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

7,151 篇 会议
15 篇 期刊文献
11 册 图书

馆藏范围

7,177 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,654 篇 工学
- 4,103 篇 计算机科学与技术...
- 3,359 篇 软件工程
- 1,070 篇 信息与通信工程
- 823 篇 控制科学与工程
- 710 篇 生物工程
- 570 篇 电气工程
- 562 篇 生物医学工程（可授...
- 337 篇 电子科学与技术（可...
- 293 篇 光学工程
- 292 篇 安全科学与工程
- 253 篇 动力工程及工程热...
- 229 篇 机械工程
- 224 篇 交通运输工程
- 215 篇 仪器科学与技术
- 212 篇 化学工程与技术
- 211 篇 网络空间安全
2,293 篇 理学
- 1,413 篇 数学
- 766 篇 生物学
- 679 篇 统计学（可授理学、...
- 435 篇 物理学
- 399 篇 系统科学
- 353 篇 化学
1,399 篇 管理学
- 799 篇 管理科学与工程(可...
- 664 篇 图书情报与档案管...
- 402 篇 工商管理
770 篇 医学
- 637 篇 临床医学
- 505 篇 基础医学(可授医学...
- 352 篇 公共卫生与预防医...
- 310 篇 药学(可授医学、理...
270 篇 法学
- 241 篇 社会学
191 篇 农学
175 篇 经济学
150 篇 教育学
58 篇 文学
11 篇 军事学

主题

1,078 篇 machine learning
718 篇 deep learning
713 篇 accuracy
398 篇 machine learning...
391 篇 support vector m...
379 篇 predictive model...
370 篇 data models
324 篇 feature extracti...
306 篇 training
246 篇 adversarial mach...
230 篇 computational mo...
222 篇 convolutional ne...
216 篇 real-time system...
201 篇 data mining
181 篇 prediction algor...
171 篇 forecasting
150 篇 random forests
145 篇 decision trees
139 篇 analytical model...
126 篇 neural networks

机构

138 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 chitkara univers...
15 篇 computer science...
14 篇 department of co...
14 篇 graphic era hill...
13 篇 vishwakarma inst...

作者

19 篇 gill kanwarparta...
17 篇 shiva mehta
16 篇 mehta shiva
14 篇 chauhan rahul
13 篇 kanwarpartap sin...
12 篇 vinay kukreja
11 篇 kukreja vinay
10 篇 singh jagendra
10 篇 rahul chauhan
8 篇 deepak upadhyay
8 篇 akhilendra prata...
8 篇 girija shankar s...
7 篇 deepak banerjee
7 篇 kumar manish
7 篇 babu tina
7 篇 singh gurjinder
7 篇 anand vatsala
7 篇 sheifali gupta
7 篇 singh anmol ratt...
7 篇 kaushik priyanka

语言

4,087 篇 英文
3,087 篇 其他
116 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 7177 条记录，以下是871-880 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：

AI Techniques for Anomaly Detection in Video Surveillance Using Deep learning Method 3

AI Techniques for Anomaly Detection in Video Surveillance Us...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Jain, Swati Choudhary, Naveen College of Technology and Engineering Department of Computer Science and Engineering Udaipur India

ISBN: (纸本)9798350372120

Researchers are becoming more interested in the topic of anomaly detection in video surveillance, which has made substantial advancements. In many domains, including industrial safety, surveillance, and healthcare monitoring, video anomaly detection is essential. This work suggests a novel approach for applying deep learning algorithms to identify abnormalities in films. The complexity and variety of real-world data sometimes pose challenges for traditional approaches, which results in a lack of accuracy and resilience. Deep learning methods, on the other hand, have demonstrated incredible promise in identifying spatiotemporal patterns in video data. This research investigates and identifies outliers in video surveillance by examining different kinds of video *** application scenario, which depends on the types of data and anomalous types of behavior with different circumstances, is the emphasis of the various methodologies. Large datasets are used to train the Convolutional Neural Network architecture, which enhances its capacity to learn general visual representations. Our work gives futuristic performance in terms of efficiency and accuracy as demonstrated by the experimental findings. © 2024 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Theoretical Foundations of Corpus-based Uzbek-English machine Translation 3

Theoretical Foundations of Corpus-based Uzbek-English Machin...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Zaynobiddin Qizi, Nilufar Abdurakhmonova Asliddin Qizi, Shamsiyeva Gulshoda National University of Uzbekistan The Department of Computational and Applied Linguistics Tashkent Uzbekistan National University of Uzbekistan Tashkent Uzbekistan

ISBN: (纸本)9798331516321

This article explores the linguistic, cultural, and historical significance of Turkic languages, with a particular focus on their evolution and impact on regional and global communication. The study delves into the roots of Turkic languages, tracing their influence across different civilizations and highlighting their role in shaping both spoken and written communication in Central Asia and beyond. It also examines the structural and grammatical intricacies of the language family, shedding light on how these unique features contribute to the richness and diversity of linguistic studies. By analyzing various aspects of language development, the article provides a comprehensive understanding of how Turkic languages have preserved their identity while adapting to modern trends. This research contributes to broader discussions in linguistics by offering insights into how language evolution and cultural exchange are intertwined. The findings emphasize the relevance of Turkic languages in the context of globalization, where the preservation of linguistic heritage remains crucial for maintaining cultural identity. © 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

Research on groundwater impurity identification based on big data fusion 3

Research on groundwater impurity identification based on big...

引用

3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Wu, Huiwen Huang, Xiaoping Natural Language Processing Software Engineering Institute of Guangzhou Guangzhou China Nanfang College Guangdong Guangzhou510970 China

ISBN: (纸本)9781510680449

With the acceleration of urbanization, the quality of groundwater has been seriously threatened, and impurity identification has become an urgent problem to be solved. Based on big data fusion technology, this study uses machine learning and deep learning algorithms to intelligently identify groundwater impurities. Firstly, groundwater quality data, including turbidity, total suspended matter, pH value, dissolved oxygen and other indicators, were collected, and pretreatment and feature extraction were carried out. Then, support vector machine, random forest and neural network algorithms are used to build a classification model to classify and predict the impurities in groundwater. The experimental results show that the groundwater impurity identification method based on big data fusion has high accuracy and stability, and can provide strong support for groundwater quality monitoring and treatment. This study not only enriched the theoretical system of groundwater impurity identification, but also provided an operable scheme for practical application. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Groundwater

来源：评论

学校读者我要写书评

暂无评论

Gear Misalignment Optimization for Electric Drive Unit Using machine learning Based Prescriptive Analytics

Gear Misalignment Optimization for Electric Drive Unit Using...

引用

2025 international conference on Advances in Design, Materials, Manufacturing and Surface engineering for Mobility, ADMMS 2025

作者： Penumatsa, Venkata Ramana Raju Thomas, Benson Black, Derrick Jain, Sachin Tata Consultancy Services Ltd. India General Motors LLC United States

In Electric vehicle Drive Unit Gears, high mesh misalignments result in shift in load distribution of a gear pair that can increase contact and bending stresses. It can move the peak bending and contact stresses to the edge of the face width and increase gear noise as well. Lower misalignment value is often required to reduce the peak bending and contact stresses and have a balanced load distribution along the gear flank, which in turn helps in reducing noise and improving durability of drive unit. This paper delineates Prescriptive Analytics method that combines virtual simulations, machine learning (ML) and optimization techniques to minimize different gear misalignments for the electric vehicle drive units. Generally, the manual optimization process is carried out by sequential modifications of stiffness of individual components. However, this process is time consuming and does not account for interactions between the components. In this study, firstly, machine learning models are developed based on design of experiments (DOE) simulations. These ML models are used as surrogates for actual simulations in generic algorithms (Differential Evolution) based optimization techniques. It finally prescribes changes in stiffness of different components to get optimum misalignment value. © 2025 SAE international and SAEINDIA;General Motors.

关键词： Electric load distribution

来源：评论

学校读者我要写书评

暂无评论

Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

引用

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

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Power System Fault Treatment Using Deep learning 3

Power System Fault Treatment Using Deep Learning

引用

3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Sahoo, Anjan Kumar Samal, Sudhansu Kumar Panigrahi, Girija Sankar Centurion University of Technology & Management Department of Electrical Engineering Odisha Bhubaneswar India Iter Siksha 'o' Anusandhan Deemed to Be University Deptt. of Ee Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

A novel complete protection scheme is proposed in this article that detects and classifies the transmission line faults. Also it estimates the location of fault. The proposed method is implemented in Kundur two area 4 machine 11 bus system over a distance of 220 km. The data is extracted automatically by the LSTM (Long Short Term Memory) networks of deep learning model. Two different LSTM network modules like LSTM (FL) and LSTM (FD) are used for this purpose. LSTM (FD) module is responsible for accurate fault detection and classification. The LSTM (FL) module is responsible for the estimation of exact location of the fault. Both symmetrical and unsymmetrical fault cases are tested under both LSTM fault modules. The protection modules are also tested under different fault locations, fault resistances and noisy signals etc. The features taken into consideration for the operation of the protection modules are different bus voltages, bus currents, zero sequence voltage, zero sequence current etc. The advantages of this method are it works at very low sampling frequency and it also does not require any communication link. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Enhancing Sign Language Classification through Reservoir Computing and Depth Camera Technology 3

Enhancing Sign Language Classification through Reservoir Com...

引用

3rd international conference on Image Processing and Robotics, ICIPRoB 2024

作者： Thongking, Witchuda Premachandra, Chinthaka Wiranata, Ardi Maeda, Shingo Shibaura Institute of Technology Department of Engineering and Science Tokyo Japan Shibaura Institute of Technology Department of Electronic Engineering Tokyo Japan University of Gadjah Mada Department of Mechanical and Industrial Engineering Yogyakarta Indonesia Tokyo Institute of Technology Department of Mechanical Engineering Tokyo Japan Research Group Tokyo Japan

ISBN: (纸本)9798350374766

The depth camera has emerged as an efficacious tool for facilitating interactions between individuals and machines. Its advantageous features, including a lightweight design, high durability, completeness, superior image quality, and versatility, have prompted researchers to explore its integration into advanced applications. Currently, the prevailing methods for classifying sign language exhibit certain limitations, such as suboptimal accuracy and inefficiency in gesture recognition technologies. In this study, we conduct a comprehensive examination of hand language characteristics rooted in American Sign Language (ASL), utilizing gesture-based interactions and augmenting sensing performance through preprocessing and the application of reservoir computing in the training model. We conduct a comprehensive evaluation of the gesture characteristics captured by the depth camera, elucidating a methodology encompassing preprocessing, machine learning, and the technical prediction of salient gesture features derived from conventional depth camera video footage. This endeavor aims to establish a robust signal model for enhanced understanding and representation of the analyzed gestures. Our machine-learning models adeptly predict American Sign Language signs. Correlation values derived from the depth camera data demonstrate a pronounced alignment with naturally occurring variations in metrics observed within specific gestures. Our methodologies for quantifying gestures with depth cameras contribute to heightened accessibility in quantitative motion analysis. The outcomes of the reservoir computing implementation exhibit the successful classification of three hand language signs, thereby manifesting high precision and recall. The robust performance of the classification is pivotal for practical applications and underscores the efficacy of our approach. © 2024 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Identification of human activity by utilizing YOLOv5s approaches 3

Identification of human activity by utilizing YOLOv5s approa...

引用

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

作者： Islam, Md. Rahatul Horio, Keiichi Kyushu Inst Technol Grad Sch Life Sci & Syst Engn Kitakyushu 8080196 Japan

ISBN: (纸本)9798350372977;9798350372984

The significance of identifying human activity for the purpose of ensuring security is progressively growing day by day. Human activity recognition is an innovative technology that detects and recognizes human actions inside video footage. This research involves performing deep learning based YOLOv5s algorithms for the detection and recognition of four types of human activity, like sitting, standing, running, and sleeping. This research uses its own data set, which contains a total of 2375 images. There are 1745 images used for training and 630 images used for validation. We use *** to classify the datasets. After classifying, we use the YOLOv5s method to train it. For this research, we utilized the Google Colabatory Environment with a V100 GPU. The results of this research indicate a precision score of 95.6%, a recall value of 95.8%, a f1-score of 95.69%, and an accuracy of 97%. The results of the experiment demonstrate that the model is capable of accurately identifying human activity. The model's capability to accurately identify human activity enables its utilization for monitoring purposes in various institutions such as daycare centers, schools, colleges, and hospitals.

关键词： deep learning yolov5s makesense.ai human activity recognition v100 gpu

来源：评论

学校读者我要写书评

暂无评论

Towards the Integration of Mixed Reality and machine learning for Older Adults Personalized Smart Home and Health Monitoring 3

Towards the Integration of Mixed Reality and Machine Learnin...

引用

3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Mahroo, Atieh Sacco, Marco Lecco Italy University of Milano-Bicocca Department of Informatics Systems and Communication Milan Italy

ISBN: (纸本)9798350378009

This contribution proposes a multi-modal system for older adults' health monitoring within the smart home framework that integrates Mixed Reality, a network of IoT sensors, wearable health sensors, and users' feedback to collect data and monitor older adults' health. It provides a comprehensive framework for older adults' health monitoring and safe exercise sessions to enhance their physical and cognitive well-being. The proposed system allows older adults to control the smart home features and benefit from assistive services via a mixed reality application. The system explores exploiting machine learning models to provide personalized physical and cognitive training routines to enhance older adults' quality of life, autonomy, and general well-being. It leverages historical data coming from wearable sensors, smart mixed reality glasses, IoT sensors, and users' feedback to predict the users' health status and activity level and suggest the proper exercise routine accordingly. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Advancing Network Anomaly Detection: An Ensemble Approach Combining Optimized Contractive Autoencoders and K-Means Clustering 3

Advancing Network Anomaly Detection: An Ensemble Approach Co...

引用

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

作者： Aktar, Sharmin Nur, Abdullah Yasin Univ New Orleans Dept Comp Sci New Orleans LA 70148 USA

ISBN: (纸本)9798350372977;9798350372984

This paper introduces a new approach for detecting unusual activities in network traffic, a critical aspect in maintaining network security. We propose an innovative model that combines the strengths of Contractive Autoencoders (CAEs) and K-means clustering, specifically designed for effective anomaly detection in network environments. Our model employs CAEs for efficient data processing and K-means clustering to identify deviations from standard network patterns. The focus is on the exploration of CAE's latent space and the impact of various deep learning parameters on the model's detection capabilities. Tested on the NSL-KDD dataset, a standard in network security research, our best-tuned model achieves an F1 Score of 0.92, making it approximately 8.2% more effective than the basic Autoencoder model and about 5.7% better than the standalone K-Means approach in terms of F1 Score. This significant improvement in performance highlights the advanced capabilities of our model in identifying potential threats in network traffic, marking a considerable advancement in the field of network security.

关键词： Network Security Anomaly Detection Deep learning Autoencoder Contractive Autoencoder K-means Clustering Network Traffic Analysis Intrusion Detection CAE-K-means Model Cybersecurity

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 << < 84 85 86 87 88 89 90 91 92 93 > >>

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：