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

17,143 篇 会议
2,565 篇 期刊文献
141 册 图书

馆藏范围

19,849 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

11,647 篇 工学
- 7,847 篇 计算机科学与技术...
- 6,576 篇 软件工程
- 3,364 篇 信息与通信工程
- 1,991 篇 控制科学与工程
- 1,798 篇 电子科学与技术（可...
- 1,764 篇 电气工程
- 1,231 篇 生物工程
- 1,060 篇 生物医学工程（可授...
- 898 篇 光学工程
- 810 篇 机械工程
- 770 篇 动力工程及工程热...
- 736 篇 仪器科学与技术
- 598 篇 化学工程与技术
- 522 篇 安全科学与工程
- 455 篇 交通运输工程
- 395 篇 材料科学与工程（可...
5,942 篇 理学
- 3,014 篇 数学
- 1,843 篇 物理学
- 1,359 篇 生物学
- 1,002 篇 统计学（可授理学、...
- 703 篇 系统科学
- 602 篇 化学
2,901 篇 管理学
- 1,828 篇 管理科学与工程(可...
- 1,237 篇 图书情报与档案管...
- 937 篇 工商管理
943 篇 医学
- 810 篇 临床医学
- 755 篇 基础医学(可授医学...
- 464 篇 药学(可授医学、理...
- 428 篇 公共卫生与预防医...
569 篇 法学
- 461 篇 社会学
355 篇 经济学
259 篇 农学
208 篇 教育学
88 篇 文学
49 篇 军事学
12 篇 艺术学
1 篇 历史学

主题

687 篇 accuracy
498 篇 deep learning
455 篇 feature extracti...
419 篇 computational mo...
351 篇 support vector m...
339 篇 real-time system...
310 篇 training
301 篇 internet of thin...
270 篇 machine learning
241 篇 predictive model...
218 篇 optimization
217 篇 convolutional ne...
211 篇 reliability
209 篇 computer science
207 篇 computer archite...
187 篇 neural networks
183 篇 wireless communi...
176 篇 data mining
171 篇 routing
168 篇 throughput

机构

84 篇 institut univers...
82 篇 north-west unive...
82 篇 tno technical sc...
81 篇 national centre ...
79 篇 infn sezione di ...
78 篇 nikhef national ...
76 篇 infn sezione di ...
76 篇 université de st...
75 篇 university würzb...
72 篇 university of jo...
66 篇 università di ca...
61 篇 university moham...
61 篇 ncsr demokritos ...
53 篇 leiden universit...
53 篇 university moham...
51 篇 dept. of compute...
50 篇 school of automa...
43 篇 in2p3 lpc campus...
42 篇 infn lnf via enr...
42 篇 chitkara univers...

作者

85 篇 ferrara g.
85 篇 ardid m.
84 篇 calvo d.
84 篇 gal t.
84 篇 bozza c.
83 篇 boumaaza j.
82 篇 kalekin o.
82 篇 díaz a.f.
81 篇 chen a.
81 篇 coleiro a.
81 篇 gozzini s.r.
81 篇 dallier r.
78 篇 anguita m.
76 篇 boettcher m.
76 篇 enzenhöfer a.
72 篇 aly z.
70 篇 huang f.
69 篇 celli s.
66 篇 dornic d.
60 篇 carretero v.

语言

18,606 篇 英文
977 篇 其他
199 篇 中文
45 篇 日文
21 篇 朝鲜文
2 篇 斯洛文尼亚文
1 篇 阿拉伯文
1 篇 德文
1 篇 法文
1 篇 土耳其文

检索条件"机构=Dept Computer Engineering and Technology"

共 19849 条记录，以下是121-130 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：

Smart Irrigation System using the Internet of Things 8

Smart Irrigation System using the Internet of Things

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Anpan, Sakshi Lanjewar, Rutuja Kedar, Rani Qureshi, Rukaiyya Zade, Nikita Gourshettiwar, Palash Faculty of Engineering and Technology Dept of Artificial Intelligence and DataScience Wardha India Faculty of Engineering and Technology Dept of Computer Science and MedicalEngineering Wardha India

ISBN: (纸本)9798350376425

Fast technical advancements have been implemented in multiple domains of life, including agriculture. technology can help the agriculture industry cut down on the energy and time lost on using traditional methods. The Internet of Things (IoT) is one of the technological developments that are currently in vogue. Real-time internet connectivity for physical items is made possible by the Internet of Things (IoT) technology. Through the use of remote water pump control and garden soil moisture monitoring, this research integrates the Internet of Things into the garden irrigation system. Garden owners may monitor and identify soil moisture levels in their plantations using the Internet of Things. After that, effectively, may control the usage of water utilized immediately. The water pump attached to the garden's water supply is designed to automatically change the moisture parameter to its ideal value when the soil moisture sensor detects low moisture levels. Additionally, the garden's owner can use a smartphone app to keep an eye on the moisture level of the soil. © 2024 IEEE.

关键词： Soil moisture

来源：评论

学校读者我要写书评

暂无评论

Detection of Cardiovascular Disease from ECG Image Using Optimized Weighted Average Ensemble Machine Learning Technique 26

Detection of Cardiovascular Disease from ECG Image Using Opt...

引用

26th International Conference on computer and Information technology, ICCIT 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Akash, Ahsan Sadek Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Pabna University of Science & Technology Dept. of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350359015

cardiovascular diseases (CVD) stand as a prominent global cause of death in worldwide. Timely identification and intervention are paramount in dealing with heart disease, as its failure to detect and treated promptly can lead to life-threatening outcomes. The Electrocardiogram (ECG) Signals are mainly used as diagnostic tools that routinely assess the electrical and muscular function of the heart. Until recently, ECG records were predominantly maintained in paper form;thus, manually examining and re-examining the ECG paper records are often proved time-consuming and laborious tasks. Therefore, this research work utilizes, the power of machine learning and deep learning in predicting four different cardiac abnormalities named Abnormal Heartbeats (AHB), History of Myocardial Infarction (HMI), Myocardial Infarction (MI), and Normal Heartbeats (NHB) from paper-based ECG images. Different image pre-processing methods have been employed to improve the quality of individual ECG images and a novel lightweight deep neural model is proposed for extracting prominent features from each ECG images. Finally, we used an optimized weighted average ensemble technique for the final prediction. The grid search technique is used to find the optimal parameters and weights of each machine-learning model. Our proposed model shows state-of-the-art results in accuracy, precision, recall, and f1-score of 99.29%, outperforming the existing work. The model has attained an Area Under Curve (AUC) score of 99.96%. © 2023 IEEE.

关键词： Cardiovascular disease convolution neural network (CNN) deep learning ECG image feature extraction image classification machine learning weighted average ensemble

来源：评论

学校读者我要写书评

暂无评论

Detection of Plant Diseases and Recommendation of Plant Cycle using Machine Learning 4

Detection of Plant Diseases and Recommendation of Plant Cycl...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Alivn Ancy, A. Vivekanandan, G. Swapna, A. Kaniha, P. Indhu Priyadharshini, S. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Plant diseases create significant challenges to productivity in agriculture, food security, and ecological stability. Pathogens and environmental variables interact to cause plant diseases. Pathogens include various microorganisms like bacteria, fungi, and viruses, each with unique modes of spread. Environmental conditions such as temperature, humidity, and soil quality impact disease development and severity. The impacts of plant diseases are far-reaching. It affects crop yields and quality. Outbreaks of plant diseases can lead to economic losses, and food shortages which ultimately lead to hunger and poverty. Effective management of plant diseases requires an integrated approach that combines detection, preventive measures, cultural practices, and chemical interventions. The solution has two modules. The first module focuses on detection of plant diseases and its management. The second module focuses on recommendations of the plant cycle. Our proposed system utilizes CNN algorithms trained on a large amount of datasets. Using image analysis the features are extracted and patterns are recognized. Therefore, the system can quickly identify the disorders.. The system also includes suggestions for preventive measures. The system integrates modules for recommending optimal plant cycles to the farmers and gardeners. It generates personalized recommendations for planting schedules and crop varieties. This system increases crop yields and decreases the risk of disease outbreaks. Our solution offers a holistic approach to plant health monitoring and management. © 2024 IEEE.

关键词： Productivity Plant diseases Pathogens Schedules Soil Feature extraction Stability analysis Pattern recognition Crop yield Thermal stability

来源：评论

学校读者我要写书评

暂无评论

A Detailed Analysis of Machine Learning Algorithm Performance in Heart Disease Prediction 4

A Detailed Analysis of Machine Learning Algorithm Performanc...

引用

4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Rabbi, Mohammad Ali Rijon, Rokibul Hasan Akhi, Sharmin Sultana Hossain, Alomgir Jeba, Samiha Maisha International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Monroe University Dept of Computer Science United States

ISBN: (纸本)9798331530938

Human deaths are natural events, but heart disease remains a leading cause of mortality worldwide. Predicting heart disease using machine learning can significantly benefit global health, though it presents clinical challenges requiring high accuracy and efficiency. Many countries face a shortage of cardiovascular experts and a high rate of misdiagnoses, which can be mitigated by accurately determining the stage of coronary disease and supporting medical decisions with automated patient records. This paper addresses the prediction of coronary disease based on input attributes using data mining techniques. The results showed that the CatBoost Classifier achieved the highest accuracy at 87.2%, followed closely by the Ridge Classifier at 86.3%. Other models, such as XGBoost and Gradient Boosting, performed similarly, achieving 84.0%, while the RandomForest Classifier had a slightly better performance with an accuracy of 85.6%. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach to Attendance Monitoring: Combining Geo Location and Facial Verification 4

A Hybrid Approach to Attendance Monitoring: Combining Geo Lo...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Ramya, R. Hariharan, S. Abishek, B. Thangamuthu, E. Harikrishna, K.L. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Attendance management is an important task for an educational institution to cater to faculty accountability and correct tracking of the same. Traditional technique is enhanced by introducing a novel system that uses three separate modules;namely face recognition, geofencing, and real-time notifications. The system can identify when faculty members enter the institution's appointed area, and it embraces them with a request to complete the attendance process. The faculty members are encouraged to log onto to face detection software, where in they have a set time frame for them to log on and log their attendance using their individual identification number. The mobile application software has additional options - check-in and check-out options to precisely monitor the working hours accurately. It ensures the attendance is not falsified, and only registered within the designated area. The bank offers an elegant interface with sections of home, reports, user details, and announcement. The report sect provides a clear overview of the attendance records, and the user detail section provides comprehensive information about the faculty members, however the announcement section is relevant for the announcement section is the latest updates about the institution. Additionally, with the advanced security on this system, the data privacy and the data integrity is secure and trustworthy to use in this contemporary educational institution. It has not only increased the efficiency of workplace, but has also improved the traditional attendance logging system. © 2024 IEEE.

关键词： Application programs

来源：评论

学校读者我要写书评

暂无评论

Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots 2

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Singh, Aashdeep Dhawan, Sakshi Verma, Jyoti Singh, Gurpreet Kaur, Amanpreet Dept. of Computer Science & Engineering Punjab Rajpura India Dept. of Computer Application Punjab Rajpura India Chitkara University Institute of Engineering & Technology Dept. of Computer Science & Engineering Punjab Rajpura India

ISBN: (纸本)9798350360523

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning. © 2024 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Federated Learning for Cybersecurity: A Promising Frontier 2

Federated Learning for Cybersecurity: A Promising Frontier

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Ananya, G. Bindu Madhavi, V. Murthy, Monisha Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Buddha Institute of Technology Computer Science and Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350368109

In today's world, Artificial Intelligence and Machine Learning are transforming many industries, but they rely on huge amounts of data leading to privacy issues. A better alternative is Federated learning. Federated Learning involves training the model from multiple sources using the decentralization technique, meaning each device trains the model on its local data thereby reducing the strain on the single server. This is useful in cases where the data is too large to be sent and maintained on a central server or in handling privacy and security concerns. Cybersecurity is a prime domain where vulnerable attacks and breaches can be prevented using this technique. Other domains like finance, healthcare, IoT etc. have transformed the idea of data-driven decisions. This paper gives an insight into the concept of Federated learning and why it is a better choice. It includes the various algorithms that can be implemented in multiple applications depicted through a comparative analysis. The choice of algorithm depends upon its efficiency and the desired cybersecurity application. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Efficiency Redefined: The Document Scanner App for Precise Entity Identification with Customized NER 3

Efficiency Redefined: The Document Scanner App for Precise E...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Javid, Sajid Saim, Mohammad Islam, Saiful Indraprastha Institute of Information Technology Delhi Dept. of Computer Science & Engineering Delhi India University of Cincinnati Dept. of Computer Science & Engineering CincinnatiOH United States Zakir Husain College of Engineering & Technology Aligarh Muslim University Dept. of Computer Engineering Aligarh India

ISBN: (纸本)9798331518592

In the era of rapid advancements in technology, the efficient digitization of paper-based documents remains a crucial challenge across various domains. The traditional approach to document scanning often struggles with accurate extraction of critical information from unstructured or diverse document formats. This paper presents the development of a web-based document scanner application that leverages Named Entity Recognition (NER) to extract entities from scanned business cards efficiently. The study aims to address the challenge of digitizing and organizing physical documents, with a focus on precise entity extraction. The methodology involves creating a custom dataset of scanned business cards, preprocessing the data, and training an NER model using machine learning techniques. The application is built using the Flask framework and integrates OpenCV for text extraction and SpaCy for NER. The key findings include the model's high F1 score in identifying entities such as names, organizations, designations, emails, and contact details. The application streamlines document digitization by automatically extracting and tagging relevant metadata, minimizing the need for manual data entry. This research contributes to enhancing document management and demonstrates broader applicability for other document types apart from business cards. The implications highlight improvements in workflow automation, data accuracy, and the potential for integrating with larger document management systems. © 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

Deep Learning Driven Multilingual System for Recognition and Categorization of Medicinal Plants with Extensive Elucidation 9

Deep Learning Driven Multilingual System for Recognition and...

引用

9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Azhaguramyaa, V.R. Janet, J. Hariharan, A. Kamaleshwaran, A. Albert Joseph, A. Computer Science And Engineering Sri Krishna College of Engineering And Technology Tamil Nadu Coimbatore India Sri Krishna College of Engineering And Technology Dept. of Computer Science And Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350365092

Medicinal plant use is widespread throughout many cultures and geographical areas, frequently entwined with customs and knowledge. The use of technology, especially deep learning, to improve the identification and classification of these plants has gained popularity in recent years. This research introduces a new multilingual system with deep learning capabilities that can identify and classify medicinal plants with a great deal of explanation. The term 'deep learning-driven multilingual system for recognition and categorization of medicinal plants with extensive elucidation' describes an advanced technology that carries out several tasks by utilizing deep learning, one of the most advanced machine learning techniques. Based on pictures, the system can recognize and classify a variety of therapeutic plants. Because users may communicate with the system, ask questions, and get information in their native tongue, it is usable by a wide variety of people across the globe. Apart from identification and classification, the method offers comprehensive clarifications or in-depth accounts of the recognized therapeutic herbs. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Real-Time Object Detection from Surveillance using Deep Learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

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 << < 9 10 11 12 13 14 15 16 17 18 > >>

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：