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

10,668 篇 会议
423 篇 期刊文献
38 册 图书
3 篇 学位论文

馆藏范围

11,132 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,570 篇 工学
- 7,285 篇 计算机科学与技术...
- 4,341 篇 软件工程
- 1,540 篇 信息与通信工程
- 1,044 篇 生物工程
- 1,022 篇 电气工程
- 952 篇 生物医学工程（可授...
- 721 篇 控制科学与工程
- 375 篇 机械工程
- 364 篇 化学工程与技术
- 324 篇 光学工程
- 271 篇 仪器科学与技术
- 224 篇 电子科学与技术（可...
- 217 篇 动力工程及工程热...
- 209 篇 安全科学与工程
3,209 篇 理学
- 1,740 篇 数学
- 1,145 篇 生物学
- 773 篇 统计学（可授理学、...
- 606 篇 系统科学
- 553 篇 物理学
- 342 篇 化学
1,889 篇 管理学
- 1,108 篇 图书情报与档案管...
- 877 篇 管理科学与工程(可...
- 390 篇 工商管理
1,092 篇 医学
- 916 篇 临床医学
- 588 篇 基础医学(可授医学...
- 379 篇 药学(可授医学、理...
- 202 篇 公共卫生与预防医...
- 200 篇 特种医学
277 篇 教育学
- 265 篇 教育学
268 篇 法学
- 224 篇 社会学
203 篇 农学
168 篇 经济学
21 篇 文学
10 篇 军事学
4 篇 艺术学

主题

2,530 篇 machine learning
819 篇 deep learning
420 篇 support vector m...
417 篇 feature extracti...
402 篇 data mining
284 篇 big data
284 篇 training
274 篇 data models
268 篇 neural networks
255 篇 predictive model...
221 篇 task analysis
188 篇 decision trees
187 篇 machine learning...
169 篇 forecasting
136 篇 computational mo...
130 篇 covid-19
130 篇 students
128 篇 artificial intel...
126 篇 classification
124 篇 internet of thin...

机构

49 篇 univ chinese aca...
38 篇 sun yat sen univ...
22 篇 university of ch...
21 篇 university of el...
20 篇 school of data a...
17 篇 tianjin univ col...
16 篇 school of comput...
16 篇 peng cheng lab p...
15 篇 sun yat sen univ...
14 篇 sun yat sen univ...
13 篇 college of compu...
12 篇 nanyang technol ...
12 篇 shanghai jiao to...
12 篇 univ chinese aca...
11 篇 school of comput...
11 篇 zhejiang univ pe...
11 篇 school of comput...
11 篇 chinese acad sci...
11 篇 peking univ ctr ...
11 篇 beijing univ pos...

作者

19 篇 liu yang
17 篇 zheng wei-shi
13 篇 li dan
13 篇 zhang bo
13 篇 wang wei
12 篇 wang ruixuan
11 篇 yin jian
11 篇 yang yang
10 篇 xiong gang
10 篇 yang zan
9 篇 li yang
9 篇 liu bo
9 篇 liu xin
9 篇 li zhen
9 篇 chen chen
9 篇 zhang kun
9 篇 nai wei
9 篇 wang tao
9 篇 zhang rui
8 篇 chen a.

语言

11,004 篇 英文
114 篇 其他
29 篇 中文
2 篇 西班牙文
1 篇 法文

检索条件"任意字段=2019 International Conference on Applied Machine Learning and Data Science, ICAMLDS 2019"

共 11132 条记录，以下是231-240 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：

Handwriting Analysis for Dysgraphia Using machine learning 3rd

Handwriting Analysis for Dysgraphia Using Machine Learning

引用

3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Sharma, Anmol Singhal, Ishica Awasthi, Naman Mehrotra, Deepti Gupta, Richa Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819725076

learning disabilities specifically Dyslexia, Dysgraphia and Dyscalculia affect around 10% of children, holding back with their academic performance along with long term consequences such as taking a toll on their self-esteem and even their attitude towards education. As a neurological condition, the poor motor skills and motor coordination, these signs are reflected upon multiple aspects of written skills. An early diagnosis enables quick detection and have remedies to overcome writing-related challenges and reach their full potential. In this paper we have shown identification of dysgraphia by understanding deep leaning algorithms that have been already done by researchers (Mekyska et al. in IEEE Trans Hum Mach Syst 47:235–248, 2019) Performance of dysgraphia students’ handwriting with different models is discussed and implementation of CNN and RNN on dataset found on Kaggle have been accomplished. This study’s objectives were to analyse and discriminate the sample of handwriting capabilities of students suffering with dysgraphia handwriting. With the help of deep learning techniques, we have applied CNN and RNN on existing data and find out result on training dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Entropy and Mobility-based Model Assignment for Multi-model Vehicular Federated learning 2

Entropy and Mobility-based Model Assignment for Multi-model ...

引用

2nd IEEE international conference on Federated learning Technologies and Applications, FLTA 2024

作者： Lobato, Wellington da Costa, Joahannes B.D. Gonzalez, Luis F.G. Cerqueira, Eduardo Rosário, Denis Sommer, Christoph Villas, Leandro A. Brazil Brazil TU Dresden Faculty of Computer Science Germany

ISBN: (纸本)9798350354812

machine learning (ML) is extensively employed for key functions of Connected and Autonomous Vehicles (CAVs), where many models are executed simultaneously within a vehicle to provide diverse applications, from perception to planning and control. One of the most appealing ML approaches for CAVs is Federated learning (FL) due to its privacy-preserving nature and distributed learning capabilities. However, current FL approaches mostly focus on single-model training and are unsuitable for parallel training of multiple models. Multi-model FL involves training multiple ML models to perform different tasks, often simultaneously, to meet the demands of different applications within the same context. In this way, this work introduces MELRO, an FL model assignment algorithm based on link duration, training latency, and data entropy from CAVs. MELRO balances computing resources and addresses high vehicle mobility while considering the heterogeneity of data and availability of resources in CAVs. The assignment algorithm takes advantage of data transmitted periodically by CAVs, such as beacons, to calculate link duration and training latency, define the model assignment matrix for CAVs, and consider data entropy. Finally, MELRO increases accuracy for FL applications by at least 11.76% while reducing training latency by 25% and maintaining computational resource usage. ©2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Impact of Feature Correlation on Classification Acuracy of machine learning Model

Analyzing the Impact of Feature Correlation on Classificatio...

引用

2023 international conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Mishra, Satabdi Pradhan, Ranjan K. Odisha University of Technology and Research Department of Biotechnology Odisha Bhubaneswar India Odisha University of Technology and Research Department of Electrical Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350322309

machine learning has been widely used in building classification models for early detection of diseases using electronic health records of patients or variety of biosensor data on the characteristics of tissues or bio-fluids. For example, machine learning algorithms have been extensively applied for cancer detection and heat disease classification using open-source clinical data repository. However, there has been increasing number of challenges and complexities in building accurate classification models from diverse healthcare datasets available today that are basically measured under a variety of clinical settings and measurement techniques. Models that were shown to attain highest accuracy often suffer from overfitting problem. A key element in enhancing the accuracy of machine learning models is selecting correlated features or eliminating redundant features associated with a given dataset. Therefore, this study examines how the degree of correlation among features affects the predictive accuracy of three popular classifier models of breast cancer detection using clinical, demographic, anthropometric and blood metabolic data (with 10 features), and cytological characteristics of breast tissues (with 11 features) of patients. It was demonstrated that eliminating highly correlated features from the input dataset does not have any negative impact on the accuracy of classification models. SVM was found to be the best classifier with highest accuracy for both the datasets. The present results suggest that increasing the number of features increases the performance of breast cancer classifiers, and there is a modest change in prediction accuracy with the removal of highly correlated features. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Federated Knowledge Graph Embedding Unlearning via Diffusion Model 1

引用

8th international Joint conference on Web and Big data and Web-Age Information Management (APWeb-WAIM)

作者： Liu, Bingchen Fang, Yuanyuan Wang, Xu Li, Xin Shandong Univ Sch Software 1500 Shunhua Rd Jinan Shandong Peoples R China Univ Finance & Econ Sch Comp Sci & Technol 7366 Second Ring East Rd Jinan Shandong Peoples R China

ISBN: (数字)9789819772353

ISBN: (纸本)9789819772346;9789819772353

Knowledge graph (KG) embedding representation provides a foundation for knowledge reasoning and applications by mapping entities and relations into vector space. Federated learning (FL) promotes the development and application of artificial intelligence technologies by enabling model sharing and collaboration while safeguarding data privacy. Federated KG embedding enables the utilization of knowledge from diverse client sources while safeguarding the privacy of local data. Due to demands such as privacy protection and the need to adapt to dynamic data changes, investigations into machine unlearning (MU) have been sparked. It is challenging to maintain the performance of KG embedding models while forgetting the influence of specific forgotten data on the model. Existing methods attempt to make simple improvements at the level of the loss function for model training, but lack a targeted design for MU task characteristics. In this paper, we propose FedDM, a novel framework tailored for MU of federated knowledge graph embedding. Leveraging diffusion models, we generate noisy data to sensibly mitigate the influence of specific knowledge on FL models while preserving the overall performance of the remaining data. We conduct experimental evaluations on benchmark datasets to assess the efficacy of the proposed model. Extensive experiments demonstrate that FedDM yields promising results in knowledge forgetting.

关键词： Federated learning Knowledge graph machine unlearning Diffusion model

来源：评论

学校读者我要写书评

暂无评论

Predicting Cloud Applications Failures from Infrastructure Level data 53

Predicting Cloud Applications Failures from Infrastructure L...

引用

53rd Annual IEEE/IFIP international conference on Dependable Systems and Networks (DSN)

作者： Domingos, Jomar Cerveira, Frederico Barbosa, Raul Madeira, Henrique Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9798350325430

Cloud computing assumes a crucial role in the internet applications and services landscape, serving as the default infrastructure for deploying and delivering web applications. To harness the cloud's technical and financial benefits, one must address the challenges of the increased consequences of software faults. Online failure prediction has emerged as a solution allowing for actions to be taken before the failures. However, conventionally, each target application requires a specific online failure predictor to be trained from application-specific data, which is impractical. In this paper we explore and assess the possibility to achieve online failure prediction for cloud applications from infrastructure-level data, i.e., data from the hypervisor. For this purpose, we present a method to accomplish this type of failure prediction, through fault injection on the hypervisor and machine learning techniques to create failure prediction models. From our experiments we find that it is possible to predict application failures from low-level infrastructural data, i.e., the infrastructure data presented enough application failure predictive power (with an accuracy of 96%, precision of 95% and a recall of 64%), showing the great potential that infrastructural data has to contribute to this task. Additionally, our study also opens interesting future research directions, such as multilevel failure prediction or generic failure prediction models.

关键词： failure prediction machine learning feature engineering time series forecast dependability cloud computing

来源：评论

学校读者我要写书评

暂无评论

A Sentiment Analysis Benchmark for Automated machine learning Applications 22

A Sentiment Analysis Benchmark for Automated Machine Learnin...

引用

22nd IEEE international conference on machine learning and Applications, ICMLA 2023

作者： Rosendo Silva, Marilia Costa De Oliveira, Vitor Augusto Salgueiro Pardo, Thiago Alexandre Institute of Mathematics and Computer Science University of Sao Paulo Brazil

ISBN: (纸本)9798350345346

Developing new applied Artificial Intelligence solutions can be complex and time-consuming. Automated machine learning (AutoML) can speed up the solutions' development, but may require hundreds of datasets to make the learning process viable, which may be a challenge for Natural Language Processing (NLP) tasks, as many of them need annotated datasets that are expensive to produce. It may also be necessary to artificially recombine classes to generate new datasets for Meta-learning (MtL), which can be considered as a particular AutoML approach. This paper addresses such challenge and proposes an AutoML benchmark for sentiment analysis, which comprises relevant tasks in NLP, resulting in 49 preprocessed datasets, more than 650 binary datasets, and 204 meta-features. More than this, a proof of concept is carried out for the hate speech detection task. © 2023 IEEE.

关键词： AutoML Benchmark Sentiment Analysis

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis between machine learning and Econometric Approaches for Travel Mode Choice Modeling

A Comparative Analysis between Machine Learning and Economet...

引用

ASCE international conference on Transportation and Development (ICTD)

作者： Javadinasr, Mohammadjavad Asgharpour, Sina Mohammadi, Motahare Mohammadian, Abolfazl (Kouros) Auld, Joshua Univ Illinois Dept Civil & Mat Engn Chicago IL 60607 USA Argonne Natl Lab Lemont IL USA

ISBN: (数字)9780784484883

ISBN: (纸本)9780784484883

As a famous step in the transportation planning process, the mode choice problem has been traditionally studied by employing the concept of random utility optimization in the last 50 years. More recently and especially in the past decade, the emergence of big data and the computational power and insights brought by machine learning techniques has resulted in incorporating data-driven approaches that can lead to more accurate predictions. In this study, we developed travel mode choice models for commute trips in Chicago. Our data set is obtained from the Chicago Metropolitan Agency for Planning (CMAP) travel tracker survey conducted in 2018-2019, including approximately 12,000 households' activity and travel records for 24 h. We also employed Google Maps Direction API to calculate travel times. We first constructed the homebased tour of each person's trips to detect the main choice and eliminate the redundant nonchoice trips. Then, we estimated a multinomial logit model to characterize the underlying factors that affect the decision of people in selecting commute travel mode. As one of our contributions and for the first time in Chicago, we have considered seven travel modes, including walking, biking, walking to transit, driving to transit (i.e., park and ride), auto driver, auto passenger, and TNC in the choice set. We also analyze the value of time to draw a comparison between auto, transit, and TNC travel modes. Moreover, we employed machine learning classifiers to model the mode choice problem and compare the performance of these models with the econometric model. Also, a feature importance analysis is performed to further scrutinize the most determining factors of the model prediction. The results of this study can improve the existing mode choice model of Chicago through the inclusion of an emerging mode (i.e., TNC) and better representation of existing modes using supply and operational characteristics.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent Prediction of Information Security Industry data Based on machine learning and Adaptive Weighting Fusion 6

Intelligent Prediction of Information Security Industry Data...

引用

6th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Ding, Lijiao Wang, Ting Sun, Jinze Jing, Changqiang School of Information Science and Engineering Linyi University Shandong Linyi China

ISBN: (纸本)9798350399059

Entering the critical year of the 14th Five Year Plan, China's information security industry has entered a new stage of development. With the increasing importance of information security, its industrial development has been paid attention to, but the data fragmentation of China's information security industry is serious, and there are few corresponding summaries and predictions. To achieve the development prediction of the industry, this article studies the intelligent prediction of information security industry data based on machine learning and new adaptive weighted fusion, and deduces the system based on the research results to promote industry development. Firstly, collect, filter, integrate, and preprocess industry data. Based on the characteristics of the data, machine learning algorithms such as linear regression, ridge regression, logical regression, polynomial regression and random forest are selected to predict the data, and the corresponding optimal parameters are found and set in the model creation. And an improved adaptive weighted fusion model based on model prediction performance was proposed. Its principle is to adaptively select the model with the lowest mean square error (MSE) value for fusion based on the real-time prediction performance of multiple machine learning models, and its weight is also calculated adaptively to improve prediction accuracy. Secondly, using technologies such as Matplotlib and Pyecharts to visualize the data and predicted results, it was found that the development trend of the information security industry is closely related to factors such as national information security laws and regulations, the situation between countries, and social emergencies. According to the predicted results of the data, it is observed that both industry input and output have shown an upward trend in recent years. In the future, China's information security industry is expected to maintain stable and rapid growth driven by the domestic market. ©

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Predicting the Success of Global Terrorist Attacks Based on machine learning 9

Predicting the Success of Global Terrorist Attacks Based on ...

引用

9th Annual international conference on Network and Information Systems for Computers, ICNISC 2023

作者： Pan, Xiaohui Shanghai University of Political Science and Law School of Artificial Intelligence and Law Shanghai China

ISBN: (纸本)9798350306682

Terrorist attacks can be analysed and predicted with detailed historical data for better prevention and early warning. In this paper, we use the Global Terrorism database (GTD) to quantitatively study the characteristics of terrorist attacks from 1970 to 2019, and construct a framework based on four machine learning classification algorithms, namely, decision trees, random forests, logistic regression, and XGBoost, to predict whether a terrorist attack can be successful. A skillful method is used to deal with the unbalanced data. Each classifier undergoes optimizing, training, and parameter search to attain optimality. The proposed framework is then evaluated to assess its performance and stability. The experimental results show that the four algorithms achieved good performance. Random Forest and XGBoost achieve the best prediction performance of high accuracy (99%) in predicting the success of terrorist attacks. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Forecasting Mutated Genome Sequences of Human Coronaviruses by Unveiling Genomic Mutations Leveraging the Seq2Seq Approach

Forecasting Mutated Genome Sequences of Human Coronaviruses ...

引用

2024 IEEE international conference on machine learning and applied Network Technologies, ICMLANT 2024

作者： Rafi, Abdullah Hasan Sajjad Das, Arkadeep Das, Moumita Shakil, Arif Department of Computer Science & Engineering BRAC University Kha 224 Bir Uttam Rafiqul Islam Avenue Merul Badda Dhaka Bangladesh

ISBN: (纸本)9798350389180

Since viruses mutate rapidly, an outbreak of SARS-CoV-2, otherwise known as Covid-19, that originated a few years ago, has persisted in humans for nearly four years and is showing its rapid unpredictable mutation nature continuously. Several human coronavirus variants have emerged, including SARS-CoV in 2002, MERS-CoV in 2012, and the most recent, SARS-CoV-2 in 2019. This virus caused a major global outbreak, leading to over 700 million infections and more than 7 million deaths worldwide by March 2024. The continuous evolution of new SARS-CoV-2 strains has created a major challenge, and researchers are working hard to find effective solutions to tackle the problem. The most recent variant JN.1 has caused serious concern among public health officials due to its rapid spread and potential impact on vaccine efficacy and disease severity. To address this, the study uses a new approach with a Seq2Seq model to predict the genomic sequences of future Human Coronavirus variants early. The model learns the patterns of genomic mutations by analyzing the genome sequences of past Human Coronavirus variants. The genome sequences were arranged chronologically according to the date of their original discovery. A sliced approach for genome sequence is used for better learning of the model. The model produced a well fit validation loss curve with an average validation accuracy of 0.93. This research work demonstrates the possibility to predict the mutated genome sequence by detecting the chronological mutation patterns through natural language processing models such as the Seq2Seq model. In order to tackle the issue of rapid mutation of SARS-CoV-2, the proposed model would work as a useful method for predicting the genomic sequences of upcoming variations and obtaining crucial genomic insights and structures of the upcoming variants. The code, data and results are available in the GitHub repository. © 2024 IEEE.

关键词： Genome

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500页 << < 20 21 22 23 24 25 26 27 28 29 > >>

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：