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

2,973 篇 会议
174 册 图书
24 篇 期刊文献

馆藏范围

3,170 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,043 篇 工学
- 1,825 篇 计算机科学与技术...
- 960 篇 软件工程
- 329 篇 电气工程
- 313 篇 信息与通信工程
- 214 篇 控制科学与工程
- 207 篇 生物工程
- 149 篇 生物医学工程（可授...
- 71 篇 光学工程
- 64 篇 机械工程
- 62 篇 化学工程与技术
- 51 篇 电子科学与技术（可...
- 47 篇 仪器科学与技术
- 46 篇 安全科学与工程
- 36 篇 交通运输工程
648 篇 理学
- 352 篇 数学
- 244 篇 生物学
- 140 篇 物理学
- 135 篇 统计学（可授理学、...
- 95 篇 系统科学
- 62 篇 化学
408 篇 管理学
- 244 篇 图书情报与档案管...
- 182 篇 管理科学与工程(可...
- 83 篇 工商管理
262 篇 医学
- 241 篇 临床医学
- 123 篇 基础医学(可授医学...
- 74 篇 药学(可授医学、理...
- 40 篇 公共卫生与预防医...
46 篇 法学
- 42 篇 社会学
38 篇 教育学
- 32 篇 教育学
33 篇 经济学
- 31 篇 应用经济学
23 篇 农学
6 篇 艺术学
4 篇 文学

主题

573 篇 machine learning
564 篇 data mining
321 篇 pattern recognit...
212 篇 deep learning
201 篇 feature extracti...
154 篇 support vector m...
145 篇 artificial intel...
129 篇 machine learning...
121 篇 training
120 篇 accuracy
90 篇 data models
89 篇 data mining and ...
72 篇 neural networks
71 篇 classification
69 篇 predictive model...
66 篇 face recognition
61 篇 learning systems
52 篇 computer imaging...
50 篇 convolutional ne...
48 篇 classification a...

机构

9 篇 school of comput...
9 篇 chinese academy ...
8 篇 zhejiang univers...
8 篇 chitkara univers...
7 篇 guangdong univer...
7 篇 school of inform...
6 篇 xi'an university...
6 篇 indian statistic...
6 篇 central south un...
6 篇 department of co...
6 篇 unsw sydney
6 篇 xi'an university...
6 篇 southern univers...
5 篇 study group aust...
5 篇 peking universit...
4 篇 centre for patte...
4 篇 chongqing univer...
4 篇 national laborat...
4 篇 sun yat sen univ...
4 篇 school of inform...

作者

11 篇 de-shuang huang
8 篇 petra perner
7 篇 krasotkina olga
7 篇 mottl vadim
7 篇 ying tan
6 篇 zheng-guang wu
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 phung dinh
6 篇 venkatesh svetha
6 篇 hongyi li
6 篇 jacek m. zurada
6 篇 chaojie li
6 篇 biao luo
6 篇 ilias maglogiann...
6 篇 lotfi a. zadeh
6 篇 leszek rutkowski
6 篇 lazaros iliadis
6 篇 long cheng
5 篇 vitoantonio bevi...

语言

2,867 篇 英文
290 篇 其他
34 篇 中文
1 篇 土耳其文

检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3171 条记录，以下是781-790 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：

A Hybrid Model for Network Anomalies Classification Based on Improved Random Forest and Frequent Itemset Matching

A Hybrid Model for Network Anomalies Classification Based on...

引用

Big data & Artificial Intelligence & Software Engineering (ICBASE), international conference on

作者： Ruiyang Zeng Bing Jiang College of Oceanography and Space Informatics China University of Petroleum (East China) Qingdao China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

With the advancement of network technology, the methods of network attacks have become increasingly diverse. To counter these threats, traditional machine learning and deep learning techniques have been employed to detect and classify network traffic, leading to significant progress in the field. However, single classification methods often struggle with distinguishing between data categories that share similar characteristics, and there has been limited exploration of hybrid models for classifying anomalous network traffic. In this paper, we develop a hybrid model composed of a improved Random Forest classifier and frequent itemset mining to address these challenges by studying anomalous network traffic data. through various evaluation metrics and experiments, we demonstrate that this hybrid model effectively leverages the advantages of different classification methods while mitigating the confusion inherent in single-model classifications. the final classification results outperform those achieved by single classifiers without hybridization.

关键词： Training machine learning algorithms Itemsets Software algorithms Telecommunication traffic data models Classification algorithms data mining Random forests Software engineering

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of CNN and Quantized CNN Model for Rheumatoid Arthritis Identification Using thermal Image 5th

Performance Analysis of CNN and Quantized CNN Model for Rheu...

引用

5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Kumar, A. S. Mahesh Mallikarjunaswamy, M. S. Chandrashekara, S. JSS Sci & Technol Univ Mysuru India JSS Sci & Technol Univ Sri Jayachamarajendra Coll Engn Mysuru India ChanRe Rheumatol & Immunol Ctr & Res Bengaluru India

ISBN: (纸本)9783031235986;9783031235993

Rheumatoid Arthritis (RA) is distinguished by antibody-mediated inflammation. the most prevalent indications of RA are inflammatory, swollen ankles, tightness, and severe joint discomfort. Finally, RAaffects function, impairment, and morbidity by damaging neighboring connective tissues like ligaments, tendons, and cartilages. A range of techniques, comprising radiography, Computed Tomography (CT), ultrasonic, Magnetic resonance imaging (MRI), and thermal imaging, are used to identify and RA diagnose. Infrared (IR) thermal imaging is a non-invasive, fast, and effective approach for assessing early RA. IR-based examinations are a common imaging modality since it is radiation-free and non-invasive. In this study, the required dataset is collected using the IR thermal camera. the collected dataset consists of normal and arthritis-affected thermal images. the dataset is then prepared to be compatible with the Convolution Neural Network (CNN) of the Deep learning (DL) model and statistical parameters such as mean, mode, mode, kurtosis, etc. are derived and the correlation between the parameters is drawn using the covariance matrix. the dataset is then visualized using graphical plots to view the distribution of the statistical parameters. the dataset is then pre-processed using the normalization method and then analyzed using the CNN model. To find the efficiency of the DL model performance metrics such as accuracy, precision, loss, F-1 score, recall, etc. are calculated. To reduce the complexity and to reduce the computational time, the dataset is quantized using the optimization method and a comparison between the trained model and the quantized model is drawn.

关键词： Arthritis thermal image data Pre-process Optimization Neural network

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Distributed Image recognition App with Federal learning Techniques 5th

Design and Implementation of Distributed Image Recognition A...

引用

5th EAI international conference on Smart Grid and Internet of things, SGIoT 2021

作者： Chan, Yu-Wei Wu, Bo-You Huang, Yi-Ming Yang, Chao-Tung Department of Information Management Providence University Taichung Taiwan Department of Computer Science Tunghai University No. 1727 Sec. 4 Taiwan Boulevard Taichung407224 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University No. 1727 Sec. 4 Taiwan Boulevard Taichung407224 Taiwan

ISBN: (纸本)9783031203978

In recent years, machine learning technology has been widely used in many fields, such as smart transportation, smart healthcare, smart finance and smart cities. Although machine learning technology has brought people a lot of convenience, the privacy problem of user data has also emerged [1]. Considering that users are not necessarily willing to upload personal privacy data to the cloud for deep learning training, therefore, instead of consuming a lot of bandwidth to upload data to the cloud, it is better to train on the local device and then use the model parameters obtained after training. (For example: weights and bias, etc.) upload to the server for aggregation. this emerging machine learning technology is called federated learning. In this way, the privacy and security of data can be guaranteed, and the purpose of decentralized learning can be achieved through aggregation. this study uses the architecture of federated learning technology and convolutional neural network algorithms to implement distributed image recognition mobile applications. this application allows users to use their mobile devices and the central servers for repeated training. After multiple rounds of repeated training, the convergence will be stabilized, and the accuracy will be significantly improved. At the same time, it can take into account privacy and achieve the machine the purpose of learning. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

On the Possibility of Developing a System for Predicting Students Academic Performance Using machine learning Methods

On the Possibility of Developing a System for Predicting Stu...

引用

international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Y.A. Tsygankov A.Iu. Solovev M.S. Sergeychik Automated and Information Control Systems Department Stary Oskol Technological Institute (Branch) NUST "MISIS" Stary Oskol Russia

this article considers the possibility of developing data mining techniques and applying machine learning models to identify hidden dependencies that uniquely characterize different categories of students in order to predict their academic performance. the application of machine learning in this subject area is conditioned by their ability to analyze large amounts of data, obtaining, as a result, generalizing models capable of classifying the objects of study and predicting the required indicators. the development of such a system will allow, based on the analysis of a priori data and current indicators, to predict the expected performance of students in disciplines of different orientation, providing information on the categories of performance in a visual form.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security with machine learning-based Finger-Vein Biometric Authentication System

Enhancing Security with Machine Learning-based Finger-Vein B...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： G Prakash Babu Upendra Singh Aswal M. Sindhu Vijay Jagdish Upadhye Natrayan L Harshal Patil Department of Computer Science and Engineering Acharya Institute of Technology Bangalore Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Business Systems R.M.K. Engineering College Thiruvallur Tamil Nadu India Department of Microbiology Research and Development Cell Parul Institute of Applied Sciences Parul University Waghodia Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. the study employs two feature extraction approaches, the Local Binary pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. the simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IoT Sensors Empowered with Deep learning for Brain Depletion recognition 5

IoT Sensors Empowered with Deep Learning for Brain Depletion...

引用

5th international conference on Inventive Computation Technologies (ICICT)

作者： Revathy, G. Karthik, Durga Sreedevi, B. SASTRA Deemed Univ Sch Comp Thirumalaisamudram India SASTRA Deemed Univ Dept Comp Sci & Engn SRC Kumbakonam India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

In recent years, Internet of things enabling applications, which have provided excellent answers to a variety of challenges. this fast-growing industry is led by wireless sensor networks. Smart medical devices and wearables, for example, play an important part in the Internet of things, as they may collect a variety of longitudinal patient-generated health data while also presenting preliminary diagnosis options. As part of their efforts to serve patients with IoT-based solutions, experts apply ml to give effective resolutions in bleeding detection. this work describes a smart IoT-based solution for human brain hemorrhage diagnostics that uses deep learning algorithms to reduce death rates and provide correct treatment recommendations. the SVM and Recurrent Neural Network were used to classify the images from the computed tomography scans for the intracranial dataset. When compared to prior techniques such as naive bayes, KNN, and K-medoids, the classification results for the SVM and Recurrent neural network are high. According to the findings, the recurrent neural network beats other methods for identifying intracranial images. the output of the classification tool offers information on the type of brain hemorrhage, which helps to validate an expert's diagnosis and is utilized as a learning tool for trainee radiologists to eliminate errors in existing systems.

关键词： IoT wireless sensor networks machine learning algorithms support vector machines recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

data mining and Network Pharmacology on Tibetan Medicine Prescriptions for the Prevention and Treatment of COVID-19 5

Data Mining and Network Pharmacology on Tibetan Medicine Pre...

引用

5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Aray, Baht Zhang, Xubao Fu, Rao Sun, Mengliao Li, Zhiyong Huang, Xiulan School of Pharmacy Minzu University of China Beijing China Capital Normal University High School Beijing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

In this paper, the compatibility rules and drug combinations of Tibetan medicine prescriptions are explored, and the possible anti-epidemic mechanism is analyzed from the perspectives of biological network and signaling pathway, so as to provide reference for scientifically elaborating the potential value of ancient Tibetan medicine in preventing epidemic diseases. Association rules and clustering analysis are used for Tibetan drug mining. A total of 18 prescriptions involving 113 Tibetan medicinal materials were included, and 26 high-frequency Tibetan medicinal materials with the statistical frequency ≥3 are included, most of which are medicinal materials for clearing heat, detoxicating, eliminating plague. thirteen potential drug combinations are obtained through association rule analysis. the KMean clustering and hierarchical clustering were used for clustering analysis to obtain five drug clusters, and the "Bamusaeconcretiosilicea - Carthami Flos"combination was selected for network pharmacology research by comparing the two methods. After drug target and pathway analysis, "Bamusaeconcretiosilicea - Carthami Flos"through MAPK cascade, response to oxygen level, reactive oxygen species metabolism process, PI3K-Akt signaling pathway, NF- κ B signaling pathway, cytokine-cytokine Receptor interaction and calcium signaling pathways have certain feasibility for the treatment of immune disease fever in three aspects: immune response, inflammatory response, and oxidative stress. © 2022 IEEE.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Survey on One-Shot learning for Image recognition using machine learning and Deep learning Techniques

Survey on One-Shot Learning for Image Recognition using Mach...

引用

Smart Electronics and Communication (ICOSEC), international conference on

作者： Ganesh Vitthal Kadam Ghanshyam Prasad Dubey Computer Science Amity School of Engineering & Technology Gwalior Maharajpura Gwalior Madhya Pradesh ASET-CSE Amity School of Engineering & Technology Maharajpura Gwalior Madhya Pradesh

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Despite their success, the traditional deep neural networks are unable to rapidly generalize from a limited number of samples. To address the issue of large training data requirements with deep neural networks, one-shot learning restricting the number of data in each category is developed to attain enhanced results. Specifically, the traditional neural networks lead to over-fitting problems in training with a limited number of samples. In contrast, machine learning algorithms are capable of one-shot learning from limited samples like humans have the ability of oneshot learning. Hence, it is necessary to analyze one-shot object recognition techniques prevalent in image processing systems. this study will explore different one-shot learning techniques and methods from the state-of-the-art models and examine the merits and challenges of the approach with 25 literature papers. the effectiveness of the existing approaches will be evaluated by various techniques, datasets, and performance metrics are discussed in detail. At last, the paper discusses the potential future studies and challenges in achieving better detection accuracy.

关键词： Training Measurement Surveys Image recognition Accuracy Training data One shot learning Artificial neural networks Reliability Object recognition

来源：评论

学校读者我要写书评

暂无评论

Time Series Decision Analysis Based on Least Squares Method and LSTM

Time Series Decision Analysis Based on Least Squares Method ...

引用

5th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Li, Aiqi School of International Business Xiamen University Tan Kah Kee College Zhangzhou China

this study explores a novel approach combining the least squares technique with LSTM neural networks for time series analysis in decision-making processes. the research framework leverages the precise parameter estimation capabilities of least squares for establishing variable relationships, while utilizing LSTM's advanced pattern recognition to identify temporal dependencies in sequential data. through practical implementation, the methodology establishes quantitative relationships between sales performance and pricing strategies using least squares, while employing LSTM architecture for forecasting inventory replenishment needs. the proposed analytical framework demonstrates significant capability in identifying complex temporal patterns, offering empirical evidence for data-informed corporate decision-making. Experimental outcomes substantiate the methodology's efficacy, presenting innovative tools and insights for temporal data analysis applications. © 2025 the Authors. Published by Elsevier B.V.

关键词： Risk perception

来源：评论

学校读者我要写书评

暂无评论

Feature recognition of Natural Gamma-Ray Spectral data Based on K-L Transformation

Feature Recognition of Natural Gamma-Ray Spectral Data Based...

引用

machine learning and Computer Application (ICMLCA), international conference on

作者： Daobing Han Qingxin Meng Hebei Geological University Shijiazhuang China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

the natural gamma-ray spectroscopy observation technique is an important nuclear geophysical detection method and is one of the main tools for solving problems related to Earth and environmental sciences. the traditional natural gamma-ray spectrum observation technique mainly focuses on the analysis and interpretation of a small number of parameters (U, th, K) and basically does not require feature compression processing, but there are certain limitations. Generally natural gamma ray spectra are measured in a very broad range, for displaying physical properties, geological bodies, geotechnical and other ray spectra, the amount of data is very large, especially for low-energy spectral data, very complex, and the need for ray spectra to be identified by the characteristics of the ray spectra. In this paper, firstly, we obtain the raw data output from the radionuclide instrument, and secondly, we use matlab software to sample the raw data in the range of 0–3 MeV, the ray spectrum interval is more flexible, and the obtained data can be used in the form of ‘uniform sampling- mean output’, and then the output results are characterised by the K-L transform to identify the features of the output results, and extract those values with the largest eigenvalues to characterise its statistical features. the obtained data can be in the form of ‘uniform sampling-mean output’, and the ray spectrum is characterised by a number of output results obtained above, and finally, the K-L transform is used to identify the features of the output results, and the values with the largest eigenvalues are extracted to characterise its statistical features. thus, it can analyse the difference between the rock and soil more accurately, and provide more accurate and faster methods and techniques for geological exploration, resource exploration, environmental monitoring and other fields.

关键词： Accuracy Gamma-rays Transforms Soil Feature extraction Rocks Eigenvalues and eigenfunctions Object recognition data mining Geologic measurements

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318页 << < 75 76 77 78 79 80 81 82 83 84 > >>

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：