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

85,838 篇 会议
395 篇 期刊文献
68 册 图书
1 篇 学位论文

馆藏范围

86,302 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

53,912 篇 工学
- 43,033 篇 计算机科学与技术...
- 31,566 篇 软件工程
- 13,286 篇 信息与通信工程
- 9,629 篇 控制科学与工程
- 8,699 篇 电气工程
- 4,910 篇 生物工程
- 4,115 篇 生物医学工程（可授...
- 3,828 篇 电子科学与技术（可...
- 3,464 篇 机械工程
- 3,263 篇 仪器科学与技术
- 2,955 篇 动力工程及工程热...
- 2,866 篇 交通运输工程
- 2,740 篇 安全科学与工程
- 2,327 篇 网络空间安全
- 2,141 篇 光学工程
20,121 篇 理学
- 10,659 篇 数学
- 5,374 篇 生物学
- 4,197 篇 统计学（可授理学、...
- 3,568 篇 物理学
- 3,175 篇 系统科学
- 2,083 篇 化学
15,731 篇 管理学
- 10,197 篇 管理科学与工程(可...
- 7,470 篇 图书情报与档案管...
- 4,867 篇 工商管理
4,251 篇 医学
- 3,507 篇 临床医学
- 3,357 篇 基础医学(可授医学...
- 2,063 篇 公共卫生与预防医...
- 1,794 篇 药学(可授医学、理...
2,408 篇 法学
- 1,966 篇 社会学
1,782 篇 经济学
- 1,770 篇 应用经济学
1,213 篇 农学
740 篇 教育学
294 篇 文学
187 篇 军事学
66 篇 艺术学
3 篇 哲学
3 篇 历史学

主题

4,177 篇 data mining
3,606 篇 data models
3,409 篇 computer science
2,417 篇 real-time system...
2,378 篇 training
2,280 篇 computational mo...
2,202 篇 feature extracti...
2,065 篇 accuracy
1,986 篇 big data
1,774 篇 machine learning
1,765 篇 predictive model...
1,478 篇 deep learning
1,413 篇 cloud computing
1,243 篇 monitoring
1,242 篇 internet of thin...
1,144 篇 neural networks
1,112 篇 conferences
1,096 篇 computer archite...
995 篇 databases
984 篇 learning systems

机构

154 篇 university of sc...
98 篇 school of comput...
91 篇 school of automa...
83 篇 chitkara univers...
77 篇 institute of inf...
77 篇 college of compu...
73 篇 school of comput...
72 篇 huazhong univers...
69 篇 school of comput...
55 篇 department of co...
54 篇 school of comput...
53 篇 shandong provinc...
52 篇 academy of mathe...
52 篇 tsinghua univers...
50 篇 school of inform...
49 篇 college of compu...
49 篇 sathyabama insti...
43 篇 lovely professio...
41 篇 shanghai jiao to...
41 篇 chennai

作者

55 篇 wang lei
51 篇 zhang lei
50 篇 li yang
44 篇 li peng
41 篇 li xin
37 篇 gill kanwarparta...
37 篇 wang jing
37 篇 jun wang
36 篇 zhang tao
33 篇 lei wang
32 篇 rui wang
32 篇 yang bo
30 篇 yu wang
27 篇 xiao liu
23 篇 zhang lin
22 篇 zheng wei
22 篇 hui wang
21 篇 anjali t.
21 篇 pareek piyush ku...
21 篇 wang hui

语言

83,212 篇 英文
2,800 篇 其他
293 篇 中文
8 篇 俄文
7 篇 西班牙文
4 篇 土耳其文
2 篇 法文
2 篇 葡萄牙文

检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 86302 条记录，以下是261-270 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 Reference-Less CDR Using SAR-Based Frequency-Acquisition Technique Achieving 55ns Constant Band-Searching Time and up to 63.64Gb/s/μs Acquisition Speed 72

A Reference-Less CDR Using SAR-Based Frequency-Acquisition T...

引用

72nd ieee international Solid-State Circuits conference, ISSCC 2025

作者： Zhao, Xiaoteng Zhang, Yuhao Chang, Hao Dong, Yilong Han, Chenxi Yang, Zekai Dong, Zhicheng Zhou, Rong Liu, Shubin Zhu, Zhangming Xidian University Xi'an China

ISBN: (纸本)9798331541019

Reference-less clock and data recovery (CDR) circuits are prevalently employed in applications that demand a continuous data rate due to their simplicity and cost-effectiveness, as they eliminate the need for an external reference [1]-[6]. The frequency acquisition (FA) speed of such CDRs is critical for link setup, particularly in systems with dynamic data traffic management that necessitate frequent transitions from sleep to wake up [7], [8]. According to [9], part of the links can be turned off without congesting the network, even in high-load scenarios like data centers. However, the interface wake-up delay has been identified as the most significant bottleneck in the overall network. To address this issue, this paper proposes a fast FA reference-less CDR using a successive approximation register (SAR)-based frequency band switching technique, achieving a constant band switching time of 55ns. With the help of the proposed charge pump (CP) with positive net current, the total FA time of less than 150ns is achieved, which is at least 4× shorter than comparable work. © 2025 ieee.

关键词： Clock and data recovery circuits (CDR circuits)

来源：评论

学校读者我要写书评

暂无评论

ResMobileNet: A Deep Ensemble Approach for Classification of Breast Cancer using Transfer Learning 3

ResMobileNet: A Deep Ensemble Approach for Classification of...

引用

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

作者： Khattar, Sonam Kumar, Vishal Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science Chandigarh University Mohali India

ISBN: (纸本)9798331527549

Breast cancer remains one of the leading causes of death in women worldwide, and thus there is a great need for reliable diagnostic tools. This paper proposes an improved ensemble approach for breast cancer classification using mammography images, which integrates the strengths of two advanced transfer learning architectures: ResNet and MobileNetV2. The ensemble model combines the feature extraction capabilities of ResNet with the computational efficiency of MobileNetV2 to achieve higher diagnostic accuracy without compromising operational effectiveness. This approach was applied to an open mammography dataset with the use of preprocessing and data augmentation techniques for the better generalization of models. The metrics such as accuracy, precision, recall, and F1-score depicted that ensemble methods perform better than the single model, hence being a good approach toward the early detection of breast cancer. This work holds promise toward further integration of deep learning frameworks for advancing computer-aided diagnosis systems in the field of medical imaging. © 2025 ieee.

关键词： Mammography

来源：评论

学校读者我要写书评

暂无评论

Optimized data Processing for Hard Drive Failure Prediction 5

Optimized Data Processing for Hard Drive Failure Prediction

引用

5th international conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Sivakumar, M. Ragavendra, R. Satapathy, Sandeep Kumar Mishra, Shruti Mallick, Pradeep Kumar Vellore Institute of Technology School of Computer Science & Engineering Tamilnadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798331522100

With the rise of big data, efficient data handling techniques have become increasingly crucial. Hard drive failure prediction is a critical domain where accurate and timely data handling is paramount. This research paper introduces a data analysis and pre-processing approach for hard drive failure prediction based on machine learning techniques. A large dataset encompassing hard drive operational data, metadata, and error rates was collected. Extensive pre-processing was performed, including data cleaning and transformation. Multiple Machine Learning algorithms, including random forests, were employed to train predictive models for hard drive failure. The results demonstrate the effectiveness of our approach in achieving high accuracy for hard drive failure prediction, highlighting the significance of our pre-processing methods. Additionally, this paper compares existing machine learning algorithms and explores the role of big data in enhancing hard drive failure prediction. The utilization of big data and advanced data handling techniques has led to improved accuracy and predictive capabilities, making it an indispensable factor in this field. © 2025 ieee.

关键词： data curation

来源：评论

学校读者我要写书评

暂无评论

Improvement and Performance of data Transmission Algorithms in Computer Communications 14

Improvement and Performance of Data Transmission Algorithms ...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Liu, Yi Liu, Hao Cheng, Yun Cheng, Siqiang Information School Hunan University of Humanities Science and Technology Loudi417000 China

ISBN: (纸本)9798331531935

In response to the current problems of low data transmission efficiency, high latency, and insufficient stability in computer communications, this paper studies a comprehensive optimized data transmission algorithm, which aims to improve network resource utilization efficiency and transmission performance and reduce the frequency of congestion by dynamically adjusting the transmission rate, adaptive congestion control and queue management optimization. Firstly, a dynamic rate adjustment mechanism based on real-time network status detection is designed to monitor the network bandwidth, latency, and packet loss rate in real time to dynamically adjust the data packet transmission rate. Secondly, an adaptive congestion control strategy is applied to reduce the impact of congestion on data transmission by monitoring the network congestion status in real time and adjusting it according to the changing trend. Finally, queue management and priority scheduling algorithms are used to reasonably arrange the transmission order of data packets, reduce queuing delays, and improve the transmission efficiency of high-priority data packets. In terms of latency and throughput, as latency increases, throughput gradually decreases. Taking the bandwidth of 100Mbps as an example, when the network latency is 20ms, the throughput is 92.15Mbps, and when the latency is 100ms, the throughput drops to 74.11Mbps. The optimized data transmission algorithm proposed in this paper significantly improves the efficiency and stability of network transmission by dynamically adapting to network conditions. In future research, the application of this algorithm can be further expanded, and its adaptability and robustness in more complex network environments and high-load applications can be enhanced to improve the reliability of data transmission. © 2025 ieee.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning 1

Optimizing Inventory Management using Demand Forecasting bas...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Pavithra, V. Abbas, Haayder M. Manjula, B.M. Chidambaram, P.K. Jothilakshmi, S. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science and Applications Ramapuram Chennai India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India

ISBN: (纸本)9798331529246

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF). © 2025 ieee.

关键词： demand forecasting ensemble stacking model hyperparameter tuning optimized inventory management seasonality and trend decomposition

来源：评论

学校读者我要写书评

暂无评论

Developing Scalable and Fault Tolerant Distributed Architecture for Big Unstructured data systems 6

Developing Scalable and Fault Tolerant Distributed Architect...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Saranya, G. Kumaran, K. Pratheep, V. Dhanush, S.R. School of Computer Science and Engineering Vellore Institue of Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the era of big data, efficiently processing and retrieving insights from unstructured data presents a critical challenge. This paper introduces a scalable leader-worker distributed data pipeline designed to handle large sets of unstructured data blobs. Leveraging data parallelism, the system distributes workload across worker nodes, ensuring high performance for embarrassingly parallel tasks with minimal inter-process communication. Fault tolerance is achieved through an optimized next-in-line leader re-election strategy, addressing cascading failures and mitigating the thundering herd effect. The use of Apache Zookeeper for service discovery ensures seamless coordination within the cluster while maintaining a loosely coupled architecture for enhanced scalability. Google Protocol Buffers are employed for secure data transmission, offering tamper resistance against eavesdropping and man-in-the-middle attacks. The system also integrates the TF-IDF algorithm for efficient information retrieval, demonstrating the ability to achieve data parallelism while preserving task uniformity across nodes. Performance evaluation highlights significant reductions in response times with increasing cluster size, validating the system’s scalability, efficiency, and robustness. The proposed architecture is well-suited for real-world applications requiring secure, fault-tolerant, and scalable solutions for unstructured data analysis. © 2025 ieee.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Autonomous Vehicle Navigation systems: Machine Learning for Real-Time Traffic Prediction

Autonomous Vehicle Navigation Systems: Machine Learning for ...

引用

2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Hundekari, Sheela Parida, Prasanta Mittal, Tanusha Sehgal, Archana Bhavana, Munugapati LTIMindtree Limited Telangana Hyderabad India School of Computer Applications Pimpri Chinchwad University Pune India School of Rural Management KIIT University Odisha Bhubaneswar India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

By making transportation safer and more efficient, autonomous vehicle navigation systems are transforming the transportation industry. Autonomous cars are able to make route selections based on accurate, up-to-theminute traffic predictions, which help them avoid congestion and maximize their trip time. The use of machine learning algorithms to reliably forecast traffic conditions in real time for use in autonomous vehicle navigation is the focus of this research. Deep learning, support vector machines, and random forests are just a few examples of the machine learning models that can accurately forecast traffic conditions by mining massive datasets collected from GPS data, traffic sensors, and past trends. The suggested method incorporates these models into an autonomous vehicle navigation system that operates in real-time, enabling adaptive modifications according to the flow of traffic. We also look at how things like construction, road accidents, and weather affect the precision of traffic predictions. Based on the outcome of decreased trip time, increased safety and efficiency, results demonstrate how navigation systems supplemented with machine learning, far surpass conventional routing approaches. The study suggests that further research needs to be conducted on hybrid algorithms for better traffic prediction, and multi modal data integration to further optimize autonomous vehicle navigation and in general, machine learning presents great promise in this area. © 2025 ieee.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Structured Process on FL for Big data Analysis 6

Structured Process on FL for Big Data Analysis

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Jayasakthi, K. Rajkumar, N. Porkodi, G. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Avadi Tamil Nadu Thiruvallur600062 India

ISBN: (纸本)9798331522667

The integration of the Internet of Things (IoT) into various aspects of daily life is accelerated by the proliferation of cognitive capabilities and AI-driven applications. Traditional automated systems rely on centralized data storage and dispensation, which may be feasible in specific application scenarios. However, the increasing flexibility of recent IoT networks and rising concerns over data confidentiality are challenging this approach. Federated Learning (FL), a decentralized collaborative AI technique, allows machine learning to occur directly on external IoT devices without the need for data to be centrally aggregated or shared. This paves the way for a wide variety of intelligent IoT applications to be developed. This article presents a thorough analysis of the along with a detailed analysis of the convergence of these two fields. After that, a full analysis of how FL is currently being utilized in several essential applications related to the IoT is provided. These include smart health, mobility, Unmanned Aerial Vehicles (UAVs), infrastructure improvements, and smart industries. In total, 49 publications from the years 2015-2022 were reviewed here. Finally, discuss future study challenges with issues and exposed questions about data with IoT. © 2025 ieee.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Role of IoT in Smart Cities: A Review, Applications, Open Challenges and Solutions 3

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

引用

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

作者： Vivek, Kolla Swetha, Gurrala Kumar M, V. T. Ram Pavan Veni, Anusuri Krishna Praveen, Neti Kale, Masthan Rao Dept. of Cse Andhra Pradesh Ongole India Dept. of Cse Telangana India Kakaraparti Bhavanarayana College Dept. of Computer Science Andhra Pradesh Vijayawada India Madanapalle Institute of Technology & Science Dept. of Computer Science & Engineering - Data Science Andhra Pradesh Madanapalle517325 India Srkr Engineering College Dept. of Computer Science and Design Andhra Pradesh Bhimavaram India Dept. of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798331509675

Smart cities have emerged as a response to the fast expansion of metropolitan areas;these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions. © 2025 ieee.

关键词： Digital Technology Drainage Monitoring Fire detection Smart City Waste Management

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Enabled Federated Learning systems with Explainable AI: A Review 2

Blockchain-Enabled Federated Learning Systems with Explainab...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Potdukhe, Nayan Gourshettiwar, Palash Zade, Sujal Waghale, Atharva Datta Meghe Institute of Higher Education and Research Sawangi Meghe Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Meghe Computer Science and Medical Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The convergence of Blockchain with Federated Learning (FL) technology and Explainable Artificial Intelligence (XAI) serves as a new framework to solve privacy protection problems, security issues, and interpretability needs. Model training collaborations supported by FL technology allow different entities to develop models while safeguarding raw data from exposure during training processes, thus addressing privacy needs in healthcare and finance. The combined challenges of model vulnerability to malicious attacks, data poisoning vulnerabilities, and explainability issues block broad industrial adoption. Blockchains strengthen FL security systems by providing decentralised solutions that produce unchangeable audit trails through their proof of transparency, immutability, and distributed consensus models. By enabling model interpretability, XAI methods build stakeholder trust and ensure system accountability. The study combines the technologies to analyse current systems and identify gaps that integrated systems should resolve. A detailed review examines explainable blockchain models and privacy preservation methods to tackle performance and implementation limitations within the framework. Combining these technologies builds a transformative process to create secure AI systems that maintain interpretability functions and protect data privacy during system development as researchers develop frameworks for wider application deployment. © 2025 ieee.

关键词： Federated learning

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 << < 23 24 25 26 27 28 29 30 31 32 > >>

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：