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

533,931 篇 会议
6,113 册 图书
5,704 篇 期刊文献
6 篇 学位论文
2 篇 科技报告

馆藏范围

545,736 篇 电子文献
44 种 纸本馆藏

日期分布

学科分类号

324,219 篇 工学
- 228,632 篇 计算机科学与技术...
- 186,724 篇 软件工程
- 73,754 篇 信息与通信工程
- 47,668 篇 控制科学与工程
- 35,814 篇 电气工程
- 33,322 篇 生物工程
- 30,195 篇 电子科学与技术（可...
- 29,785 篇 机械工程
- 27,910 篇 生物医学工程（可授...
- 20,333 篇 光学工程
- 18,402 篇 仪器科学与技术
- 18,356 篇 化学工程与技术
- 17,249 篇 动力工程及工程热...
- 13,455 篇 安全科学与工程
- 13,190 篇 材料科学与工程（可...
- 11,934 篇 交通运输工程
- 10,922 篇 土木工程
148,277 篇 理学
- 80,261 篇 数学
- 37,265 篇 物理学
- 35,953 篇 生物学
- 24,578 篇 统计学（可授理学、...
- 17,262 篇 化学
- 17,133 篇 系统科学
86,700 篇 管理学
- 53,740 篇 管理科学与工程(可...
- 37,013 篇 图书情报与档案管...
- 25,982 篇 工商管理
23,101 篇 医学
- 19,616 篇 临床医学
- 18,703 篇 基础医学(可授医学...
- 12,263 篇 药学(可授医学、理...
14,349 篇 法学
- 11,711 篇 社会学
10,783 篇 教育学
10,749 篇 经济学
8,250 篇 农学
3,969 篇 文学
1,502 篇 军事学
1,243 篇 艺术学
61 篇 哲学
37 篇 历史学

主题

23,912 篇 computer science
15,029 篇 accuracy
14,879 篇 computational mo...
14,641 篇 feature extracti...
12,223 篇 training
10,538 篇 deep learning
8,835 篇 data mining
8,824 篇 machine learning
8,803 篇 computer archite...
8,435 篇 predictive model...
8,280 篇 support vector m...
7,365 篇 data models
7,299 篇 real-time system...
7,276 篇 neural networks
7,142 篇 software enginee...
6,856 篇 artificial intel...
6,853 篇 convolutional ne...
6,500 篇 educational inst...
6,115 篇 optimization
5,952 篇 visualization

机构

1,915 篇 chitkara univers...
1,337 篇 school of comput...
1,079 篇 school of comput...
1,043 篇 school of comput...
870 篇 school of comput...
661 篇 department of co...
647 篇 school of comput...
589 篇 lovely professio...
566 篇 department of co...
545 篇 school of comput...
537 篇 department of co...
533 篇 college of compu...
530 篇 school of comput...
518 篇 college of compu...
514 篇 maharishi school...
502 篇 school of comput...
465 篇 school of electr...
462 篇 college of compu...
451 篇 department of co...
420 篇 college of compu...

作者

283 篇 wang wei
252 篇 li wei
241 篇 liu yang
241 篇 yang liu
239 篇 yang yang
234 篇 china
228 篇 kukreja vinay
228 篇 vinay kukreja
206 篇 wei wang
167 篇 wei li
162 篇 li li
160 篇 zhang yu
152 篇 zhang wei
151 篇 wei zhang
147 篇 wang lei
145 篇 zhang lei
128 篇 liu wei
125 篇 li yang
111 篇 yang li
109 篇 li yan

语言

521,221 篇 英文
20,543 篇 其他
5,410 篇 中文
34 篇 西班牙文
31 篇 土耳其文
28 篇 法文
12 篇 葡萄牙文
8 篇 德文
6 篇 俄文
2 篇 乌克兰文
1 篇 阿拉伯文
1 篇 捷克文

检索条件"任意字段=International Conference on Advances in Computer Science and Engineering"

共 545761 条记录，以下是541-550 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：

Single Shot Detector Based MobileNet for Automatic Detection and Recognition of License Plates

Single Shot Detector Based MobileNet for Automatic Detection...

引用

2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Kiruthuka, D.C. Blessy, M. Prasanna Premalatha, M. Pooja, S. Artificial Intelligence & Data Science Dr.Mahalingam College Of Engg. & Tech Pollachi India Dr.Mahalingam College Of Engg.&Tech Pollachi India Artificial Intelligence & Data Science Kathir College Of Engineering Coimbatore India Computer Science & Engineering Sri Raganathar Institute Of Engg.& Tech Pollachi India

ISBN: (纸本)9798350371567

An Automatic motor vehicle Registration Number Recognition and Monitoring Solution is crucial for the shift towards technological advances in numerous everyday life features such as the management of parking spaces, tollbooth facilities, and roadway control situations. The proposed research concentrates on the databases for automobile registration numbers, regions, and forward images. The suggested approach concentrates on collecting deformed registration number plates caused by angled perspectives. The Single Shot Detector (SSD) is used to recognize as well as distinguish distorted registration plate numbers in frame by frame. Following license plate recognition, distortion on the registration plate is reduced using computerized processing methods. Additionally, it crops the text after retrieving them from the registration plate utilizing a sort outline technique. With the use of a lightweight in nature neural network such as MobileNets, the characters are subsequently turned into electronic characters through Optical Character Recognition (OCR). The suggested model also uses an evaluation measure, like stopping training when the value of the loss does not decrease after five epochs. As a result, it achieves 99.54% overall precision throughout the validation dataset and a precision of 98.26% after 23 training epochs. In order to prevent unauthorized entrance into susceptible sites, the suggested solution uses automated automotive vehicle identification validation through vehicle numbers. © 2024 IEEE.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

Uncertainty-Aware Reservoir Permeability Prediction using Gaussian Processes Regression and NMR Measurements 24

Uncertainty-Aware Reservoir Permeability Prediction using Ga...

引用

8th international conference on advances in Artificial Intelligence, ICAAI 2024

作者： Lawal, Ahmad Yang, Yingjie Baisa, Nathanael L. He, Hongmei School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Science Engineering and Environment University of Salford Manchester United Kingdom

ISBN: (纸本)9798400718014

This study investigates the challenges of permeability prediction in reservoir engineering, focusing on addressing uncertainties inherent in the data and modelling process, and leveraging Nuclear Magnetic Resonance (NMR) log data from the Northern Sea Volve field. The study uses a probabilistic machine learning method called Gaussian Process Regression (GPR) with different kernels, such as Matern52, Matern32, and Radial Basis Function (RBF). LSboost, K-nearest neighbour (KNN), and XGBoost are some of the existing models that are used for comparison. Performance metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination (R2) are utilized for assessment. Additionally, the uncertainty associated with different GPR kernels is analyzed, and confidence intervals are generated to provide insights into model behaviour. The inclusion of confidence intervals enhances interpretability by quantifying the range within which the true permeability value is likely to fall with a specified level of confidence, offering valuable information for decision-making processes in reservoir engineering applications. Findings demonstrate the effectiveness of GPR with Matern52 and Matern32 kernels in permeability prediction, offering competitive performance and robust uncertainty quantification. This research contributes to advancing reservoir engineering by providing a comprehensive and uncertainty-aware approach to permeability prediction. © 2024 Copyright held by the owner/author(s).

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

Transforming Education with Innovative Virtual Learning Environments

Transforming Education with Innovative Virtual Learning Envi...

引用

2024 IEEE international conference on Recent advances in science and engineering Technology, ICRASET 2024

作者： Shwetha, G.K. Shetty, Pratham S. Baliga, Samarth N. Rathod, Jayant Kumar A. Divya, C. Department of Computer Science and Engineering Karnataka Nitte India Alva's Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Moodubidire India Kalpataru Institute of Technology Department of Information Science and Engineering Karnataka Tiptur India

ISBN: (纸本)9798350388602

There are several issues with traditional schooling, including poor student participation, rigid scheduling, and restricted accessibility. We require a cutting-edge virtual classroom management system that uses technology to create a dynamic and engaging learning environment in order to solve these problems. The Innovative Virtual Learning Environments is a modern web-based application developed using the MERN stack (MongoDB, ***, ***, ***). It offers a dynamic virtual learning environment, bridging the gap between traditional and remote education by providing features like live video lectures, chat messaging, assignment submissions. By enabling remote attendance, the system removes geographical constraints, enhances accessibility, and offers flexibility and convenience to educators and students. It aims to lower education costs, making high-quality education more affordable and accessible. Utilizing modern web development tools and real-time communication protocols like WebSocket's or WebRTC, the system ensures a interactive and engaging learning experience, promoting teamwork, personalized learning, and real-time communication. The objective is to foster an environment of active learning and continuous interaction, so equipping our users with the critical thinking, creativity, and problem-solving skills necessary to thrive in an increasingly digital and interconnected world. © 2024 IEEE.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Development of IoT-Based Smart Bin System for Effective Garbage Management 14

Development of IoT-Based Smart Bin System for Effective Garb...

引用

14th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Chobhare, Prachi Bagul, Gayatri Soyam, Harshad Pardeshi, Rahulkumar Pujari, Siddharaj Patane, Atul Walchand College of Engineering Computer Science and Engineering Sangli India Walchand College of Engineering Department of Computer Science and Engineering Department of Civil Engineering Sangli India

The cleanliness situation related to garbage management is rapidly declining, and it is essential to implement proper garbage management techniques to address the widespread garbage crisis, particularly in the Urban Environment. In order to address this problem, we developed a Smart Bin System for efficient waste management utilizing IoT technology. The system involves the use of sensors to detect garbage levels and Android applications for monitoring purposes. The integration of wireless technologies, micro-electromechanical systems, microservices, and the internet has given rise to IoT technology. This innovative development of an IoT-Based smart bin System for effective Garbage Management, also known as the Smart Bin, aims to keep cities clean and free from pollution. © Grenze Scientific Society, 2023.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Deep Learning Framework for Estimating Human 3D Pose from 2D Joint Positions

A Hybrid Deep Learning Framework for Estimating Human 3D Pos...

引用

2024 international conference on advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Sultana, Zinia Islam, Muhammad Nazrul Kabir, Md. Hasanul Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Computer Science and Engineering Islamic University of Technology Gazipur Bangladesh

ISBN: (纸本)9798350350289

For computers to understand human activity or behavior in a variety of scenarios, reliable 3D human posture estimation is a prerequisite. Several difficulties have made such work more complex as it is influenced by various factors, including image quality, background, garment texture and diversity, body shape, and the presence of other objects alongside persons in the image which has depicted the necessity of adopting the technique of computer vision. While much work has been done on 2D human pose estimation, showing state-of-the-art performance, the objective of this research is to expand the capacity for 3D human pose estimation. We have investigated deep neural networks comprising of linear layers with residual blocks. And proposed a hybrid deep learning framework and raised the number of residual blocks to achieve this objective. The hybrid network comprises of two parallel models estimation and combines them to estimate 3D pose. This research also showed comparative training results. Finally, the proposed architecture was evaluated on H3WB dataset and presented the evaluation results considering the evaluation metrics of the mean per joint position error (MPJPE) and the percentage of correct keypoints (PCK). The proposed architecture performed about 50% better in terms of MPJPE and PCK@150mm for three residual blocks. © 2024 IEEE.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

A Comparison of Machine Learning Methods for Forecasting Solar Energy Production

A Comparison of Machine Learning Methods for Forecasting Sol...

引用

2024 international conference on advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Prajapati, Dharmistha K. Joshi, Ritesh A M Patel Institute of Computer Studies Ganpat University Master of Computer Application Mehsana India Prestige Institute of Engineering Management and Research Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Fundamentally, the study's findings show how important machine learning algorithms are to improving solar power forecasts and system optimization. The study illustrates how well the ANN algorithm predicts solar energy generation by utilizing large datasets and advanced computational methods. This insight opens the door for proactive maintenance plans and increased PV installation reliability in addition to helping energy firms and decision-makers make well-informed judgments. Looking ahead, the conclusions of this study provide insightful advice for industrial solar energy forecasting procedures and further research. Through the identification of ideal model configurations and performance measures, interested parties can enhance their forecasting abilities and optimize the solar resource usage. Additionally, the incorporation of machine learning algorithms into real-time monitoring systems facilitates quick decisions, enabling communities to maximize energy production and adjust to shifting environmental conditions. The coming together of data-driven insights, technical innovation, and sustainability imperatives offers a once-in-a-lifetime chance to quicken the shift to renewable energy. We can fully utilize solar energy and lessen the negative consequences of climate change by utilizing machine learning and predictive analytics. In addition to fostering economic growth and energy security, this revolutionary change also builds a more resilient, just, and sustainable society for coming generations. In conclusion, a major advancement in the global search for clean and sustainable energy solutions is the use of machine learning algorithms into solar energy forecasts. We can take advantage of these developments and work together to create a more innovative and environmentally friendly future for everybody. © 2024 IEEE.

关键词： Clean energy

来源：评论

学校读者我要写书评

暂无评论

AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud Computing

AI-Enhanced Public Safety Lighting for Smart Sustainable Cit...

引用

2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tidke, Bharat Pragadeeswaran, S. Lakshmi, R. Poornima Bhuvaneswari, G. Manikandan, G. Murugan, S. University Nagpur Campus Pune India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering college Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Kavaraipettai Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

This paper presents a novel method that combines cloud computing infrastructure and Reinforcement Learning (RL) algorithms to improve public safety lighting in smart cities. By dynamically adjusting lighting levels in response to real-time sensor inputs, the proposed system aims to maximize visibility while minimizing energy consumption. The cloud-based framework's RL agents continuously interact with the environment and human activity patterns to learn the best lighting policies. The system utilizes data from multiple sensors, including ambient light and motion detectors, to adjust real-time to daylight levels, weather, and pedestrian traffic variations. By integrating RL, the lighting system can adapt to changing urban dynamics and safety requirements over time, all while reducing energy waste. Compared to conventional static lighting systems, case studies and simulations show how the system can significantly improve visibility and response times while enhancing safety in various urban settings. RL algorithms can be easily deployed and managed throughout extensive smart city networks due to cloud computing network scalability and adaptability. This study advances sustainable urban infrastructure by showcasing the effectiveness of RL-based artificial intelligence (AI) solutions in public safety lighting optimization, thereby laying the groundwork for future smarter and safer cities. © 2025 IEEE.

关键词： Sustainable city

来源：评论

学校读者我要写书评

暂无评论

Detection and Segregation of Plastic Waste using a Robotic Arm 15

Detection and Segregation of Plastic Waste using a Robotic A...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kangabam, Bikash Huidrom, Bhupendra Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim India

ISBN: (纸本)9798331300579

Plastic waste pollution has become one of the most significant environmental concerns in recent years. The accumulation of plastic waste has been observed in various locations worldwide, leading to environmental damage and health hazards. In this paper, a system is proposed for the detection and segregation of plastic waste using a Robotic arm manipulator based on YOLOv5 and Raspberry Pi. YOLOv5 is an advanced object detection algorithm that can identify plastic waste in images. The Raspberry Pi is a low-cost, small-sized computer that can be used as the central processing unit for the proposed system. The system includes a camera module that captures images of the waste material in real time, and an ultrasonic sensor to measure distance of the object. It detects and identifies the plastic waste in the image. Then, using the 3-D coordinate values calculated from the data gathered from the camera and ultrasonic sensor, plastic waste is separated using a robotic arm by applying inverse kinematics, controlled by the Raspberry Pi. The proposed system's performance was evaluated using various custom datasets, and the results show that it can effectively detect and segregate plastic waste from other types of waste materials. © Grenze Scientific Society, 2024.

关键词： Inverse kinematics

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for DoS Attack Detection in IoT Systems 19

Machine Learning for DoS Attack Detection in IoT Systems

引用

19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Kikissagbe, Brunel Rolack Adda, Mehdi Célicourt, Paul Tchappihaman, Igor Najjar, Amro Mathematics Computer Science and Engineering Departement University of Quebec RimouskiQC Canada National Institute of Scientific Research QC Canada University of Luxembourg Luxembourg

This study focuses on enhancing DoS attack detection in IoT systems through a Machine Learning approach that combines class balancing, feature selection, and optimized classifiers. Utilizing the Edge IIoT dataset, we applied SMOTE and Random Undersampling for class balance and employed DNN, Random Forest, and PCA for feature selection. We evaluated six technique combinations across four classifiers (DNN, SVM, XGBoost, and Random Forest), finding that certain combinations notably improve detection efficiency and accuracy. This research contributes to IoT security by offering an effective methodology for DoS attack detection, setting a foundation for further advances in IoT system protection against security threats. © 2024 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning - Based Crop Management: Towards Intelligent Recommendations for Crop, Fertilizer and Pesticide Optimization

Machine Learning - Based Crop Management: Towards Intelligen...

引用

2023 international conference on Recent advances in science and engineering Technology, ICRASET 2023

作者： Manoj Kumar, D.P. Advithi, A.R. Hamsaveni, A.R. Meghana, G. Kavya, M.S. Priya, J. Kalpataru Institute of Technology Computer Science Department Karnataka Tiptur572201 India

ISBN: (纸本)9798350306927

India heavily relies on agriculture as a key income source, but frequent natural disasters lead to crop failures and farmer suicides. Accurate crop production predictions can aid farmers and government in planning storage, pricing, and trade. Data science, with its analysis of variables like soil quality, weather, and more, offers a suitable approach for precise crop yield forecasting. Research focuses on data science methods for this purpose. The machine learning model achieved an impressive accuracy score of 96.44%, surpassing the target accuracy of 90%. This accomplishment is particularly notable considering that the fertilizer recommendation component relies solely on a dictionary-based solution. The last module of the system provides pesticide advice, which is determined through the implementation of a deep learning model used to identify pests. © 2023 IEEE.

关键词： Digital storage

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 << < 51 52 53 54 55 56 57 58 59 60 > >>

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：