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

1,942 篇 会议
6 册 图书
1 篇 期刊文献

馆藏范围

1,949 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,442 篇 工学
- 1,321 篇 计算机科学与技术...
- 1,055 篇 软件工程
- 348 篇 信息与通信工程
- 318 篇 生物工程
- 204 篇 控制科学与工程
- 191 篇 生物医学工程（可授...
- 131 篇 电气工程
- 121 篇 光学工程
- 83 篇 机械工程
- 74 篇 化学工程与技术
- 67 篇 网络空间安全
- 65 篇 安全科学与工程
- 58 篇 电子科学与技术（可...
- 51 篇 仪器科学与技术
793 篇 理学
- 431 篇 数学
- 328 篇 生物学
- 182 篇 物理学
- 175 篇 统计学（可授理学、...
- 134 篇 系统科学
- 115 篇 化学
458 篇 管理学
- 262 篇 管理科学与工程(可...
- 232 篇 图书情报与档案管...
- 132 篇 工商管理
215 篇 医学
- 198 篇 临床医学
- 161 篇 基础医学(可授医学...
- 117 篇 药学(可授医学、理...
- 73 篇 公共卫生与预防医...
85 篇 法学
- 77 篇 社会学
82 篇 教育学
- 82 篇 教育学
52 篇 农学
- 51 篇 作物学
46 篇 经济学
8 篇 文学
4 篇 军事学

主题

333 篇 machine learning
151 篇 deep learning
79 篇 support vector m...
66 篇 machine learning...
55 篇 forecasting
48 篇 feature extracti...
39 篇 accuracy
37 篇 decision trees
35 篇 computational mo...
34 篇 computer vision
32 篇 predictive model...
29 篇 training
29 篇 convolutional ne...
28 篇 data mining
28 篇 students
26 篇 neural networks
23 篇 object detection
23 篇 artificial intel...
22 篇 real-time system...
21 篇 internet of thin...

机构

13 篇 chitkara univers...
7 篇 shenyang ligong ...
6 篇 uttaranchal inst...
5 篇 department of co...
5 篇 maharishi school...
5 篇 alibaba cloud bi...
4 篇 school of comput...
4 篇 college of compu...
4 篇 key laboratory o...
4 篇 lovely professio...
4 篇 jilin agricultur...
4 篇 uttaranchal univ...
4 篇 key laboratory o...
4 篇 department of co...
4 篇 master of comput...
4 篇 school of inform...
3 篇 school of comput...
3 篇 vivekananda glob...
3 篇 department of el...
3 篇 college of compu...

作者

4 篇 liu gang
4 篇 kumar rakesh
4 篇 waseemullah
4 篇 liu peng
3 篇 zhang tao
3 篇 kumar ajay
3 篇 rakesh kumar
3 篇 babu tina
3 篇 siddhant
3 篇 sharma arun
3 篇 faisal rehman
3 篇 wang xiaofei
3 篇 sharif hanan
3 篇 gupta rajeev kum...
3 篇 singh sunil kuma...
3 篇 rehman faisal
3 篇 tina babu
3 篇 yang wei
3 篇 sharma avinash
3 篇 prajwal

语言

1,483 篇 英文
464 篇 其他
57 篇 中文

检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1949 条记录，以下是1051-1060 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：

Vegetation Coverage and Urban Amenity Mapping Using computer Vision and machine learning

Vegetation Coverage and Urban Amenity Mapping Using Computer...

引用

3rd international conference on Image Processing and Vision Engineering, IMPROVE 2023

作者： Karkut, Nicholas Kiriluk, Alexey Zhang, Zihao Zhu, Zhigang Computer Science Department The City College of New York - CUNY New YorkNY10031 United States Graduate Landscape Architecture Program The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper proposes a computer vision-based workflow that analyses Google 360-degree street views to understand the quality of urban spaces regarding vegetation coverage and accessibility of urban amenities such as benches. Image segmentation methods were utilized to produce an annotated image with the amount of vegetation, sky and street coloration. Two deep learning models were used -- Monodepth2 for depth detection and YoloV5 for object detection -- to create a 360-degree diagram of vegetation and benches at a given location. The automated workflow allows non-expert users like planners, designers, and communities to analyze and evaluate urban environments with Google Street Views. The workflow consists of three components: (1) user interface for location selection;(2) vegetation analysis, bench detection and depth estimation;and (3) visualization of vegetation coverage and amenities. The analysis and visualization could inform better urban design outcomes. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Vegetation mapping

来源：评论

学校读者我要写书评

暂无评论

Analyze the application of new media technology in environmental art design 3

Analyze the application of new media technology in environme...

引用

3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Jiecheng, Wei Gongqing College Nanchang University Jiujiang City Jiangxi Province Gongqing City332020 China

With the continuous development of society and the continuous improvement of economic level, science and technology have also been greatly developed. Multimedia technology has also been rapidly developed and promoted in the new era field and background. New media art has been developing rapidly in the new social development process. Therefore, it has played a more and more important role in the design and influence of art. The participation of new media art has also gone deeper and deeper into various fields, and has also had an important role and impact on people's lives. The application of new media technology in environmental art design has two main purposes. First, it has a visual impact on the audience. Second, it can help the audience understand how they are affected by the depletion of the environment and natural resources. The first purpose is to achieve it by using video clips or animations, which are produced by different kinds of technologies, such as 3D modeling software, projection mapping, virtual reality, etc. It can also be realized by using various types of media, such as photography, recording and music production. They are used to create an audio-visual expression, which can not only attract people's attention. This article mainly analyzes and explores the application of new media art in environmental art design, so as to help us better understand the connotation of new media art. © 2023 Elsevier B.V.. All rights reserved.

关键词： 3D modeling

来源：评论

学校读者我要写书评

暂无评论

Soil Mineral Prediction of Crops Using machine learning 3

Soil Mineral Prediction of Crops Using Machine Learning

引用

3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Akkasaligar, Prema T. Biradar, Sunanda Gudgeri, Manjula C. Mulla, Sana Mohammadi A BLDEA's V. P. Dr. P. G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapur586103 India Karnataka Vijayapur586103 India Kle Dr M S Sheshgiri College of Engg and Tech Department of Engg Science and Humanities Mathematics Karnataka Belagavi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

In India, agriculture is the primary source of income. Agriculture employs the majority of the people. Because India's financial system is so reliant on agriculture, there is a pressing need to boost agricultural output generally. Soil is the most important natural resource in agriculture. The most important requirement is good soil quality. The pH of the soil is crucial in this regard. The acidity of the soil is measured by its pH. The total yield is influenced by the pH level. Nitrogen, Potassium, and Phosphorus all influence pH of soil. Farmers can then begin cultivating crops as a result of this. pH sensors are commonly used to determine the pH of soil. However, because they increase the cost of manufacture, they are not always practicable. To address this problem, the proposed method determines the pH value while simultaneously recommending a crop. The crop recommendation method takes into account factors such as soil pH, season, and temperature. © 2022 IEEE.

关键词： Nitrogen

来源：评论

学校读者我要写书评

暂无评论

Compensation mechanism and machine learning application for cooperative watershed management in pollution control 25

Compensation mechanism and machine learning application for ...

引用

Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Fenglian Zhuo School of Management Shanghai University Shanghai Shanghai China

ISBN: (纸本)9798400713231

The management of urban and rural watersheds faces challenges due to outdated methods, highlighting the need to strengthen cost-sharing mechanisms and establish an ecological compensation framework. In this study, a comprehensive analysis was conducted using cooperative game theory and machine learning techniques in the Fujian Minjiang River Basin as an example. Based on the average tree solution (A-T solution), a fairer cost-sharing scheme is proposed, and a practical ecological compensation model is constructed by integrating the pollution intensity compensation factor. In addition, a machine learning approach was used to predict wastewater discharge trends and identify factors affecting water quality. The Random Forest model identified key factors including 'water temperature (\(^ \circ C\))', 'chemical oxygen demand (COD) (mg/L)' and 'dissolved oxygen (mg/L)'. Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models were used to predict industrial wastewater discharges in Putian City from 1994 to 2022. The results suggest that regional geography has a significant impact on cost sharing and that coalition constraints contribute to a more equitable and balanced distribution of costs. The results of the study highlight the importance of pollution intensity in determining management costs and the need to develop compensation criteria. This take into account regional pollution levels to improve watershed management coordination. The LSTM model showed the highest accuracy with an R2 of 0.09, which exceeded the performance of traditional linear regression. This study provides insights that can inform improved watershed management, equitable cost sharing and ecological compensation, while highlighting the benefits of combining machine learning with traditional methods to improve forecasting and decision making.

关键词： Compensation Mechanism

来源：评论

学校读者我要写书评

暂无评论

Glioma/Glioblastoma Detection in Brain MRI using Pre-trained Deep-learning Scheme 3

Glioma/Glioblastoma Detection in Brain MRI using Pre-trained...

引用

3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Sujitha, R. Angel Balaji, Gangadharam Mohammed, Mazin Abed Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Vytautas Magnus University Department of Applied Informatics Kaunas44404 Lithuania St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Omr Chennai600119 India M/S Tata Consultancy Services Limited Tcsl Siruseri Sez Unit Sipcot I.T. Park TN Chennai603103 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Convolutional Neural Network (CNN) supported medicinal image examination is extensively accepted due to its reputation and improved accuracy. The investigational outcome obtained with DLS along with a chosen classifier helps to achieve better detection results than the traditional and machine-learning methods. The proposed research examines the performance of pre-trained VGG16 and VGG19 schemes in detecting the brain tumour (Glioma/Glioblastoma) grade using different pooling methods. The classification is performed using SoftMax with five-fold cross-validation, and the products are compared and presented. The brain tumour images considered in this study are collected from The Cancer Imaging Archive (TCIA) dataset. This work considered 2000 images (1000 Glioma and 1000 Glioblastoma) of axial-plane with dimension of 224×224×3 pixels for the assessment, and the attained results are compared. The experimental outcome achieved with Python® confirms that the VGG16 with average-pooling provides a better classification accuracy (>99%) with Decision Tree (DT) compared with other methods considered. © 2022 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

machine learning Framework for Identification of Abnormal EEG Signal 3rd

Machine Learning Framework for Identification of Abnormal EE...

引用

3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

作者： Rao, A. Prabhakara Bhaskar, J. Kumar, G. Prasanna Department of ECE Vishnu Institute of Technology AP Bhimavaram India

ISBN: (纸本)9783031289743

Epilepsy is a chronic, noncommunicable disease (NCD) causing disorder in the brain’s neurological activity. It may be due to genetic disorder or brain injuries caused by some accidents. This may cause seizures, loss of awareness, unusual sensations and behavior. Globally 50 million people are suffering from epilepsy, so it is one of the most prominent neurological diseases globally according to World Health Organization (WHO) statistics in 2021. It is estimated that up to 70% of people are suffering with epilepsy and they can be saved by timely diagnosis and proper treatment of epilepsy. Electroencephalograms (EEGs) are universally used to detect this chronic non-communicable disease. Furthermore, assessing a specific type of abnormality by visual examination of an EEG signal is an intuitive process that can vary from radiologist to radiologist. It is a challenging task for the radiologists to visually examine the EEG signal by looking for a shift in frequency or amplitude in long-duration signals. It may give rise to inaccurate categorization. Identification of epileptic seizure from the recorded EEG signal is a primary task in the treatment of epilepsy. In this work, wavelets were used to obtain the appropriate features from EEG signals. These features were fed to different classifiers. This work proposes a machine learning (ML) framework to detect the abnormality in the EEG signal automatically to assist the radiologists in their diagnosis. The ML framework uses 7 classifiers (KNN, SVM, Random Forest, Logistic Regression, Decision Tree, AdaBoost, and Bagging). Among these classifiers, Bagging Classifier was shown better performance in terms of accuracy and ROC. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Classifying Customers' Journey from Online Reviews of Amazon Fresh via Sentiment Analysis and Topic Modelling 3

Classifying Customers' Journey from Online Reviews of Amazon...

引用

3rd international conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Rana, Jyoti Gaur, Loveleen Santosh, K.C. Amity University Amity College of Commerce and Finance Noida India Amity University Amity International Business School Noida India University of South Dakota Department of Computer Science South Dakota United States

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

A positive customer journey experience is necessary to maintain customer loyalty in online retailing. After the outbreak of Covid-19, there has been a significant increase in the number of customers who buy online groceries. Due to the anonymity and convenience throughout the customer journey, E-grocery shopping platforms have become a reliable source for gathering online customer reviews. In the study, we used text mining and machine learning (ML) models to an e-grocery customer review database from the Amazon Fresh website to forecast customer feelings in the data set. To be more specific, this study aimed to determine whether the customers are satisfied with the online purchase of products or not. Further, the study aims to analyze whether the customers would recommend the purchased products or not. For sentiment analysis a sample of 78,619 reviews was used. We used a linguistic approach consisting of ML and dictionary scoring algorithms to forecast customers' sentiment based on their reviews. Topic modeling (TM) on 3,26,120 customer reviews was used to reveal 'themes' from customer reviews to grasp a better knowledge of customers experiences. © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Arabic Cyberbullying Detection from Imbalanced Dataset Using machine learning 3rd

Arabic Cyberbullying Detection from Imbalanced Dataset Using...

引用

3rd international conference on Soft Computing and its Engineering applications, icSoftComp 2021

作者： AlFarah, Meshari Essa Kamel, Ibrahim Al Aghbari, Zaher Mouheb, Djedjiga University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9783031057663

In recent years, the number of online social networks users is dramatically increased. Cyberbullying is a serious threat in online social networks especially toward children and teenagers. Victims are harassed by perpetrators even with no physical attendance. Cyberbullying causes psychological damage to victims resulting in anxiety, depression, and even suicide. Cyberbullying is language and culture sensitive. Many prior works in cyberbullying detection was conducted in English and only few papers studied Arabic cyberbullying detection. In this research, Arabic cyberbullying detection using five machine learning techniques were studied using real Arabic messages from Twitter and YouTube. Many performance techniques were used to evaluate the robustness of the model. Naïve Bayes (NB) achieves the highest Receiver Operating Characteristic (ROC) curve (AUC) of 89% while Support Vector machine (SVM) and Logistic Regression (LR) both achieve 88%. © 2022, Springer Nature Switzerland AG.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

A New machine learning Algorithm for Users' Movie Recommendation 3

A New Machine Learning Algorithm for Users' Movie Recommenda...

引用

2022 3rd international conference on computer Information and Big Data applications, CIBDA 2022

作者： Liu, Zhongyuan Wang, Xuefei Zhu, Hongzheng Faculty of rail transit Wuxi University Wuxi214105 China Shanghai Fuxing Senior High School Shanghai200434 China College of computer and information science Faculty of automation Southwest University Chongqing400700 China

ISBN: (纸本)9783800758760

In this information era, even a movie lover may feel overwhelmed when facing numerous movies on the movie website. Therefore, an efficient movie recommendation system is needed. In this paper, two different recommendation systems were compared, based on the user-based collaborative filtering and the content-based collaborative filtering respectively. Basically, several relevant studies were referred to, and a huge dataset was used from MovieLens. After pre-processing the data, where the data was transformed into matrixes of each user's rating and the corresponding movie (the rating matrix represented the eigenvector of each user), cosine similarity was used to obtain the similarity relationship between users by calculating the cosine similarity between those eigenvectors. Then, K-NearestNeighbor (KNN) algorithm was used to select a certain number of users having the highest similarity with the target, based on which predicted how the target would rate a movie and recommended the movie with the highest predicted score to the target. In the part of using the content-based collaborative filtering, films in each category were ranked based on the average and the number of the scores and the user's favorite movie types were obtained by the user-movie genre preference matrix based on the scores that users gave to each type of movies. The movies with the highest score in each target user's favorite movie type would be recommended to the target. Experimental outcomes indicate that both algorithms generally meet the needs of users despite of the differences in the results. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

IoT-based Battery Management System by Deploying an machine learning Model 3

IoT-based Battery Management System by Deploying an Machine ...

引用

3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Prabha, A. Saravanan, S. Balaji, S. Malini, V.K. Thopate, Kaushalya Sivakami, T. Ramkumar, M.Siva Sa Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai600077 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Kcg College of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600097 India Sri Sairam College of Engineering Department of Electrical and Electronics Engineering Bengaluru562106 India Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune411037 India Bharath Institute of Higher Education and Research Department of Electronics and Communication Tamil Nadu Chennai600126 India Karpagam Academy of Higher Education Department of Eee India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

A battery is a device that can be used to provide power for a variety of purposes. Regardless of the battery's size, the SoC is an important consideration. The abbreviation SoC stands for 'State of Charge."It refers to the battery's remaining capacity. SoC is used in both main and secondary batteries. However, accurately calculating the SoC necessitates a high level of skill. This research aims to solve this problem by developing an Artificial Intelligence (AI) model that can predict the SoC of *** construct this battery management system, Mendeley data was used to obtain a fundamental dataset encompassing all vital properties that compose a battery. An AI model is built using the Artificial Neural Network (ANN) architecture. This model is then trained using the data from the dataset. The model had the highest accuracy and a very low loss value at the end of the training and validation;thus, it could be ignored. After validation, the model's accuracy is greater than 0.98 and the loss value is less than 0.05, indicating that it is very effective at predicting the battery's SoC *** model is then implemented into a software application to make it more user-friendly for common people. The user can enter information about the battery, such as the voltage, current, and temperature values, using the application's straightforward user interface. The application shows the battery's SoC as well as the actual SoC for comparative purposes based on the numbers entered. This application reduces the need for an electrician for simple tasks like the prediction of the SoC of the battery. © 2022 IEEE.

关键词： Forecasting

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195页 << < 102 103 104 105 106 107 108 109 110 111 > >>

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：