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

20,103 篇 会议
103 篇 期刊文献
49 册 图书

馆藏范围

20,255 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

14,206 篇 工学
- 11,477 篇 计算机科学与技术...
- 8,851 篇 软件工程
- 3,234 篇 信息与通信工程
- 2,496 篇 生物工程
- 2,262 篇 控制科学与工程
- 1,800 篇 电气工程
- 1,487 篇 生物医学工程（可授...
- 1,045 篇 机械工程
- 1,028 篇 光学工程
- 1,026 篇 电子科学与技术（可...
- 797 篇 安全科学与工程
- 777 篇 动力工程及工程热...
- 761 篇 交通运输工程
- 681 篇 仪器科学与技术
- 660 篇 化学工程与技术
- 483 篇 材料科学与工程（可...
6,783 篇 理学
- 3,489 篇 数学
- 2,659 篇 生物学
- 1,498 篇 统计学（可授理学、...
- 1,365 篇 物理学
- 844 篇 系统科学
- 738 篇 化学
4,236 篇 管理学
- 2,538 篇 管理科学与工程(可...
- 2,023 篇 图书情报与档案管...
- 1,280 篇 工商管理
1,434 篇 医学
- 1,287 篇 临床医学
- 1,240 篇 基础医学(可授医学...
- 764 篇 药学(可授医学、理...
- 631 篇 公共卫生与预防医...
693 篇 教育学
- 667 篇 教育学
560 篇 法学
487 篇 农学
450 篇 经济学
43 篇 军事学
27 篇 文学
8 篇 艺术学
1 篇 哲学

主题

1,325 篇 deep learning
1,128 篇 machine learning
836 篇 accuracy
561 篇 data mining
541 篇 support vector m...
534 篇 feature extracti...
427 篇 training
413 篇 data models
405 篇 convolutional ne...
397 篇 predictive model...
379 篇 internet of thin...
363 篇 real-time system...
332 篇 computational mo...
322 篇 neural networks
314 篇 big data
233 篇 reinforcement le...
216 篇 forecasting
180 篇 object detection
179 篇 machine learning...
174 篇 optimization

机构

227 篇 chitkara univers...
64 篇 centre of interd...
58 篇 maharishi school...
43 篇 maharishi school...
34 篇 college of techn...
33 篇 school of comput...
31 篇 pune
29 篇 the islamic univ...
28 篇 school of comput...
28 篇 chitkara univers...
26 篇 karnataka bangal...
26 篇 uttaranchal inst...
25 篇 uttaranchal univ...
24 篇 computer science...
24 篇 computer science...
23 篇 department of ar...
22 篇 graphic era deem...
22 篇 school of inform...
22 篇 department of in...
22 篇 national univers...

作者

22 篇 gill kanwarparta...
21 篇 wang lei
21 篇 kukreja vinay
20 篇 shiva mehta
19 篇 liu yang
18 篇 wang jing
15 篇 zhang tao
15 篇 malik bader alaz...
15 篇 joshi kapil
15 篇 singh jagendra
14 篇 yang yang
13 篇 zhang lei
13 篇 li yang
13 篇 chauhan rahul
12 篇 meena belwal
12 篇 novais paulo
12 篇 li li
11 篇 babu tina
11 篇 liu xin
11 篇 rupesh gupta

语言

15,418 篇 英文
4,818 篇 其他
125 篇 中文
3 篇 土耳其文
2 篇 西班牙文
1 篇 俄文

检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20255 条记录，以下是501-510 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：

SmartLeafNet: An automated Deep learning based Efficient Plant Disease Detection using RNN-GRU Model 3

SmartLeafNet: An Automated Deep Learning based Efficient Pla...

引用

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

作者： Prakash, M. Gnana Sungeetha, D. Saveetha University Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9798331509675

Plant leaf disease has consequences for health and productivity maintenance, hence making early interventions on them possible, and preventing loss caused by disease. They minimize the usage of pesticides for agricultural benefits, conserve other valuable resources, and provide much better yield for betterment in agriculture. Most lately, during this enhanced tendency of technological reliance, different deep learning techniques find prominent support in automating this type of plant disease detection process. This paper presents a new approach in detecting diseases in a plant leaf using deep learning, by implementing the structure of the Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU). This methodology uses a PlantVillage dataset for the extraction of images covering a wide range of aspects of plant diseases. Our approach has done extensive preprocessing in the form of resizing, normalizing, and augmenting the data so that real-world diverse plant images could be fed into the model. These models were trained based on the robust strategy for splitting up the training-validation data and further evaluated on separate testing for assessing their generalization capability. Accuracy, precision, recall, F1-score, time, and AUC are used for the performance analysis of the proposed model. It considers deep learning techniques, more precisely the RNN-GRU method, a promising outlook for changing plant disease detection and agricultural management. The model meets scalability and efficiency demands that will be helpful to farmers in the early detection of diseases by applying much more sustainable and efficient agriculture. © 2025 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions 3

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yashas, D. Kashyap, M. Shivani Charan, P.R. Hemanth Kumar, G.P. Abhishek, B.K. Deekshith, C. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Dept of Information Science and Engineering Bengaluru India Bnm Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Siddaganga Institute of Technology Dept of Artificial Intelligence and Data Science Tumkur India Vidyavardhaka College of Engineering Dept of Computer Science and Engineering Mysore India

ISBN: (纸本)9798331515911

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field. © 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

TLNet: A Novel Model to Detect Wheat Leaf Diseases using Deep Neural Network Architecture 3

TLNet: A Novel Model to Detect Wheat Leaf Diseases using Dee...

引用

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

作者： Nagaraju, M. Jahnavi, G. Sri Vaishnavi, V. Vaishnavi, M. Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798331527549

Wheat crop diseases are a major challenge to food security, significantly impacting agricultural yields worldwide. Timely detection and effective management of these diseases are essential to reduce crop losses. Recent advances in deep learning have demonstrated potential in automating disease identification in crops. This research presents a novel approach for detecting wheat crop diseases using a Convolutional Neural Network (CNN) model. The CNN architecture is designed for optimal efficiency and flexibility, making it well-suited for deployment across various computational platforms, such as mobile and edge devices. By incorporating transfer learning and attention mechanisms, the model achieves high accuracy in disease diagnosis. It has been evaluated using a dataset of wheat leaf images, encompassing diseases like yellow rust, leaf blotch, and powdery mildew. Evaluation of the system on these images, sourced from the Plant Village Repository, shows that the model achieves an accuracy of 87%. This method provides a practical solution for early detection of wheat crop diseases, enabling timely action to reduce yield loss and support food security. © 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

IoT Environmental Monitoring with Anomaly Detection 3

IoT Environmental Monitoring with Anomaly Detection

引用

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

作者： Abhiram, D. Dhanush Reddy, K. Koushik Reddy, P. Rajagopal, Shinu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798331527549

This project proposes an IoT-based environmental evaluation system taking into consideration its surroundings through a microcontroller ESP8266 and sensor DHT11. The system reads the current temperature and humidity in addition to providing alerts for changes as well as threshold and sensor integrity issues. It contains context awareness and adaptive monitoring. It connects to the Arduino IoT Cloud, which allows users to monitor data in real-time, or receive notifications, or set up thresholds right from the mobile or web application. The features such as real-time monitoring, real-time anomaly detection, and remote access make this project practical as the key application for smart homes, farming and industries applications. © 2025 IEEE.

关键词： Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence and Supervised learning based Solid Waste Segregation Mechanism using ESP 32 3

Artificial Intelligence and Supervised Learning based Solid ...

引用

3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Srinevasan, M. Parameshwari, V. Abirami, T. Divyaa, N. Nandha Engineering College Dept of Ece Erode India M. Kumarasamy College of Engineering Dept of Ece Karur India Velalar College of Engineering and Technology Dept of Ece Erode India

ISBN: (纸本)9798350306989

Garbage separation must take place at the household level because it is time-consuming and frequently unsuccessful to do it at the disposal. This Proposed method attempts to decrease mannual effort in segregation of waste in order to achieve proper waste segregation with the least amount of effort. This proposed system suggests an automatic trash segregator system powered by IOT. In addition to detecting and classifying household rubbish into dry, moist, and metallic waste streams, it aids in real-time monitoring the levels of garbage in trash discharges. A metallic sensor is then used to determine whether there is any metal in the garbage after an Ultrosonic [radar based] has detected the arrival of the trash. Additionally, the trash is separated using the capacitive detector into dry and moist residue. The conveyor helps to move the waste to the proper bins before the platform rotates once it has been detected and segregated using a supervised learning-based algorithm. Additionally, the system uses ESP 32 to display a note to clean the trash can when it is occupied and to show the amount of compost in the trash discharges on the LCD screen. Experimental results show that the proposed method is effective in categorizing waste into moist and dry waste as well as metal and non-metal waste. © 2023 IEEE.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Predictive Analysis of Airborne Contaminant Spread with Cloud-Connected Sensors and Deep learning Model 3

Predictive Analysis of Airborne Contaminant Spread with Clou...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Ranganathan, Chitra Sabapathy Jothi, M. Srinivasan, V. Prasanna Sathyanathan, P. Meenakshi, B. Sujatha, S. Mphasis Corporation Tamil Nadu Chennai India Srm Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Trichy India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technica Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331528201

The rapid dispersal of contaminants in the air poses serious threats to human and environmental health. By combining sensors linked to the cloud with a deep learning (DL) model built on Long Short-Term Memory (LSTM) networks, this research proposes a novel way to address this problem. It will help with risk mitigation and proactive decision-making by predicting how airborne pollutants will disperse. The proposed method relies on real-time data gathered from dispersed sensors placed in key areas. These sensors provide data on air quality metrics to a central cloud platform that is analyzed in real-time. Using this data, the LSTM model may be trained to understand the dynamics of contamination distribution over time and identify patterns and relationships. The LSTM model captures complex interactions between pollutant dispersion and various environmental parameters to predict future spread trajectories. The effectiveness of proposed approach is by conducting thorough experiments and validating them against historical contamination data. The considerably outperforms conventional approaches by providing precise and timely estimates of the spread of airborne contaminants. To lessen the impact of possible contamination exposure concerns, these predictions may provide stakeholders with the data need to act swiftly, such as by releasing public health warnings or modifying ventilation systems. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Implementation of intelligent Image Editing Algorithms in Art Design 3

Implementation of Intelligent Image Editing Algorithms in Ar...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Dai, Sharina Li, Wenlian School of Culture and Media Changchun College of Electronic Technology Changchun130000 China School of Design Huzhou College Hunan Huzhou313000 China

ISBN: (纸本)9798331519032

With the rapid development of artificial intelligence technology, intelligent image editing algorithms are increasingly widely used in the field of art and design. This paper firstly outlines several key intelligent image editing algorithms, including style migration, image synthesis and enhancement, and discusses the practical application of these algorithms in art creation. Then, this paper analyzes the impact of intelligent image editing on traditional art design, pointing out its advantages in enhancing creative efficiency and expanding design boundaries. At the same time, this paper also discusses the ethical and philosophical issues raised by AI-generated artworks. Finally, this paper looks forward to the future development trend of intelligent image editing algorithms in art design, and points out the challenges that still need to be overcome in this field. Through the research in this paper, we hope to provide new perspectives and ideas for the integration of art design and AI technology. © 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

QuickPark: Efficient Parking with Real-Time Pre-Booking 3

QuickPark: Efficient Parking with Real-Time Pre-Booking

引用

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

作者： Dharshan, J.Y. Amarnath, Arjun R. Adithya, Gadamsetty Siva Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The parking is one of the biggest problems. One of the emerging features of parking management is congestion in urban centres due to the increase in the number of vehicles on the road, which has added anxiety to drivers. These conventional or legacy parking systems fail to respond adequately to the increasing needs, especially in Smart City contexts. To this end, a Smart Parking Management System (SPMS) is developed with real-time data, incorporating a parking sensor, parking information, an easy-to-navigate parking lot, and favourable methods of payment accepted. This prototype optimises parking space, provides better convenience to drivers, and leads to the sustainability of such a structure in the urban area. The proposed SPMS promises to be a revolutionary and practical approach to addressing some of the modern parking management issues in delivering an efficient mobility regime. © 2025 IEEE.

关键词： Parking

来源：评论

学校读者我要写书评

暂无评论

Research on Deep learning-based Driver Fatigue Driving Behavior Recognition Methods 3

Research on Deep Learning-based Driver Fatigue Driving Behav...

引用

3rd international conference on Big data, Information and Computer Network, BDICN 2024

作者： Sun, Miao Liu, Fan He, Haijun Yang, Zhuomin Lou, Wen Guo, Dudu School of Intelligent Manufacturing Modern Industry Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Public Security Department Traffic Police Corps Urumqi China Traffic Management Research Institute of the Ministry of Public Security Key Laboratory of Road Traffic Safety Ministry of Public Security Wuxi China School of Traffic and Transportation Engineering Xinjiang University Urumqi China Xinjian Key Laboratoryof Gn. Construction and Smart Traffic Control of Transportation Infrastructure Urumqi China

ISBN: (纸本)9798350363821

Aiming at the problem of motor vehicle drivers' fatigue driving, this paper proposes a deep learning-based fatigue driving behavior recognition method. The four parameters of PERCLOS parameter, blinking frequency, continuous eye closure time and continuous mouth opening time are used as the recognition indexes to establish a driver fatigue driving recognition model based on deep learning, and the experimental results show that the method proposed in this paper has a better recognition effect in different light environments. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Research on the Application of Big data Deep learning Algorithms in Management Accounting 3

Research on the Application of Big Data Deep Learning Algori...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Bian, Ying Dongying Vocational Institute Shandong Province Dongying City257091 China

ISBN: (纸本)9798350354171

Design management is an important part of data analysis, and it is also the main aspect of economic management of enterprises. Big data can provide qualifications for accounting management, so it has great potential for *** applied research is critical in intelligent management accounting, however it has an issue with erroneous performance positioning. The typical Neural network algorithms is unable to address the inaccurate application positioning issue in intelligent management accounting, and the result is insufficient. As a result, a Big data deep learning algorithms-based application research in management accounting is provided, and application research in management accounting is assessed. To begin, the gradient descent theory is used to discover the influencing elements, and the indicators are split based on the applied research's needs to decrease interference factors in the applied research. The gradient descent theory is then used to create a Big data deep learning algorithms applied research accounting table, and the outcomes of the applied research are thoroughly examined. The MATLAB simulation results reveal that, under particular evaluation conditions, the Big data deep learning algorithms outperforms the standard Neural network algorithms in terms of applied research accuracy and time of influencing variables. The method can improve the overall level of accounting management, with an increase of between 10% and 20%. Therefore, the big data analysis method can meet the requirements of equipment analy sis and management. © 2024 IEEE.

关键词： Accounting Applied research big data deep learning algorithm gradient descent theory Management

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 << < 47 48 49 50 51 52 53 54 55 56 > >>

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：