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

448 篇 期刊文献
297 篇 会议
4 册 图书

馆藏范围

749 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

405 篇 工学
- 274 篇 计算机科学与技术...
- 251 篇 软件工程
- 85 篇 生物工程
- 70 篇 信息与通信工程
- 70 篇 生物医学工程（可授...
- 50 篇 控制科学与工程
- 37 篇 电气工程
- 25 篇 机械工程
- 25 篇 化学工程与技术
- 25 篇 安全科学与工程
- 22 篇 电子科学与技术（可...
- 21 篇 光学工程
- 18 篇 动力工程及工程热...
- 16 篇 环境科学与工程（可...
300 篇 理学
- 137 篇 数学
- 113 篇 生物学
- 59 篇 统计学（可授理学、...
- 57 篇 物理学
- 39 篇 系统科学
- 25 篇 化学
- 22 篇 地球物理学
137 篇 管理学
- 73 篇 图书情报与档案管...
- 59 篇 管理科学与工程(可...
- 36 篇 工商管理
133 篇 医学
- 110 篇 临床医学
- 62 篇 基础医学(可授医学...
- 46 篇 公共卫生与预防医...
- 38 篇 药学(可授医学、理...
31 篇 农学
19 篇 教育学
17 篇 法学
- 17 篇 社会学
15 篇 经济学
- 15 篇 应用经济学

主题

31 篇 machine learning
27 篇 deep learning
18 篇 data models
17 篇 feature extracti...
17 篇 training
16 篇 forecasting
15 篇 covid-19
14 篇 predictive model...
14 篇 industries
13 篇 support vector m...
13 篇 semantics
12 篇 task analysis
12 篇 data mining
12 篇 artificial intel...
11 篇 neural networks
11 篇 data analysis
10 篇 machine learning...
9 篇 big data
9 篇 computational mo...
9 篇 visualization

机构

19 篇 institute for bi...
18 篇 school of comput...
16 篇 institute for co...
15 篇 national enginee...
13 篇 msc. in big data...
11 篇 school of comput...
10 篇 institute of com...
10 篇 national enginee...
10 篇 university of ba...
9 篇 institute for bi...
9 篇 peking universit...
8 篇 college of elect...
8 篇 national enginee...
7 篇 technology innov...
7 篇 physics and appl...
7 篇 college of compu...
7 篇 center for moder...
7 篇 lugano
7 篇 university of pe...
7 篇 university of tw...

作者

31 篇 matwin stan
19 篇 liu jun
19 篇 pal supratik
12 篇 li chen
10 篇 gao zeyu
10 篇 hewahi nabil
10 篇 soares amilcar
9 篇 sarvmaili mahtab
9 篇 zhang lingling
9 篇 etemad mohammad
9 篇 bernasconi alice
9 篇 zanga alessio
9 篇 zare nader
8 篇 ahmed m. zeki
8 篇 rui yang
8 篇 yuan kun
8 篇 chen li
8 篇 gong tieliang
8 篇 wu jialun
8 篇 bao yukun

语言

611 篇 英文
127 篇 其他
13 篇 中文
1 篇 德文
1 篇 法文
1 篇 土耳其文

检索条件"机构=Data Science and Big Data Analytics Unit"

共 749 条记录，以下是31-40 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：

An Enhanced BERTopic Framework and Algorithm for Improving Topic Coherence and Diversity 24

An Enhanced BERTopic Framework and Algorithm for Improving T...

引用

24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Sawant, Sahil Yu, Jinhong Pandya, Kirtikumar Ngan, Chun-Kit Bardeli, Rolf Data Science Program Worcester Polytechnic Institute WorcesterMA United States Big Data and Consumer Analytics Vodafone GmbH NRW Duesseldorf Germany

ISBN: (纸本)9798350319934

In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even moderate dataset. More specifically, the contributions of this work are threefold: (1) integrate a dynamic and advanced optimizer into the existing BERTopic framework to learn the optimal number of dimensions of different document embeddings, (2) develop a k-means-based algorithm in the optimizer to support the dimension-embedding learning, and (3) conduct an extensive experimental study on three distinct types of datasets, including DBPedia, AG News, and Reuters, to evaluate the performance of our approach in terms of the topic quality (TQ) score computed by the topic coherence and the topic diversity. From the results, we can conclude that our enhanced, automated BERTopic framework with its dimension-embedding learning algorithm on documents outperforms the TQ score of the existing framework by 4.49% (before removing the stop words) and 16.52% (after removing the stop words) among all the four representable document-embedding approaches, including the BERTopic's Default Sentence Transformer, Google's Universal Sentence Encoder, OpenAI GPT-2, and our investigators' developed Context-aware Embedding Model, on all the three datasets. © 2022 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Aviation Collision Risk Warning for Non-Tower Airport Based on Trajectory Prediction 3

Aviation Collision Risk Warning for Non-Tower Airport Based ...

引用

2023 3rd International Conference on Mechatronics Technology and Aerospace Engineering, ICMTAE 2023

作者： Yang, Lijian Zeng, Zhuo Liu, Kai Zi, Feiyue Wang, Yazhong Unit 95616 of the PLA Qionglai611531 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611721 China Chengdu Union Big Data Tech. Inc. Chengdu610041 China

With the development of the civil aviation industry, the throughput of airports continues to increase, including non-tower airports (small airports without the ability to actively communicate with aircraft). These non-tower civil airports can handle small aircraft such as personal aircraft well and lay a solid foundation for future automated aircraft. However, there is a high collision risk in taking off or landing because the probability of avoiding risks highly relies on the pilot's flight proficiency and experience. To address this problem, we studied the collision risk of aircraft in non-tower airports and proposed a collision risk warning method based on trajectory prediction. The proposed method obtains the probability of future collisions by learning the flight patterns of all aircraft in non-tower airports, the impact of environmental wind speed on pilot behavior, and the interaction patterns between aircraft. At the same time, we conducted in-depth optimization of the probability integration calculation process under a conditional threshold to reduce the time complexity greatly. The simulation experiment results on non-tower airports show that the designed method for collision risk warning can effectively address the aircraft collision prediction problem in non-tower airports, and outperform other benchmark learning methods. It has significant guiding implications for improving airport intelligence and aircraft automation in the future. © Published under licence by IOP Publishing Ltd.

关键词： Airports

来源：评论

学校读者我要写书评

暂无评论

Response to yarmolinsky, tzoulaki, gunter, et al

引用

Journal of the National Cancer Institute 2024年第5期116卷 766-767页

作者： Jing Sun Xue Li Department of Big Data in Health Science School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China.

来源：评论

学校读者我要写书评

暂无评论

RespoBot: Chatbot used for the prediction of diseases using Machine Learning and Deep Learning with respect to Covid-19 2

RespoBot: Chatbot used for the prediction of diseases using ...

引用

2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Sreedevi, B. Rosewelt, L. Antony Ramesh, Neela Sri Sai Ram Institute of Technology Computer Science and Engineering Chennai India Sri Sai Ram Institute of Technology Big Data Analytics Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Resembling man's evolution over time the life threatening diseases have also evolved with him a disease is basically a disorder in structure or functioning of an organism's body. There are a several kinds of diseases but the most dangerous ones are the infectious diseases. Infectious diseases occur when a foreign body such as bacteria, fungi, and virus enter the body and attack the immune system. That being the smaller problem of infectious diseases as the main problem it's a communicable disease that spreads when a uninfected person comes in contact with an infected person they are highly likely to contract the same. The prediction is performed using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent SGD), Gradient Boosting (GD), Decision Trees (DT), Naive Bayes Classifier (NBC) and Voting Classifier (VC) ensemble technique. After the prediction a comparative analysis in terms of accuracy is performed. A Chatbot using Natural Learning Processing on a Neural Network is employed. The user is asked to upload a Chest X-ray if in possession as a second step confirmation for Tuberculosis, Pneumonia, Covid-19 and Healthy lung. In terms of highest accuracy for Covid-19 the GBC, VC, 56 tops the charts and for other diseases also GBC, VC, ANN top the charts. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

引用

2023 International Conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

引用

2023 IEEE Conference on Engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

Logic-Aware Knowledge Graph Reasoning for Structural Sparsity under Large Language Model Supervision 25

Logic-Aware Knowledge Graph Reasoning for Structural Sparsit...

引用

34th ACM Web Conference, WWW 2025

作者： Pan, Yudai Hong, Jiajie Zhao, Tianzhe Song, Lingyun Liu, Jun Shang, Xuequn School of Computer Science Northwest Polytechnical University Shaanxi Xi’an China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Shaanxi Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an China

ISBN: (纸本)9798400712746

Knowledge Graph (KG) reasoning aims to predict missing entities in incomplete triples, which requires adequate structural information to derive accurate embeddings. However, KGs in the real world are not as dense as the idealized benchmarks, where sparse graph structures restrict the comprehensive structural information for superior performance. Although the logical semantics in KGs shows its potential in alleviating the impact of structural sparsity, there still exist some challenges. The deficient supervision and the semantic gap of logic make it difficult to introduce logical semantics in sparse KG reasoning. To this end, we propose a novel KG reasoning approach LoLLM injecting logic with the supervised information supplied by the Large Language Model (LLM), which is proved to be effective in evaluating and scoring. Firstly, LoLLM derives structural embeddings employing a graph convolutional network (GCN) with relation-aware and triple-aware attention. LoLLM secondly constructs reasoning paths instantiated from the first-order logic rules extracted from sparse KGs, and injects the logical semantics by a designed LLM-enhanced tuning strategy. We propose a textual loss (TL) and a logical loss (LL) in the optimization and obtain logical tuning embeddings of KG in this process. Finally, LoLLM fuses structural embeddings from the GCN and logical tuning embeddings from the LLM-enhanced tuning for scoring and incomplete triple prediction. Extensive experiments on two sparse KGs and a benchmark show that LoLLM outperforms state-of-the-art structure-based and Language Model (LM)-augmented baselines. Moreover, the logic rules with corresponding confidences provide explicit explanations as an interpretable paradigm. © 2025 Copyright held by the owner/author(s).

关键词： Structural analysis

来源：评论

学校读者我要写书评

暂无评论

Distributed Bilevel Optimization with Communication Compression 41

Distributed Bilevel Optimization with Communication Compress...

引用

41st International Conference on Machine Learning, ICML 2024

作者： He, Yutong Hu, Jie Huang, Xinmeng Lu, Songtao Wang, Bin Yuan, Kun Peking University China University of Pennsylvania United States IBM Research United States Zhejiang University China National Engineering Labratory for Big Data Analytics and Applications China AI for Science Institute Beijing China

Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conventional distributed bilevel methods, each worker must transmit full-dimensional stochastic gradients to the server every iteration, leading to significant communication overhead and thus hindering efficiency and scalability. To resolve this issue, we introduce the first family of distributed bilevel algorithms with communication compression. The primary challenge in algorithmic development is mitigating bias in hypergradient estimation caused by the nested structure. We first propose C-SOBA, a simple yet effective approach with unbiased compression and provable linear speedup convergence. However, it relies on strong assumptions on bounded gradients. To address this limitation, we explore the use of moving average, error feedback, and multi-step compression in bilevel optimization, resulting in a series of advanced algorithms with relaxed assumptions and improved convergence properties. Numerical experiments show that our compressed bilevel algorithms can achieve 10× reduction in communication overhead without severe performance degradation. Copyright 2024 by the author(s)

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Comparing the Performance of Machine Learning Algorithms for Predicting Employees' Turnover

Comparing the Performance of Machine Learning Algorithms for...

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Aldossary, Yasmeen Ebrahim, Majeed Alhaddad, Salman Aldossary, Manar Hilal, Sawsan University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain Department of Mathematics College of Science Bahrain

ISBN: (纸本)9781665490580

Employee turnover directly impacts companies ' performance due to losing qualified staff and invested costs, so predicting the ability to turnover or staying will help companies reduce these effects. Therefore, this study aimed to predict turnover by building a model using selected machine learning algorithms based on secondary data of a big data company. The algorithms are Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, and Random Forest, which have been improved with different methods for better performance, like balancing data and important feature selection. The Random Forest was characterized by the best performance with 80.2% accuracy and 72.4% AUC, with city development index, experience, and university enrolment being the most important features. However, the performance of the Random Forest classifier can be enhanced by including other important features besides those highlighted by the model. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Factors that Impact Tension String Tennis Racket

Factors that Impact Tension String Tennis Racket

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Yusuf, Husain Yusuf, Salman Hilal, Sawsan University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Science Department of Mathematics Bahrain

ISBN: (纸本)9781665490580

Tennis is growing in popularity year after year. It is an expensive sport that requires special equipment, such as rackets, balls, and tennis strings. One of the problems with tennis is tennis strings that have to be changed in short periods due to the loss of their tension. Most tennis strings are not recyclable as their excessive consumption negatively affects the environment. This study aims at identifying the factors that impact tennis strings. The data were extracted from the Tennis Warehouse University website which specializes in tennis and its equipment. After deleting missing values, the 80:20 ratio was used to divide the data into two distinct sets: training and testing. To this end, three regression methods were implemented: least squares, ridge, and lasso. Across all variables, only the Friction between the strings themselves and the Spin Potential was found to have an insignificant impact on the tennis strings. The results showed that polyester strings could withstand the most tension and those made of the gut were the worst performers. Furthermore, it's better to string the racket at medium tension instead of low tension. Despite the interesting results obtained concerning the factors that impact the tennis strings, the study can be improved by including other variables such as the tennis racket type, the racket shape, the methods of stringing the rackets, the types of balls, and the string thicknesses. © 2022 IEEE.

关键词： Tennis

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75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：