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

623 篇 会议
28 册 图书
8 篇 期刊文献

馆藏范围

659 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

470 篇 工学
- 440 篇 计算机科学与技术...
- 224 篇 软件工程
- 83 篇 信息与通信工程
- 77 篇 控制科学与工程
- 70 篇 电气工程
- 33 篇 生物医学工程（可授...
- 30 篇 生物工程
- 13 篇 化学工程与技术
- 13 篇 安全科学与工程
- 13 篇 网络空间安全
- 12 篇 仪器科学与技术
- 12 篇 石油与天然气工程
- 10 篇 矿业工程
- 9 篇 机械工程
- 8 篇 建筑学
- 7 篇 动力工程及工程热...
139 篇 理学
- 86 篇 数学
- 38 篇 生物学
- 35 篇 系统科学
- 35 篇 统计学（可授理学、...
- 17 篇 化学
- 15 篇 物理学
127 篇 管理学
- 84 篇 图书情报与档案管...
- 60 篇 管理科学与工程(可...
- 23 篇 工商管理
32 篇 医学
- 28 篇 基础医学(可授医学...
- 26 篇 临床医学
- 20 篇 药学(可授医学、理...
15 篇 法学
- 14 篇 社会学
12 篇 教育学
- 9 篇 教育学
5 篇 经济学
3 篇 文学
3 篇 农学

主题

210 篇 data mining
133 篇 machine learning
31 篇 feature extracti...
29 篇 support vector m...
29 篇 machine learning...
26 篇 sentiment analys...
23 篇 accuracy
20 篇 big data
20 篇 classification
18 篇 predictive model...
16 篇 artificial intel...
16 篇 data models
16 篇 training
15 篇 data mining and ...
13 篇 deep learning
13 篇 natural language...
13 篇 decision trees
13 篇 prediction algor...
13 篇 information retr...
13 篇 feature selectio...

机构

3 篇 pimpri chinchwad...
3 篇 chinese acad sci...
3 篇 peking universit...
3 篇 univ chinese aca...
2 篇 chitkara univers...
2 篇 wuhan institute ...
2 篇 wuhan unversity ...
2 篇 tilburg universi...
2 篇 dai-labor techni...
2 篇 university of sa...
2 篇 tsinghua univ de...
2 篇 department of co...
2 篇 shanghai univers...
2 篇 global univ dept...
2 篇 carnegie mellon ...
2 篇 fstg cadi ayyad ...
2 篇 university of bu...
2 篇 rensselaer polyt...
2 篇 kasetsart univ d...
2 篇 lovely professio...

作者

4 篇 petra perner
3 篇 waiyamai kitsana
3 篇 yuhui shi
3 篇 gallinari patric...
3 篇 sunil vadera
3 篇 unold olgierd
3 篇 gammerman alex
3 篇 ying tan
3 篇 zhongzhi shi
2 篇 lebrun gilles
2 篇 asri hiba
2 篇 steele robert
2 篇 rakthanmanon tha...
2 篇 alhajj reda
2 篇 coenen frans
2 篇 coldham nick
2 篇 xiang li
2 篇 xu bin
2 篇 faria pedro
2 篇 krasotkina olga

语言

652 篇 英文
6 篇 其他
1 篇 中文

检索条件"任意字段=7th International Conference on Machine Learning and Data Mining, MLDM 2011"

共 659 条记录，以下是1-10 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：

11th mining Humanistic data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on machine learning and Big data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

引用

11th mining Humanistic data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on machine learning and Big data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

the proceedings contain 35 papers. the special focus in this conference is on mining Humanistic data. the topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;the Use of Robotics in Critical Use Cases: the 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for machine learning-Based Resource Management;efficient data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep learning Based Facial Expressions An

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analysis of Arabic Tweet Sentiment About Trending Hashtags Using Transfer learning and machine learning Models 7

Analysis of Arabic Tweet Sentiment About Trending Hashtags U...

引用

IEEE 7th international conference on Advanced Technologies, Signal and Image Processing (ATSIP)

作者： Al-Qahtani, Waad Nasier Muniasamy, Anandhavalli King Khalid Univ Coll Comp Sci Abha Saudi Arabia

ISBN: (纸本)9798350351491;9798350351484

this study used a multi-pronged approach to examine how Gulf region Twitter users felt about COVID-19 trending topics. the data collection phase of the study started with the selection of relevant hashtags and time frames for analysis. the Twitter API was then used to compile a representative dataset of Arabic tweets pertaining to the COVID-19 trending topics. Subsequently, the research proceeded to the data annotation stage, employing a hybrid annotation technique that fused the transfer learning model and lexicon-based approach to assign a sentiment label to every tweet. Analyzing the patterns of tweet distribution over time exposed interesting patterns and possible sentiment expression influencers. the research obtained good accuracy scores by using a sentiment analysis model that combined three popular machine learning algorithms (Multinomial Naive Bayes, CountVectorizer, and TfidfVectorizer) with three feature representations (Ngram, TfidfVectorizer, and CountVectorizer). the sentiment tendencies of Arabic-speaking Twitter users toward trending topics were revealed by these scores. With the Ngram(1,2) representation, the LinearSVC algorithm achieved an impressive accuracy score of 89.1%, making it stand out as the best performer among all feature representations.

关键词： machine learning Opinion mining Sentiment Analysis Natural Language Processing Twitter Lexical Semantic Arabic Language

来源：评论

学校读者我要写书评

暂无评论

epiDAMIK 2024: the 7th internationalWorkshop on Epidemiology meets data mining and Knowledge Discovery 24

epiDAMIK 2024: The 7th InternationalWorkshop on Epidemiology...

引用

30th ACM SIGKDD conference on Knowledge Discovery and data mining

作者： Rodriguez, Alexander Adhikari, Bijaya Srivastava, Ajitesh Pei, Sen Charpignon, Marie-Laure Wang, Kai Chang, Serina Vullikanti, Anil Prakash, B. Aditya Univ Michigan Ann Arbor MI 48109 USA Univ Iowa Iowa City IA USA Univ Southern Calif Los Angeles CA 90007 USA Columbia Univ New York NY USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Georgia Inst Technol Atlanta GA 30332 USA Stanford Univ Stanford CA 94305 USA Univ Virginia Charlottesville VA USA

ISBN: (纸本)9798400704901

the epiDAMIK workshop serves as a platform for advancing the utilization of data-driven methods in the fields of epidemiology and public health research. these fields have seen relatively limited exploration of data-driven approaches compared to other disciplines. therefore, our primary objective is to foster the growth and recognition of the emerging discipline of data-driven and computational epidemiology, providing a valuable avenue for sharing state-of-the-art research and ongoing projects. the workshop also seeks to showcase results that are not typically presented at major computing conferences, including valuable insights gained from practical experiences. Our target audience encompasses researchers in AI, machine learning, and data science from both academia and industry, who have a keen interest in applying their work to epidemiological and public health contexts. Additionally, we welcome practitioners from mathematical epidemiology and public health, as their expertise and contributions greatly enrich the discussions. Homepage: https://***/.

关键词： Epidemiology public health forecasting AI for good

来源：评论

学校读者我要写书评

暂无评论

2024 7th international conference on Artificial Intelligence and Big data, ICAIBD 2024

2024 7th International Conference on Artificial Intelligence...

引用

7th international conference on Artificial Intelligence and Big data, ICAIBD 2024

ISBN: (纸本)9798350385106

the proceedings contain 98 papers. the topics discussed include: patient clustering to improve process mining for disease trajectory analysis using Indonesia health insurance dataset;a comprehensive review of transformer-based models: ChatGPT and bard in focus;pneumonia image classification: deep learning and machine learning fusion;exploring the pathways to optimize immersive imaging experiences using AIGC technology;sentiment analysis of song dynasty classical poetry using fine-tuned large language models: a study with LLMs;a random forest stock prediction model based on Bayesian optimization;multi-level generative pretrained transformer for improving malware detection performance;and research on building a competency model for managers in private express enterprises based on recruitment big data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Undersampling Method based on the Distance to Hyperplane for Highly Imbalanced Classification 7

An Undersampling Method based on the Distance to Hyperplane ...

引用

7th international conference on Computer Information Science and Application Technology, CISAT 2024

作者： Qin, Haiou Nanchang Institute of Technology School of Information Engineering Nanchang China

ISBN: (纸本)9798350375107

Imbalanced classification is an important topic in the fields of data mining and machine learning because the models produced by most general learning algorithms are useless at identification of the minority samples. To solve this problem, three categories of methods are proposed: oversampling, undersampling, and cost-sensitive learning. In this paper, a novel undersampling method based on the distance of the majority samples to the original classification hyperplane is presented. Specifically, An algorithm which belongs the method is presented. the algorithm can balance the datasets thoroughly and lead to deterministic undersampling results. To verify the effectiveness of our method, the comprehensive experiments are performed on 20 imbalanced datasets. Results show that our method is comparable to the classical undersampling methods and the latest PSU (Particle Stacking Undersampling). © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

A Selective Ensemble learning Method for Time Series Forecasting 7

A Selective Ensemble Learning Method for Time Series Forecas...

引用

7th international conference on machine learning and Natural Language Processing, MLNLP 2024

作者： Wang, Yuxin Dong, Yilin Shanghai Maritime University College of Information Engineering Shanghai China

ISBN: (纸本)9798350354973

Time series forecasting, as an important field of data mining, is of great significance in revealing future trends. Traditional statistical models have limitations in handling nonlinear and non-stationary time series. In contrast, machine learning models, especially ensemble learning, have become a research hotspot due to their advantages in balancing biases of multiple models and improving overall performance. Aiming at the existing time series forecasting methods that have the problems of single model and insufficiently accurate forecasting results, this paper proposes a time series forecasting model based on selective ensemble learning (s-SMLE), which significantly improves the forecasting accuracy by combining the advantages of different component models through the steps of data preprocessing, model screening and model weight generation. the experiments verify the effectiveness of the proposed model, providing new ideas and methods for time series forecasting. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

An Optimization Method TSDAE -based for Unsupervised Sentence Embedding learning 24

An Optimization Method TSDAE -based for Unsupervised Sentenc...

引用

7th international conference on Signal Processing and machine learning, SPML 2024

作者： Zhu, YueYing Zhang, Xu Zhao, Qi School of Computer Science Liupanshui Normal University China School of Education Science Liupanshui Normal University China

ISBN: (纸本)9798400717192

As for the research on learning sentence embeddedness, most of the research methods involved at present are designed for labeled data in the general field. the evaluation is also basically carried out on a single task Semantic Textual Similarity (STS). However, many tasks in specific fields are involved in the real society, which presents certain challenges compared with general fields. For example, there are most unlabeled data sets and certain professional knowledge. the learning methods in general fields are difficult to adapt to specific fields or tasks. To solve the problem of unlabeled data sets, specialized knowledge in specific fields or tasks, we propose an optimization method TSDAE-based for unsupervised sentence embedding learning(AOM), to optimize and improve the existing unsupervised sentence embedding learning based on pre-trained transformers and sequential denoising auto encoder (TSDAE)[9]. We firmly believe that improving the training difficulty of noise reduction autoencoders in unsupervised sentence representation learning is the key to achieve further breakthroughs in semantic text similarity (STS) tasks. Based on this idea, this study innovates the existing technology. Our research results show that in STS (Semantic Text similarity) downstream task testing, our proposed improvement strategy only needs one-tenth of the corpus data of the original method to exceed the performance of the original model. © 2024 ACM.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Analyzing data Scientist Salaries dataset through machine learning Algorithms Using Tool 'Orange' 7

Analyzing Data Scientist Salaries Dataset Through Machine Le...

引用

2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mittal, Shikha Monga, Chetna Bansal, Ankit Singla, Neeraj Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304268

the buzz surrounding artificial intelligence has made machine learning a hot topic right now. Although there are many tools for visualising data, scripting languages are primarily used for model training. the Orange data mining tool gives you a lot of ways to change how data is preprocessed, how it is displayed, how models are trained, and how models are tested. In order to establish which strategy has the best Classification Accuracy and Precision, the proposed research uses machine learning techniques to predict the size of an organization based on a variety of parameters, including employee experience, income, job type, employee type, etc. the effectiveness of various machine learning techniques, including Naive Bayes, random forests, support vector machines, neural network, logistic regression, was evaluated. Classification Accuracy evaluations are performed by cross validation. For this paper, we consulted the Kaggle dataset. © 2023 IEEE.

关键词： classification confusion matrix cross validation employee dataset machine learning orange tool for data mining

来源：评论

学校读者我要写书评

暂无评论

Heart Attack Prediction Using Big data Analytics 7th

Heart Attack Prediction Using Big Data Analytics

引用

7th IFIP TC 12 international conference on Computational Intelligence in data Science, ICCIDS 2024

作者： Akalya, A. Swedha, V. Department of IT Saveetha Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9783031699856

Acute myocardial infarction, sometimes known as a heart attack, is among the most fatal conditions that patients encounter. Large-scale data analysis, comparison, and mining for insights that can be applied to forecast, prevent, manage, and cure chronic illnesses like heart attacks are crucial to effectively treating cardiovascular illness. With tremendous efficacy, big data analytics—which is widely recognized in the corporate world for its application in regulating, comparing, and managing massive datasets—can be utilized to forecast, prevent, manage, and treat cardiovascular disease. Large-scale datasets can be analyzed using big data technologies like Hadoop, data mining, and visualization. © IFIP international Federation for Information Processing 2024

关键词： Big data data mining data mining Health care Personalized patient care Heart attack Internet of things (IoT) Internet of things (IoT) machine learning machine learning Nursing Privacy Telecardiology Telecardiology

来源：评论

学校读者我要写书评

暂无评论

An Automated machine learning Workflow to Classify Soil Texture for Crop Yield 7

An Automated Machine Learning Workflow to Classify Soil Text...

引用

7th international conference on Computer Information Science and Application Technology, CISAT 2024

作者： Chuah, Chai Wen Arbaiy, Nureize Binti Li, Siqiao Guangdong University of Science & Technology Dongguang China Universiti Tun Hussein Onn Malaysia Johor Malaysia Guangxi Academy of Sciences Guangxi China

ISBN: (纸本)9798350375107

Soil classification is used for crop selection to determine the type of activity that can be done on a particular piece of land. this is especially important for plantation purposes and crop selection. the current method of assessing soil type is time-consuming which relies on the expertise of agricultural professionals. the implementation of machine learning is expected to improve soil classification and provide better crop recommendations. the use of machine learning to classify soil could lead to more effective farming methods and improved crop suggestions. In this study, three algorithms were tested: Random Forest, Naïve Bayes, and k-Nearest Neighbor. Classification techniques were chosen as a data mining task to create a classification model. Random Forest had the best accuracy (99.81%), followed by K-nearest Neighbors (97.47%) and Naïve Bayes (89.11%). this means that Random F orest outperformed the other methods. the findings suggest that Random Forest could be effective in the agricultural domain for accurate soil classification. the results of the study will focus on the differences, capabilities, accuracy, and performance of the classifiers for the selected machine learning algorithms. the findings of the study are also beneficial to the plantation industry, as the classified soil data can be used to determine which crop is best suited to a certain soil type. © 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

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66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

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：