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

25,871 篇 会议
99 篇 期刊文献
22 册 图书

馆藏范围

25,993 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

13,981 篇 工学
- 12,722 篇 计算机科学与技术...
- 9,106 篇 软件工程
- 2,794 篇 信息与通信工程
- 2,190 篇 控制科学与工程
- 1,407 篇 电气工程
- 1,028 篇 生物工程
- 1,003 篇 生物医学工程（可授...
- 933 篇 网络空间安全
- 771 篇 安全科学与工程
- 643 篇 交通运输工程
- 638 篇 机械工程
- 614 篇 动力工程及工程热...
- 598 篇 电子科学与技术（可...
- 592 篇 光学工程
- 429 篇 环境科学与工程（可...
- 404 篇 仪器科学与技术
4,272 篇 理学
- 2,211 篇 数学
- 1,236 篇 生物学
- 1,154 篇 统计学（可授理学、...
- 807 篇 物理学
- 708 篇 系统科学
3,013 篇 管理学
- 2,256 篇 管理科学与工程(可...
- 1,144 篇 图书情报与档案管...
- 1,033 篇 工商管理
1,389 篇 医学
- 995 篇 基础医学(可授医学...
- 988 篇 临床医学
- 931 篇 公共卫生与预防医...
- 397 篇 药学(可授医学、理...
817 篇 法学
- 747 篇 社会学
495 篇 经济学
- 491 篇 应用经济学
361 篇 农学
296 篇 教育学
287 篇 文学
15 篇 军事学
3 篇 艺术学

主题

5,187 篇 machine learning
4,006 篇 accuracy
2,898 篇 deep learning
2,755 篇 machine learning...
2,110 篇 adversarial mach...
1,967 篇 predictive model...
1,821 篇 computational mo...
1,683 篇 training
1,506 篇 support vector m...
1,374 篇 real-time system...
1,266 篇 feature extracti...
1,255 篇 data models
1,025 篇 convolutional ne...
909 篇 random forests
862 篇 prediction algor...
696 篇 federated learni...
581 篇 reliability
564 篇 internet of thin...
550 篇 technological in...
508 篇 analytical model...

机构

167 篇 chitkara univers...
97 篇 lovely professio...
43 篇 school of comput...
41 篇 chitkara centre ...
40 篇 department of co...
37 篇 school of comput...
36 篇 pune
35 篇 department of me...
33 篇 school of comput...
32 篇 chennai
32 篇 department of co...
31 篇 graphic era deem...
30 篇 yeshwantrao chav...
29 篇 computer science...
25 篇 school of comput...
24 篇 department of co...
24 篇 school of comput...
23 篇 department of co...
22 篇 maharishi school...
21 篇 computer science...

作者

19 篇 rishabh sharma
19 篇 satvik vats
18 篇 manjunatha
18 篇 andersson karl
16 篇 majeed anwar p. ...
16 篇 singh jagendra
15 篇 shiva mehta
15 篇 mahendra kumar g...
14 篇 nagpal amandeep
13 篇 amandeep nagpal
13 篇 gill kanwarparta...
12 篇 manoharan geetha
12 篇 liu yang
12 篇 ramya maranan
12 篇 himanshu sharma
10 篇 kumar ajay
10 篇 rakesh kumar
10 篇 samadhi rathnaya...
10 篇 sudeep tanwar
10 篇 sharif mhd saeed

语言

23,616 篇 英文
2,371 篇 其他
358 篇 中文
1 篇 法文
1 篇 葡萄牙文

检索条件"任意字段=2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024"

共 25993 条记录，以下是51-60 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：

learning from Noisy pairwise dissimilarities and unlabeled data 24

Learning from Noisy pairwise dissimilarities and unlabeled d...

引用

2nd international conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Huang, Shuying Li, Junpeng Hua, Changchun Yang, Yana Yanshan University Hebei China

ISBN: (纸本)9798400710681

machine learning from weak supervision has become a hot topic recently, particularly in classification using similar/dissimilar data and unlabeled data. learning pairwise similar and dissimilar data enhances data security. However, in real-world scenarios, pairwise dissimilarity data often gets contaminated with pairwise similarity data. To overcome this bottleneck, this paper proposes a model for learning from noisy pairwise dissimilarities and unlabeled (nDU) data, aiming to address the issue of pairwise similarity data being mixed into pairwise dissimilarity data. The proposed method establishes an empirical risk function for noisy pairwise dissimilarity data, provides theoretical guarantees through estimation error bounds, and validates the approach through experiments. This novel weakly supervised learning algorithm effectively mitigates the impact of noise in pairwise dissimilar samples, resulting in an effective binary classifier. © 2024 Copyright held by the owner/author(s).

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 the Application of Generative Artificial Intelligence in Human-machine Cooperative Teaching 24

Research on the Application of Generative Artificial Intelli...

引用

7th international conference on Educational Technology Management, ICETM 2024

作者： Mi, Zejia Li, Kangkang School of Smart Education Jiangsu Normal University Jiangsu Xuzhou China

ISBN: (纸本)9798400717468

The rise of Generative Artificial Intelligence (GAI) has brought new transformations to education. AI-driven educational applications that employ human-machine collaborative teaching are expected to become one of the mainstream teaching methods in the future. This study proposes a human-machine collaborative teaching model based. It developed a human-machine collaborative learning system utilizing the SparkDesk and the WeChat system, providing learners with functions such as conversation, resource sharing, and intelligent teaching. The study adopts a quasi-experimental research method, using the learning tasks of the"Smart Application of Educational Media" course as an example to test the effectiveness of the model above. The experimental results show that this model and its system can significantly enhance online collaborative learning interaction, improve online collaborative learning performance, and elevate the online collaborative learning experience. Finally, based on the experimental research, three recommendations are made: expanding the application scenarios of human-machine collaborative teaching, cultivating students’ abilities and competencies in human-machine collaborative inquiry, and strengthening the evaluation and management of human-machine collaborative teaching, with the aim of profoundly integrating generative AI into human-machine collaborative teaching. © 2024 Copyright held by the owner/author(s).

关键词： Collaborative learning

来源：评论

学校读者我要写书评

暂无评论

Research on the intelligent Path of Asset Recovery Based on Big Data Technology 1

Research on the Intelligent Path of Asset Recovery Based on ...

引用

1st international conference on intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Yang, Fang Faculty of Public Administration Guizhou University Guizhou Guiyang550001 China

ISBN: (纸本)9781643685779

This paper explores the intelligent path of asset recovery based on big data technology, aiming to enhance the accuracy and efficiency of financial crime tracking. By integrating data, extracting features, and optimizing machine learning algorithms, an intelligent prediction system is constructed that can effectively identify high-risk transactions and improve the asset recovery rate. Experiments show that the random forest algorithm performs best in handling multidimensional data, with a model accuracy of 92.5%. The research results demonstrate the practicality and feasibility of intelligent systems in the field of asset recovery, providing significant technical support for financial risk prevention. © 2025 The Authors.

关键词： asset recovery Big data technology intelligent path

来源：评论

学校读者我要写书评

暂无评论

Exploring educational transformation and innovation pathways in the era of artificial intelligence 24

Exploring educational transformation and innovation pathways...

引用

2024 international conference on Artificial Intelligence and Future Education, AIFE 2024

作者： Sun, Weiwei Qin, Minwu Yin, Zhenyao Guangzhou Institute of Science and Technology Guangzhou Guangdong China Guangdong Baiyun University Guangzhou Guangdong China

ISBN: (纸本)9798400710650

This study analyzes the profound impact of artificial intelligence (AI) on education, exploring the applications of educational reform theory, technological innovation theory, and the theory of equal educational opportunities in the context of AI-driven educational transformation. The rapid advancement of AI technologies-particularly deep learning, intelligent image recognition, big data, and educational robotics-is driving education from traditional models toward personalization, lifelong learning, and intelligent approaches. Technological innovation not only revamps teaching tools and resources but also enables differentiated instruction through intelligent data analysis, promoting educational equity and enhancing students' self-learning abilities. AI demonstrates immense potential in supporting lifelong learning, optimizing educational processes, and enriching the educational ecosystem;however, it also raises ethical challenges, including privacy concerns and risks of educational alienation. Consequently, educators should focus on the responsible application of AI technologies, leveraging the intrinsic strengths of education, to improve teaching quality and foster harmonious development between humans and machines. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Improving Federated learning Efficiency with PruneFL: A Healthcare Perspective 24

Improving Federated Learning Efficiency with PruneFL: A Heal...

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Shahid, Alvina Arshad, Muhammad Muneeb Azam, Farooque Anwar, Muhammad Waseem Shafi, Imran Department of Computer and Software Engineering EME College National University of Sciences and Technology Punjab Islamabad Pakistan School of Interdisciplinary Engineering and Sciences National University of Sciences and Technology Punjab Islamabad Pakistan

ISBN: (纸本)9798400717482

In healthcare, accessing diverse and large datasets for machine learning poses challenges due to data privacy concerns. Federated learning (FL) addresses this by training models on decentralized data while preserving privacy. We propose PruneFL, a FL paradigm that leverages model pruning to reduce complexity and enhance efficiency. PruneFL strikes a balance between model accuracy and efficiency by reducing parameters, accelerating convergence, and minimizing communication overhead. Our approach addresses critical FL challenges and demonstrates efficacy in resource-constrained environments like healthcare. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

12th international conference on Recent Trends in computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

引用

12th international conference on Recent Trends in computing, ICRTC 2024

ISBN: (纸本)9789819789450

The proceedings contain 34 papers. The special focus in this conference is on Recent Trends in computing. The topics include: Deep learning-Based Prediction of Human Ageing Related Proteins;the Insight of Clustering Algorithms for Emerging Networking Paradigms;Revolutionizing Cardiovascular Health: Exploring the Potential of AI-Driven Approaches in Diagnosing Arrhythmia Disorders;humor Detection in Telugu Social Media Text Using Cost-Sensitive learning and Indian Language Embeddings;empirical Comparison of Sleep Disorder Prediction Using machine learning Techniques;securing Smart Grids: Decentralized Anomaly Detection Using Federated learning and Recurrent Neural Networks;machine learning-Powered Intrusion Detection for Network Security;secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality;deep Neural Networks for Early Diabetes Mellitus Prediction;advancements in Image Deblurring and Performance Metrics Using Deep learning Technique;emotion-Levitating Music Recommendation System Through Real-Time Facial Recognition;smartVoice TrashMate: The intelligent Voice-Controlled Trash Bin;an Innovative Image Enhancement Algorithm for Color Filter Array Images: A Novel Approach;Enhancing Secure and Efficient Communication in Free-Space Optical Networks Using Spectral Amplitude Coding - OCDMA Technology;enhancing Citrus Fruit Classification and Quality Assessment Through Advanced machine learning Techniques;connecting Health for a Better Tomorrow Through Internet of Medical Things;image Processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency;Leaf Checker: AI-Based Plant Diagnostic Tool Using Alexnet and Mobilenet;ioT-Supported Indoor Navigation System Using machine learning Technique;a Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities;An Unequal Cluster-Based Routing Mechanism Using Lyrebird Optimization Algorithm to Achieve Network Longevity in WSNs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

12th international conference on Recent Trends in computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

引用

12th international conference on Recent Trends in computing, ICRTC 2024

ISBN: (纸本)9789819788354

关键词：

来源：评论

学校读者我要写书评

暂无评论

Role of linear algebra and algorithms in machine learning 4th

Role of linear algebra and algorithms in machine learning

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Rohit Jhamb, Shelja Parmar, Mamta Chandigarh Engineering College-CGC Landran Punjab Mohali India

ISBN: (纸本)9781032911571

All machine learning procedures consume a mathematical foundation. The aforementioned is applicable to Deep learning, Optimization, and any additional Statistics Science processes since Deep Knowledge is a subset of machine learning. We are abetted by these algorithms in extracting information from the data. When we inscribe these procedures down in a software design language, the processor system that runs them on the dataset seems brainy, which is how artificial intelligence gets its name. A classical that dismiss learn since statistics and generate approximations expending an algorithm is produced via machine learning. It’s employed to determine the workings of a mechanism and the reasons for model superiority. A mathematical requirement is included into machine learning. In order to create more intelligent software, this discipline mixes computer science, probability, statistics, linear algebra, and algorithms. These programmers are capable of extracting insightful and relevant material since statistics to attain at business visions. © 2025 the Author(s).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

8th international conference on Cognitive computing, ICCC 2024, Held as Part of the Services conference Federation, SCF 2024

8th International Conference on Cognitive Computing, ICCC 20...

引用

8th international conference on Cognitive computing, ICCC 2024, Held as Part of the Services conference Federation, SCF 2024

ISBN: (纸本)9783031779534

The proceedings contain 8 papers. The special focus in this conference is on Cognitive computing. The topics include: A Survey on Anomaly Detection with Few-Shot learning;electromyography-Based Intentional-Deception Behavior Analysis in an Interactive Social Context: Statistical Analysis and machine learning;an Adaptive Hot Ranking Algorithm for Popular Item Recommendation in the Express Industry;Retrieval-Augmented Generation Architecture Framework: Harnessing the Power of RAG;application and Optimization of Multi-agent Reinforcement learning in Collaborative Decision-Making;LLM-Based Automating Product Information Retrieval for Industry Analysis: A Real-World Application.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Empowering Students: Leveraging No-Code and Training Models with Google machine learning for Accessible App Development 5th

Empowering Students: Leveraging No-Code and Training Models ...

引用

5th international Symposium on intelligent computing Systems

作者： Paul, Sujni Higher Coll Technol Dubai Acad City Fac Dubai U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

The rapid advancement of technology has created a demand for tools that enable individuals without a computer science background to develop applications. This research focuses on leveraging no-code and low-code platforms, integrated with Google's machine learning technologies, to empower non-computer science students to create functional and innovative applications. The goal is to democratize app development by providing accessible tools that allow students from various disciplines to harness the power of machine learning without extensive programming knowledge. No-code and low-code platforms offer intuitive development environments where users can design and deploy applications using drag-and-drop interfaces, pre-built components, and minimal coding. By incorporating Google's machine learning capabilities, such as natural language processing, image recognition, and predictive analytics, these platforms enable students to create intelligent applications that solve real-world problems. This integration enhances learning experiences, fosters creativity, and broadens skill sets. The study examines the potential of these platforms in educational settings, exploring their ability to facilitate technically sophisticated yet easy-to-create applications. It also investigates the challenges and opportunities of teaching non-computer science students to use these tools, including the need for tailored instructional materials and support systems. Through case studies and user testing, the research evaluates the impact of no-code and low-code app development on student engagement, creativity, and integration of machine learning into diverse academic projects. By contributing to accessible technology education, this paper highlights how these platforms, combined with Google's machine learning, can foster a new generation of developers from non-traditional backgrounds.

关键词： Predictive analytics

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 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：