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

3,080 篇 会议
141 册 图书
24 篇 期刊文献

馆藏范围

3,244 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,110 篇 工学
- 1,926 篇 计算机科学与技术...
- 1,072 篇 软件工程
- 412 篇 信息与通信工程
- 286 篇 电气工程
- 265 篇 生物工程
- 226 篇 控制科学与工程
- 169 篇 生物医学工程（可授...
- 81 篇 化学工程与技术
- 76 篇 机械工程
- 68 篇 光学工程
- 68 篇 仪器科学与技术
- 64 篇 网络空间安全
- 63 篇 电子科学与技术（可...
- 62 篇 安全科学与工程
- 57 篇 动力工程及工程热...
767 篇 理学
- 435 篇 数学
- 290 篇 生物学
- 149 篇 统计学（可授理学、...
- 137 篇 物理学
- 128 篇 系统科学
- 90 篇 化学
502 篇 管理学
- 304 篇 图书情报与档案管...
- 228 篇 管理科学与工程(可...
- 93 篇 工商管理
222 篇 医学
- 191 篇 临床医学
- 123 篇 基础医学(可授医学...
- 87 篇 药学(可授医学、理...
- 59 篇 公共卫生与预防医...
69 篇 法学
- 67 篇 社会学
52 篇 教育学
- 47 篇 教育学
35 篇 经济学
28 篇 农学
6 篇 艺术学
4 篇 军事学
1 篇 文学

主题

571 篇 data mining
546 篇 machine learning
241 篇 pattern recognit...
183 篇 deep learning
173 篇 feature extracti...
156 篇 artificial intel...
151 篇 support vector m...
114 篇 accuracy
113 篇 machine learning...
111 篇 training
93 篇 data models
82 篇 neural networks
77 篇 data mining and ...
65 篇 classification
62 篇 sentiment analys...
61 篇 classification a...
59 篇 big data
57 篇 predictive model...
50 篇 face recognition
48 篇 prediction algor...

机构

7 篇 maharishi school...
6 篇 guangdong univer...
5 篇 centre of interd...
5 篇 chitkara univers...
5 篇 uttaranchal univ...
5 篇 uttaranchal inst...
5 篇 institute of aut...
4 篇 shanghai jiao to...
4 篇 chitkara univers...
4 篇 school of inform...
4 篇 indian statistic...
4 篇 south china univ...
4 篇 galgotias univer...
4 篇 zhejiang univers...
4 篇 maharishi school...
4 篇 department of cs...
4 篇 pune
4 篇 university of ca...
4 篇 school of comput...
4 篇 department of co...

作者

9 篇 de-shuang huang
7 篇 coenen frans
7 篇 venkatesh svetha
7 篇 petra perner
7 篇 tianrui li
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 jacek m. zurada
6 篇 krasotkina olga
6 篇 lotfi a. zadeh
6 篇 mottl vadim
6 篇 leszek rutkowski
5 篇 vitoantonio bevi...
5 篇 sankar k. pal
4 篇 waiyamai kitsana
4 篇 sozo inoue
4 篇 sergei o. kuznet...
4 篇 duoqian miao
4 篇 shengli xie
4 篇 hepu deng

语言

3,100 篇 英文
135 篇 其他
32 篇 中文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是3081-3090 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：

Exploring students eating habits through individual profiling and clustering analysis 3rd

Exploring students eating habits through individual profilin...

引用

3rd Workshop on mining data for Financial Applications, MIDAS 2018 and 2nd international Workshop on Personal Analytics and Privacy, PAP 2018 held at 18th European conference on machine learning and Knowledge Discovery in databases, ECML PKDD 2018

作者： Natilli, Michela Monreale, Anna Guidotti, Riccardo Pappalardo, Luca University of Pisa Largo B. Pontecorvo 3 Pisa Italy KDDLab ISTI-CNR Via G. Moruzzi 1 Pisa Italy

ISBN: (纸本)9783030134624

Individual well-being strongly depends on food habits, therefore it is important to educate the general population, and especially young people, to the importance of a healthy and balanced diet. To this end, understanding the real eating habits of people becomes fundamental for a better and more effective intervention to improve the students’ diet. In this paper we present two exploratory analyses based on centroid-based clustering that have the goal of understanding the food habits of university students. The first clustering analysis simply exploits the information about the students’ food consumption of specific food categories, while the second exploratory analysis includes the temporal dimension in order to capture the information about when the students consume specific foods. The second approach enables the study of the impact of the time of consumption on the choice of the food. © Springer Nature Switzerland AG 2019.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Electronic Music Type Identification Based on Multimodal learning Optimized Residual Neural Networks

Electronic Music Type Identification Based on Multimodal Lea...

引用

Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Jingying Guo Peng Wang School of Media and Performing Arts Coventry UniversityCoventry CV1 5RW UK Beijing City University Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

The classification effect of electronic music may be low due to the complexity of its type branches. Therefore, a research on electronic music type identification method based on multimodal learning optimization residual neural network is proposed. This paper analyzes the characteristics of electronic music from three perspectives of short-time energy, time-domain distribution and frequency-domain distribution, divides the original continuous music signal into several short segments (frames), and extracts the corresponding short-time energy, time-domain variance and frequency-domain variance information. In the stage of classification and recognition, the residual module is introduced into the neural network, and three characteristic modes are fitted to determine the final classification results. In the test results, the AUC of the recognition results of rhythmic electronic music and atmospheric electronic music is basically above 0.7, and the minimum value is also above 0.68. On the overall perspective, the AUG mean is 0.0783 and 0.0615 lower than that of the control group, respectively.

关键词： Training Accuracy Frequency-domain analysis Feature extraction User experience Electronic music data mining Time-domain analysis Residual neural networks Software engineering

来源：评论

学校读者我要写书评

暂无评论

Emotion Representation in Whole-brain Functional Connectivity patterns: An fMRI Study

Emotion Representation in Whole-brain Functional Connectivit...

引用

2017 3rd international conference on Artificial Intelligence and Industrial Engineering (AIIE2017)

作者： Yin LIANG School of Computer Science and Technology Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjin University

The representation of emotion in human brain is an important question in the cognitive neuroscience. However, it was remained unclear to what extent the connections between different brain regions contribute to the emotion recognition. The present study mainly focused on the emotion decoding based on the functional connectivity patterns. We designed the experiment and collected the neural activities while participants viewed emotion stimuli using the functional magnetic resonance imaging(f MRI) technology. We constructed the whole-brain functional connectivity patterns for each emotion, and performed emotion classification using multivariate pattern analysis combined with machine learning algorithms. We found that emotions could be successfully decoded from the whole-brain functional connectivity patterns. These results provide new evidence that large-scale functional connectivity patterns contain rich emotion information and contribute to the emotion recognition. Our study extends exist f MRI studies on emotion perception and may further our understanding of how human beings achieve easy and quick recognition of emotions.

关键词： Emotion Functional magnetic resonance imaging Functional connectivity Multivariate pattern analysis machine learning

来源：评论

学校读者我要写书评

暂无评论

Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks 13th

引用

13th international conference on machine learning and data mining in pattern recognition, MLDM 2017

作者： Antony, Joseph McGuinness, Kevin Moran, Kieran O’Connor, Noel E. Insight Centre for Data Analytics Dublin City University Dublin Ireland School of Health and Human Performance Dublin City University Dublin Ireland

ISBN: (纸本)9783319624150

This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints;next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN).We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This joint training further improves the overall quantification of knee OA severity, with the added benefit of naturally producing simultaneous multi-class classification and regression outputs. Two public datasets are used to evaluate our approach, the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST), with extremely promising results that outperform existing approaches. © Springer international Publishing AG 2017.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A Survey on Wireless Network Traffic Analysis using machine learning Algorithms

A Survey on Wireless Network Traffic Analysis using Machine ...

引用

international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： M. Praveen Kumar P Ashwitha Noble V S Esha Malavika G Geethanjali A S Farheen Department of CSE Sri Eshwar College of Engineering Coimbatore India

Analyzing and forecasting network traffic for different applications has gained popularity in recent years. Several tests have been carried out to find issues with the computer network apps that are in use today. Anticipating and evaluating network traffic can assist in implementing preemptive steps to guarantee safe, dependable, and excellent network connection. Numerous methods, including data mining approaches and neural network-based techniques, have been suggested and assessed for the purpose of predicting patterns of network traffic. For this goal, other linear and nonlinear models have also been developed. The main objectives of combining different network analysis and prediction techniques are efficiency and optimization. The goal of this survey study is to present a thorough review of the variety of methods used in traffic prediction and network analysis. The paper delves into the distinctive features and findings of previous research efforts and brings to light the patterns that have emerged from these studies. Furthermore, it provides an overview of the various areas in which network traffic analysis and prediction have shown their effectiveness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Apple Leaf Disease Identification and Classification using ResNet Models

Apple Leaf Disease Identification and Classification using R...

引用

IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Xin Li Laxmisha Rai College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

With the development and popularization of intelligent agricultural system, more and more research and attention have been paid to the detection and identification of leaf diseases. We used data sets of apple grey-spot disease, black star disease, cedar rust disease and healthy leaves to study the identification and classification of apple leaf diseases. Image segmentation SVM classifier and ResNet and VGG convolutional neural network model were used for comparison and improvement. In the final experiment, ResNet-18 with fewer layers of ResNet obtained an accuracy rate of 98.5% achieving better recognition effects.

关键词： Training Symbiosis Support vector machines Image segmentation machine learning Classification algorithms Diseases

来源：评论

学校读者我要写书评

暂无评论

Basic Structure on Artificial Intelligence: A Revolution in Risk Management and Compliance

Basic Structure on Artificial Intelligence: A Revolution in ...

引用

international conference on Intelligent Sustainable Systems (ICISS)

作者： Pradeep Bedi S B Goyal Jugnesh Kumar Graphic Era Hill University Dehradun INDIA City University MALAYSIA St. Andrews Institute of Technology & Management Gurgaon INDIA

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

This paper discusses how risk assessment is changed through artificial intelligence (AI) solutions. The key AI strategies of benefit to risk management are first given a non-technical description. A study of the application of these methods to the risk management fields of credit risk, market risk, organizational risk, and enforcement is then presented, utilizing existing experience and empirical data. Further, this research will be concluded with some reflections on existing constraints and views on how in the short- to medium-term the sector is expected to evolve. Overall, an ambitious image of AI's position will be provided for risk management, but notice some realistic restrictions about effective data management practices, accountability and lack of requisite skill sets inside organizations.

关键词： Artificial intelligence data mining Risk management machine learning Industries data models Business

来源：评论

学校读者我要写书评

暂无评论

An Ideology for the Prediction of Critical Haplotype Blocks of Variants in Genes (Cyp2c9 And Vkorc1) for Warfarin (Anticoagulant) Drug Dosage to Treat Heart Patients Efficiently by Using Ml (machine learning) and data Stream mining Techniques 19

An Ideology for the Prediction of Critical Haplotype Blocks ...

引用

Proceedings of the 2019 3rd international conference on Advances in Image Processing

作者： Hina Saeeda Muhammad Adil Abid College of information science and engineering Ocean University of China Qingdao China Computer Science & Technology Shandong University Qingdao China

ISBN: (纸本)9781450376754

Now a day's on time treatment of heart diseases is a very critical part of medical diagnoses. So far there are total 50 SNP (Single Nucleotide Polymorphism) diagnosed that are responsible for the heart problems. But it is very hard to study all of the SNP together because of their different base pairs' locations or changes in base pairs positions (variations in genetic code A C G T). These all 50 SNP are present in all individuals with different variations, it is a tough job to calculate all the changes in this SNP set as there are total of (50^50) positions to calculate which is making it a huge data set. For acquiring a data set of all these positions, we will need some good data Stream mining (data mining techniques) to find out all the possible locations of all the variants responsible for the heart problems. In this research paper, we are giving a short analysis and introduction to the problem of heart patients drug dosage associated with anticoagulant (Warfarin) and its risks, solution for the challenge of calculating all variants of two genes (CYP2C9 and VKORC1) and advantages of the proposed solution in the future.

关键词： machine learning Single Nucleotide Polymorphism genes VKORC1 data stream mining data mining CYP2C9 warfarin Haplotype Blocks

来源：评论

学校读者我要写书评

暂无评论

A Brute-Force Attack Detection Method Based on the Combination of Artificial Neural Network and Auto Encoder

A Brute-Force Attack Detection Method Based on the Combinati...

引用

Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Xiaojun Ma Qingyuan Li Xiaofei Wang Quan Wen Yingming Zeng Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted research on the deliberate disruption of network traffic. Two issues currently impede the efficacy of machine learning and deep learning models in detecting brute force attacks. First, the presence of noise in the traffic data reduces the accuracy and robustness of detection. Second, when an attacker modifies the frequency, intensity, time, and other parameters of their attack to evade detection, the characteristics of the attack traffic become more complex and diverse. This, in turn, results in a notable decline in detection accuracy. In order to address the aforementioned issues, this paper presents a novel detection model that integrates the capabilities of an artificial neural network (ANN) and an auto-encoder (AE). The proposed model leverages the ANN's proficiency in recognizing intricate data patterns and the AE's effectiveness in removing noise, thereby facilitating precise detection of brute-force attacks. The experimental results demonstrate that the proposed detection model exhibits an enhanced accuracy rate of 99.93% in CICIDS-2017, exhibiting superior overall detection performance compared to existing algorithmic models such as AE and DAE. Additionally, it demonstrates a reduced false alarm rate and effective mitigation of the impact of noise and interference on attack data.

关键词： Time-frequency analysis Accuracy Prevention and mitigation Noise Telecommunication traffic Feature extraction Brute force attacks Robustness pattern recognition Software engineering

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based Functional Prediction Model for Genome Sequence

Deep Learning-based Functional Prediction Model for Genome S...

引用

data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Bo Lan United World College of South East Asia East Singapore Singapore

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

This study proposes a deep learning-based genome sequence function prediction model and systematically evaluates its performance. The study begins with an overview of the importance of genome sequence function prediction and its applications in biomedicine, followed by a detailed description of the main current research methods and advances. We use Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Transformer model for capturing local features, sequence dependencies and long-range dependencies, respectively. Through the combined application of multiple feature extraction methods, the model demonstrates excellent prediction performance on different genomic datasets. The experimental results show that the deep learning model can effectively improve the accuracy of genomic sequence function prediction, especially in complex feature extraction and pattern recognition. Convolutional neural networks have obvious advantages in local feature extraction, recurrent neural networks excel in dealing with the time dependence of sequence data, and the Transformer model demonstrates its superiority in dealing with long-distance dependencies and parallel computation.

关键词： Deep learning Recurrent neural networks Computational modeling Biological system modeling Genomics Predictive models Feature extraction Transformers Convolutional neural networks Bioinformatics

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325页 << < 305 306 307 308 309 310 311 312 313 314 > >>

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：