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

13,377 篇 会议
17 篇 期刊文献
16 册 图书

馆藏范围

13,410 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,105 篇 工学
- 6,398 篇 计算机科学与技术...
- 5,095 篇 软件工程
- 1,730 篇 信息与通信工程
- 1,008 篇 控制科学与工程
- 735 篇 生物工程
- 721 篇 电气工程
- 667 篇 生物医学工程（可授...
- 582 篇 网络空间安全
- 511 篇 光学工程
- 451 篇 安全科学与工程
- 398 篇 电子科学与技术（可...
- 344 篇 交通运输工程
- 328 篇 动力工程及工程热...
- 288 篇 机械工程
- 267 篇 仪器科学与技术
2,667 篇 理学
- 1,298 篇 数学
- 879 篇 生物学
- 637 篇 统计学（可授理学、...
- 535 篇 物理学
- 328 篇 系统科学
- 242 篇 化学
1,877 篇 管理学
- 1,300 篇 管理科学与工程(可...
- 789 篇 图书情报与档案管...
- 573 篇 工商管理
947 篇 医学
- 687 篇 临床医学
- 681 篇 基础医学(可授医学...
- 659 篇 公共卫生与预防医...
- 264 篇 药学(可授医学、理...
508 篇 法学
- 471 篇 社会学
262 篇 经济学
- 262 篇 应用经济学
223 篇 农学
210 篇 教育学
198 篇 文学
9 篇 军事学
1 篇 艺术学

主题

2,085 篇 accuracy
1,690 篇 deep learning
1,200 篇 machine learning
1,188 篇 computational mo...
1,044 篇 training
1,026 篇 data models
968 篇 predictive model...
724 篇 real-time system...
687 篇 convolutional ne...
684 篇 feature extracti...
676 篇 machine learning...
585 篇 support vector m...
531 篇 federated learni...
484 篇 adversarial mach...
330 篇 contrastive lear...
311 篇 medical services
295 篇 random forests
289 篇 analytical model...
278 篇 prediction algor...
271 篇 neural networks

机构

105 篇 lovely professio...
101 篇 chitkara univers...
57 篇 department of co...
47 篇 school of comput...
45 篇 school of comput...
45 篇 department of co...
42 篇 the islamic univ...
42 篇 department of co...
40 篇 shandong provinc...
36 篇 college of compu...
32 篇 university of ch...
28 篇 department of co...
27 篇 department of co...
26 篇 computer science...
26 篇 graphic era deem...
25 篇 department of co...
25 篇 amrita vishwa vi...
25 篇 the islamic univ...
23 篇 department of co...
23 篇 college of intel...

作者

23 篇 satvik vats
18 篇 shiva mehta
18 篇 kukreja vinay
18 篇 vats satvik
17 篇 anjali t.
17 篇 rishabh sharma
17 篇 deepak upadhyay
16 篇 senthil kumar th...
15 篇 singh jagendra
15 篇 venugopalan manj...
13 篇 manjunatha
13 篇 rakesh kumar
12 篇 ramya maranan
11 篇 selvanayaki kola...
11 篇 s. kaliappan
10 篇 andersson karl
10 篇 muthukumaravel a...
10 篇 yang yang
10 篇 ayodeji olalekan...
10 篇 surendran r.

语言

12,140 篇 英文
1,269 篇 其他
306 篇 中文
1 篇 西班牙文

检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是71-80 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：

Federated Impression for learning with Distributed Heterogeneous data 9th

Federated Impression for Learning with Distributed Heterogen...

引用

27th international conference on Medical Image computing and Computer Assisted Intervention (MICCAI)

作者： Arya, Atrin Ayromlou, Sana Saadat, Armin Abolmaesumi, Purang Li, Xiaoxiao Univ British Columbia Elect & Comp Engn Dept Vancouver BC V6T IZ4 Canada Vector Inst Toronto ON M5G 0C6 Canada

ISBN: (纸本)9783031776090;9783031776106

Standard deep learning-based classification approaches may not always be practical in real-world clinical applications, as they require a centralized collection of all samples. Federated learning (FL) provides a paradigm that can learn from distributed datasets across clients without requiring them to share data, which can help mitigate privacy and data ownership issues. In FL, sub-optimal convergence caused by data heterogeneity is common among data from different health centers due to the variety in data collection protocols and patient demographics across centers. Through experimentation in this study, we show that data heterogeneity leads to the phenomenon of catastrophic forgetting during local training. We propose FedImpres which alleviates catastrophic forgetting by restoring synthetic data that represents the global information as federated impression. To achieve this, we distill the global model resulting from each communication round. Subsequently, we use the synthetic data alongside the local data to enhance the generalization of local training. Extensive experiments show that the proposed method achieves state-of-the-art performance on both the BloodMNIST and Retina datasets, which contain label imbalance and domain shift, with an improvement in classification accuracy of up to 20%. The code is available at https://***/Atrin78/FedImpress.

关键词： Federated learning Catastrophic Forgetting data Synthesis data Heterogeneity

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cross-Lingual Named Entity Recognition via Dual Contrastive learning Based on MRC Framework 13th

Enhancing Cross-Lingual Named Entity Recognition via Dual Co...

引用

13th international conference on Natural Language Processing and Chinese computing

作者： Zhuo, Aiqing Shi, Kunli Gu, Jinghang Qian, Longhua Zhoul, Guodong Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Chinese & Bilingual Studies Hong Kong 999077 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Cross-lingual Named Entity Recognition (NER) has recently become a research hotspot because it can transfer knowledge from high-resource languages to low-resource languages, thus meeting the challenge of data scarcity in low-resource languages. Most of the current model-transfer methods rely on directly using multilingual models to represent text yet ignoring the cross-lingual word alignment information in multi-lingual models, and also neglecting the utilization of prior knowledge. We propose a dual contrastive learning method based on machine reading comprehension (MRC) framework, by combining Query Contrastive learning (QCL) and Translation Word Contrastive learning (TWCL), to mitigate above problems. Specifically, we utilize QCL to design contrastive objectives for different query templates to enhance the representation ability of prior knowledge. In addition, we utilize TWCL to help the model capture the word alignment relationship between the source language and target language via a pseudo-parallel corpus. We conducted extensive experiments on 4 different datasets and the experimental results demonstrate the effectiveness of our method.

关键词： Cross-lingual Named Entity Recognition Contrastive learning machine Reading Comprehension

来源：评论

学校读者我要写书评

暂无评论

Advances in Multimodal Fusion of EHR and Medical Imaging data Using deep learning techniques for advanced treatment of brain cancer 24

Advances in Multimodal Fusion of EHR and Medical Imaging Dat...

引用

8th international conference on Algorithms, computing and Systems, ICACS 2024

作者： Siva Raja, P.M. Vidhya, S. Sumithra, R.P. Anjana, S. Rejini, K. Ramanan, K. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Nagercoil Tamil Nadu Nagercoil India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798400718304

In this research, the performance of different deep learning models, including CNN + RNN, VGG16 + RNN, VGG19 + RNN, ResNet50 + RNN, and InceptionV3 + RNN in predicting brain tumour grades by employing Electronic Health Record (EHR) and imaging data are evaluated. Confusion matrices show the models' abilities to predict, showing the results of the prediction capability of each model over tumour grades. From the result and analysis, InceptionV3+RNN is performing better than all other ML models. With the multimodal fusion techniques, an informed decision can be made about the grades of the cancer cells, and the clinicians can give the proper treatment to the patients. The proposed approach can be implemented in real-time to predict the cancer grades with the highest level of accuracy for better treatment. © 2024 Copyright held by the owner/author(s).

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Structural Health Monitoring data Analysis Using Deep learning Techniques 24

Structural Health Monitoring Data Analysis Using Deep Learni...

引用

8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Yang, Linfei Huang, Enzo Faculty of Science and Technology University of Macao China Faculty of Business Administration University of Macao China

ISBN: (纸本)9798400710094

With the rapid development of deep learning technology, its application in the field of Structural Health Monitoring (SHM) represents a significant leap from traditional detection methods. This paper aims to explore how deep learning algorithms enhance the assessment of civil infrastructure integrity by analyzing large datasets from sensors. We first introduce the background of SHM, highlighting the shortcomings of traditional methods in terms of accuracy and efficiency. Next, we employ deep learning models for data analysis, investigating their applications in structural damage detection and prediction. Through a comprehensive analysis of existing literature, we find that deep learning not only overcomes the limitations of manual inspections but also significantly improves the overall performance of SHM by enhancing real-time monitoring capabilities. Furthermore, this paper discusses hybrid systems that combine data-driven models with physical understanding, as well as the distributed intelligence brought about by edge computing. These factors collectively provide more reliable and flexible solutions for future infrastructure management. Our key findings indicate that the application of deep learning can significantly improve the accuracy and efficiency of SHM, enabling a quicker response to the needs of dynamic communities. © 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

Residual-INR: Communication Efficient On-Device learning Using Implicit Neural Representation 24

Residual-INR: Communication Efficient On-Device Learning Usi...

引用

43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Chen, Hanqiu Yao, Xuebin Subedi, Pradeep Hao, Cong Georgia Institute of Technology United States Samsung Semiconductor Inc. United States

ISBN: (纸本)9798400710773

Edge computing is a distributed computing paradigm that collects and processes data at or near the source of data generation. The on-device learning at edge relies on device-to-device wireless communication to facilitate real-time data sharing and collaborative decision-making among multiple devices. This significantly improves the adaptability of the edge computing system to the changing environments. However, as the scale of the edge computing system is getting larger, communication among devices is becoming the bottleneck because of the limited bandwidth of wireless communication leads to large data transfer latency. To reduce the amount of device-to-device data transmission and accelerate on-device learning, in this paper, we propose Residual-INR, a fog computing-based communication-efficient on-device learning framework by utilizing implicit neural representation (INR) to compress images/videos into neural network weights. Residual-INR enhances data transfer efficiency by collecting JPEG images from edge devices, compressing them into INR format at the fog node, and redistributing them for on-device learning. By using a smaller INR for full image encoding and a separate object INR for high-quality object region reconstruction through residual encoding, our technique can reduce the encoding redundancy while maintaining the object quality. Residual-INR is a promising solution for edge on-device learning because it reduces data transmission by up to 5.16 × across a network of 10 edge devices. It also facilitates CPU-free accelerated on-device learning, achieving up to 2.9 × speedup without sacrificing accuracy. Our code is available at: https://***/sharclab/Residual-INR. © 2024 Copyright is held by the owner/author(s).

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Anomaly detection using different types of machine learning models in the context of "smart maintenance technologies in the manufacturing industry" 6

Anomaly detection using different types of machine learning ...

引用

6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Giliyana, San Mälardalen University Universitetsplan 1 Västeras Sweden Mälardalen Industrial Technology Center John Engellaus Gata 1 Eskilstuna633 61 Sweden

Industry 4.0 presents nine technologies, including the Industrial Internet of Things (IIoT), Big data and Analytics, Cloud computing, etc. The progress of Industry 4.0 places a new demand for maintenance. Some of the nine technologies of Industry 4.0, such as IIoT, Big data and Analytics, Cloud computing and Augmented Reality (AR), as well as Cyber-Physical System (CPS) and machine learning, play an important in the development of smart maintenance technologies. In smart maintenance research, it is presented how IIoT can be used for machine connection and maintenance data collection, machine learning models for maintenance data analysis and failure prediction, AR for maintenance instructions, etc. Although previous smart maintenance research presents many technologies for smart maintenance, the manufacturing companies still face many implementation challenges when implementing and using to add benefits to maintenance organizations in line with companies main goals. Many manufacturing companies still utilize reactive maintenance and are experiencing too much downtime. In this paper, I have shown how two machine learning models, Isolation Forest and Regression Learner, and the statistical technique, Interquartile range (IQR), can be applied in order to detect anomalies in an unsupervised dataset, consisting of travel time for a linear guide, and temperature, in a drill station, which is part of a Cyber-Physical production system, located at a smart production laboratory in Sweden. © 2025 The Authors.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Emotion detection: Facial emotion detection using ML 4th

Emotion detection: Facial emotion detection using ML

引用

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

作者： Sharma, Sharad Chauhan, Ritik Vajpayee, Pooja School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Cognitive perception of images is an intense task, like guessing the truth of a thought or a mystery. In this process, we use different methods to solve the need to know the job. In recent years, emotional intelligence has received widespread attention due to its important role in improving human-computer interaction and emotional analysis. Because of its processes for extracting visual features, deep neural networks-particularly convolutional neural network (CNN)-are frequently employed in FER. A lot of research has been done on CNN, but only a few areas have solved the facial recognition (FER) problem. After many reports and studies, the shortcoming of most existing models is that they often consider the front profile and ignore the side profile of the current image, although different angular views are also important for the perspective model. This helps you make informed decisions about personal needs, providing support, or security threats. However, this proved to be too complex for computers, and feature extraction needed a wide range of image processing methods. Numerous machine learning techniques are appropriate for this particular purpose. To use machine learning for any kind of detection, an algorithm must first be trained and then tested on relevant data. This article examines some machine learning algorithms and strategies for extraction that aid in the analysis of human emotion. This review contains a comprehensive review of a variety of materials, methods, and measures commonly used in psychology research. It also highlights the challenges and limitations of current models, including dealing with uncertainties and cultural differences. Face recognition in large datasets poses additional challenges due to the diversity of images as well as the differences between side and front views. © 2025 the Author(s).

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Zero-Shot Object Detection via Semantic Decoupling 24

Fine-Grained Zero-Shot Object Detection via Semantic Decoupl...

引用

8th international conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Duan, Lijuan Zhang, Zichen Gong, Zhi Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing China School of Computer Science Beijing University of Technology Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing China

ISBN: (纸本)9798400718182

Recent deep learning models have advanced object detection capabilities, but their reliance on labeled data limits scalability. Fine-grained zero-shot detection remains challenging due to the need to generalize to unseen classes and distinguish subtle features. To address this, we propose Fine-Grained Zero-Shot Object Detection via Semantic Decoupling (ZSD-SD). Our approach introduces a semantic decoupling module to separate visual features into semantic-related and semantic-unrelated components, facilitating alignment between visual and semantic spaces. A semantic-visual learning module further decouples these features, enhancing visual representations relevant to external semantic knowledge. This semantic-related representation significantly improves zero-shot performance. Experiments on two fine-grained object detection datasets demonstrate that ZSD-SD achieves state-of-the-art results. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Outsourced and Robust Multi-party Computation with Identifying Malicious Behavior and Application to machine learning 19th

Outsourced and Robust Multi-party Computation with Identifyi...

引用

19th international conference on Information Security Practice and Experience

作者： Qin, Hong He, Debiao Feng, Qi Yang, Xiaolin Luo, Qingcai Wuhan Univ Sch Cyber Sci & Engn Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan Peoples R China Inspur Cloud Informat Technol Co Ltd Jinan Peoples R China Inspur Acad Sci & Technol Jinan Peoples R China

ISBN: (纸本)9789819790524;9789819790531

This work proposes an efficient n-party computation framework with malicious security in the outsourced case. We consider a non-symmetrical trust setting that n - 1 parties, including P-0, is semi-honest and one party is malicious. Once the party in {P-1, P-2, ..., Pn-1} misbehaves, we will caught and exclude it, achieving robustness by executing the further computations among semi-honest parties. Unlike previous works that focused on traditional robustness, we do not need to delegate the computation to an identified honest party, which contradicts the usual expectation of privacy and hinder the adoption in practice. Our framework bridges the gap between semi-honest and malicious honest-majority multi-party protocols, and is well suited for privacy-preserving machine learning. In general, machine learning depends on large amounts of data and powerful computing power, leading resource-constrained clients to resort to secure outsourced computing (SOC) approach. In this work, we utilize the n-party framework to design efficient building blocks for machine learning. We significantly improve the efficiency of blocks and implement machine learning inference in LAN and WAN setting, respectively. Theoretical analysis and experimental results show that our framework outperforms the existing honest-majority protocols, such as SWIFT (USENIX Security'21) in terms of "robustness" and Fantastic Four (USENIX Security'21) in terms of efficiency.

关键词： multiparty computation machine learning robustness secure outsourced

来源：评论

学校读者我要写书评

暂无评论

Entropy Based Embedded Unsupervised Feature Selection 4th

Entropy Based Embedded Unsupervised Feature Selection

引用

4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Rastogi, Reshma Aich, Era South Asian Univ Machine Learning & Stat Inference MLSI Lab Delhi India

ISBN: (纸本)9783031837920;9783031837937

machine-learning techniques often encounter significant challenges when dealing with high-dimensional data due to the substantial memory requirements and extended processing times involved. In this paper, we introduce a novel unsupervised feature selection method called Entropy based Embedded Unsupervised Feature Selection (EEUFS). This method aims to select a suitable and concise subset of features while minimizing redundancy by assessing the entropy of each feature. computing entropy for each feature, evaluating correlation between features and eliminating redundant ones, our approach reduces the feature set size while retaining essential information from the original dataset. Experimental evaluations conducted across six datasets using six different entropy measures demonstrate that our proposed Entropy based Embedded Unsupervised Feature Selection(EEUFS) method is effective and often outperforms state-of-the-art unsupervised feature selection methods.

关键词： Unsupervised feature selection Entropy Information theory Feature dependency Feature redundancy

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 << < 4 5 6 7 8 9 10 11 12 13 > >>

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：