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

510 篇 会议

馆藏范围

510 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

405 篇 工学
- 355 篇 计算机科学与技术...
- 259 篇 软件工程
- 95 篇 信息与通信工程
- 95 篇 控制科学与工程
- 87 篇 电气工程
- 51 篇 交通运输工程
- 50 篇 仪器科学与技术
- 47 篇 安全科学与工程
- 45 篇 网络空间安全
- 43 篇 动力工程及工程热...
- 41 篇 生物医学工程（可授...
- 38 篇 生物工程
- 16 篇 机械工程
- 14 篇 光学工程
- 12 篇 电子科学与技术（可...
176 篇 理学
- 84 篇 数学
- 49 篇 化学
- 44 篇 生物学
- 43 篇 统计学（可授理学、...
- 29 篇 系统科学
- 24 篇 物理学
104 篇 管理学
- 83 篇 管理科学与工程(可...
- 64 篇 图书情报与档案管...
- 26 篇 工商管理
35 篇 医学
- 35 篇 临床医学
- 34 篇 基础医学(可授医学...
- 22 篇 药学(可授医学、理...
- 14 篇 公共卫生与预防医...
14 篇 法学
- 13 篇 社会学
13 篇 经济学
- 13 篇 应用经济学
9 篇 农学
5 篇 教育学

主题

106 篇 machine learning
52 篇 adversarial mach...
45 篇 training
35 篇 deep learning
32 篇 security
31 篇 federated learni...
26 篇 contrastive lear...
26 篇 privacy
25 篇 data models
22 篇 smart cities
22 篇 feature extracti...
18 篇 machine learning...
16 篇 accuracy
15 篇 predictive model...
13 篇 reinforcement le...
13 篇 self-supervised ...
13 篇 neural networks
13 篇 deep reinforceme...
12 篇 support vector m...
10 篇 data privacy

机构

7 篇 school of comput...
6 篇 school of cyber ...
5 篇 institute of inf...
4 篇 school of cyber ...
3 篇 school of comput...
3 篇 the chinese univ...
3 篇 utah state unive...
3 篇 university of li...
3 篇 munich institute...
3 篇 zhongguancun lab...
3 篇 development and ...
2 篇 school of cyber ...
2 篇 school of inform...
2 篇 intel technol in...
2 篇 florida atlantic...
2 篇 department of fa...
2 篇 department of in...
2 篇 shiga university...
2 篇 naturalpad montp...
2 篇 ludwig maximilia...

作者

6 篇 singh jagendra
4 篇 shelke nitin arv...
4 篇 ebadi ashkan
3 篇 hara takahiro
3 篇 hamdi shah muham...
3 篇 kashef rasha
3 篇 shukla rishi pra...
3 篇 kumar ashish
3 篇 schewe sven
3 篇 alsahlanee abbas...
3 篇 liu xudong
3 篇 upreti kamal
2 篇 agrawal ankit
2 篇 popovic tomo
2 篇 lucas kupssinskü
2 篇 kretinsky jan
2 篇 liu ziang
2 篇 anguita davide
2 篇 xie xia
2 篇 jiang siyang

语言

482 篇 英文
27 篇 其他
1 篇 葡萄牙文

检索条件"任意字段=23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024"

共 510 条记录，以下是1-10 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：

Proceedings - 2024 international conference on machine learning and applications, icmla 2024

Proceedings - 2024 International Conference on Machine Learn...

引用

23rd ieee international conference on machine learning and applications, icmla 2024

ISBN: (纸本)9798350374889

The proceedings contain 287 papers. The topics discussed include: exploring machine learning for faster mapping and scheduling of automotive applications on ADAS platforms;application of machine learning techniques to drive immunological insights towards malaria prognosis using microarray data;applied machine learning for surrogate modeling: a spatio-temporal approach;aircraft engine remaining useful life (RUL) prediction using machine learning;exploring machine learning engineering for object detection and tracking by unmanned aerial vehicle (UAV);quantum machine learning for computer vision: a survey;a machine learning approach for identifying HLA variants associated with symptomatic and asymptomatic COVID-19;injury risk prediction in soccer using machine learning;combining transfer learning and representation learning to improve predictive analytics on small materials data;and comparative study of machine learning techniques in prediction of superfund sites.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AutordF2GML: Facilitating rdF Integration in Graph machine learning 23rd

AutoRDF2GML: Facilitating RDF Integration in Graph Machine ...

引用

23rd international Semantic Web conference, ISWC 2024

作者： Färber, Michael Lamprecht, David Susanti, Yuni ScaDS.AI & TU Dresden Dresden Germany metaphacts GmbH Walldorf Germany Fujitsu Ltd. Kanagawa Japan

ISBN: (纸本)9783031778469

In this paper, we introduce AutordF2GML, a framework designed to convert rdF data into data representations tailored for graph machine learning tasks. AutordF2GML enables, for the first time, the creation of both content-based features—i.e., features based on rdF datatype properties—and topology-based features—i.e., features based on rdF object properties. Characterized by automated feature extraction, AutordF2GML makes it possible even for users less familiar with rdF and SPARQL to generate data representations ready for graph machine learning tasks, such as link prediction, node classification, and graph classification. Furthermore, we present four new benchmark datasets for graph machine learning, created from large rdF knowledge graphs using our framework. These datasets serve as valuable resources for evaluating graph machine learning approaches, such as graph neural networks. Overall, our framework effectively bridges the gap between the Graph machine learning and Semantic Web communities, paving the way for rdF-based machine learning applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing User Authentication Through EEG Based P300 Speller Response 23rd

Enhancing User Authentication Through EEG Based P300 Speller...

引用

23rd Mexican international conference on Artificial Intelligence

作者： Khan Khalil, Adnan Elahi Perez-Diaz, Jesus Arturo Cantoral-Ceballos, Jose Antonio Antelis, Javier M. Tecnol Monterrey Sch Sci & Engn Monterrey 64849 Nuevo Leon Mexico

ISBN: (纸本)9783031755422;9783031755439

Brain computer interface is employed in several applications providing explicit mode of communication between the brain and computers. Particularly, electroencephalography (EEG) is one of the most conventional methods for acquiring visual evoked potentials that is acquired from external stimuli, such as the P300 speller elicits the P300 potential from the presentation of characters and symbols. By employing machine learning classifiers and P300 potential has shown promising results for identifying and authenticating users since the brainwaves generated by each person while facing a particular stimulus are distinctive. But the current authentication research have not fully explore the P300 potentials and are not very successful when analyzing the most suitable processing and machine learning based classification techniques. In this study an approach for user recognition scheme utilizing the P300 speller is proposed to validate it on 8 users creating a non-invasive EEG based user authentication scheme. This framework achieved a performance of 100% accuracy in user recognition for the deep neural network (DNN) classifier, highlighting its effectiveness in accurately identifying and authenticating users thus, indicating the probability of performing EEG based user authentication using P300 speller paradigm.

关键词： Electroencephalogram (EEG) machine learning P300 Potentials User Recognition

来源：评论

学校读者我要写书评

暂无评论

Towards Estimating Water Consumption in Semi-arid Urban Landscaping: A machine learning Approach 23rd

Towards Estimating Water Consumption in Semi-arid Urban Land...

引用

23rd Mexican international conference on Artificial Intelligence

作者： Espinoza, Damian L. Gallegos Chavira, Luis A. Garnica Villanueva-Rosales, Natalia Rodriguez Gaeta, Juan Manuel Navarro Gomez, Carmen Julia Univ Texas El Paso El Paso TX 79968 USA Univ Autonoma Chihuahua Chihuahua Chihuahua Mexico

ISBN: (纸本)9783031755392;9783031755408

Escalating water scarcity, increasing demand, and rising costs require residents of semi-arid urban areas to make informed decisions on water management. Outdoor landscaping typically represents 40-70% of total water household consumption in some semi-arid areas;thus, outdoorwater use is a significant factor that contributes to urban water sustainability. Evapotranspiration (ET) indicates the water and energy exchange between the atmosphere and the soil. This interaction is interdependent on solar radiation, evaporation, transpiration, and other biophysical parameters. ET has become a reference for water management in agriculture (e.g., crop irrigation). However, these water management models do not typically include outdoor vegetation. Advances in Artificial Intelligence (AI) provide promising alternatives for Reference Evapotranspiration (ETo) prediction. This work aims to create an ETo model for small-scale outdoor irrigation to support water and vegetation management in semi-arid urban households. Four Artificial Neural Networks (ANN) models, Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional-LSTM (Conv-LSTM), were implemented and trained on ETo using meteorological data calculated with a modified Penman-Monteith equation from 33 stations. The ANN models were compared against four benchmark models: linear regression (LR), XGBRegressor (XGBR), support vector regressor (SVR), and random forest regressor (RFR). All models were evaluated using metrics such as R-squared (R-2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). From the ANN models, the MLP network produced the best data fit with an R-2 of 0.9665 on the test data, followed by the Conv-LSTM, LSTM, and GRU with R-2 of 0.8412, 0.8202, and 0.8069, respectively. However, the regression models show a slightly better performance than MLP regarding R-2.

关键词： machine learning applications Evapotranspiration Modeling Water Sustainability

来源：评论

学校读者我要写书评

暂无评论

Unguided machine learning-Based Computation Offloading for Near-Memory Processing

Unguided Machine Learning-Based Computation Offloading for N...

引用

international conference on VLSI Design

作者： Satanu Maity Manojit Ghose Avinash Kumar Anol Chakraborty Ankit Chakraborty Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Assam India Department of Computer Science and Engineering Jorhat Engineering College Assam India

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Recent data-intensive applications encounter memory wall bottlenecks in the traditional processor-centric computing architecture due to the need for frequent and extensive off-chip data movement. The emerging 3D-enabled near-memory processing (NMP) is a potential solution where computation can be offloaded and performed near memory, eliminating the need for expansive off-chip data transfer. Recently, some crucial works in this context have been proposed that use a supervised machine learning approach that requires a well-labeled dataset, which necessitates creating a time-consuming simulation process. Furthermore, their major focus was on profiling and offloading the entire application to the NMP side, causing a sub-optimal performance because of the low processing power on the NMP cores. This paper presents an unguided machine-learning-based approach (UCO) for computation offloading in NMP architecture that offloads only the memory-intensive regions of an application. The approach extracts several application performance characteristics and creates two clusters using them - one for the CPU side and another for the NMP side. The proposed approach's effectiveness is demonstrated through a series of comprehensive experiments with standard simulators and dataintensive benchmark applications. The results indicate that UCO improves performance (1.31x and 1.10x), saves energy (16% and 12%), and reduces off-chip data transfer (78% and 73%) compared to conventional and state-of-the-art approaches.

关键词： learning systems Energy consumption Embedded systems Instruments Computer architecture machine learning Very large scale integration Benchmark testing Data transfer Standards

来源：评论

学校读者我要写书评

暂无评论

Enhanced Distribution Matching for Multiclass Quantification 23

Enhanced Distribution Matching for Multiclass Quantification

引用

23rd ieee international conference on machine learning and applications, icmla 2024

作者： Malinda, Danuka Senarathna, Danushka Tragoudas, Spyros School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale United States

ISBN: (纸本)9798350374889

Quantification is the task of estimating the class distribution of a given dataset. This paper presents an Enhanced Distribution Matching method that can directly quantify multiclass datasets. Our method utilizes inter-class conditional probability distributions for quantification. We also propose a heuristic to speed up the distribution matching to obtain class distributions with a minimum number of iterations. The proposed methods were tested with 14 binary and multiclass datasets. The proposed method outperformed 12 datasets by a minimum 26% Mean Absolute Error (MAE) reduction. © 2024 ieee.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Quantum machine learning for Computer Vision: A Survey 23

Quantum Machine Learning for Computer Vision: A Survey

引用

23rd ieee international conference on machine learning and applications, icmla 2024

作者： Islam, Md Majedul He, Jing Selena Kennesaw State University Department of Computer Science Marietta United States

ISBN: (纸本)9798350374889

This research delves into quantum machine learning (QML) in the context of computer vision analysis by exploring the progress made in quantum computing and its impact on machine learning applications such as managing datasets and improving large-scale data processing efficiency through QML techniques specialised for tasks like image segmentation and classification in computer vision projects, along with findings, from trials conducted using the EMNIST benchmark *** tests reached an accuracy level above 90% successfully categorising tasks, with precision. This study explores the uses of quantum machine learning (QML) in areas like identification medical scans and distant monitoring. It also delves into the existing constraints and hurdles linked to quantum computer technologies. © 2024 ieee.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

learning-Based Attitude Estimation with Noisy Measurements and Unknown Gyro Bias 23

Learning-Based Attitude Estimation with Noisy Measurements a...

引用

23rd ieee international conference on machine learning and applications, icmla 2024

作者： Oveissi, Parham Mirtaba, Mohammad Goel, Ankit University of Maryland Department of Mechanical Engineering BaltimoreMD21250 United States

ISBN: (纸本)9798350374889

This paper introduces a learning-based, data-driven attitude estimator, called the retrospective cost attitude estimator (RCAE), for the SO(3) attitude representation. RCAE is motivated by the multiplicative extended Kalman filter (MEKF). However, unlike MEKF, which requires computing a Jacobian to compute the correction signal, RCAC uses retrospective cost optimization that depends only on the measured data. Moreover, due to the structure of the correction signal, RCAE does not require explicit estimation of gyro bias. The performance of RCAE is verified and compared with MEKF through both numerical simulations and physical experiments. © 2024 ieee.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

Harnessing machine learning and Stock Market Techniques for Signal Detection in Underwater Sensing Technologies 23

Harnessing Machine Learning and Stock Market Techniques for ...

引用

23rd ieee international conference on machine learning and applications, icmla 2024

作者： Ebadi, Ashkan Auger, Alain Gauthier, Yvan National Research Council Canada Digital Technologies Research Centre Toronto Canada S&t Foresight and Risk Assessment Unit Defence Research and Development Canada Quebec City Canada National Research Council Canada Digital Technologies Research Centre Ottawa Canada

ISBN: (纸本)9798350374889

Detecting emerging technologies is crucial as it enables industries and policymakers to anticipate future trends, allocate resources efficiently, and foster innovation. Research plays a pivotal role in developing and advancing emerging technologies, driving innovation and shaping the trajectory of future technological landscapes. In this work, inspired by stock market analysis and leveraging machine learning, we analyze three decades of scientific publications in the field of underwater sensing, as the case technology, to identify bursty terms. We also employ historical data to develop a classifier predicting their future popularity trends. © 2024 ieee.

关键词： Marketplaces

来源：评论

学校读者我要写书评

暂无评论

Leveraging A* Pathfinding for Efficient Deep Reinforcement learning in Obstacle-Dense Environments 23

Leveraging A* Pathfinding for Efficient Deep Reinforcement L...

引用

23rd ieee international conference on machine learning and applications, icmla 2024

作者： Lopez-Yepez, Junior Samuel Fagette, Antoine Dansereau, Charles Carvalhais-Sanches, Filipe Thales Digital Solutions Montreal Canada

ISBN: (纸本)9798350374889

This study introduces a novel approach to integrate A* pathfinding algorithms with deep reinforcement learning (Deep RL) for navigating vehicles in environments laden with obstacles, without requiring pre-existing map information during execution. Our method demonstrates enhanced data efficiency and improved success rates in target tracking and collision avoidance, even in unfamiliar terrains. © 2024 ieee.

关键词： Deep reinforcement learning

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51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：