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

105,915 篇 会议
684 篇 期刊文献
554 册 图书
4 篇 学位论文

馆藏范围

107,156 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

60,454 篇 工学
- 49,571 篇 计算机科学与技术...
- 39,558 篇 软件工程
- 13,351 篇 信息与通信工程
- 11,480 篇 生物工程
- 8,434 篇 控制科学与工程
- 6,702 篇 电气工程
- 6,321 篇 生物医学工程（可授...
- 3,803 篇 光学工程
- 3,314 篇 机械工程
- 2,969 篇 电子科学与技术（可...
- 2,729 篇 网络空间安全
- 2,689 篇 安全科学与工程
- 2,503 篇 动力工程及工程热...
- 2,317 篇 化学工程与技术
- 2,171 篇 仪器科学与技术
25,849 篇 理学
- 12,106 篇 生物学
- 11,963 篇 数学
- 5,129 篇 物理学
- 4,858 篇 统计学（可授理学、...
- 3,515 篇 系统科学
- 2,324 篇 化学
17,336 篇 管理学
- 10,934 篇 管理科学与工程(可...
- 7,575 篇 图书情报与档案管...
- 5,114 篇 工商管理
13,503 篇 教育学
- 13,302 篇 教育学
6,377 篇 医学
- 5,441 篇 临床医学
- 5,191 篇 基础医学(可授医学...
- 3,408 篇 药学(可授医学、理...
- 2,855 篇 公共卫生与预防医...
3,345 篇 法学
- 2,897 篇 社会学
1,965 篇 文学
1,753 篇 农学
1,654 篇 经济学
267 篇 艺术学
105 篇 军事学
11 篇 历史学
10 篇 哲学

主题

7,093 篇 machine learning
6,838 篇 deep learning
5,239 篇 accuracy
5,005 篇 training
4,787 篇 computational mo...
4,149 篇 education
3,826 篇 students
3,516 篇 feature extracti...
3,006 篇 support vector m...
2,999 篇 machine learning...
2,985 篇 predictive model...
2,907 篇 convolutional ne...
2,378 篇 real-time system...
2,297 篇 data models
2,223 篇 neural networks
1,982 篇 cloud computing
1,805 篇 teaching
1,469 篇 reinforcement le...
1,412 篇 artificial intel...
1,379 篇 internet of thin...

机构

823 篇 chitkara univers...
145 篇 lovely professio...
109 篇 computer science...
98 篇 karnataka bangal...
92 篇 college of compu...
87 篇 school of comput...
80 篇 chitkara univers...
76 篇 school of comput...
72 篇 department of co...
68 篇 department of co...
64 篇 department of co...
58 篇 department of co...
57 篇 school of comput...
49 篇 department of co...
48 篇 chitkara univers...
43 篇 department of me...
42 篇 school of automa...
42 篇 vellore institut...
41 篇 chennai
37 篇 sathyabama insti...

作者

70 篇 shiva mehta
69 篇 anjali t.
53 篇 li yang
42 篇 sharma vikrant
40 篇 kukreja vinay
39 篇 wang lei
39 篇 rakesh kumar
37 篇 gill kanwarparta...
35 篇 sharma neha
34 篇 wang jing
32 篇 vinay kukreja
31 篇 lei wang
31 篇 zhang lei
30 篇 huang yueh-min
29 篇 liu bo
29 篇 yu wang
27 篇 li xin
26 篇 amandeep nagpal
25 篇 vinay gautam
25 篇 kumar ajay

语言

102,616 篇 英文
4,178 篇 其他
687 篇 中文
27 篇 西班牙文
17 篇 葡萄牙文
5 篇 阿拉伯文
3 篇 法文
3 篇 土耳其文
2 篇 俄文

检索条件"任意字段=International Conference on Learning and Teaching in Computing and Engineering"

共 107157 条记录，以下是4551-4560 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 Comparative Study of Deep Image Retrieval Models Leveraging Deep Features

A Comparative Study of Deep Image Retrieval Models Leveragin...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Jain, Ashish Varshney, Sudeep SET Sharda University Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

This review investigates how deep learning methods can be utilized for efficient image retrieval based on content. Obtaining accurate images from vast digital collections poses significant challenges, motivating research in CBIR. The effectiveness of these methods varies depending on the dataset's type and size, with certain algorithms excelling with specific dataset characteristics. An extensive and well-structured review of successful image retrieval techniques is given in this paper. Our primary objective is to evaluate various deep learning models applied by researchers and compare their performance based on the outcome of evaluation matrices. These models encompass CNNs, DBNs, and other deep architectures tailored for image retrieval tasks. By synthesizing insights from this review, researchers can make informed decisions regarding model selection and potentially enhance retrieval performance by leveraging advanced deep learning features. The importance of deep features in image retrieval is the ability to capture complex visual patterns and semantic information that cannot be easily extracted by traditional handcrafted features. With the increasing volume of online images, Image retrieval using deep learning has become crucial for applications like object recognition, image retrieval, and image search engines. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Accuracy of Manufacturing Process Error Detection Through SMOTE-Based Oversampling Using Machine learning and Deep learning Techniques

Enhancing the Accuracy of Manufacturing Process Error Detect...

引用

2024 international conference on Integrated Circuits, Communication, and computing Systems, ICIC3S 2024

作者： Boyapati, Sai Venkat Rakshitha, Guduru Bhanu Sri Reddy, Maryada Rishitha Saleti, Sumalatha SRM University Department of Computer Science Engineering AP Guntur India

ISBN: (纸本)9798350364088

A production competency study leads to a rise in the manufacturing sectors' strategic emphasis. Developing semiconductor materials is a highly complex approach that necessitates numerous evaluations. It is impossible to emphasize the significance of the quality of the product. We put up a number of methods for automatically creating a prognostic model that is effective at identifying equipment flaws throughout the semiconductor materials' wafer fabrication process. The SECOM dataset is representative of semiconductor production procedures that go through numerous tests performed. There are imbalanced statistics in the dataset, so our proposed methodology incorporates SMOTE (Synthetic Minority Over-sampling Technique) functionality that is introduced to mitigate the imbalance of the training dataset by leveling off any unbalanced attributes. Detecting faults in the manufacturing process improves semiconductor quality and testing efficiency, and is used to validate both approaches to Machine learning and Deep learning algorithms. This is accomplished by collecting performance metrics during the development process. Another aspect of our effort to cut down on the training time for testing is highlighted in our research report. © 2024 IEEE.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Adaptive Multi-Path Video Streaming Using AI-Driven Edge computing for Enhanced Quality of Experience (QoE) 2

Adaptive Multi-Path Video Streaming Using AI-Driven Edge Com...

引用

2nd international conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798331529529

With the increasing demand for high-quality video streaming services, there is a need to enhance the Quality of Experience (QoE) for users, especially in environments with variable network conditions. This research paper proposes a novel approach leveraging AI-driven edge computing to implement adaptive multi-path video streaming. The proposed method uses machine learning algorithms to dynamically select the optimal streaming paths and adjust video quality in real-time based on network conditions and user preferences. By utilizing edge computing resources, this approach aims to reduce latency, improve bandwidth utilization, and provide a more reliable and high-quality video streaming experience. © 2024 IEEE.

关键词： Video streaming

来源：评论

学校读者我要写书评

暂无评论

Safeguarding Audio Integrity: Ensemble Architectures for Real-World Fake Audio Identification 15

Safeguarding Audio Integrity: Ensemble Architectures for Rea...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kasturi, Soundarya Lahari Nivetha, S. Selva Rani, B. Vairamuthu, S. Tiwari, Sourabh Yadav, Devendra Shankarappa, Rashmi T. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Voice Intelligence Group Samsung R&d Institute Bengaluru India

ISBN: (纸本)9798350370249

Through this research, we introduce a unique Audio Spoof Detection System for distinguishing between actual, fraudulent, and generated audio recordings. Strong detection algorithms are required since the spread of audio modification techniques poses a major threat to the integrity of audio-based systems. Combination of 7 key voice features from cepstral coefficients and spectrograms were used to cover all possible spoofing. We are proposing ensemble methods to enhance the accuracy of conventional machine learning models such as Random Forest, State Vector Classifier (SVC), K-Nearest Neighbors (KNN) etc. and deep learning models such as Recurrent Neural Networks (RNN), Time-Delay Neural Networks (TDNN), SqueezeNet etc. The proposed model is evaluated using the ASVspoof 2021 logical access (LA) and Deep Voice dataset. Experimental results show that our proposed model significantly elevates performance compared to the baseline and state-of-the-art models. © 2024 IEEE.

关键词： Audio Spoof Detection CNN Deep learning Ensemble learning Machine learning Random Forest TDNN

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Liver Disease Prediction Using Supervised Machine learning Classification Models 1

Performance Analysis of Liver Disease Prediction Using Super...

引用

1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Singh, Ramveer Sharma, Rahul Chaudhary, Shalbha Yadav, Deepika Jain, Jaishree Kumar, Pawan Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Raj Kumar Garg Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India R.D. Engineering College Department of Management Studies UP Ghaziabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India Teerthanker Mahaveer University Department of Computer Science & Engineering UP Moradabad India Satyug Darshan College of Engineering Department of Computer Science Faridabad India

ISBN: (纸本)9798350367720

Liver is most fundamental part of human body. It is highly dependable to performing several body functions such as helping in food digestion, waste filtering, enzyme activation, detoxification, nutrition and glycogen storage, carbohydrate metabolism, vitamin and mineral storage etc. It has been found from various survey reports that across the world, 58 million people are suffering from liver disorders and nearly 01 million liver disorder patients are present in India. The growth rate of liver disorder patients is increasing rapidly worldwide due to a sharp rise in obesity, eating more junk foods, more alcohol consumption and unhealthy lifestyles. People who have liver disorders problem need medical treatment at the correct time and early disease prediction helps to reduce the number of deaths. The research focuses on designing an intelligent system that helps for prediction liver disorder by using various classification models of supervised machine learning and it also helps to prevent becoming India a global hub of liver diseases by 2030. The research emphasis on comparative performance analysis of different classification algorithms and ensemble methods. It also helps to select best accurate model for prediction of the liver diseases. © 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

DenseNet201-Powered Dental Radiograph Classification: A Deep learning Approach for Multi-class Dental Pathology Detection 4

DenseNet201-Powered Dental Radiograph Classification: A Deep...

引用

4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Kaushik, Pratham Sharma, Pooja Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India

ISBN: (纸本)9798331530402

In this study, the automated dental radiograph classification problem was addressed by a deep learning approach using DenseNet201 architecture. The research utilized a comprehensive dataset of 29, 815 dental X-ray images across five categories: 69.0%, fillings (20.3%), implants (6.9%), cavity (2.1%), and impacted teeth (1.7%). To tackle class imbalance, the dataset was pre-processed with resizing to 224×224 pixels, normalization, and data augmentation techniques. 85.3% of the data were used for training, 9.1% for validation, and 5.6% for testing. With ImageNet weights and using Adam optimizer, the model achieved an overall accuracy of 91% Performance analysis revealed excellent results for normal cases (Fl-score: 0.95) and implants (Fl-score: 0.91), while cavity detection showed lower performance (Fl-score: 0.27) due to limited samples. While showing the capabilities of deep learning in dental radiography classification, the study also identifies where deep learning can improve on minority class detection. Finally, future work is proposed to incorporate attention mechanisms and increase the dataset to improve diagnostic accuracy in clinical applications. © 2025 IEEE.

关键词： Diagnostic radiography

来源：评论

学校读者我要写书评

暂无评论

Experimental Analysis of Plant Disease Identification Methodology Based on Improved Classification and Deep learning Principle

Experimental Analysis of Plant Disease Identification Method...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Shibu, S. Priyadharshini, S. Madhini, M. Radha, K. Giri, Pallavi Samhan, Ahmad A. Ali Panimalar Engineering College Department of Ece Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India S.A. Engineering College Department of Ece Chennai India Dr. Mahalingam College of Engineering and Technology Department of Cse Pollachi Coimbatore India Laxminarayan Innovation Technological University Department of Civil engineering Nagpur India Zarqa University Faculty of Information Technology Deoartment of Software Engineering Zarqa Jordan Universityof Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The agriculture industry is essential for providing excellent food and significantly contributes to the growth of economies and people. Plant diseases can result in substantial reductions in food output and diminish species diversity. The prompt identification of plant diseases by precise or automated detection methods can improve food production quality and save economic losses. In recent years, deep learning has significantly enhanced the identification accuracy of image categorization and object detection systems. In this paper, a novel method termed learning Enabled Disease Detection Logic (LDDL) is introduced for the efficient identification of plant diseases, which is cross-validated against the traditional Convolutional Neural Network (CNN) to assess the efficacy of the proposed scheme. The report delineates the specific crop illnesses addressed, the models utilized, the data sources referenced, and the overall efficacy as measured by the performance criteria applied in each study for plant disease diagnosis. The findings demonstrate that the proposed model, LDDL, achieves superior accuracy compared to prevalent illness detection methods and identifies the primary parameters influencing the efficacy of deep learning-based tools. The research offers significant insights for researchers, practitioners, and industry professionals in plant disease detection by addressing challenges and limitations, presenting a thorough understanding of the current research landscape, elucidating the advantages and drawbacks of various methods, and suggesting potential solutions to facilitate their implementation. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Load-Balanced Task Scheduling in Fog-Cloud Architecture: A Machine learning Approach 1

引用

5th international conference on Soft computing and its engineering Applications (IcSoftComp)

作者： Keshri, Rashmi Vidyarthi, Deo Prakash Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

ISBN: (数字)9783031537318

ISBN: (纸本)9783031537301;9783031537318

The swift expansion of internet-of-things (IoT) devices and the rise in the pace of task requests sent from these IoT devices to the cloud data centres led to Congestion and delays in the service. To meet the challenges, fog computing emerged as a new computer paradigm that offers services near the request-generating devices and reduces delays, particularly for real-time and delay-sensitive queries. It is crucial to consider issues like balancing the load, lowering energy consumption, and scheduling requests that impact the fog-cloud ecosystem's performance to accomplish these aims. This work suggests a Machine learning based Task scheduling algorithm with load balancing for the fog-integrated cloud. It first deals with the task offloading to decide the layer where the service should be placed in the fog-cloud ecosystem. Then, it allocates the best available node considering the load balance of the overall ecosystem. The simulation experiments show that the suggested algorithm better balances the load and decreases reaction time compared to the state-of-art algorithms. It is also energy efficient as it minimises the number of active devices and their run time.

关键词： Task Scheduling Fog computing Cloud computing Load Balancing Clustering

来源：评论

学校读者我要写书评

暂无评论

Development of Ratio Water Index for Surface Water Mapping using Landsat 8 OLI Imagery 4

Development of Ratio Water Index for Surface Water Mapping u...

引用

4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Nagaraj, R. Kumar, Lakshmi Sutha Sri Sai Ram Engineering College Chennai600044 India National Institute of Technology Puducherry Thiruvettakuddy Karaikal609609 India

ISBN: (纸本)9798331530402

Mapping surface water is vital for managing and preserving water resources. However, accurately identifying and measuring water bodies from satellite images is challenging due to similar spectral responses from various land types. Techniques such as spectral indices, Machine learning (ML), and Deep learning (DL) have been explored for water body identification. Implementation of ML and DL techniques requires labeled datasets and high computational resources. Spectral indices, in contrast, are straightforward, do not require labeled data, have better i n t e s t a b i l i t y, and require less computational resources, but requires dynamic threshold determination. This paper proposes a Ratio Water Index, which eliminates the need of dynamic threshold and additionally compared with fourteen spectral indices formulated using Landsat 8 OLI images to determine the best index for extracting water bodies. Experimental results show that the proposed RWI outperforms by the performance metrics Overall Accuracy, Fl-Score, and Kappa Coefficient and recommended for efficient and accurate water body identification. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Transfer learning-Based Evolutionary Multi-task Optimization 18th

Transfer Learning-Based Evolutionary Multi-task Optimization

引用

18th international conference on Bio-Inspired computing: Theories and Applications (BIC-TA)

作者： Li, Shuai Zhu, Xiaobing Li, Xi Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China

ISBN: (纸本)9789819722716;9789819722723

Multi-task optimization (MTO) is an emerging research topic to optimize multiple related tasks simultaneously. It aims to enhance task interrelationships by leveraging shared information and features, thereby improving model performance. Evolutionary transfer optimization (ETO), applied to address multitask problems using evolutionary algorithms, incorporates the principles of transfer learning. It utilizes knowledge and experience from source tasks to expedite the optimization process of target tasks. We introduce a transfer learning-based strategy where valuable information from one task is transferred as comprehensively as possible to another task. This article proposes an idea that is based on joint distribution adaptation (JDA) and employs population individual replacement methods as knowledge transfer, differential evolution as the underlying optimizer, called transfer learning-based evolutionary multi-task optimization algorithm (TLEMTO). To validate the effectiveness of the proposed algorithm, the experiment is conducted on CEC17 multi-task optimization problem benchmarks, the results show that TLEMTO is superior to the compared state-of-the-art algorithms.

关键词： Evolutionary transfer optimization Multi-task optimization Differential evolution Transfer learning Joint distribution adaptation

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 << < 452 453 454 455 456 457 458 459 460 461 > >>

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：