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

60,107 篇 会议
10,797 篇 期刊文献
550 册 图书
34 篇 科技报告

馆藏范围

71,488 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

46,386 篇 工学
- 35,742 篇 计算机科学与技术...
- 29,808 篇 软件工程
- 10,914 篇 信息与通信工程
- 6,614 篇 控制科学与工程
- 4,483 篇 电气工程
- 4,391 篇 生物工程
- 4,218 篇 电子科学与技术（可...
- 3,443 篇 生物医学工程（可授...
- 2,678 篇 光学工程
- 2,444 篇 机械工程
- 1,936 篇 仪器科学与技术
- 1,824 篇 动力工程及工程热...
- 1,776 篇 化学工程与技术
- 1,458 篇 安全科学与工程
- 1,331 篇 建筑学
- 1,230 篇 交通运输工程
24,217 篇 理学
- 14,952 篇 数学
- 5,464 篇 物理学
- 4,912 篇 生物学
- 4,487 篇 统计学（可授理学、...
- 2,681 篇 系统科学
- 1,904 篇 化学
12,324 篇 管理学
- 6,695 篇 管理科学与工程(可...
- 6,265 篇 图书情报与档案管...
- 3,890 篇 工商管理
2,794 篇 医学
- 2,452 篇 临床医学
- 2,259 篇 基础医学(可授医学...
- 1,464 篇 药学(可授医学、理...
2,182 篇 法学
- 1,827 篇 社会学
1,277 篇 经济学
- 1,275 篇 应用经济学
1,165 篇 教育学
911 篇 农学
289 篇 文学
152 篇 军事学
85 篇 艺术学
39 篇 哲学
17 篇 历史学

主题

1,772 篇 computer science
1,645 篇 accuracy
1,547 篇 computational mo...
1,479 篇 feature extracti...
1,221 篇 training
1,024 篇 deep learning
990 篇 machine learning
831 篇 data mining
826 篇 predictive model...
777 篇 semantics
757 篇 real-time system...
730 篇 support vector m...
711 篇 artificial intel...
711 篇 data models
665 篇 computer archite...
590 篇 visualization
572 篇 optimization
557 篇 internet of thin...
554 篇 convolutional ne...
544 篇 neural networks

机构

91 篇 institute of inf...
84 篇 dept. of compute...
83 篇 north-west unive...
82 篇 dept. of compute...
81 篇 infn sezione di ...
76 篇 infn sezione di ...
69 篇 institut univers...
66 篇 università di ca...
65 篇 dept. of compute...
62 篇 national centre ...
60 篇 dept. of compute...
56 篇 dept. of compute...
54 篇 dept. of informa...
52 篇 school of automa...
44 篇 leiden universit...
42 篇 infn lnf via enr...
42 篇 in2p3 lpc campus...
39 篇 dept. of compute...
38 篇 dept. of compute...
37 篇 hokkaido univers...

作者

97 篇 ferrara g.
94 篇 ardid m.
87 篇 calvo d.
82 篇 chen a.
75 篇 gal t.
72 篇 bozza c.
62 篇 ahmad jalal
60 篇 kulikovskiy v.
53 篇 kalekin o.
41 篇 gagliardini s.
40 篇 benoit d.m.
40 篇 eddymaoui a.
40 篇 labalme m.
37 篇 díaz a.f.
36 篇 androulakis g.
35 篇 huang f.
34 篇 crawford h.j.
34 篇 cafagna f.
34 篇 de palma m.
33 篇 haungs a.

语言

69,363 篇 英文
1,407 篇 其他
527 篇 中文
108 篇 日文
63 篇 朝鲜文
11 篇 德文
7 篇 法文
6 篇 西班牙文
3 篇 葡萄牙文
2 篇 斯洛文尼亚文

检索条件"机构=Dept. of Computer Science and Data Science"

共 71488 条记录，以下是4951-4960 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：

Decision Making Algorithm for Blind Navigation Assistance using Deep Learning 1

Decision Making Algorithm for Blind Navigation Assistance us...

引用

1st International Conference on Computational science and Technology, ICCST 2022

作者： Zulaikha Beevi, S. Harish Kumar, P. Harish, S. Lakshan, S.J. Vsb Engineering College Dept. of Computer Science and Engineering Karudayampalayam. Tamil Nadu Karur639 111 India Vsb Engineering College Dept. of Artificial Intelligence and Data Science Karudayampalayam Tamil Nadu Karur639 111 India

ISBN: (纸本)9781665476553

Blind people face several obstacles in their daily lives and technological interventions can help overcome these obstacles. In this research, we provide an AI-based autonomous assisting device that recognizes many objects and it will provide acoustic input to the user to help visually blind people to understand the surrounding better to understand their environment better. Multiple photos of objects relevant to visually impaired people were used to build a deep-learning model. Training photos are enhanced and manually annotated to improve the trained model's resilience. A distance-measuring sensor is included which recognise the objects using computer vision. The gadget is made more inclusive by recognizing the obstacles coming out of one place to another. After stage segmentation and obstacle detection, the aural information sent to the user is adj usted to get a lot of details in minimum time and speed up video processing. © 2022 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

引用

Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of problems present in wireless communications.

关键词： Wireless communication Heating systems Accuracy Neural networks Channel estimation Estimation Massive MIMO Robustness Compressed sensing Convergence

来源：评论

学校读者我要写书评

暂无评论

Efficient Protocols for the Secure Transmission of Training Parameters in a Federated Network: Research Frontiers, Taxonomy, and Future Directions

Efficient Protocols for the Secure Transmission of Training ...

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Pamula, Narendra Babu Khan, Ajoy Kumar Sarkar, Arindam Mizoram Central University Tanhril Department of Computer Engineering Mizoram Aizawl796004 India Ramakrishna Mission Vidyamandira Belur Math Dept. of Computer Science & Electronics WB Howrah711202 India

ISBN: (纸本)9798350369175

Federated Learning (FL), which ensures data privacy while jointly increasing model accuracy, has emerged as a viable method for collaborative machine learning across decentralized and heterogeneous data sources. However, because of differing computational capacities and security requirements, securely communicating training parameters across heterogeneous participants continues to be a significant difficulty. The present study offers an extensive overview of the state of research in the field and employs a taxonomy based on cryptography techniques, including Differential Privacy (DP), Homomorphic Encryption (HE), Secure Multiparty Computation (SMPC), and Blockchain technology, to classify the current secure transmission protocols. Additionally, it suggests future lines of inquiry for optimization and hybrid approaches to improve security, scalability, and efficiency in heterogeneous federated networks. For scholars and practitioners looking to propel the field of FL into more robust and privacy-preserving collaborative learning settings, the insights and taxonomy offered provide a road map © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Improving Image Classification Using Attention Mechanisms in Deep Learning Models 4

Improving Image Classification Using Attention Mechanisms in...

引用

4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sharma, Vinod Mishra, Atul Kumar Kumar, Anil Shakti, Nanda Sharma, Madhuri Srinju, M. Slp University Dept of Cse Gwalior India Madhav University Dept. of Commerce & Management Pindwara India Mangalayatan University Dept. of Eee Aligarh India Usha Martin University Dept. of Engineering and Applied Sciences Ranchi India Chri Campus University Dept. of Life Sciences Gwalior India Dhanalakshmi Srinivasan University Dept. of Ai and Data Science Coimbatore India

ISBN: (纸本)9798350364729

In this study, we explore the application of attention mechanisms to enhance deep learning models in the context of image classification. We assess several types of attention mechanisms across diverse image classification datasets. Our findings indicate substantial enhancements in model accuracy facilitated by these attention mechanisms. Additionally, we delve into implementation challenges such as computational overhead and the necessity for extensive datasets. Furthermore, we investigate the potential benefits of transfer learning and fine-tuning pretrained models integrated with attention mechanisms to optimize performance and streamline training efforts. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

HALDA: An Adaptive IoT Algorithm for Reducing Water Losses in Jordanian Homes

HALDA: An Adaptive IoT Algorithm for Reducing Water Losses i...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Abu Arqoub Eman Alduweib Waseem Alromema Ali A. Titinchi dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Electrical and Computer Engineering University of Nizwa Nizwa Sultanate of Oman

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Water is considered an extremely valuable commod-ity in Jordan, which happens to be among those countries of the world that have the lowest water availability per capita. This paper introduces the Hybrid Adaptive Leak Detection Algorithm (HALDA), an IoT-based framework adapted for resi-dential water management in resource-constrained environments. HALDA integrates real-time sensing, adaptive thresholding, and predictive analytics into leakage detection and mitigation at the household level. The system, by continuously sending notifications related to data-driven analysis, helps lower not only acute water wastage events, but also encourages better pro-sustainable habits among home holders. The accuracy results for the experiments in detecting accurate detection and power draw from ten conducted field trials all over Amman was as highly as 93 %, with each sensor as well as being that low at each 50 mW, reducing widely previously undetected leaks. Results stress the fact that HALDA is one of the few promising approaches toward finding a cost-effective and energy-efficient solution to the water scarcity problem of Jordan and many other arid and semi-arid regions of the world.

关键词： Accuracy data integrity Urban areas Water conservation Predictive models Real-time systems Blockchains Leak detection Predictive analytics Water resources

来源：评论

学校读者我要写书评

暂无评论

Increase the Degree of Accuracy by Employing A More Accurate Classification Approach 2

Increase the Degree of Accuracy by Employing A More Accurate...

引用

2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Khan, Muhammad Farhan Sajid Farooq, Muhammad Joghee, Shanmugan University of Health Sciences Dept. of Forensic Sciences Lahore Pakistan Lahore Garrison University Dept. of Computer Science Lahore Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan Skyline University School of Business Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

Traditional artificial neural networks and machine learning have traditionally been used to classify images;however, these approaches have not only struggled to keep up with the processing demands of such datasets but have also performed inefficiently and with poor classification accuracy due to the processing demands of enormous image datasets during feature extraction and model training. Large photo collections make it challenging for machine learning and traditional artificial neural networks to keep up with their processing demands. Deep learning significantly surpasses other, more traditional techniques for photo classification. Another difficulty was that when dealing with really large pictures, standard artificial neural networks were forced to exceed their data storage limits. As a direct result of their research, the authors of the study developed a deep-learning model for image tagging. This model was developed as a tool to aid in the identification and categorization of pictures on a large scale. Following a brief explanation of neural network theory, the study's focus shifted to an examination of the various convolutional neural network types and the general strategy for employing such networks for picture categorization. The present model of a convolutional neural network was utilized to minimize noise and alter the parameters used in the feature extraction operation. This was supposed to improve the results' dependability. An upgraded convolutional neural network was used to develop the model's architecture. The resulting deep learning model improved operational effectiveness and data categorization accuracy significantly. To achieve this purpose, the structure must be modified so that it can work as effectively as possible. Experiments were carried out to evaluate if the number of times an image classification network model is put through its training process influences the level of accuracy obtained by the model. This was done to see how effectively

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Smart Medical Box using Arduino

Smart Medical Box using Arduino

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： Sweadha. M M. Selvi Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Smart Medical Box is a sophisticated IoT-driven solution designed to enhance medication adherence by utilizing computer vision and machine learning. The system automates the identification and extraction of essential information from medication labels, ensuring precision in dosage reminders and reducing human error. It incorporates a high-resolution camera, an OCR engine, and an Arduino-based microcontroller to enable seamless image processing and real-time analysis. With an intuitive interface and GPS-enabled location services, users can efficiently manage their medication schedules while accessing nearby pharmacies when needed. This research focuses on designing and implementing a scalable and cost-effective system that improves medication compliance and minimizes health risks associated with missed or incorrect dosages. By integrating advanced image processing techniques and AI-driven recognition algorithms, the Smart Medical Box ensures high accuracy in identifying medications. Additionally, features such as automated alerts, real-time monitoring, and pharmacy locator tools enhance the system's usability and accessibility. This innovation is particularly beneficial for elderly individuals and patients with chronic conditions, offering a simplified and automated approach to medication tracking. Performance evaluations of the system indicate a significant improvement in medication adherence, a high recognition accuracy rate, and positive user feedback. Future enhancements, including AI-based OCR, voice assistance, and mobile application connectivity, are expected to further improve functionality and ease of use. This paper presents the conceptualization, development, and testing of the Smart Medical Box, highlighting its potential to revolutionize personal healthcare management by providing an intelligent, efficient, and user-friendly solution.

关键词： Accuracy Image recognition Microcontrollers Optical character recognition Medical services Real-time systems Usability Older adults Biomedical imaging Global Positioning System

来源：评论

学校读者我要写书评

暂无评论

Detection of DDoS Attack in IoT Networks Using Sample elected RNN-ELM

Detection of DDoS Attack in IoT Networks Using Sample electe...

引用

2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Venkateswara Reddy, B. Basha Sk, Khader Roja, D. Rao Purimetla, NagaMalleswara Vellela, Sai Srinivas Kumar, K Kiran Chalapathi Institute of Technology Dept. of CSE-Cyber Security AP Guntur India Chalapathi Institute of Technology Dept. of CSE-Data Science AP Guntur India Chalapathi Institute of Technology Dept. of Cse AP Guntur India

ISBN: (纸本)9798350306927

IoT, a global technology paradigm, allows a variety of devices to be online at any time and from any location. But the widespread use of IoT devices has made them vulnerable to risks and vulnerabilities. data breaches within IoT networks, particularly those that exist beyond the fog layer, have escalated as more businesses use IoT applications. In order to address this security concern, deep learning models have gained popularity as threat detection and mitigation techniques. An SSRNN-ELM hybrid approach is suggested in this study for the identification and mitigation of IoT data breaches. The SSRNN-ELM model consists of RNNs for supervised learning and ELMs for unsupervised classification. This hybrid design between the devices and fog layer protects IoT networks. Feature selection is the main innovation of the algorithm. These particular features impart these significant feature behaviour patterns to an RNN. Malicious and benign network data are separated by an ELM classifier in the final *** assess our hybrid SSRNN-ELM model's ability to identify DDoS attacks on IoT devices outside of the fog node by utilising the NSL-KDD benchmark dataset. Impressively, our model obtained 99% accuracy on the NSL-KDD dataset. When our strategy is compared with current models, its benefits become *** sum up, our hybrid SSRNN-ELM model offers a sophisticated and reliable solution to problems with IoT network security, especially DDoS detection that goes beyond the fog layer. With the help of feature selection, RNN-based learning, and ELM classification, our model effectively mitigates data vulnerabilities. The security of the IoT ecosystem and application integrity across industries are supported by this research. © 2023 IEEE.

关键词： Fog

来源：评论

学校读者我要写书评

暂无评论

An Innovative Online Monitoring System in Knitting Industry 16

An Innovative Online Monitoring System in Knitting Industry

引用

16th Textile Bioengineering and Informatics Symposium, TBIS 2023

作者： Elkateb, Sherien Métwalli, Ahmed Shendy, Abdelrahman Textile Engineering Dept. Faculty of Engineering Alexandria University Alexandria21544 Egypt Electrical Engineering Dept. Arab Academy for Science Technology and Maritime Transport Alexandria1029 Egypt Computer Engineering Dept. Arab Academy for Science Technology and Maritime Transport Alexandria1029 Egypt

ISBN: (纸本)9781713879473

This study aims to introduce an innovative online monitoring system for the knitting industry using internet of things and machine learning concepts. The system measures and presents productivity through its dashboard. A double knitting machine was equipped with sensors. Statistical analysis results show that the system has great accuracy and precision. The T-Test outcome demonstrates that there is no significant difference between actual and device measured productions. The proposed system enables real-time tracking, maintenance, and costeffective quality for knitting machines. © TBIS 2023. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Exploring Climate Change Prediction Dynamics Using Machine Learning Approach: A Review

Exploring Climate Change Prediction Dynamics Using Machine L...

引用

Computing, sciences and Communications (ICCSC), International Conference on

作者： Shashank Tiwari Shikha Verma Rosey Chauhan Neha Gupta Dept. of Computer Science Sharda Unviersity Gerater Noida India Dept. of Computer Science ABES Engineering College Ghaziabad UP India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Climate refers to the long-term weather patterns of a region. Variations in the weather have a lot of implications on our daily lives. Explore the application of deep learning techniques in climate prediction and related fields. It focuses on Long Short-Term Memory (LSTM) models for forecasting air quality, greenhouse temperature control, and urban flood water accumulation using Gradient Boosted Decision Trees (GBDT). The analysis concludes that deep learning, particularly LSTM, offers promising accuracy for various climate-related predictions. Challenges such as data scarcity, non-stationarity, and high dimensionality are addressed, highlighting the need for advanced models to capture complex climate system dynamics and human influences.

关键词： Deep learning System dynamics Reviews Atmospheric modeling Predictive models Temperature control Decision trees Forecasting Long short term memory Meteorology

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 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：