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

4,122 篇 会议
38 册 图书
6 篇 期刊文献

馆藏范围

4,166 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,726 篇 工学
- 2,455 篇 计算机科学与技术...
- 2,045 篇 软件工程
- 616 篇 生物工程
- 581 篇 信息与通信工程
- 362 篇 生物医学工程（可授...
- 266 篇 控制科学与工程
- 212 篇 光学工程
- 212 篇 电气工程
- 148 篇 安全科学与工程
- 137 篇 机械工程
- 124 篇 电子科学与技术（可...
- 119 篇 化学工程与技术
- 106 篇 交通运输工程
- 92 篇 动力工程及工程热...
1,369 篇 理学
- 675 篇 数学
- 646 篇 生物学
- 332 篇 统计学（可授理学、...
- 307 篇 物理学
- 197 篇 系统科学
- 127 篇 化学
722 篇 管理学
- 395 篇 管理科学与工程(可...
- 388 篇 图书情报与档案管...
- 239 篇 工商管理
340 篇 医学
- 315 篇 临床医学
- 308 篇 基础医学(可授医学...
- 221 篇 药学(可授医学、理...
- 125 篇 公共卫生与预防医...
160 篇 法学
- 138 篇 社会学
156 篇 教育学
- 154 篇 教育学
142 篇 农学
- 141 篇 作物学
91 篇 经济学
9 篇 军事学
5 篇 文学

主题

517 篇 machine learning
240 篇 deep learning
118 篇 support vector m...
99 篇 adversarial mach...
92 篇 accuracy
92 篇 machine learning...
82 篇 forecasting
74 篇 convolutional ne...
70 篇 predictive model...
69 篇 feature extracti...
56 篇 artificial intel...
53 篇 students
51 篇 learning systems
48 篇 decision trees
48 篇 training
47 篇 reinforcement le...
47 篇 computational mo...
47 篇 computer vision
46 篇 internet of thin...
41 篇 sentiment analys...

机构

46 篇 chitkara univers...
15 篇 graphic era hill...
10 篇 college of manag...
9 篇 graphic era deem...
8 篇 computer science...
8 篇 beijing universi...
7 篇 school of comput...
7 篇 school of comput...
7 篇 school of comput...
7 篇 department of in...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 computer science...
6 篇 lovely professio...
6 篇 in2p3 lpc campus...
6 篇 kkr and ksr inst...
6 篇 institut univers...
6 篇 national centre ...
6 篇 greater bay area...

作者

18 篇 kukreja vinay
15 篇 mehta shiva
10 篇 william p.
8 篇 sharma vikrant
7 篇 mahamudul hasan
7 篇 shiva mehta
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.
6 篇 huang f.

语言

4,020 篇 英文
145 篇 其他
34 篇 中文

检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4166 条记录，以下是251-260 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：

Review of Research Progress on Small Sample learning 4

Review of Research Progress on Small Sample Learning

引用

4th international conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Zhao Zhihao Hong Kong Baptist Univ Comp Sci Hong Kong 999077 Peoples R China

ISBN: (纸本)9781728146898

the neural network model has achieved very good results in the field of computer vision, but the requirements for the amount of data are directly proportional to performance. Data hungry is not only the cause of the high performance of the neural network model, but also a hindrance to its further development. there is not a lot of available data in real-world application scenarios, so how to obtain better performance and more robust models in a small sample environment becomes the goal of less sample learning research. this paper reviews the past small sample learning methods, sorts out its performance and theoretical principles, and points out the existing deficiencies and proposes possible future research directions.

关键词： Few shot learning Meta-learning machine learning computer Vision Data Science

来源：评论

学校读者我要写书评

暂无评论

Research on YOLOv4 Object Detection Based on K-means Algorithm and Fusion Attention Mechanism 4

Research on YOLOv4 Object Detection Based on K-means Algorit...

引用

4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Peng, Chufei Shenzhen MSU-BIT University Shenzhen China

ISBN: (纸本)9798350308082

Object detection technology is a machine learning application based on deep learning and computer vision technology, which can replace error-prone manual inspections in various scenarios, such as mask recognition applications. In view of the problems of easy omission and difficult error detection in traditional object detection algorithms, this paper proposes a research on YOLOv4 object detection based on K-means and fusion attention mechanism, with mask recognition as the research object. Firstly, the preprocessing of the dataset is carried out. the K-means algorithm is used to cluster and analyze the face mask dataset, generate more suitable anchor box prior boxes, and use the Mosaic method for data augmentation. then, the SE module and SPP module of the channel attention mechanism are introduced to improve the basic YOLOv4 model, and the SE-SPP3-YOLOv4 detection model is constructed. the experiment shows that the proposed improved model has significantly improved the detection rate and accuracy of both face and mask targets, and can perform accurate recognition of the wearing of masks on faces in specific application scenarios. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Generative AI learning Environment for Non-computer Science Engineering Students: Coding Versus Generative AI Prompting 4

Generative AI Learning Environment for Non-Computer Science ...

引用

4th international conference on AI Research, ICAIR 2024

作者： Alphinas, Robert Tambo, Torben Department of Business Development and Technology Denmark

ISBN: (纸本)9798331309466

this paper addresses the need for a positive and effective learning environment for engineering students in non-computer science fields to grasp Generative AI principles while navigating the intricate balance between its application and developmental insights. Drawing from pedagogical theories and cognitive science, especially Leinenbach and Corey (2004)’s Universal Design for learning, this study proposes a framework tailored to the unique needs and backgrounds of engineering students. the framework emphasizes active learning strategies, collaborative problem-solving, and real-world applications to engage learners in meaningful experiences with Generative AI concepts. the central learning context is a ***. program in management engineering with a course/training opportunity in machine learning Fundamentals using Python based on Google Collab. the introduction of Generative AI is based on selected Google libraries for Python. Furthermore, this paper explores various instructional approaches and tools to scaffold students' understanding of Generative AI, including hands-on projects, case studies, and interactive simulations. It also addresses ethical considerations and societal implications associated with Generative AI deployment, encouraging students to critically reflect on the broader impacts of their technical decisions. through a synthesis of pedagogical best practices and AI development principles, this paper contributes to the ongoing discourse on effective AI education for non-computer science disciplines. By embracing a holistic approach that integrates theory with practical application, educators can empower engineering students to harness the transformative potential of Generative AI while navigating its complexities responsibly and ethically. © proceedings of the 4th international conference on AI Research, ICAIR 2024.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

machine learning Comparison in application of Power System Instability Detection 4

Machine Learning Comparison in Application of Power System I...

引用

4th international conference on High Voltage Engineering and Power Systems, ICHVEPS 2023

作者： Rahmani, Rizky Hariyanto, Nanang Anggoro, Bambang Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350318678

this research paper aims to compare machine learning techniques for detecting transient instability in power systems. the delayed construction of infrastructure and the increasing presence of converter-based power plants, which reduce system inertia, contribute to operating states that can lead to instability. In such scenarios, disturbances in the network backbone can disrupt system synchronism, potentially causing angle instability and leading to blackouts. Transient stability, characterized by rapid responses before a blackout, requires proactive defense schemes to predict the outcomes of disturbances and take preventive actions. However, predicting whether a disturbance will lead to instability or not is challenging due to multiple factors involved. machine learning techniques offer the ability to address this challenge by leveraging their capacity for problem generalization. the results shows that several machine learning algorithms have very good accuracy to predict transient instability. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Comparison of CNN models for picture description application 4

Comparison of CNN models for picture description application

引用

4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Srivastava, Ayushi Meena, Isha Manisha, K. Bansal, Ankita Nsut Department of It New Delhi India

ISBN: (纸本)9781665474368

Blindness makes life tough for those who suffer from it, but machine learning can assist the visually impaired with their day - to - day tasks. Presently, image to speech is a relatively new and naïve topic. It's a machine learning problem that requires both natural language processing and computer vision to analyze visual content. In this context, this work focuses on the development of a photo-to-speech application for the visually impaired people. the work's ideology aims to make an impact on society so that visually impaired people can achieve equality. We merged picture and text processing in this work to build a practical deep learning application. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Survey on Chinese Word Segmentation 4

Survey on Chinese Word Segmentation

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Liu, Ying Li, DanPing Zhou, Xiaolu Xu, Shuai Institute of Computer Application Caep Mianyang China

ISBN: (纸本)9798400709449

With the rapid development of information technique, the amount of text is expanding, which puts forward higher requirements for the accuracy and the speed of Chinese word segmentation. Word segmentation technology is widely used in search engines, AI customer service, man-machine dialogue and other scenarios. this paper summarizes the existing research and challenges. Finally, we looks forward to the possible research directions in the future, and provides a large number of references. © 2023 ACM.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

Automatic Diagnosis of Congenital Conditions in Pediatric Age Using Pupillometry with machine learning 4

Automatic Diagnosis of Congenital Conditions in Pediatric Ag...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Vijayakumari, E.N. Swetha, Kora. Kumar, Atul Tulasi, Rajesh Mlr Institute of Technology Computer Science and Engineering Hyderabad India The ICFAI University Department of Fst Jharkhand India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350300888

Children who suffer from inherited retinal illnesses have a significantly reduced ability to see. this can cause damage to either the inner or outer retina, which can lead to blindness in infants. the clinical measures that clinics use to screen children and young adults for visual impairments are insufficient. these are not the tactics that should be used with infants. Chromatic Pupillometry is a state-of-the-art method that has been developed to evaluate both the exterior and inner functioning of the retina. this study has used a technology called the Support Vector machine (SVM), which is based on machine learning and makes use of pupillometry. Additionally, a system called 'Clinical Decision Support System (CDSS)' is used. It is guaranteed that the desired outcome will be accomplished. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Using machine learning to Detect and Classify URLs: A Phishing Detection Approach 4

Using Machine Learning to Detect and Classify URLs: A Phishi...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mahesh Ananth Dheepthi Hindustan Institute of Technology and Science Computer Science and Engineering India

ISBN: (纸本)9798350300093

It has become absolutely necessary to identify malicious URLs in real time due to the growing number of cyber-attacks and fraudulent activities that take place on the internet. Within the scope of this study, proposing a method that makes use of machine learning to identify four distinct categories of URLs: phishing, malware, benign, and defacement. the training and testing dataset using for our models contains over 651,191 URLs with a variety of features, such as the length of the URL, the presence or absence of symbols, the length of the hostname, the length of the path, and many more. In order to find the machine learning algorithm and architecture that produces the best results for the classification task, by investigating a variety of options. Based on the results of our experiments, a multi-layer perceptron (MLP) architecture performs significantly better than other models, achieving an accuracy of 95.6percent. this study has implemented a parallel data processing pipeline so that handle the large dataset. this pipeline preprocesses and extracts features from URLs in parallel, which significantly reduces the amount of time needed for training. Our proposed method offers a practical answer to the problem of identifying potentially harmful URLs and is adaptable enough to be incorporated into existing infrastructure in order to improve the safety of internet users. © 2023 IEEE.

关键词： Statistical tests

来源：评论

学校读者我要写书评

暂无评论

From Cloud Down to things: An Overview of machine learning in Internet of things 4

From Cloud Down to Things: An Overview of Machine Learning i...

引用

4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Humayoun, Muneeba Sharif, Hanan Rehman, Faisal Shaukat, Shahbaz Ullah, Muhbat Maqsood, Hadia Ali, Chaudhry Nouman Iftikhar, Razia Chandio, Adil Hussain Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan Mehran University of Engineering and Technology Szab Department of Computer Science Khairpur Mir's Pakistan Quaid e Awam University of Engineering Science and Technology Department of Electronic Engineering Larkano Pakistan

ISBN: (纸本)9798350335316

Due to the large number of things and information devices, not all IoT applications can be satisfied by processing data in the cloud. Due to the cloud's constrained ability to process and share data, edge computing, or the act of initiating IoT edge data processing and connected devices' transformation from intelligent devices to gadgets, was developed. machine learning is the key instrument. It is important to include information inference as a continuum in the cloud-to-things approach. Reviewing machine functions that are connected to the Internet, from the cloud all the way down to embedded devices. Many uses for machines learning to handle application data management and processing responsibilities are examined. the most current machine learning apps for IoT are gathered, and they all agree on their feedback and application space. the type of data, the machine learning methods used, and the locations belong to the continuum from clouds to objects. the issues and future directions of IoT machine learning research are spoken about. Additionally, employing methods for categorization using machine learning, papers on 'machine' learning in IoT are meticulously retrieved and reviewed. Next, with the expansion of recognized subjects and application domains, difficulties and search are moving in the direction of effective machine learning for the IoT. In addition, articles on the IoT's 'machine' learning are painstakingly retrieved, then classified using machine learning methods. © 2023 IEEE.

关键词： Metadata

来源：评论

学校读者我要写书评

暂无评论

DA PUF: dynamic adversarial PUF against machine learning attacks 4

DA PUF: dynamic adversarial PUF against machine learning att...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Kou, Yuping Jiang, Haolin Deng, Ding Mou, Weihua College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

With the rise of the Internet of things (IoT), some emerging mobile devices have been widely used such as wireless sensor networks, Radio Frequency Identification (RFID) chips, and smart cards etc. However, their communication security issues in open environments are increasingly prominent. Physical Unclonable Function (PUF) is a new type of "hardware fingerprint" that can authenticate IoT devices in the aspect of hardware. However, the Challenge-Response Pair (CRP) mechanism of PUF is vulnerable to machine learning (ML) modeling attacks. Based on this, the paper proposes a Dynamic Adversarial (DA) PUF through modifying the original CRP mechanism of APUF. Experimental results show that the PUF can effectively resist ML modeling attacks, while maintaining good uniformity, uniqueness, and reliability. © 2024 SPIE.

关键词： Internet of things

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417页 << < 22 23 24 25 26 27 28 29 30 31 > >>

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：