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181-190 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：

CNN-Bi-LSTM Based Household Energy Consumption Prediction 3

CNN-Bi-LSTM Based Household Energy Consumption Prediction

引用

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

作者： Gaur, Kshitij Singh, Sandeep Kumar Natl Inst Technol Dept Elect & Commun Engn Hamirpur 177005 India

ISBN: (纸本)9781665428644

Driven by technological advances, there is a increase in electricity-based equipments and this leads to excessive energy consumption (EC) and demand for power every day. To enhance power management and collaboration between electricity used in a building and the smart grid, the EC must be predicted. Forecasting techniques used for prediction of the energy accurately are limited due to challenges like dynamic behaviour of residents and climatic condition. So, to conquer such challenges we proposed a deep learning based methodology. The proposed methodology uses hybrid model consisting of CNN and Bi-LSTM for predicting EC. The performance of the proposed methodology is tested using publically available real dataset. Test results shows that the proposed methodology are able to predict the consumption with very small error. The proposed methodology helps in management for producing optimum amount of power.

关键词： machine learning deep learning CNN LSTM Bi-LSTM

来源：评论

学校读者我要写书评

暂无评论

Pothole Detection using Accelerometer and Computer Vision with Automated Complaint Redressal 3

Pothole Detection using Accelerometer and Computer Vision wi...

引用

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

作者： Kandoi, Aditi Agarwal, Harsh Revadekar, Abhishek Chaudhari, Bhalchandra N. Sardar Patel Inst Technol Dept Comp Engn Mumbai 400058 Maharashtra India

ISBN: (纸本)9781665428644

Road transport is the most widely used means of transportation around the world. With this high use of road transport, the safety of travellers' becomes the prime concern for any governing authority. While some safety concerns arise from driver errors and environmental factors, most cases are a result of poor maintenance of these roads. Potholes, specifically, are one of the leading causes of road accidents throughout the world and need to be taken care of immediately, by the authorities. This paper presents a solution that makes use of civilians' mobile sensors, along with image-based alternatives to detect potholes in real-time, using machine learning. The concerned authorities are then notified about the same through a web-based portal, to take the necessary action. The solution also incorporates pivoting existing complaints, location tagging and prioritization. Additionally, the solution provides a forecast of the likelihood of issues regarding potholes, constantly updating time series data of the locations.

关键词： Road Safety Pothole Detection Sensors Computer Vision machine learning Complaint Redressal Predictive Maintenance

来源：评论

学校读者我要写书评

暂无评论

SMART SURVEILLANCE OF DRIVER USING machine learning 3

SMART SURVEILLANCE OF DRIVER USING MACHINE LEARNING

引用

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

作者： Rani, T. P. Sree, Sai Kaavya . M. Sharmila, . P. Sri Sairam Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9781665428644

Road accidents are often caused by drunk driving and drowsiness. This paper detects the drowsiness of the driver. Besides that, intake of alcohol by the driver is also detected. The main goal of this proposed system is to reduce the number of accidents due to driver's Drowsiness and alcohol intake. This increases the transportation safety. This system also makes use of a USB camera and Alcohol sensor (MQ-135)by which alcohol intake is detected and drowsiness of the driver is also monitored. OpenCV, a machine learning software examines vision-based applications. It is the one which detects the driver's *** idea comes with an application which helps to track the drowsiness and also alert the passenger and the owner if the driver is *** will perform tasks like notifying and the customer with alarm by a message notification. The ultimate aim of this system is to design a feasible system that decreases the fatal accidents caused by the drowsiness of the driver and can also display the percentage of alcohol consumed by the driver.

关键词： Fatigue Detection Alcohol intoxication Arduino UNO OpenCv Application

来源：评论

学校读者我要写书评

暂无评论

A Survey on Attack Detection Methods For IOT Using machine learning And Deep learning 3

A Survey on Attack Detection Methods For IOT Using Machine L...

引用

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

作者： Babu, Meenigi Ramesh Veena, K. N. Reva Univ Sch Elect & Commun Engn Bangalore Karnataka India

ISBN: (纸本)9781665428644

The Internet of Things (IoT) models are getting more complicated day by day with the rising demand in IoT automated network system. As the devices use wireless medium for broadcasting the data, it is easy to target for an attack. Due to the addition of different protocols in IoT, lakhs of attacks are emerging every day, which often provokes the computing process worsen, unstable, non-effective as well. In the local network, the normal communication attack is restricted to small local domain or local nodes. However, the attack present in IoT devices gets expanded to a large area that would cause destructive effects. The heterogeneity, distribution of IoT services/applications make the security of IoT a more challenging and complex one. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Thus, existing security techniques should be improved to secure the IoT environment viably. (ML/DL) have progressed impressively throughout the most recent couple of years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in various significant applications. The objective of this work is to give a thorough study of ML techniques and ongoing advances in DL methods that can be utilized to create upgraded attack detection models for IoT frameworks. We discuss the features and research gaps for each method in applying machine learning and deep learning to IoT security.

关键词： IoT Security Attack detection t machine learning Deep learning

来源：评论

学校读者我要写书评

暂无评论

Comparison of machine learning Methods for Classification and Diagnosis of Skin Disease

Comparison of Machine Learning Methods for Classification an...

引用

international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Kakarla Deepti S. Aruna Deepthi Department of ECE Vasavi College of Engineering Hyderabad INDIA

Skin disease is one of the most prevalent diseases around the world. To ease the complexity of conventional computational methods, deep learning techniques are adopted. By leveraging transfer learning and deep learning models, Convolution neural networks have been built, trained and evaluated. From the available data set of 10,082 using CycleGAN data augmentation a total of 39507 images are generated. One challenge faced during the implementation is mainly to improve the stability of CycleGAN during training phase. To analyze the performance of CycleGAN, three CNN models based on architecture of MobileNet, LeNet and modified LeNet are considered. A comparative analysis based on performance of three architectures is done and it is concluded that modified LeNet has outperformed with a higher accuracy of 95.03%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EEMD and Double Thresholds Integrated Voice Activity Detection 3

EEMD and Double Thresholds Integrated Voice Activity Detecti...

引用

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

作者： Meng, Shan Ablimit, Mijit Hamdulla, Askar Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang Urumqi China Xinjiang University Xinjiang Key Laboratory of Multilingual Information Technology School of Information Science and Engineering Xinjiang Urumqi China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Voice activity detection (VAD) is an important preprocessing for voice applications. Anti-noise performance is the most important evaluation index of VAD algorithm. The traditional dual-threshold-based VAD algorithm has very low detection accuracy in a low signal-to-noise ratio environment. This paper proposes a voice activity detection algorithm based on Ensemble Empirical Mode Decomposition (EEMD) combined with the dual-threshold method, which integrates the decomposition of EEMD. The denoising feature is combined with VAD based on dual thresholds, and dual thresholds are set for VAD to improve the anti-noise performance and accuracy of the algorithm. VAD is divided into three categories: based on feature parameters, based on pattern recognition, and based on deep learning. The VAD algorithms based on pattern recognition and deep learning require the support of big training data to achieve good detection results. And it is too complex and requires a large amount of computation, thus its application and real-time deployment have been greatly affected. The traditional VAD methods based on feature parameters only needs to use the short-term energy and short-term zero-crossing rate as the judgment criteria for activity detection. Thise algorithm is simple, easy to deploy, and applicable even if it is dozens of voices. At the same time, the EEMD changes the extreme point characteristics of the signal by adding different white noises of the same amplitude each time, and then performs the overall average of the corresponding IMF(intrinsic mode function) obtained by multiple EMDs to offset the added white noise, therefor effectively suppressing the mode-mixing. Thus the production of state aliasing can better improve the anti-noise performance of the VAD algorithm. © 2022 IEEE.

关键词： Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

A machine learning Model to Classify Indian Taxi System in Tourism Industry

A Machine Learning Model to Classify Indian Taxi System in T...

引用

international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Krutibash Nayak Saroj Kumar Panigrahy School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India

India is now becoming a tourism hotspot for tourist. To support the growing tourism industry, the taxi services play a major role and also it plays an important role in urban transportation. In view of the popularity of Taxi services, we have analyzed the sentiment of the taxi industry by taking the reviews of the customer on different taxi service providers. In this research, we addressed text sentiment analysis of taxi reviews, posted by customers on online review sites. All the reviews are based on Indian review sites only. We have compared many machine learning techniques with the dataset. To determine the sentiments of text reviews, machine learning techniques are used, which explore the feeling of a customer and also give the in-hand idea of the taxi services and its amenities. The study presents that among all the common machine learning techniques, Support Vector machine (SVM) performs better than other algorithms. Considering different evaluation parameters like Accuracy, F1Score, and Recall value, SVM gives the best result with 89%, 82%, and 86% respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A portable spirometer using machine learning approach 3

A portable spirometer using machine learning approach

引用

3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Priya, E. Nivethitha, R. Shivani, B.P. Srijee, P. Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665462754

Wheezing is a prime non-communicable disease among all age groups. It affects the air flow towards lungs by inflaming and narrowing the passage. Asthma relevant mortality occur in low and lower-middle revenue countries. In global arena, according to a report by world health organization about 417,918 deaths occur due to asthma. These sort of chronic obstructive pulmonary disease affects more than 10 million people every year. Spirometry is the universally used test for pulmonary functions. The obstructive and restrictive disease diagnosis and prognosis could be evaluated with this test. In this work, a lab model of a portable spirometer is aimed to be developed. This spirometer utilizes a flow sensor to acquire the respiratory signal and transmits the signal to Raspberry Pi for further processing. The identification or prediction of certain lung disease could be identified with the help of demographic parameters and particular symptoms of the subjects. Prime attributes are extricated from the acquired signals. Automatic decision support is executed via algorithms such as support vector machine, Naive Bayes, voting classifier are implemented to demarcate wheeze from normal subjects. Results demonstrate that the methodology adopted here is a promising method for lung disease identification and classification. © 2022 IEEE.

关键词： Pulmonary diseases

来源：评论

学校读者我要写书评

暂无评论

Automated Text Categorization 3

Automated Text Categorization

引用

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

作者： Patel, Atul Pathak, Samprati Khan, Md Irfan Galgotias Univ Sch Comp Sci & Engn Greater Noida Uttar Pradesh India

ISBN: (纸本)9781665428644

one of the central motivations behind NLP (Natural Language processing) is distinguishing designs. Agreed a content report, errand of recognizing the setting is referred to be as theme order. Text classification is an important advance in the characteristic non-standard speech process. It is also performed utilizing totally different classification algorithms. Its cut back is wide utilized in text classification to perform classification on large live of text information. However, Map Reduce needed plenty of your time to perform the tasks thereby increasing latency and since the information is distributed over the cluster it builds time and so reducing process speed. Also, It utilizes a long queue of code. driven by this, we propose a basic nonetheless compelling machine learning methodology that uses a Naive mathematician classifier for text information. In the machine learning approach, the classifier is made mechanically by learning the properties of classes from a collection of predefined coaching information. Hence, it will method complicated moreover, multi assortment information in dynamic things. Here we tend to propose a naive Bayes classifier that scales directly with a range of indicators and information points that may be used for each binary and multiclass classification issue. We tend to enforce the conferred schemes victimization machine learning tool. The experiment results demonstrate the performance improvement within the classification technique..This manuscript investigates the performance of three various classifiers to be specific Decision Tree, and Naive Bayes and KNN on a theme characterization commission (within six subject divisions). An Assessment is finished based on meticulousness, truthfulness, recall, and f1-attain based outcomes. Along with those three previously mentioned classifiers, we have selected the Naive Bayes as our best model utilizing which we achieved 87.1% exactness

关键词： machine learning Natural Language processing and Text Classification

来源：评论

学校读者我要写书评

暂无评论

Classification Techniques for Binary Motor Imagery signal for Brain-Computer Interfaces 3rd

Classification Techniques for Binary Motor Imagery Signal fo...

引用

3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： Kant, Piyush Laskar, S.H. Hazarika, Jupitara Department of Electronics and Instrumentation Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9789811627606

Brain-computer interface (BCI) is a tool to decode human brain signals in order to enable communication with the outer world just by using electroencephalogram (EEG) signals. Motor imagery (MI) signals recorded from the scalp during the imagination of movements are used as inputs for the BCI applications. Authors did review various classification methods, used to classify MI data. machine learning (ML) has proven to be a very effective tool in classifying MI signals. Several machine learning techniques are available to train ML models. Conventional machine learning methods can provide good classification results. However, deep learning (DL) methods are also found to provide better performance for such problems. The study compares conventional ML and deep neural networks based transfer learning as two classification approaches. Conventional machine learning was evaluated using SVM, KNN, and LDA classifiers, whereas transfer learning is implemented using alexnet, vgg16, and vgg19 networks. The study has shown the dominance of deep learning techniques over conventional classification techniques in our experiments. Transfer learning using the VGG net has resulted in a classification accuracy of 94.29%. The finding of the study suggests that although deep neural networks are computationally complex, and these networks can be very vital in the implementation of BCI technology. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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 << < 15 16 17 18 19 20 21 22 23 24 > >>

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：