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

3,008 篇 会议
4 册 图书
4 篇 期刊文献

馆藏范围

3,016 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,283 篇 工学
- 2,033 篇 计算机科学与技术...
- 1,640 篇 软件工程
- 894 篇 信息与通信工程
- 655 篇 控制科学与工程
- 468 篇 网络空间安全
- 418 篇 生物工程
- 321 篇 电气工程
- 273 篇 生物医学工程（可授...
- 241 篇 电子科学与技术（可...
- 219 篇 仪器科学与技术
- 218 篇 石油与天然气工程
- 175 篇 动力工程及工程热...
- 146 篇 光学工程
- 119 篇 安全科学与工程
- 109 篇 机械工程
1,354 篇 理学
- 792 篇 数学
- 436 篇 生物学
- 278 篇 统计学（可授理学、...
- 241 篇 化学
- 192 篇 物理学
- 130 篇 系统科学
828 篇 管理学
- 526 篇 管理科学与工程(可...
- 435 篇 图书情报与档案管...
- 229 篇 工商管理
306 篇 医学
- 236 篇 临床医学
- 235 篇 基础医学(可授医学...
- 167 篇 药学(可授医学、理...
- 166 篇 公共卫生与预防医...
138 篇 法学
- 102 篇 社会学
87 篇 农学
- 86 篇 作物学
80 篇 经济学
80 篇 教育学
10 篇 军事学
2 篇 文学

主题

89 篇 deep learning
87 篇 machine learning
66 篇 internet of thin...
61 篇 blockchain
53 篇 big data
47 篇 support vector m...
46 篇 forecasting
45 篇 data mining
36 篇 students
34 篇 artificial intel...
34 篇 convolutional ne...
33 篇 feature extracti...
32 篇 long short-term ...
30 篇 decision trees
29 篇 sentiment analys...
26 篇 object detection
26 篇 training
25 篇 cybersecurity
24 篇 computer vision
24 篇 power electronic...

机构

58 篇 maharishi school...
57 篇 uttaranchal univ...
39 篇 vishwakarma inst...
36 篇 chitkara univers...
35 篇 vivekananda glob...
32 篇 national univers...
32 篇 the islamic univ...
29 篇 centre of interd...
27 篇 lovely professio...
24 篇 chitkara univers...
24 篇 vishwakarma inst...
24 篇 graphic era deem...
23 篇 sr university sc...
21 篇 school of comput...
20 篇 uttaranchal inst...
19 篇 graphic era hill...
18 篇 maharishi school...
16 篇 vishwakarma inst...
16 篇 college of pharm...
15 篇 ajloun national ...

作者

29 篇 chauhan rahul
17 篇 alazzam malik ba...
16 篇 joshi kapil
14 篇 dhabliya dharmes...
13 篇 gill kanwarparta...
13 篇 gehlot anita
13 篇 tiwari mohit
10 篇 kukreja vinay
10 篇 mondal dipannita
10 篇 vats satvik
10 篇 anand vatsala
9 篇 sharma neha
9 篇 gupta rupesh
9 篇 kumar bura vijay
8 篇 anjali t.
8 篇 kumar ritesh
8 篇 singh vaishali
8 篇 singh akhilendra...
8 篇 singh rajesh
8 篇 abdelminaam diaa...

语言

2,985 篇 英文
31 篇 其他
5 篇 中文

检索条件"任意字段=2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023"

共 3016 条记录，以下是1-10 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：

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

2023 IEEE 3rd International Conference on Data Science and C...

引用

2023 ieee 3rd international conference on data science and computer application, icdsca 2023

ISBN: (纸本)9798350341546

The proceedings contain 306 papers. The topics discussed include: CloneBAS: a code clone detection method based on abstract syntax tree and Simhash;research on the a-star algorithm based on path finding;study on vibration isolation characteristics of UHVDC converter valve saturable reactor;a market transaction model based on blockchain technology for multimicrogrid networks;optimization research on radar signal processing technology based on MTI-MTD;stock prediction using evolutionary attention-based LSTM;the reasons and improvement measures for the long time of replacing cross arm with 10 kV live;research on accurate three-dimensional modeling technology and intelligent mining in coal mines;evaluation on power equipment operation status monitoring system based on data mining;exploring deep learning-based visual localization techniques;and intelligent detection and recognition of pulley paying-off defects based on improved YOLOv5.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advances in AI for Women’s Healthcare: A Review of Deep Learning and NLP Approaches 3

Advances in AI for Women’s Healthcare: A Review of Deep Lea...

引用

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

作者： Choudhary, Jyoti Thakur, Madhuri School of Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798331527549

Women’s health faces critical challenges, particularly in maternal and reproductive healthcare, where complications significantly contribute to global mortality rates. Despite advancements in medical science, inequities in healthcare access and diagnosis persist, especially in underserved regions. Innovative approaches leveraging artificial intelligence (AI) technologies, such as deep learning (DL) and natural language processing (NLP), are transforming healthcare by enabling early diagnosis, personalized treatment, and scalable preventive care. This paper evaluates the transformative role of these AI-driven methods in addressing critical women’s health issues, including cancer detection, maternal health, and reproductive disorders, with a focus on advancements from 2018 to 2023. These years represent a period of rapid AI evolution and its integration into healthcare systems. The present study captures important challenges like restricted diversity of datasets, issues concerning transparency in models, and obstacles in actual deployment. It collates findings to give a global perspective of AI development in women's health. It is also prepared with practical future action steps for its research to lead AI-influenced innovations shaping the future of women's healthcare. © 2025 ieee.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Automated Image Captioning: A CNN and Transformer Model Analysis 3

Deep Learning for Automated Image Captioning: A CNN and Tran...

引用

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

作者： Saarika, Kathula Varsha, Vasaraju Sri Harsha, P. Sheikh, Anjum Nabi Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology India

ISBN: (纸本)9798331527549

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and ieeeXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning. © 2025 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Construction of an Innovation Information Mining Platform Integrating Knowledge Graph 3

Construction of an Innovation Information Mining Platform In...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Wan, Dawei Huang, Peisen Sichuan University of Arts and Science Sichuan Dazhou635000 China

ISBN: (纸本)9798331509675

With the continuous expansion of data mining scenarios, an accurate data analysis algorithm framework is urgently needed. In this study, the construction of an innovation information mining platform integrating knowledge graph is discussed. The designed platform combines multiple layers of knowledge fusion, matching and problem solving. This architecture can greatly improve the intelligent extraction and mining of innovative information. The designed platform mainly consists of four key parts: 1. At the data acquisition level, network logs are analyzed through Web data mining. At the same time, GoAccess tool is used to extract access logs and conduct preliminary analysis. 2. At the data mining level, this study uses a probabilistic graph model based on a word pair model to perform cluster analysis on large-scale text data. At the same time, the innovative information points are classified through calorific value calculation and information entropy theory to obtain the mining effect. 3. At the system improvement level, we integrate knowledge graphs and data mining algorithms, and use the Jena inference engine to reason and complete data relationships. 4. At the platform optimization level, the cloud platform architecture of this study further optimizes the allocation of computing resources and application deployment, while using Kubernetes and Docker container technologies to ensure the high performance of the system. Through data mining accuracy test, K-mean and FCM algorithms were used to verify the algorithm effect of this study. © 2025 ieee.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Research on Network Security Intrusion Detection Based on Devnet 24

Research on Network Security Intrusion Detection Based on De...

引用

3rd international conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Zheng, Hao Sun, Dongxu Han, Xiao Zhang, Xiaohua Zhao, Yan Co. Ltd. Hebei Shijiazhuang China Guodian Hefeng Wind Power Development Co. Ltd. Liaoning Shenyang China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China

ISBN: (纸本)9798400710834

With the promotion of industrial 4.0 and intelligent manufacturing, the network security of industrial control system becomes particularly important. In the face of new network attacks, the traditional intrusion detection system (IDS) has been difficult to meet the protection requirements, so it is particularly urgent to research and develop efficient network security detection technology. In this study, an intrusion detection method of industrial control system based on DevNet network is proposed, which uses deep convolution neural network architecture to detect events and locate evidence by analyzing the characteristics of key frames, which significantly improves the performance of multimedia event detection. This method optimizes the anomaly score through end-to-end learning, makes effective use of the limited marked abnormal data, and enhances the accuracy and real-time performance of detection. The research first examines the challenges of network security in industrial control systems, and then describes the DevNet framework and its core algorithms, including exception score learner, normal a priori and Z-Score deviation loss function. Through the experimental evaluation of multiple data sets, DevNet performs well in key evaluation indicators such as AUC-ROC and AUC-PR, especially in dealing with large-scale network traffic and defending against zero-day attacks. The interpretability of DevNet provides strong support for security analysis and enhances the credibility of the model prediction. This research not only enhances the protection of network security, but also promotes technological innovation, and provides new ideas for the field of network security. With the increase of IIoT equipment, DevNet is expected to become the key technology to protect the safety of industrial control system. The results of this research are of great significance in theory, and show broad prospects in practical application, and contribute to the progress of network security. ©

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Research on the application of Cloud Computing-based Dual-Machine Hot Standby System in Railway Signal Safety Management 3

Research on the Application of Cloud Computing-based Dual-Ma...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Ji, Zhe Wang, Ding Liu, Bing Liu, Yiming Research and Design Institute Co. Ltd Shenzhen China China Academy of Railway Sciences Co. Ltd Beijing China

ISBN: (纸本)9798331508456

This paper investigates the application of cloud computing-based dual-computer hot standby computer security platform in railway signalling security management, aiming to enhance the reliability and security of railway signalling system. With the increasing importance of railway transport to modern society, the safety of railway signal system has become a key factor for public safety and social stability. This paper first analyses the security challenges faced by railway signalling systems and explores the potential of dual-machine hot-standby systems in improving system availability and security. Then, this paper details the design and implementation of a cloud-based dual-machine hot standby architecture, including system architecture, signal monitoring module, data encryption module, security audit module, and event response module. Through experimental validation, this paper compares the performance of the cloud-based dual-machine hot-standby architecture with that of the traditional single-machine system in terms of failure recovery time, resource utilisation and system response time, and the results show that the dual-machine hot-standby architecture has significant advantages in these key performance indicators. Finally, the paper discusses the limitations of the research and proposes directions for future research, including cost-benefit analysis, resource optimisation, technology adaptation research and intelligent development, with a view to providing a more efficient and intelligent solution for railway signal safety management. © 2025 ieee.

关键词： Subway signal systems

来源：评论

学校读者我要写书评

暂无评论

Towards Floating Point-Based Attention-Free LLM: Hybrid PIM with Non-Uniform data Format and Reduced Multiplications 24

Towards Floating Point-Based Attention-Free LLM: Hybrid PIM ...

引用

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

作者： Guo, Lidong Zhu, Zhenhua Liu, Tengxuan Ning, Xuefei Li, Shiyao Dai, Guohao Yang, Huazhong Fu, Wangyang Wang, Yu Tsinghua University Beijing China Tsinghua University Infinigence-AI Beijing China Shanghai Jiao Tong University Infinigence-AI Shanghai China

ISBN: (纸本)9798400710773

Attention-free large language models (LLMs), such as Mamba and RWKV, have emerged as promising architectures to address the quadratic attention complexity of Transformer models. The inference bottleneck of these models lies in memory-bound matrix-vector multiplications (MVMs) and element-wise multiplications (EWMs). The emerging RRAM/SRAM-based Processing-In-Memory (PIM) architectures have shown great potential to overcome the memory wall problem. However, constrained by the supported data format and operator type, directly adopting PIM architectures for attention-free models faces three challenges: (1) RRAM-based analog PIM architectures perform integer (INT) MVMs using voltage, current, and conductance in the analog domain, limiting their application to the more accurate floating point (FP) data format;(2) SRAM-based digital PIM architectures require additional decoder circuits to support FP format, and the SRAM capacity cannot satisfy the storage requirement of LLMs;(3) When performing EWMs using PIM architectures, only one row/column or the diagonal memory cells are activated, resulting in severe device under-utilization. To tackle the above challenges, this paper proposes an RRAM and 3D-SRAM-based hybrid PIM architecture with non-uniform data format, achieving FP-based algorithm accuracy, high device utilization, and high energy efficiency. At the software level, we first analyze the impact of quantization errors on the accuracy of attention-free LLMs. For the quantization error-insensitive MVM operations, we propose the PIM-oriented exponent-free non-uniform (PN) data format. The proposed PN format can be flexibly adjusted to fit the data distribution and approach the accuracy of the FP format using bit-slicing-based full INT operations. For the quantization error-sensitive EWM operations, we introduce the multiplication-free approximated FP multiplications to reduce the additional hardware overhead for PIM. At the hardware level, we propose a hybrid PIM archi

关键词： RRAM

来源：评论

学校读者我要写书评

暂无评论

Identifying Human Movement Patterns: Multivariate Gait Analysis Through Machine Learning 3rd

Identifying Human Movement Patterns: Multivariate Gait Analy...

引用

3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Kumar, Raunak Mittal, Usha Chawla, Priyanka School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India National Institute of Technology Warangal India

ISBN: (纸本)9789819719426

The ability to identify walking conditions correctly is essential for both diagnosing and treating gait abnormalities. This study utilized machine learning algorithms to analyze multivariate gait data obtained from 10 healthy participants walking under three distinct settings, including normal treadmill walking while wearing an ankle brace on the right. In order to increase the precision and efficiency of the ML models, the authors adopted a pipeline technique to handle the data. This research unveils the preeminence of random forest, achieving an impressive (92%) accuracy, surpassing logistic regression, neural network, naive Bayes, and perceptron. It exemplifies the formidable potential of machine learning algorithms for gait classification. The application of the results may be restricted to the particular dataset and walking conditions employed in the study, and the proposed study’s limitations include the use of a constrained number of algorithms and hyperparameter tuning settings. The proposed study has implications for the design of diagnostic tools and assistive devices for people with gait abnormalities and emphasizes the value of paying close attention to hyperparameters and other important model parameters to achieve the highest level of accuracy and performance in machine learning models. Future studies might build on this strategy by utilizing more datasets, additional algorithms, and sophisticated optimization methods to boost the precision of gait categorization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Exercise equipment

来源：评论

学校读者我要写书评

暂无评论

Deep Ensemble Learning Approach for Multimodal Emotion Recognition 3rd

Deep Ensemble Learning Approach for Multimodal Emotion Recog...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Dave, Maheak Mukherjee, Shivesh Krishna Singh, Pawan Kumar Mahmud, Mufti Department of Computer Science and Engineering Techno India University West Bengal Kolkata700091 India Department of Information Technology Jadavpur University Jadavpur University Second Campus Salt Lake City Plot No. 8 Salt Lake Bypass LB Block Sector III West Bengal Kolkata700106 India School of Science and Technology Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Department of Computer Science Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University NottinghamNG11 8 NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University NottinghamNG11 8 NS United Kingdom

ISBN: (纸本)9789819739363

In this research initiative, our central objective is the development of an emotion recognition system through the utilization of cutting-edge deep learning methodologies. Our methodology involves adopting a subject-independent approach to classify emotions based on Electroencephalogram (EEG) signals sourced from a standard benchmark DEAP dataset. To ensure data quality and reliability, we initiate our process by meticulously preprocessing the raw signal data, which includes the application of Normalization and Common Average Reference (CAR) techniques. Subsequently, we employ Discrete Wavelet Transform (DWT) technique to extract salient features from the cleaned EEG data. These extracted features serve as the foundation for training three distinct deep learning models: the CNN-LSTM, CNN-GRU, and 2D-CNN models. To consolidate their predictive capabilities, we employ a Majority voting algorithm, effectively combining the strengths of these models. Notably, our proposed deep ensemble learning approach yields an impressive accuracy rate of 88% when evaluated on the challenging DEAP dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

SMOTE-Based Sampling for Addressing Class Imbalance 3rd

SMOTE-Based Sampling for Addressing Class Imbalance

引用

3rd international conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Chaudhary, Shweta Parashar, Jyoti Malik, Nisar Ahmad Ali, Shalbbya Upreti, Kamal Vats, Prashant Department of Computer Science and Engineering JIMS Engineering Management Technical Campus Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Department of Information Technology Government Degree College Kulgam Jammu and Kashmir India Delhi NCR Ghaziabad India Department of CSE SCSE Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784639

Various real-world applications, including as text categorization, categorization of gender in facial recognition for medical evaluation, fraud detection, and satellites analysis of images for oil-spill monitoring, are frequently plagued by imbalanced data. The majority class is commonly the primary focus of machine learning algorithms, with the minority samples being ignored or classified in a secondary manner. Nevertheless, despite their rarity, these minority samples are very important. When it comes to classification tasks, the issue of class imbalance—where one class is underrepresented relative to another—presents a significant barrier. Specialized approaches including SMOTE, ADASYN, and cost-sensitive voting classifiers have been developed to address this problem. The minority class is oversampled in these methods, synthetic samples are created adaptively, and different prices are placed on misclassification mistakes in order to solve the issue of class imbalance. As a result, rigorous assessment utilizing pertinent metrics and cost considerations are required. The efficacy of these strategies, however, depends on dataset features and problem-specific factors. Class imbalance is still a hot topic for study, and there has been constant innovation in novel methods that are adapted to certain dataset characteristics and application fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Oil spills

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302页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：