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

2,966 篇 会议
174 册 图书
25 篇 期刊文献

馆藏范围

3,164 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
- 1,824 篇 计算机科学与技术...
- 958 篇 软件工程
- 331 篇 电气工程
- 314 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...
- 48 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
649 篇 理学
- 353 篇 数学
- 245 篇 生物学
- 139 篇 物理学
- 135 篇 统计学（可授理学、...
- 95 篇 系统科学
- 63 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

572 篇 machine learning
563 篇 data mining
320 篇 pattern recognit...
209 篇 deep learning
201 篇 feature extracti...
154 篇 support vector m...
145 篇 artificial intel...
128 篇 machine learning...
120 篇 training
117 篇 accuracy
90 篇 data models
89 篇 data mining and ...
71 篇 neural networks
71 篇 classification
67 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

2,999 篇 英文
152 篇 其他
25 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 3165 条记录，以下是451-460 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：

IDDM 2022 - Proceedings of the 5th international conference on Informatics and data-Driven Medicine

IDDM 2022 - Proceedings of the 5th International Conference ...

引用

5th international conference on Informatics and data-Driven Medicine, IDDM 2022

the proceedings contain 27 papers. the topics discussed include: simple neuro-fuzzy system with combined learning for pattern recognition under conditions of short training set in medical diagnostics tasks;appraisal of artificial intelligence for fall prevention fall risk assessment;modeling of small data with unsupervised generative ensemble learning;the structure of the blockchain-based multi-agent system for secure management of medical information;evaluating autonomous-energy-harvesting device lifetime for the Internet of medical things with a petri net formulation considering battery SoH;application Of MLOps practices for biomedical image classification;impact of Russian war on COVID-19 dynamics in Germany: the simulation study by statistical machine learning;and a data-driven approach for neonatal mortality rate forecasting.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Critical Analysis Toward Sustainable Farming: the Role of AI in Weed Identification and Management 5th

A Critical Analysis Toward Sustainable Farming: The Role of ...

引用

5th international conference on Communication and Intelligent Systems (ICCIS)

作者： Singh, Shweta Yadav, Pritaj Rabindranath Tagore Univ Bhopal India

ISBN: (纸本)9789819720521;9789819720538

An artificial intelligence-based weed detection system is a computerized system designed to automatically identify and classify different types of weeds in agricultural fields. the system utilizes advanced computer vision techniques and machine learning algorithms to accurately detect and differentiate weeds from crops or other elements in the field. the weed detection system typically consists of hardware components such as cameras or drones which capture high-resolution images or videos of the agricultural area. these images are then analyzed by the artificial intelligence algorithms which have been trained on large datasets of weed images to recognize and distinguish various weed species. this paper examines the pivotal role of AI in weed detection, a critical aspect of farming that determines crop yield and health. through a comprehensive review, we shed light on the diverse AI-driven techniques including image recognition using Deep learning, real-time automation, data augmentation, multispectral imaging, and predictive analysis, among others. the ability of AI to distinguish between crops and weeds, often in real-time and under varied environmental conditions, underscores its transformative potential. As weed management represents a significant challenge in agriculture, the precise and proactive capabilities offered by AI can lead to optimized herbicide usage, reduced costs, and enhanced crop productivity.

关键词： Image processing Image enhancement Weed detection machine learning

来源：评论

学校读者我要写书评

暂无评论

Appraisal of Artificial Intelligence for fall prevention & fall risk assessment 5

Appraisal of Artificial Intelligence for fall prevention & f...

引用

5th international conference on Informatics and data-Driven Medicine (IDDM)

作者： Campos, Jaime Linnaeus Univ Dept Informat PG Vejdes 6 & 7 S-35195 Vaxjo Sweden

the current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored.

关键词： Fall prevention fall risk assessment artificial intelligence A.I machine learning sensors eHealth

来源：评论

学校读者我要写书评

暂无评论

machine learning solutions for securing IoT-based healthcare: A Review 5

Machine learning solutions for securing IoT-based healthcare...

引用

5th international conference on pattern Analysis and Intelligent Systems, PAIS 2023

作者： Kamir, Kharoubi Sarra, Cherbal Ferhat Abbas University of Setif-1 LRSD Laboratory Faculty of Sciences Setif Algeria

ISBN: (纸本)9798350381450

the Internet of things (IoT) has become a powerful force that is revolutionizing various fields to improve human life, including healthcare. By establishing interconnectedness among devices through network connectivity, IoT facilitates real-Time data exchange, enables remote patient diagnosis, and ensures ubiquitous access. However, the exchange of sensitive patient data within healthcare IoT systems presents significant security and privacy challenges that must be addressed to achieve a secure Healthcare IoT (H-IoT) ecosystem, resilient to attacks and threats. Among these issues, there are integrity, privacy and availability of data. It highlights the pressing need for robust security measures to safeguard patient information and explores the promising role of the ML techniques in addressing these challenges. ML algorithms offer the ability to detect and prevent both known and unknown attacks at an early stage, enhancing the security of H-IoT systems. this paper provides an overview about H-IoT, its requirements in security and some existing attacks. then, it presents ML techniques and their utilization for security. Finally, it conducts an extensive review of the contemporary solutions employed for enhancing the security of H-IoT systems during the period spanning from 2020 to 2023. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of machine learning-Based Website Phishing Detection Using URL Information 5

A Comparative Analysis of Machine Learning-Based Website Phi...

引用

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

作者： Uddin, Md. Milon Arfatul Islam, Kazi Mamun, Muntasir Tiwari, Vivek Kumar Park, Jounsup The University of Texas at Tyler Electrical Engineering Department TX United States University of Dhaka Institute of Energy Dhaka Bangladesh University of South Dakota Computer Science Department United States

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

the growing popularity of the internet help to expand e-commerce business, but such activities have security challenges caused by cybercriminals defrauding and stealing personal and financial information through website phishing. Phishing attacks are becoming increasingly complex and challenging to detect. An anti-phishing machine learning technique can help to identify a legitimate website from a phishing website by extracting various features from various sources like URL, page content, search engine, etc. this paper represents a comparative analysis of machine learning (ML) based website phishing detection and discusses some ML approaches for identifying phishing. We have compared five machine learning techniques-Decision tree, Random Forest, KNeighbors, Gaussian Naïve Byes, and XGBoost. Essential features which contribute much to the accuracy of the results have been chosen. the results show that the random forest algorithm outperforms the other proposed algorithms with an accuracy of 97.0 %. © 2022 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Toward an Ecient in-Network Caching Using Federated learning 5

Toward an Ecient in-Network Caching Using Federated Learning

引用

5th international conference on pattern Analysis and Intelligent Systems, PAIS 2023

作者： Djekidel, Malak Safa Manel, Habib Kerrache, Chaker Abdelaziz Calafate, Carlos T. Universite Amar Telidji de Laghouat Laboratoire d'Informatique et de Mathematiques Laghouat Algeria Valencia46022 Spain

ISBN: (纸本)9798350381450

In today's digital age, the Internet has experienced remarkable growth, accompanied by an exponential increase in both the diversity of available content and the number of users. Consequently, the demand for server resources and the volume of server requests have surged significantly. this places a significant strain on servers, diminishing their ability to handle user demands effectively. To alleviate this issue, caching is employed to store frequently requested content in memory that is closer to users. However, determining which content should be cached poses a challenge. Ecient cache management plays a vital role in enhancing data access speed and overall eciency. this challenge has been extensively studied and applied in the context of federated learning engineering, where effective cache management techniques are crucial for optimizing the performance of distributed machine learning models. By addressing cache management challenges, researchers aim to improve scalability, eciency, and overall system performance, ultimately enhancing the effectiveness of federated learning methodologies. In this paper, we conducted a study on enhancing network caching eciency by implementing federated learning. Our study involved the creation of different users, each of whom was assigned different databases with the same purpose (e.g., movies). the main aim is to identify the most popular content using artificial neural networks and cache them for each user, thus improving delivery services within the network by bringing these contents closer to the respective users. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

ISM 2022 - 4th international conference on Industry 4.0 and Smart Manufacturing

ISM 2022 - 4th International Conference on Industry 4.0 and ...

引用

4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

the proceedings contain 199 papers. the topics discussed include: infrastructure network support and leapfrogging Africa to Industry 4.0: the case of Tanzania;comparison of energy-use efficiency for lettuce plantation under nutrient film technique and deep-water culture hydroponic systems;an edge-cloud based reference architecture to support cognitive solutions in process industry;logistics 4.0 in intermodal freight transport;analysis of sustainable concrete obtained from the by-products of an industrial process and recycled aggregates from construction and demolition waste;intelligent concrete surface cracks detection using computer vision, pattern recognition, and artificial neural networks;spatial change recognition model using artificial intelligence to remote sensing;smart trip prediction model for metro traffic control using data mining techniques;encryption and generation of images for privacy-preserving machine learning in smart manufacturing;and using analytical and data-driven methods to develop a soft-sensor for flow rate monitoring in tube extrusion.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SUMAC '23: 5th Workshop on the analySis, Understanding and proMotion of heritAge Contents: Advances in machine learning, Signal Processing, Multimodal Techniques and Human-machine Interaction 23

SUMAC '23: 5th Workshop on the analySis, Understanding and p...

引用

31st ACM international conference on Multimedia, MM 2023

作者： Gouet-Brunet, Valérie Kosti, Ronak Weng, Li Lastig Ign Gustave Eiffel University Saint-Mandé France Picsart Ai Research Lab Berlin Germany Zhejiang Financial College Hangzhou China

ISBN: (纸本)9798400701085

SUMAC 2023 is the fifth edition of the workshop on analySis, Understanding and proMotion of heritAge Contents. It is held in Ottawa, Canada on November 2, 2023 and is co-located with the 31st ACM international conference on Multimedia. the workshop's objective is to present and discuss the latest and most significant trends, challenges and advances in the fields of machine learning, signal processing, multimodal techniques and human-machine interaction. the workshop is dedicated to the valorization of cultural heritage, with the emphasis on the unlocking of and access to the big data of the past. A representative scope of Computer Science methodologies dedicated to the processing of multimedia heritage contents and their exploitation is covered by the works presented, with the ambition of advancing and raising awareness about this fully developing research field. the complete SUMAC'23 workshop proceedings are available at: https://***/doi/proceedings/10.1145/3581783.3610949. © 2023 Owner/Author.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

Be Server in Rotation: Combining Model Reuse with Knowledge Distillation for Heterogeneous Federated learning

Be Server in Rotation: Combining Model Reuse with Knowledge ...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xintong Wen Ming An Tong Wang Chengwei Zhang Rong Chen College of Information Science and Technology Dalian Maritime University China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Dealing with data and model heterogeneity is crucial to federated learning practices. In this work, we introduce a novel mechanism termed SIO, which asks clients to take turn to be the server for aggregating model parameters in the procedure of federated mutual learning. Concretely, we propose a new federated learning framework, called FedSIO, which implements the proposed mechanism and especially exploit channel distil-lation and decoupled knowledge distillation to better address data heterogeneity issues. To assess the effectiveness of FedSIO, we conduct extensive experiments on three datasets involving different heterogeneity settings. Empirical results demonstrate that, compared to state-of-the-art methods, FedSIO significantly improves the generalization performance of the client group models.

关键词： Federated learning conferences data models pattern recognition Servers

来源：评论

学校读者我要写书评

暂无评论

Big data Analytics with Wild Horse Optimizer based Deep learning Model for Healthcare Management 5

Big Data Analytics with Wild Horse Optimizer based Deep Lear...

引用

5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Vamsikrishna, M. Rs, Rajasree Gopika, G.S. Suganthi, D. Ala Walid, Md. Abul Kuchipudi, Ramu Department of Information Technology Andhra Pradesh Surampalem India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science Tamilnadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Tamilnadu Chennai India Department of Computer Science and Engineering Khulna9203 Bangladesh Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798350321425

Big data analytics in health service is the procedure of research in huge and different datasets, designed for uncovering hidden patterns, correlations, and trends for making the best decisions in medical field. the health and medical services are developed modern and smart healthcare platforms are made the analysis further robust for the treatment. the proper diagnosis of health records depends on primarily disease recognition and the value of accuracy is decreased if the clinical data quality is worse. But, the existing methods failed to use the learning model for handling heterogeneous healthcare information. therefore, this article introduces Big data Analytics with Wild Horse Optimizer based deep learning (BDAWHO-DL) model for Healthcare Management. the proposed BDAWHO-DL technique investigates the big data in medical field and makes decisions. To do so, the BDAWHO-DL technique exploits attention based long short term memory (ABLSTM) method for data classification purposes. Moreover, the WHO system was used for the optimal hyperparameter tuning procedure of the ABLSTM algorithm to boost the classifier outcomes. the experimental outcomes indicate the promising outcomes of the BDAWHO-DL algorithm over recent approaches. © 2023 IEEE.

关键词： Big data

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317页 << < 42 43 44 45 46 47 48 49 50 51 > >>

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：