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

1,890 篇 会议
6 册 图书
1 篇 期刊文献

馆藏范围

1,897 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,395 篇 工学
- 1,275 篇 计算机科学与技术...
- 1,022 篇 软件工程
- 318 篇 生物工程
- 311 篇 信息与通信工程
- 190 篇 生物医学工程（可授...
- 162 篇 控制科学与工程
- 124 篇 电气工程
- 120 篇 光学工程
- 83 篇 机械工程
- 74 篇 化学工程与技术
- 65 篇 安全科学与工程
- 63 篇 网络空间安全
- 54 篇 电子科学与技术（可...
- 48 篇 仪器科学与技术
760 篇 理学
- 399 篇 数学
- 327 篇 生物学
- 173 篇 统计学（可授理学、...
- 152 篇 物理学
- 134 篇 系统科学
- 83 篇 化学
449 篇 管理学
- 253 篇 管理科学与工程(可...
- 232 篇 图书情报与档案管...
- 131 篇 工商管理
175 篇 医学
- 166 篇 临床医学
- 159 篇 基础医学(可授医学...
- 117 篇 药学(可授医学、理...
- 63 篇 公共卫生与预防医...
82 篇 教育学
- 82 篇 教育学
77 篇 法学
- 69 篇 社会学
52 篇 农学
- 51 篇 作物学
46 篇 经济学
4 篇 军事学

主题

326 篇 machine learning
135 篇 deep learning
79 篇 support vector m...
66 篇 machine learning...
55 篇 forecasting
46 篇 feature extracti...
37 篇 decision trees
36 篇 accuracy
35 篇 computational mo...
34 篇 computer vision
31 篇 predictive model...
30 篇 training
29 篇 convolutional ne...
28 篇 data mining
28 篇 students
26 篇 neural networks
22 篇 object detection
22 篇 artificial intel...
21 篇 sentiment analys...
20 篇 internet of thin...

机构

12 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

1,779 篇 英文
116 篇 其他
18 篇 中文

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

共 1897 条记录，以下是501-510 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：

Research on named entity identification method based on medical field 3

Research on named entity identification method based on medi...

引用

3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Zhou, Runnan Li, Mingxuan Ruan, Peiguang Tong, Wang Gong, Bing Lv, Wei Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Guangdong Zhuhai519000 China Beijing LongChuangYueDong Network Technology Co. Ltd Beijing100000 China Guangdong Polytechnic of Science and Technology Computer Engineering Technology College Guangdong Zhuhai519000 China

ISBN: (纸本)9781510663473

With the development of Internet technology, more and more scholars are applying computer technology to research in the medical field. In this paper, we will investigate the named entity recognition method. Under the study of BERT-CRF model, we propose a named entity recognition model based on multi-task pre-training model with adversarial learning and network sharing and apply it to entity recognition in medical field with the aim of improving the accuracy of entity recognition in medical field. The model introduces multi-task joint learning and adversarial learning modules to improve the entity boundary effect and solve the noise problem of word boundary information, while achieving the purpose of information enhancement. On the CMeEE (Chinese Medical Entity Extraction) dataset, the model showed a significant improvement in accuracy, recall, and F1 score. © 2023 SPIE.

关键词： learning systems

来源：评论

学校读者我要写书评

暂无评论

Short-Term Stock Price Forecasting using Exogenous Variables and machine learning Algorithms 3

Short-Term Stock Price Forecasting using Exogenous Variables...

引用

3rd international conference on Intelligent Cybernetics Technology and applications, ICICyTA 2023

作者： Wong, Albert Whang, Steven Sagre, Emilio Sachin, Niha Dutra, Gustavo Lim, Yew-Wei Hains, Gaetan Khmelevsky, Youry Chang Zhang, Frank Mathematics and Statistics Langara College Vancouver Canada Lacl Université Paris-Est Créteil France Computer Science Okanagan College Kelowna Canada University of the Fraser Valley School of Computing Abbotsford Canada

ISBN: (纸本)9798350394559

Creating accurate predictions in the stock market has always been a great challenge in the finance world but is foundational for a good algorithmic trading system. With the rise of machine learning as the next level of development in the forecasting area, this research paper implements and compares four machine learning algorithms and their accuracy in forecasting three well-known stocks traded in the North American stock market in the short term over the period from March 2020 to May 2022. In addition to the time series of historical stock prices, several exogenous financial and economic variables such as stock market indexes, interest rates, and inflation-related variables are included in the modelling process to mimic their anticipated impact on individual stock prices. We deploy, develop, tune, and evaluate several XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models and report those that produce the highest level of accuracy from our evaluation metrics: Root Mean Square Error, Mean Average Percent Error, and Mean Positive Error. The evaluation also includes an evaluation of the average training time of these algorithms. Using a training data set of 240 trading days, we find that the XGBoost algorithm gives the highest level of accuracy despite taking longer (up to 10 seconds) to run. Results from this study may improve with the further tuning of the individual parameters of the algorithms and the introduction of other exogenous variables such as company demographics or social sentiment on the stock market. © 2023 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 Smart Crop Recommender and Yield Predictor 3

Machine Learning based Smart Crop Recommender and Yield Pred...

引用

3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Chhikara, Sonam Kundu, Nidhi Manipal University Jaipur Computer Science and Engineering Department Jaipur India Manipal University Jaipur Computer and Communication Engineering Department Jaipur India

ISBN: (纸本)9781665462006

Commercialization of agriculture industry raises the demand for increasing the crop yield. Crop production is highly dependent on the type, and nutrition value of soil, pH, temperature, humidity, rainfall etc. Farmers lack in determining the precise requirements of a crop, crop yield, and profit. This may cause a huge loss to farmers. Therefore, there is a need for a crop recommender system. Various researchers proposed the crop recommender systems but, these systems lack in automatic prediction of nutrient requirements, and crop yield. Also, the existing systems do not consider the export and import policies, hence fails in precise profit estimation. In this research, The authors address the above-stated challenges, and propose a machine learning based crop recommender system that predict crop yield of 22 varieties of crops. they employ machine learning models namely decision tree, light GBM, naive bayes, support vector machine, logistic, random forest, and Xgboost. The models naive bayes, random forest, and Xgboost outperforms other ML models and reports an accuracy of 99%. Furthermore, the system analyses the profit of these crops and recommend the most profitable crops to grow on a specific piece of land. © 2022 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of machine learning models for anomaly detection in manufacturing 3

Comparative analysis of machine learning models for anomaly ...

引用

3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Kharitonov, Andrey Nahhas, Abdulrahman Pohl, Matthias Turowski, Klaus Otto von Guericke Univ Fac Comp Sci Magdeburg Res & Competence Cluster Very Large Bus Univ Pl 2 D-39106 Magdeburg Germany

The introduction of various technologies in the context of Industry 4.0 allowed collecting monitoring data for various fields in manufacturing. Shop-floor and production data can be used for further analysis to extract knowledge. In this paper, an extensive evaluation of ten machine learning (ML) models for anomaly detection in manufacturing is conducted. The evaluation is conducted on multiple distinct ML algorithms, including conventional ML and a representative of Deep Neural Network (DNN) based algorithms. The ML models are trained on real production schedules to detect anomalous behavior in the overall system efficiency as well as violations in the delivery date of jobs. Multiple combinations of relevant features are tested during the training of the models. In essence, the objective of the ML models is to detect anomalous unknown breakdowns in the machines that lead to disruption in the overall performance of the system. The evaluation of the ML models is conducted on independent datasets with artificially injected machine breakdowns to establish the ground truth. These schedules with machine breakdowns are obtained through a simulation model. The results point to the high performance of a conventional ML algorithm, KNN. The performance demonstrated by a DNN-based AutoEncoder, conventional Local Outlier Factor-based Feature Bagging, and a recent Copula-Based Outlier Detection (COPOD) algorithms suggest limited applicability, which strongly depends on the shape of the data. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Industry 4.0 Anomaly detection machine learning Predictive Maintenance

来源：评论

学校读者我要写书评

暂无评论

Semantic Matching Method-Based question and answer classification algorithm research 3

Semantic Matching Method-Based question and answer classific...

引用

3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Li, Pinyue Feng, Xiwei Zhao, Chi Hou, Wei Chen, Hao Gui, Yafei Wu, Jiansheng Wang, Chaoqi Liaoning Petrochemical University School of Inforamtion and Control Engineering Liaoning Fushun024-56860423 China

ISBN: (纸本)9781510663473

A deep learning multi-classification algorithm is proposed to address the problem that machines cannot accurately understand the semantic differences of the same words in different sentences in the current research on semantic matching question-and-answer classification algorithms. The HanLP word separation algorithm is used to classify the problem first, then the words commonly used in the sentences are replaced according to the correspondence in the dictionary library, and finally the replaced data are imported into the BERT model for encoding and learning. It was verified that the combination of the HanLP word classification algorithm and the BERT model allowed the machine to understand the user's input test to a greater extent and with a higher accuracy rate than other methods. When applied to real-world scenarios, this method will make the user experience more efficient and convenient. With the rapid development of AI, this method will also help in the exploration of natural language. © 2023 SPIE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Literature Survey on application of Deep learning in Face Recognition 3

Literature Survey on Application of Deep Learning in Face Re...

引用

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

作者： Anil, J. Padma Suresh, L. Krishna Veni, S.H. Noorul Islam Centre for Higher Education Kumaracoil Department of Electrical & Electronics Engineering Tamil Nadu Kanyakumari District Thuckalay India Baselios Mathews Ii College of Engineering Sasthamcotta Department of Electrical & Electronics Engineering Kerala Kollam India Baselios Mathews Ii College of Engineering Department of Computer Science & Engineering Kerala Sasthamcotta Kollam India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Deep learning is an emerging technique in artificial intelligence in which a machine tries to copy the learning capability of human brain. As deep learning tries to mimic human brain it has the capability of overcoming many challenges in face recognition like change in expressions, pose, illumination, age etc. to an extent. This research explores some of the most advanced deep learning methods for face recognition. The algorithms that are being reviewed in this article are based on various techniques such as data augmentation, generation of synthetic training data, batch normalisation, linear binary pattern network, and others, which are used in conjunction with conventional Convolutional Neural Network to improve the performance of the algorithms. When comparing the approaches presented in this article to traditional face recognition algorithms, it can be concluded that the performance of face recognition is significantly enhanced. © 2022 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

The Role of machine learning in IoT: A Survey 3

The Role of Machine Learning in IoT: A Survey

引用

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

作者： Arikumar, K.S. Tamilarasi, K. Prathiba, Sahaya Beni Chalapathi, M M Venkata Moorthy, Rajalakshmi Shenbaga Deepak Kumar, A. Vijayawada India Chennai India Sri Ramachandra Institute of Higher Education and Research Faculty of Engineering and Technology Chennai India St.Joseph's Institute of Technology Computer Science and Engineering Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The recent advancements in various communication technologies have paved the way for sensor devices to connect with the internet. It can be expected that the quantity of internet-connected devices can reach a maximum of 75.44 billion by 2025. The quantities of such devices directly influence the amount of data to be transferred. The Internet of Things (IoT) is one of the technologies that provide connectivity among such devices through the existing internet. In addition to this, the IoT generates big data with the characteristics such as time-dependent, location-dependent, and multi-modal data quality. This paper presents a detailed survey about various machine learning techniques that treat the issues of the data generated by IoT devices. Further, the applications of various machine learning algorithms to the IoT data for extracting higher-level information have been presented. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis and Topic Modeling of E-Grocery application Reviews Using Naive Bayes and Support Vector machine: A Case Study of Segari Data Review on the Google Play Store 3

Sentiment Analysis and Topic Modeling of E-Grocery Applicati...

引用

3rd international conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Dhammananda, Jefka Budi, Indra Santoso, Aris Budi Universitas Indonesia Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350327762

Segari is a customer-centric company with a core value of being obsessed with its customers. The lack of human resources and the abundance of customer reviews that need to be analyzed hinder the process of extracting information from these reviews. Therefore, a machine learning model is needed to automatically perform sentiment analysis and topic modeling. The information extracted from sentiment analysis can be used as a reference to maintain service quality based on positive sentiments, while the results of negative sentiments can be used for evaluation to improve Segari's services and application. The data used on this research were customer reviews from the Google Play Store. The model development process includes data collection, data labeling, data preprocessing, feature extraction, sentiment classification model, model evaluation, and topic modeling. The researcher utilized two classification algorithms, NB and SVM, on a total of 10,507 reviews. The data shows that 74.37% express positive sentiments, while 25.63% express negative sentiments. The results of the study indicate that SVM with oversampling achieved the best model performance, with a recall of 89.98%. Additionally, the researcher used LDA to identify topics related to customer perspectives on Segari, which will be communicated to the relevant team. The analysis revealed that some customers are satisfied while others are disappointed with the product delivery process, application, prices, promotions, and vouchers. Customers generally expressed satisfaction with the quality and freshness of the products. Some customers felt disappointed due to missing or incomplete items in their orders, also to customer service. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Lessons Learned from Industrial Augmented Reality applications 3

Lessons Learned from Industrial Augmented Reality Applicatio...

引用

3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Stuebl, Gernot Ebenhofer, Gerhard Bauer, Harald Pichler, Andreas Profactor GmbH Stadtgut D1 A-4407 Steyr Gleink Austria

Smart production concepts are on the way to revolutionize industrial processes. A good example is the application of Extended Reality techniques that have emerged in recent years. This paper presents six different use cases where Extended Reality is realized utilizing tablets and digital projectors. The use cases reach from virtual sticky notes to artificial intelligence guided projections. The main aim of this work is to not only tell the success stories, but also give a sight behind the curtain and highlight challenges and lessons learned for each application such that the community can profit from the presentations. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Extended Reality Augmented Reality Spatial Augmented Reality Deep learning applications

来源：评论

学校读者我要写书评

暂无评论

machine learning Approach to Crisis Management Exercise Analysis: A Case Study in SURE Project 3rd

Machine Learning Approach to Crisis Management Exercise Ana...

引用

3rd international conference on Intelligent Edge Processing in the IoT Era, Edge-IoT 2022, and the 4th international conference on Smart Governance for Sustainable Smart Cities, SmartGov 2022

作者： Joutsijoki, Henry Mäenpää, Sari Karppi, Ilari Sankala, Iina Insta Advance Oy Sarankulmankatu 20 Tampere33900 Finland Faculty of Management and Business Tampere University Kanslerinrinne 1 Tampere33014 Finland

ISBN: (纸本)9783031359811

Crisis management exercises play a key role in preparation for different scenarios such as natural disasters, or large crowd events. Moreover, they act as a platform for testing new technologies and finding common practices between different actors. In this preliminary paper, we apply a collection of machine learning methods to a questionnaire data to find factors, where authorities’ and non-authorities’ perspectives differ when a large number of questions are covered simultaneously. Such factors may be focal elements in hindering the co-operation between different actors in a real-world case. Therefore factors are essential to be revealed. Feedback data was collected from a multi-organizational crisis management exercise that was organized as part of an EU funded Smart Urban Security and Event Resilience (SURE) project. Dataset was examined in four question set scenarios and in each case we obtained around 80% accuracy when classifying authorities and non-authorities. This indicates that parties’ perspectives differ about the crisis management exercise and it strengthens the idea of recognizing the possible obstacles of co-operation. Although crisis management exercises are relatively well researched area in the literature, the use of machine learning methods in this context gives a novel approach for analyzing the results. machine learning based analysis of results enables improving crisis management exercises in the future. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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190页 << < 47 48 49 50 51 52 53 54 55 56 > >>

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：