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

6,818 篇 会议
15 篇 期刊文献
11 册 图书

馆藏范围

6,844 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,366 篇 工学
- 3,826 篇 计算机科学与技术...
- 3,201 篇 软件工程
- 882 篇 信息与通信工程
- 710 篇 生物工程
- 588 篇 控制科学与工程
- 562 篇 生物医学工程（可授...
- 500 篇 电气工程
- 304 篇 电子科学与技术（可...
- 290 篇 光学工程
- 284 篇 安全科学与工程
- 238 篇 动力工程及工程热...
- 223 篇 机械工程
- 217 篇 交通运输工程
- 210 篇 化学工程与技术
- 199 篇 仪器科学与技术
- 176 篇 网络空间安全
2,144 篇 理学
- 1,267 篇 数学
- 766 篇 生物学
- 655 篇 统计学（可授理学、...
- 399 篇 系统科学
- 331 篇 物理学
- 234 篇 化学
1,306 篇 管理学
- 715 篇 管理科学与工程(可...
- 650 篇 图书情报与档案管...
- 393 篇 工商管理
597 篇 医学
- 531 篇 临床医学
- 502 篇 基础医学(可授医学...
- 310 篇 药学(可授医学、理...
- 282 篇 公共卫生与预防医...
200 篇 法学
190 篇 农学
- 186 篇 作物学
173 篇 经济学
137 篇 教育学
11 篇 军事学
2 篇 文学

主题

1,001 篇 machine learning
668 篇 accuracy
630 篇 deep learning
393 篇 machine learning...
382 篇 support vector m...
359 篇 predictive model...
356 篇 data models
305 篇 training
300 篇 feature extracti...
246 篇 adversarial mach...
223 篇 computational mo...
207 篇 convolutional ne...
197 篇 data mining
179 篇 real-time system...
179 篇 prediction algor...
175 篇 forecasting
144 篇 decision trees
142 篇 random forests
132 篇 analytical model...
118 篇 classification a...

机构

131 篇 chitkara univers...
69 篇 maharishi school...
53 篇 centre of interd...
22 篇 department of co...
21 篇 uttaranchal inst...
21 篇 school of electr...
20 篇 department of el...
19 篇 maharishi school...
19 篇 department of ar...
18 篇 graphic era deem...
18 篇 computer science...
17 篇 college of techn...
17 篇 department of in...
16 篇 pune
15 篇 graphic era hill...
15 篇 computer science...
14 篇 chitkara univers...
14 篇 department of co...
14 篇 graphic era hill...
13 篇 vishwakarma inst...

作者

19 篇 gill kanwarparta...
17 篇 shiva mehta
15 篇 mehta shiva
14 篇 chauhan rahul
14 篇 kanwarpartap sin...
12 篇 vinay kukreja
11 篇 kukreja vinay
10 篇 singh jagendra
8 篇 deepak upadhyay
8 篇 akhilendra prata...
8 篇 girija shankar s...
8 篇 rahul chauhan
7 篇 deepak banerjee
7 篇 kumar manish
7 篇 babu tina
7 篇 anand vatsala
7 篇 sheifali gupta
7 篇 vatsala anand
7 篇 radha d.
6 篇 banerjee deepak

语言

4,877 篇 英文
1,964 篇 其他
97 篇 中文
3 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是411-420 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 Heart Disease Prediction using K-Means Clustering and Semi supervised Label Propagation 3

A Hybrid Model for Heart Disease Prediction using K-Means Cl...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Venugopal, N.L.V. Sneha, A. Babu, Vunnava Dinesh Swetha, G. Banerjee, Sourajit Kumar Lakshmanarao, A. GMR Institute of Technology Department of CSE Rajam India Aditya University Department of IT Surampalem India RV Institute of Technology Department of CSE Guntur India CMR Engineering College Kandlakoya Medchal Department of IT Hyderabad India Centurion University of Technology and Management Department of Optometry A.P Vizianagaram India

ISBN: (纸本)9798350354171

In this paper, a hybrid model for heart disease prediction was developed by integrating unsupervised and semi-supervised learning techniques. Initially, baseline supervised were applied to the heart disease dataset to establish a performance benchmark. While these conventional machine learning models provided reasonable accuracy, the need for improved predictive performance in scenarios with limited labeled data prompted the development of a hybrid approach. The proposed hybrid model leveraged the K-Means clustering algorithm to identify inherent patterns in the dataset, followed by the application of Label Propagation to enhance classification accuracy. K-Means clustering was utilized to generate pseudo-labels, which served as an initial guide for the semi-supervised learning phase. Subsequently, the Label Propagation algorithm propagated these labels across the dataset, utilizing both the pseudo-labeled and partially labeled data. © 2024 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Power Density Prediction in PEM Fuel Cells Using Semi-Supervised and Supervised Deep learning Algorithms 3

Power Density Prediction in PEM Fuel Cells Using Semi-Superv...

引用

3rd IEEE international conference on data, Decision and Systems, ICDDS 2024

作者： Ravichandran, Abhilash Sankar, K. NIT Department of Chemical Engineering Andhra Pradesh Tadepalligudem534101 India NIT Department of Chemical Engineering TamilNadu Tiruchirappalli620015 India

ISBN: (纸本)9798350363890

Proton Exchange Membrane (PEM) fuel cells are emerging as a promising technology for clean and efficient energy conversion, offering significant advantages in terms of high efficiency, low emissions, and operational flexibility. In recent years, machine learning (ML) and deep learning algorithms have shown great potential in modeling complex PEM fuel cell systems by capturing intricate patterns and relationships within the data. This study aims to leverage the capabilities of semi-supervised and supervised deep learning algorithms to predict the power density of PEM fuel cells, focusing on a comparative analysis between Graph Convolutional Network (GCN) and Artificial Neural Network (ANN) enhanced with Bayesian Optimization. The standard tests of Nafion 112 membrane dataset from the data Analytics Laboratory, ECSIM Organization, were utilized in predicting the power density of that fuel cell. The novel implementation of a semi-supervised (GCN) algorithm gave an overall model accuracy of 81%, which was then compared with a traditional supervised (ANN) algorithm that gave an overall accuracy greater than 98%. The high accuracy achieved by the ANN model indicates that deep learning algorithms hold great promise for advancing PEM fuel cell technology. Thus, leveraging these algorithms can achieve more efficient and optimized systems, paving the way for more reliable energy solutions. © 2024 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Research on quick access recorder data preprocessing based on kernel extreme learning machine and wavelet transform 3

Research on quick access recorder data preprocessing based o...

引用

3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Liu, Yutong Bai, Jie Civil Aviation University of China Faculty of Aerospace Engineering Tianjin China

ISBN: (纸本)9781510680531

At present, the Quick Access Recorder (QAR) is the most widely used device for storing aircraft recording data. QAR data contains a wealth of valuable and unexplored information, detailing the true state of the aircraft. Therefore, the mining and development of QAR data is particularly crucial. The decoded QAR data contains a large number of outliers, and is always doped with nonlinear and non-stationary noise signals, and the wavelet denoising method is widely used in the signal field because of its simple algorithm and small amount of calculation, which improves the accuracy and efficiency of data processing. The extreme learning machine has strong learning ability and good generalization performance. In order to further improve the noise reduction effect of the sensor signal, a hybrid noise reduction method based on Kernel Extreme learning machine (KELM) and wavelet algorithm is proposed, which designs a filter based on the KELM algorithm to reduce the noise of the sensor signal on the one hand, and further smooths the KELM noise reduction signal by using the classical wavelet transform on the other hand, so as to further improve the noise reduction effect under the premise of ensuring that the signal is not distorted. Simulation results show that the noise reduction effect of the proposed hybrid noise reduction method is better than that of the single noise reduction method. © 2024 SPIE.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Visionary Cardioscan: Heart Risk Detection through Retinal Imaging 3

Visionary Cardioscan: Heart Risk Detection through Retinal I...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Arun Kumar, S. Dharshini, B. Hemanth, M. Vignesh, S. Saratha, M. Department of Artificial Intelligence and Machine Learning M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Heart attacks and hypertension are two cardiovascular disease risk factors that have a major impact on the microvascular system's structure and function. Fundus camera images can detect abnormalities in retinal blood vessels that indicate vascular injury. Using machine learning techniques, preclinical signs of damage, which may not be visible to an observer, can be detected. It proposes a system that uses retinal images to predict heart attack risk. The model leverages vessel segmentation to extract morphological details of retinal blood vessels. The results can assist in identifying heart disease early, especially in young patients. © 2025 IEEE.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

Utilizing Big data and machine learning to Improve Aerobic Exercise data Collection and Analysis 3

Utilizing Big Data and Machine Learning to Improve Aerobic E...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Qiao, Shu Wuhan Qingchuan University Hubei Wuhan420204 China

ISBN: (纸本)9798331519032

Traditional data collection and analysis methods are manually recorded and analyzed, with low efficiency and poor accuracy. Additionally, due to limitations in the number of athletes and equipment, only a small number of data can be recorded, which cannot fully reflect the effectiveness of aerobic exercise. This poses significant limitations for the development of scientific fitness programs and the evaluation of their effectiveness. This article aims to improve the accuracy, real-time performance, and personalization of exercise data, in order to better guide and improve the exercise effectiveness and health status of users. This article utilizes BD (Big data) and ML (machine learning) techniques to improve the collection and analysis of aerobic exercise data. This can improve the efficiency and accuracy of data collection and analysis, more comprehensively reflect the effects of aerobic exercise, and provide stronger support for scientific research and practice in the field of health. The accuracy rates of collecting athlete data for the control group and experimental group were 72.41% and 85.56%, respectively. The accuracy rate of collecting data for the experimental group was higher. The results of this article can optimize personalized exercise recommendations and predict health risks, improve exercise effectiveness and health management level. © 2024 IEEE.

关键词： data accuracy

来源：评论

学校读者我要写书评

暂无评论

Advanced machine learning Models for Electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. Performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： Power transmission lines

来源：评论

学校读者我要写书评

暂无评论

Research on the automatic crack detection system of asphalt pavement based on deep learning 24

Research on the automatic crack detection system of asphalt ...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Chi, Fengxia Wang, Yangyang Wei, Jintao Sun, Yihan Road Engineering Research Institute Zhejiang Scientific Research Institute of Transport Hangzhou Zhejiang China Key Laboratory of Road and Bridge Detection and Maintenance Technology Research of Zhejiang Province Zhejiang Hangzhou China Zhejiang University Zhejiang Hangzhou China

ISBN: (纸本)9798400718120

This study aims to develop a deep learning-based automatic asphalt pavement crack detection system to improve road maintenance efficiency and safety. Through detailed analysis of crack features, deep learning model is used for crack detection, and data processing, model training and optimization to ensure the efficient identification of the system in different scenarios. The experimental results show that the system has stability and reliability, providing strong technical support for road maintenance. © 2024 Copyright held by the owner/author(s).

关键词： Crack detection of asphalt pavement

来源：评论

学校读者我要写书评

暂无评论

Adaptive machine learning Model for Complex data Processing 3

Adaptive Machine Learning Model for Complex Data Processing

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Ma, Rui Wei, Xiuzhuo Yang, Yuzhu Wan, Ruoyan Zhao, Huinan Changchun Humanities and Sciences College Jilin Changchun130000 China

ISBN: (纸本)9798331519032

With the rapid development of information technology today, data is emerging at an unprecedented speed and scale, with diverse forms of existence and extremely wide sources. Especially in the medical field, the complexity of data is particularly prominent, including multi-source, incremental, high-dimensional, and significant noise interference. To address these challenges, this article delves into adaptive machine learning (ML) models for complex data processing and focuses on their application in the field of medical diagnosis. ML technology, with its excellent data processing and pattern recognition capabilities, has opened up new paths for the precision and intelligence of medical diagnosis. This paper proposes a medical diagnosis model based on multimodal data fusion and ML to address the complexity of medical data. This model integrates multiple types of data from different sources and utilizes advanced fusion techniques to achieve comprehensive information extraction and deep fusion. The experimental results show that the medical diagnosis model proposed in this paper exhibits excellent performance in various disease diagnosis tasks, significantly improving the accuracy and efficiency of diagnosis. © 2024 IEEE.

关键词： data fusion

来源：评论

学校读者我要写书评

暂无评论

Research on Optimization of Korean Translation Model Based on Deep learning 3

Research on Optimization of Korean Translation Model Based o...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Wang, Xinfeng Han, Debin Weifang Engineering Vocational College Shandong Qingzhou262500 China

ISBN: (纸本)9798331519032

This paper studies the optimization of Korean translation model based on deep learning (DL). In view of the unique grammatical structure and rich vocabulary changes in Korean, this paper first constructs a rich training data set by using multiple open parallel corpora and supplementing specific domain data through web crawler and manual translation. Then, the Transformer model is adopted as the infrastructure, and the morphological analyzer and Byte-Pair Encoding (BPE) technology are introduced to deal with the lexical morphology and unknown words in Korean. By designing several groups of comparative experiments, including baseline model, model for optimizing Korean characteristics, model for enhancing the diversity of data sets and model for pre-training and fine-tuning, the influence of different optimization strategies on translation quality is evaluated. The experimental results show that the optimized model has significantly improved the evaluation indexes such as BLEU, METEOR and NIST, and the translation quality, fluency and naturalness have been significantly improved. Specifically, the optimized model 3 performed best in all evaluation indicators, with an average increase of 6% for BLEU Score, 8% for METEOR Score and 17% for NIST Score. This study not only provides an effective strategy for optimizing the Korean translation model, but also provides a useful reference for multilingual translation research. © 2024 IEEE.

关键词： machine translation

来源：评论

学校读者我要写书评

暂无评论

Real-Time Evolving Deep learning Models for Predicting Hydropower Generation 3

Real-Time Evolving Deep Learning Models for Predicting Hydro...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Girard, William Xu, Haiping Yan, Donghui Univ Massachusetts Dartmouth Comp & Informat Sci Dept Dartmouth MA 02747 USA Univ Massachusetts Dartmouth Dept Math Dartmouth MA 02747 USA

ISBN: (纸本)9798350372977;9798350372984

Deep learning models have shown great promise for predicting hydropower generation. Previous research has focused on energy output prediction or predictive maintenance using traditional artificial neural networks (ANNs). However, these models lack sustainability in the face of changing environmental conditions. The need for dynamic, real-time modeling becomes apparent in rapidly changing environments, where speed and accuracy of execution are critical. In this paper, we present a framework for real-time evolving deep learning (RT-EDL) models designed to accurately predict hydropower generation on a daily, weekly, and monthly basis. Our evolving model employs backpropagation techniques and a stochastic gradient descent optimizer to continuously fine-tune the model using newly acquired data points in real time. To validate our approach, we conduct a case study using the RT-EDL model and show how the hyperparameters in the evolving model can be adjusted to achieve optimal operation. Our experimental results not only demonstrate the feasibility and effectiveness of our real-time evolving model, but also highlight its superiority over traditional deep learning methods.

关键词： deep learning model real-time evolving model changing environments fine-tuning hydropower generation

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500页 << < 38 39 40 41 42 43 44 45 46 47 > >>

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：