model...
3 篇 convolutional ne...
3 篇 heuristic algori...
2 篇 visualization
2 篇 decision trees
2 篇 feature extracti...
2 篇 hydrogen
2 篇 prediction algor...
2 篇 emotion recognit...
2 篇 fault detection
2 篇 machine learning...
1 篇 internet of thin...

机构

2 篇 private hualian ...
2 篇 school of inform...
2 篇 electronic engin...
2 篇 sensors and biom...
2 篇 advanced telecom...
2 篇 shenyang ligong ...
2 篇 school of comput...
2 篇 beijing junyu in...
2 篇 graduate school ...
2 篇 moscow aviation ...
1 篇 c. v. raman glob...
1 篇 air defense - ai...
1 篇 thapar institute...
1 篇 department of co...
1 篇 college of engin...
1 篇 school of comput...
1 篇 college of mecha...
1 篇 nit rourkela dep...
1 篇 department of ma...
1 篇 department of el...

作者

2 篇 manan
2 篇 watanabe hideyuk...
2 篇 rushambwa munyar...
2 篇 ohsaki miho
2 篇 katagiri shigeru
2 篇 govindaraj p.
1 篇 shi hongbo
1 篇 gong shan
1 篇 liu qingli
1 篇 jagarlapudi akhi...
1 篇 rodríguez-pérez ...
1 篇 huang shutao
1 篇 ivan ivanov
1 篇 joan romera s.
1 篇 senda masahiro
1 篇 huang ying
1 篇 wei wei heng
1 篇 venkatadri abhin...
1 篇 ghosh soumya san...
1 篇 nan zhihong

语言

93 篇 英文
1 篇 其他

检索条件"任意字段=2nd International Conference on Signal Processing and Machine Learning, CONF-SPML 2022"

共 94 条记录，以下是41-50 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：

相关度排序

相关度排序
时效性降序
时效性升序

Proceedings of the 2nd international conference on signal and Data processing 1

引用

丛书名： Lecture Notes in Electrical Engineering

1000年

作者： K. P. Ray Arati Dixit Debashis Adhikari Ribu Mathew

ISBN: (数字)9789819914104

ISBN: (纸本)9789819914098;9789819914128

This volume comprises the select proceedings of the 2nd international conference on signal & Data processing (ICSDP) 2022. The contents focus on the latest research and developments in the field of artificial intelligence & machine learning, Internet of things (IoT), cybernetics, advanced communication systems, VLSI embedded systems, power electronics and automation, MEMS/ nanotechnology, renewable energy, bioinformatics, data acquisition and mining, antenna & RF systems, power systems, biomedical engineering, aerospace & navigation. This volume will prove to be a valuable resource for those in academia and industry.

关键词： signal, Image and Speech processing Wireless and Mobile Communication Circuits and Systems Image processing and Computer Vision Energy Systems

来源：评论

学校读者我要写书评

暂无评论

machine learning Approach for Estimation of High Acquity ECG Monitoring System 2

Machine Learning Approach for Estimation of High Acquity ECG...

引用

2nd international conference on signal and Information processing, IConSIP 2022

作者： Nikhare, Shiwani Chaskar, Uttam Misar, Surabhi College of Engineering Department of Instrumentation & Control Engineering Pune India

ISBN: (纸本)9781728168852

Electrocardiogram (ECGs) are simple, painless tests that are used to measure the electrical activity of the heart. Various cardiovascular conditions are early diagnosed using this test. It is also useful for detecting heart-related disorders such as arrhythmia, and heart valve complications. In this paper, a threelead monitoring system is designed using Arduino UNO and an ECG sensor AD8232. The ECG signals of a patient are collected by the AD8232 ECG sensor. Then acquired signals are further processed by an Arduino Uno, which digitizes the collected data and displays the continuous ECG on an OLED screen to make the system portable. These OLEDs are used to obtain, process, and display the amplitudes and intervals of some critical components. We approach a method to estimate P, QRS Complex, and T amplitudes as well as time durations (PR, RR, QRS, and QT). It is used to diagnose the heart condition, whether it functions normal or abnormal. In order to classify the data samples, supervised machine learning algorithms namely, Support Vector machines(SVM) and Logistic Regression are applied, resulting in an accuracy of 92.5 % and 87.5 %, respectively. © 2022 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Simulation Study of Financial Securities Market Volatility Model Based on BP Neural Network 2

Simulation Study of Financial Securities Market Volatility M...

引用

2nd international signal processing, Communications and Engineering Management conference, ISPCEM 2022

作者： Xiaofang, Niu Beijing Junyu International Import and Export Trading Co. Limited Beijing102600 China

ISBN: (纸本)9781665492713

With the continuous development and improvement of financial markets, the study of the nature and laws of the stock market has put forward higher requirements, of which predicting stock market volatility has become an important financial topic. The volatility of the stock market is its essential feature and attribute, but if the price volatility shows a sharp amplitude and too fast frequency, it may lead to the price distortion of the market, and then lead to the violent stock market shocks. Neural network is a powerful and widely used machine learning algorithm that is very good at discovering nonlinear relationships between data and is suitable for stock price prediction problems. The paper takes into account the factors influencing stock prices, extracts trading indicators and financial indicators, reduces the dimensionality through principal component analysis, and uses the reduced-dimensional principal components as the input variables of the network. Based on the actual stock trading market, this paper establishes a multilayer neural network classifier and uses four learning algorithms in BP neural network to fit the simulated data, and the results show that the algorithm has high accuracy for volatility pattern recognition. Under the modern fierce market competition, enterprises must strengthen the control of corporate financial risks and financial crisis prevention if they want to survive, develop and make profits. © 2022 IEEE.

关键词： Financial markets

来源：评论

学校读者我要写书评

暂无评论

machine learning Based System for Sleep Apnea Classification using EEG signal 2

Machine Learning Based System for Sleep Apnea Classification...

引用

2nd international conference on Computer Science, Engineering and Applications, ICCSEA 2022

作者： Khursheed, Mohd Danish Parveen, Sara Farooq, Omar Aligarh Muslim University Dept. of Electronics Engineering Aligarh India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Sleep Apnea is a condition in which people and animals cease breathing for a short period of time while sleeping. EEG signals are important in detecting sleep apnea because they detect and record brain activity. This study proposes a method in which the filtered EEG signal is moved for sub-band decomposition after pre-processing and disbanded into five frequency bands: Sigma, Alpha, Beta, Delta, and Theta. The features determined for each frequency band acquired from the filtered EEG signals are energy, kurtosis, MAD, and skewness, which are then imported for the classification process using Decision Tree and Random Forest ML classifiers. Physionet's publicly available dataset is used to assess each classifier's accuracy, sensitivity, precision, and specificity. Using the bagging technique, Random Forest method achieves the highest classification accuracy of 99.68 percent, sensitivity of 100 percent, and specificity of 99.37 percent. © 2022 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Research on Traffic Sign Recognition Based on Several machine learning Methods 2

Research on Traffic Sign Recognition Based on Several Machin...

引用

2nd international conference on Advanced Algorithms and signal Image processing, AASIP 2022

作者： Qi, Hao Qin, Zhuohang Yang, Yue Liu, Siyuan Ren, Huilin Chongqing University of Arts and Scientists No.319 Honghe Ave Chongqing402160 China

ISBN: (纸本)9781510660571

With the in-depth development of intelligent transportation, traffic sign recognition has attracted widespread attention as an essential part of intelligent transportation. This paper studies several machine learning methods for traffic sign recognition. Through comparative analysis, it is found that Convolutional Neural Network (CNN) is superior to Support Vector machine (SVM) and K Nearest Neighbor (KNN) methods in recognizing traffic signs. And adding Gaussian noise to the image data for enhancement can further improve the accuracy of applying a Convolutional Neural Network to identify traffic signs. The accuracy of applying a Convolutional Neural Network to identify traffic signs is 99.2%. After adding Gaussian noise with a mean of 0 and a standard deviation of 1 to the image set, the accuracy of applying a Convolutional Neural Network to identify traffic signs was increased to 99.6%. We also compared the CNN-based traffic signs recognition experiment in this paper with the experiments of two other scholars. Our experiment has higher accuracy in a particular data range and environment. © 2022 SPIE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

The Recent Advancements of Radio Frequency machine learning (RFML) Approaches in Enhancing Wireless Security Using Multi Regression Analysis Approach (MRAA) 2

The Recent Advancements of Radio Frequency Machine Learning ...

引用

2nd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Khan, Zarrarahmed Z Dhinakaran, M. Deepthi, P. Johar, Siti Sarawati Ventayen, Randy Joy Magno Kalpana, R. Anjuman i Islam Mumbai University Maharashtra Mumbai India Government College of Engineering Department of Electronics and Communication Engineering Tamilnadu Salem India VelTech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Tamilnadu Avadi Chennai India Centre for General Studies and Co-curricular Universiti Tun Hussein Onn Malaysia Department of Social Science Parit Raja Johor Darul Takzim Batu Pahat86400 Malaysia Pangasinan State University Philippines ECE Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engg College Anna University Chennai India

ISBN: (纸本)9781665437899

Whereas the deep learning (DL) methods were already widely used in state-of-the-art Computer Vision (CV) as well as Natural Language processing (NLP) implementations, it has only been in recent times that these innovations have begun to grow up a bit adequately in applications involving wireless communications, a field known as Radio Frequency machine learning (RFML). Particularly noteworthy is new studies that has demonstrated that deep learning (DL) is an enabler innovation for Cognitive Radio (CR) applications, as well as a valuable tool for enhancing the performance of skillfully specified techniques for frequency recognition applications including such signal identification, prediction, or categorization. Since there is an availability of comparable raw Radio Frequency (RF) information available to enable learning and assessment, almost no previous information about the target spectrum environment is necessary. This is a primary motivation for the use of RFML. Apart from the basic necessity for adequate information, there are many other important aspects to taken into perspective before using RFML systems in genuine wireless communication applications. These concerns include trustworthiness, security, and technical specifications. The sharing and disseminated nature of wireless medium makes wireless systems susceptible to a variety of threats such as interference and surveillance. machine learning (ML) offers an autonomous technique of learning from and adapting to wireless communication features that are difficult to obtain using custom characteristics & algorithms, which may be used to assist both attacking and defensive tactics. Based on this research ways which support the application of machine learning in enhancing the security. © 2022 IEEE.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Prediction of COVID-19 & Pneumonia using machine learning & Deep learning Model 2

Prediction of COVID-19 & Pneumonia using Machine Learning & ...

引用

2nd international conference on signal and Information processing, IConSIP 2022

作者： Mhatre, Prajakta Bhole, Kalyani Dept. of Instrumentation and Control College of Engineering Pune Pune411005 India

ISBN: (纸本)9781728168852

As a result of the COVID-19 pandemic, medical examinations (RTPCR, X-ray, CT-Scan, etc.) may be required to make a medical decision. COVID-19's SARS-CoV-2 virus infects and spreads in the lungs, which can be easily recognized by chest X-rays or CT scans. However, along with COVID-19 instances, cases of another respiratory ailment known as Pneumonia began to rise. As a result, clinicians are having difficulty distinguishing between COVID-19 and Pneumonia. So, more tests were required to identify the condition. After a few days, the COVID-19 SARS-CoV-2 virus multiplied in the lungs, causing pneumonia and COVID-19 named Novel Corona virus infected Pneumonia. We employ machine learning and Deep learning models to predict diseases such as COVID-19 Positive, COVID-19 Negative, and Viral Pneumonia in this research. A dataset of data is used in a machine learning model. A dataset of 120 images was used in the machine learning model. By extracting eight statistical elements from an image texture, we calculated accuracy. Adaboost, Decision Tree & Naive Bayes have overall accuracy of 88.46%, 86.4% and 80%, respectively. When we compared the algorithms, Adaboost algorithm performs the best, with overall accuracy of 88.46%, sensitivity of 84.62%, specificity of 92.31%, F1-score of 88% and Kappa of 0.8277. VGG16 Architecture is used in CNN model for 838 images in Deep learning model. The model's total accuracy is 99.17 %. © 2022 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Reinforcement learning Based on Cross Task Information Sharing 2

Multi-Agent Reinforcement Learning Based on Cross Task Infor...

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Gao, Yu Zhu, Lizhong Liu, Yunting Yang, Jiaming Harbin First Machinery Group Corporation Harbin China Shenyang Ligong University Shenyang China

ISBN: (纸本)9798350368888

This article selects StarCraft as the experimental environment, which has a complex and vast action and state space, making it difficult for intelligent agents to make optimal decisions. In this article, the sharing of parameter experience values among multiple agents in a single task is used to enhance the collaboration ability between agents and avoid repetitive learning of basic strategies. In addition, after distilling and refining the core strategies of individual tasks, policy interaction is carried out in multiple tasks of the same type. By establishing a joint objective function for multiple tasks and alternating optimization for mutual constraints and updating iterations, information exchange between cross task multi-agent systems is completed, solving the problem of difficulty in obtaining effective data in complex scenarios and improving the learning efficiency of micro unit joint decision-making. This paper also adds qualification tracking to solve the problem of reward delay at the initial stage of model training, and uses course transfer learning to complete incremental joint training. © 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

Detection of Inter-Turn Short Circuit in Permanent Magnet Synchronous Motor using machine learning 2

Detection of Inter-Turn Short Circuit in Permanent Magnet Sy...

引用

2nd international conference on signal and Information processing, IConSIP 2022

作者： Patil, Prajakta Mane, Sunil B. Dhamija, Lohit Manufacturing Engineering and Industrial Management College of Engineering Pune Pune India Computer Engineering and It College of Engineering Pune Pune India Research and Development Varroc Engineering Ltd Pune India

ISBN: (纸本)9781728168852

Electric vehicles (EVs) utilize permanent magnet synchronous motors (PMSM) as traction motors due to their advantages like long lifetime, lower maintenance cost, high speed, and noiseless operation. PMSMs are subjected to operating stresses and high temperatures which lead to faults in them. Insulation breakdown results in faulty stator windings and are prevalent. They begin as short circuit in internal turns (ITSC) and when propagated further can lead to other types of faults. Hence, timely identification of ITSC is essential. machine learning algorithms can detect relationships among features which can be used for fault detection. In this paper, machine learning algorithms are used for identifying early-stage inter-turn short circuit faults under various operating conditions using simulation dataset. Three-phase current signals are used for deriving features like root mean square and negative sequence current, hence no additional sensors are required to detect the fault. Support Vector machine, Random forest and KNN are trained and the accuracy obtained is 90%, 95.83% and 100% respectively. The model is then validated using simulation data to detect fault under unknown operating conditions and the accuracy obtained is 97.5%. © 2022 IEEE.

关键词： Fault detection

来源：评论

学校读者我要写书评

暂无评论

Detection of Pneumonia from X-ray Images using Eigen Decomposition and machine learning techniques 2

Detection of Pneumonia from X-ray Images using Eigen Decompo...

引用

2nd international conference on signal and Information processing, IConSIP 2022

作者： Kabi, Subrat Patra, Dipti Panda, Ganapati NIT Rourkela Department of Electrical Engineering Odisha India C. V. Raman Global University Department of Electronics and Tele Communication Odisha Bhubaneswar India

ISBN: (纸本)9781728168852

The mortality rate due to pneumonia is very high across the world and particularly in India. This respiratory and lung-based disease can be controlled by early detection and treatment. Many reported articles are available on this topic. In the current investigation an attempt has been made to develop and test k-nearest neighbor (k-NN), random forest (RF), and light gradient boosting machine (LGBM) based pneumonia detection models using novel eigen domain extracted features from the X-ray images of patients. By conducting simulation-based experiments on the developed models using publicly available standard data set, the various performance matrices have been obtained and compared. The analysis of results demonstrates that the proposed LGBM with eigen domain features provide the best classification performance compared to other reported models. © 2022 IEEE.

关键词： Nearest neighbor search

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10页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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

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：