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3191-3200 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 Evaluation of Intelligent Network data Analytics Based on machine learning In 5G data Networks

An Evaluation of Intelligent Network Data Analytics Based on...

引用

international conference on Futuristic Technologies (INCOFT)

作者： Parth Batra Vikas Sagar Kanishk Kandoi Department of CSE(Data Science and AI) Nanyang Technological University Greater Noida India and Kendriya Vidyalaya Embassy of India Singapore and Noida Institute of Engineering and Technology Kathmandu Nepal Department of CSE(AI) Nanyang Technological University Department of Science Nanyang Technological University

Big data Analytics has developed as a judgment technique for unopened organizations to uncover hidden patterns, relationships, industry trends, and consumption patterns. Newline Of the most common sources of big data is Viral marketing data sets, as Web 2.0 null byte technology generate massive social corpora from our everyday routines. Newline in basic language Web 2.0 technology applications including Internet newline data analytics, relationship management, Text Analytics, and opinion line break mining depend heavily on *** before compared to prestige cellular networks, 5G cellular networks have many major updates, such as network data analytics-based network data analytics, which will allow network administrators to either implement their own machine learning (ML)-based data analytics methodologies or incorporating third-party solutions into their networks. This study originally presents the structure and protocols of network data analytics based on the 3rd Generation Partnership Project (3GPP) standard standards. Then, based on the fields specified by the 3GPP specification, a cell-based artificial data set for 5G networks is *** slice, a major 5G technology, divides a physical network into many virtual end-to-end networks, each of which may receive logically separate network resources to offer richer services. 5G mobile data and sensor data are combining to produce an increasing network traffic. Traffic explosion has grown into a mixed network type, involving network viruses, worms, network theft, and hostile assaults. How to differentiate traffic kinds, restrict fraudulent traffic, and make optimal use of sensor data in the context of a 5G network slice, as well as the importance of this researchAdditionally, some abnormalities are added to this collected data (for examples, unexpected increased volume in a particular tissue), and so these irregularities are categorized within each compartment, subscriber’s category, and remote controlle

关键词： Cellular networks Analytical models data analysis 5G mobile communication Web 2.0 Linear regression Big data

来源：评论

学校读者我要写书评

暂无评论

Epileptic Seizure Diagnosis Using Wavelet Transform and Approximate Entropy in EEG Signals

Epileptic Seizure Diagnosis Using Wavelet Transform and Appr...

引用

international conference on Electrical Power and Energy Systems (ICEPES)

作者： Shyam Babu Arun Kumar Wadhwani Dept. of Electrical Engineering Madhav Institute of Technology and Science Gwalior India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Determining epileptic events in EEG signals poses a considerable challenge as it necessitates a detailed study of comprehensive long-term EEG data acquired from different channels. In this study, author conceived an unexplored approach to automatically recognize seizures from continuous EEG monitoring using a attribute named approximate entropy (ApEn) obtained from the operation of wavelet transforms. They examined the data using a machine learning algorithm, applying the approach to different sets of EEG data gathered by various healthcare organizations and research institutions. The results showed that the suggested approach acquired an outstanding 96.34 % accuracy in noticing seizures in a detailed database and displayed an excellent 100% precision when determining data from healthy individuals and people with epilepsy. The ApEn approach revealed excellent accuracy and efficiency resembled to other entropy methods. This study underlines prospects of ApEn as a practical tool for determining and evaluating practices of brain activity associated with epilepsy. This approach could prove to be a useful implement in determining epileptic seizures, finally enhancing the diagnosis and practice of remedies for people with epilepsy.

关键词： Support vector machines Accuracy Sensitivity Epilepsy pattern classification Medical services Organizations

来源：评论

学校读者我要写书评

暂无评论

A machine learning Distracted Driving Prediction Model 2019

A Machine Learning Distracted Driving Prediction Model

引用

Proceedings of the 3rd international conference on Vision, Image and Signal Processing

作者： Samira Ahangari Mansoureh Jeihani Abdollah Dehzangi Department of Transportation and Infrastructure Studies Morgan State University USA Department of Computer Science Morgan State University USA

ISBN: (纸本)9781450376259

Distracted driving is known to be one of the core contributors to crashes in the U.S., accounting for about 40% of all crashes. Drivers' situational awareness, decision-making, and driving performance are impaired due to temporarily diverting their attention from the primary task of driving to other tasks not related to driving. Detecting driver distraction would help in adapting the most effective countermeasures. To find the best strategies to overcome this problem, we developed a Bayesian Network (BN) distracted driving prediction model using a driving *** this study, we use a Bayesian Network classifier as a powerful machine learning algorithm on our trained data (80%) and tested (20%) with the data collected from a driving simulator, in which the 92 participants drove six scenarios of hand-held calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performances such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were *** we investigated different optimization models to build the best BN in which a Genetic Search Algorithm obtained the best performance. As a result, we achieved a 67.8% prediction accuracy using our model to predict driver distraction. We also achieved 62.6% true positive rate, which demonstrates the ability of our model to correctly predict distractions.

关键词： data mining Driving Simulator machine learning Distracted Driving Bayesian Network

来源：评论

学校读者我要写书评

暂无评论

Corn Crop Yield Prediction using Deep learning

Corn Crop Yield Prediction using Deep Learning

引用

Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Iniyan S Pidikiti Keerthi Shruti Pawar Department of Computing Technologies SRM Institute of Science and Technology Kaatankulathur Chennai Tamil Nadu Department of Computer Science and Engineering SRM Institute of Science and Technology Kaatankulathur Chennai Tamil Nadu India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Accurate crop yield prediction has emerged as a critical area of research, especially in light of global food security challenges and the need for sustainable agricultural practices. In this study, we focus on predicting the yield of US corn, a staple crop with significant economic and nutritional value. By leveraging data mining techniques, we extract meaningful patterns from large agricultural datasets, aiming to enhance our understanding of the factors influencing crop yields. Accurate predictions can guide farmers and policymakers in making informed decisions, such as optimizing planting schedules, managing resources more effectively, and anticipating potential shortages. In this study, we utilized a variety of machine learning models, such as Logistic Regression, Decision Trees, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Random Forest, Support Vector machines (SVM), and Long Short Term Memory (LSTM) networks. These models were selected to explore their varying capabilities in handling complex agricultural data and providing accurate predictions. The final outcome, Linear Regression, achieved an impressive accuracy of 93%, demonstrating its effectiveness in predicting crop yields based on the given dataset.

关键词： Support vector machines Accuracy Recurrent neural networks Biological system modeling Linear regression Predictive models data models Crop yield Random forests Long short term memory

来源：评论

学校读者我要写书评

暂无评论

pattern recognition Based on Weighted and Supervised ART2

Pattern Recognition Based on Weighted and Supervised ART2

引用

2008 3rd international conference on Intelligent System and Knowledge Engineering(ISKE 2008)

作者： Chu Na, Ma Lizhuang Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai, China

It is crucial for TCM (Traditional Chinese Medicine) post-hepatitis cirrhosis diagnosis to accurately identify the syndrome. Meanwhile, the selection of features which are relevant to a certain TCM post-hepatitis cirrhosis syndrome not only improves the performance of the classifiers, but also provides well measure for treatment. Therefore, in this paper, we analyze the classical ART2(Adaptive Resonance Theory 2) neural network, such as the problem of pattern drifting and the same phase data with different amplitudes. Based on this, here, a novel network named SWART2 is proposed by taking dispersion testing and centroid computation learning, and introducing the weighted and supervised mechanism, which aims at improving ART2's ability of classification greatly for post- hepatitis cirrhosis diagnosis. Experimental results in this paper showed that the new SWART2 performed better than classical ART2.

关键词： pattern recognition Based on Weighted and Supervised ART2

来源：评论

学校读者我要写书评

暂无评论

Embedding naïve Bayes algorithm data model in predicting student graduation 19

Embedding naïve Bayes algorithm data model in predicting st...

引用

Proceedings of the 3rd international conference on Telecommunications and Communication Engineering

作者： Ace C. Lagman Joseph Q. Calleja Corazon G. Fernando Joseph G. Gonzales John Benedict Legaspi John Heland Jasper C. Ortega Ronel F. Ramos Maria Vicky S. Solomo Regina C. Santos FEU Institute of Technology Manila Philippines

ISBN: (纸本)9781450371803

In the Philippines, according to Philippine Authority of Statistics, there is an imbalance between the student enrollment and student graduation. Almost half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on time. The study aims to utilize how Naïve Bayes algorithm - a data classification algorithm that is based on probabilistic analysis - can be used in educational data mining specifically in student graduation. The study is focused on the application of the Naïve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions.

关键词： data mining machine learning algorithm big data analytics classification algorithm Naïve Bayes prediction student graduation

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis in Micro Grid Using Wavelet Transform in Combination with machine learning

Fault Diagnosis in Micro Grid Using Wavelet Transform in Com...

引用

Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Chidurala Saiprakash S Ramana Kumar Joga Bera Sowmya Teki Vamsee Krishna V. Lakshmi Narayana Vasupalli Manoj Department of Electrical and Electronics Engineering SR University Warangal India EEE Department Dadi Institute of Engineering and Technology Anakapalle India Department of EEE Sasi Institute of Technology & Engineering (Autonomous) Tadepalligudem India BS&H Department Dadi Institute of Engineering and Technology Anakapalle India EEE Department GMRIT Rajam Andhra Pradesh India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Microgrids, which are defined by their decentralized methods of generating and distributing electricity, have a vital role in improving the resilience and sustainability of energy systems. Ensuring the stability and efficiency of micro grids relies heavily on the identification of faults. Conventional fault detection techniques often lack the necessary sophistication to effectively manage the intricacy and variety of contemporary micro grid systems. This paper focuses on the issue of fault identification in micro grids. It presents a new method that combines wavelet transform and data mining approaches to tackle this difficulty. The goal is to provide a reliable approach that can accurately detect and categorize defects, resulting in reduced downtime and enhanced system performance. The wavelet transform allows for the analysis of signals by breaking them down into several frequency components, thereby offering a comprehensive depiction of the dynamics of a system. The suggested technique provides various benefits over existing fault detection methods, which often depend on unsophisticated threshold-based approaches. As a consequence, there is an enhancement in precision and dependability, which allows for prompt detection and resolution of errors, while reducing interruptions in the provision of energy. Moreover, the use of sophisticated data analytics improves the capability to distinguish between typical operational circumstances and distinct fault situations, hence minimizing inaccurate alerts and enhancing the efficiency of the system. The effectiveness of the suggested technique in improving the accuracy of fault detection and assuring the dependable functioning of micro grids is confirmed via simulation results.

关键词： Wavelet transforms Fault diagnosis Fault detection Microgrids machine learning Power system stability Wavelet analysis Feature extraction data mining Resilience

来源：评论

学校读者我要写书评

暂无评论

machine learning Prediction of TBI from Mobility, Gait and Balance patterns

Machine Learning Prediction of TBI from Mobility, Gait and B...

引用

IEEE international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Bhoomi Patel Srinarayan Srikanthan Florina Asanit Emmanuel Agu Worcester Polytechnic Institute Worcester

ISBN: (纸本)9781665439664

Traumatic Brain Injury (TBI) 1 , caused by a violent blow to the head, has become a major cause of death and disability with 56,800 deaths reported in 2014. Early detection of TBI can reduce emergency visits and save lives. Prior methods were clinical tests such as Computerized Tomography (CT) that are expensive and required hospital visits, or mobile health applications that required active involvement that is burdensome. Sensor-rich smartphones have emerged as viable platforms for continuous health monitoring. In this paper, we explore using smartphone sensors for detecting TBI at the onset of injury. Mobility patterns, gait and balance are three human attributes that distinguish subjects with TBI from non-TBI controls. Our work is the first to use machine learning approaches to detect TBI using mobility, gait and balance features extracted from passively gathered smartphone sensor data. Three machine learning pipelines were investigated and compared, namely; (1a) computing hand-crafted features on raw sensor data; (1b) computing hand-crafted features on pre-processed sensor data; (2) using an auto-encoder based approach to encode raw mobility patterns that were then combined with handcrafted gait and balance features. In total, we explored 6 location features, 9 gait and 4 balance statistical features extracted from the smartphone's location and accelerometer sensor data using different segmentation methods. These features were then normalized and classified using machine learning algorithms. Of the three approaches, the best results were achieved using classification of handcrafted gait, balance and mobility features using tree-based classifiers. However, encoding mobility features using an autoencoder that were classified using the Random Forest classifier achieved the best results in terms of early detection. Hand-crafted feature extraction on raw-sensor data gave the best results on the 3rd day after injury using a 24-hour window size with XGBoost achieving S

关键词： Sensitivity Computed tomography Pipelines Sensor phenomena and characterization Feature extraction data mining Smart phones

来源：评论

学校读者我要写书评

暂无评论

Classifying Paintings into Movements using HOG and LBP Features 19

Classifying Paintings into Movements using HOG and LBP Featu...

引用

Proceedings of the 3rd international conference on Big data Research

作者： Aakansha Mathur Shruti Arippara Vinodh Siddhaling Urolagin Department of Computer Science Birla Institute of Technology & Science Pilani - Dubai Campus Dubai UAE

ISBN: (纸本)9781450372015

With the increase in availability of paintings on the web, the importance of organizing art collections cannot be overstated. By classifying paintings based on art movements, information about paintings on the web can be well structured. This will also help us garner insights into more obscure paintings and the styles they embody. This paper discusses a method of classifying paintings into two art movements, namely Cubism and Romanticism, using two texture descriptors: Local Binary pattern (LBP) and Histogram of Oriented Gradients (HOG). The paper also extends the applicability of the method to different classifiers. Some classifiers used for classification are KNN, NuSVC, LinearSVC, GaussNB, Decision Tree, Random Forest, AdaBoost and Gradient Boost. A subset of the Pandora database is used as a dataset. From the results, it can be inferred that using the Gradient Boost classifier gives the highest overall accuracy for Cubism and Romanticism when LBP and HOG are used as texture descriptors. Moreover, it can be seen that LBP emerges as the best feature for classifying paintings into the Cubism and Romanticism Art Movements.

关键词： support vector machine classification texture descriptors visual data mining movements random forest genre classification gradient boost classifier local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Structural Damage Detection by Integrating Non-negative Matrix Factorization and Relevance Vector machines

Structural Damage Detection by Integrating Non-negative Matr...

引用

2012 3rd international conference on Applied Mechanics and Mechanical Engineering(ICAMME 2012)

作者： Shumei Zhou Yuequan Bao School of Civil Engineering Harbin Institute of Technology

This paper proposes a structural damage detection method based on wavelet packet decomposition, non-negative matrix factorization(NMF) and a relevance vector machine(RVM). First, vibration data at multiple points are used to calculate the wavelet packet node energies and construct a non-negative damage feature matrix. Second, to increase the damage detection accuracy, the NMF technique is employed to obtain the reduced dimensional representation of the non-negative damage feature matrix and extract the underlying features. Last, the RVM, a powerful tool for classification and regression, that can obtain the probability estimation for classification, is used to determine the relationship between features extracted with NMF and the corresponding damage patterns by considering the measurement noise. The trained RVMs are then used to perform damage pattern identification and classification of an unknown state structure. Numerical study on the Binzhou Yellow River Highway Bridge is carried out to validate the ability of the proposed method in damage detection. The results show that the RVM can achieve a high accuracy in damage pattern identification accuracy using the features extracted by NMF.

关键词： Structural damage detection pattern recognition Non-negative matrix factorization Relevance vector machine Wavelet packet transform

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 << < 316 317 318 319 320 321 322 323 324 325 > >>

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：