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

7,262 篇 会议
1,312 篇 期刊文献
94 册 图书

馆藏范围

8,668 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

5,476 篇 工学
- 4,123 篇 计算机科学与技术...
- 3,436 篇 软件工程
- 1,525 篇 信息与通信工程
- 882 篇 控制科学与工程
- 557 篇 电气工程
- 557 篇 生物工程
- 548 篇 电子科学与技术（可...
- 396 篇 生物医学工程（可授...
- 382 篇 光学工程
- 340 篇 机械工程
- 334 篇 仪器科学与技术
- 262 篇 动力工程及工程热...
- 219 篇 化学工程与技术
- 194 篇 交通运输工程
- 194 篇 安全科学与工程
- 164 篇 建筑学
2,844 篇 理学
- 1,606 篇 数学
- 745 篇 物理学
- 585 篇 生物学
- 511 篇 统计学（可授理学、...
- 349 篇 系统科学
- 233 篇 化学
1,409 篇 管理学
- 852 篇 管理科学与工程(可...
- 629 篇 图书情报与档案管...
- 443 篇 工商管理
355 篇 医学
- 300 篇 临床医学
- 277 篇 基础医学(可授医学...
- 172 篇 公共卫生与预防医...
- 170 篇 药学(可授医学、理...
238 篇 法学
- 185 篇 社会学
155 篇 农学
148 篇 教育学
133 篇 经济学
37 篇 文学
19 篇 军事学
10 篇 艺术学
1 篇 哲学

主题

218 篇 feature extracti...
217 篇 accuracy
188 篇 computational mo...
157 篇 training
139 篇 deep learning
135 篇 computer science
130 篇 real-time system...
122 篇 machine learning
121 篇 data mining
116 篇 predictive model...
115 篇 support vector m...
100 篇 internet of thin...
100 篇 servers
98 篇 optimization
95 篇 artificial intel...
93 篇 computer archite...
93 篇 data models
91 篇 visualization
91 篇 software
90 篇 semantics

机构

95 篇 dept. of compute...
84 篇 institut univers...
84 篇 tno technical sc...
82 篇 north-west unive...
82 篇 national centre ...
82 篇 dept. of informa...
81 篇 nikhef national ...
79 篇 infn sezione di ...
79 篇 university würzb...
79 篇 university of jo...
78 篇 ncsr demokritos ...
77 篇 infn sezione di ...
76 篇 infn sezione di ...
76 篇 institute of inf...
76 篇 université de st...
74 篇 infn sezione di ...
70 篇 university moham...
68 篇 infn sezione di ...
66 篇 università di ca...
61 篇 western sydney u...

作者

85 篇 ferrara g.
85 篇 ardid m.
84 篇 calvo d.
84 篇 gal t.
84 篇 bozza c.
84 篇 boumaaza j.
84 篇 coleiro a.
84 篇 gozzini s.r.
83 篇 díaz a.f.
83 篇 dallier r.
82 篇 kalekin o.
81 篇 chen a.
80 篇 anguita m.
80 篇 enzenhöfer a.
80 篇 celli s.
78 篇 bendahman m.
77 篇 boettcher m.
77 篇 dornic d.
77 篇 carretero v.
77 篇 brunner j.

语言

7,541 篇 英文
1,035 篇 其他
65 篇 中文
20 篇 日文
6 篇 朝鲜文
3 篇 斯洛文尼亚文
1 篇 德文

检索条件"机构=Dept. of Information Engineering and Computer Science and Mathematics"

共 8668 条记录，以下是321-330 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：

Comparative Analysis of Covid Detection using Chest X-rays by SVM-PCA and Deep Learning Techniques 1

Comparative Analysis of Covid Detection using Chest X-rays b...

引用

1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent information Systems, ICAECIS 2023

作者： Jaswanth, Kunisetty Nikhil, Masabattula Teja Siddhartha, Mutyala Sai Sri Jayan, Sarada Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9798350348057

In the medical field, it is very crucial to obtain the results accurately in less amount of time. nowadays the covid cases have been increasing gradually and as a reason, the time of computations has to be decreased. In this paper, an SVM-based covid detection model on chest x-rays has been proposed with a relatively small dataset by preprocessing the images with a pooling operation and performing principal component analysis. © 2023 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Decentralized Privacy-Preserving Federated Learning for Ultrasonic Nerve Image Segmentation 50

Decentralized Privacy-Preserving Federated Learning for Ultr...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Vinjamuri, Gowtham Nayak, Suvendu Chandan Sahu, Rekha Shukla, Raj Mani Das, Tapadhir School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Dept. of Computer Science and Engineering Silicon University Bhubaneswar India Department of Computer Science University of the Pacific Stockton United States

ISBN: (纸本)9781665464543

Currently, Federated Learning is a research approach where multiple parties can train a model together without sharing each other’s data to solve complex problems in machine learning. Ultrasound Nerve Segmentation is a computer vision technique that automatically identifies and segments nerve structures in ultrasound images. This technique is particularly important in medical applications where accurate localization of nerves is crucial, such as during anesthesia, nerve blocks, or surgical procedures. Ultrasound Nerve Segmentation can help doctors find nerves better during medical procedures. This could make patients feel better and have better results. Talking about surgery can make even brave patients scared because it hurts and can cause a lot of pain afterward. To reduce pain, people use drugs called narcotics, but these drugs can have bad side effects. The goal of this project is to improve pain management by using indwelling catheters that block or reduce pain at its source. These catheters reduce the need for painkillers and hasten patient healing. It is crucial to precisely identify nerve structures in ultrasound images to guarantee the exact insertion of a patient’s pain management catheter. We attempt to build an algorithm that can recognize nerve structures in a dataset of neck ultrasound images in this study. To do this, we created a U-net architecture model that will accept an image as input and forecast an image with the source of the pain highlighted as the output. Achieving this objective would improve catheter placement precision and help in the future with reduced pain. © 2024 IEEE.

关键词： Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

RF-Based Lightweight Machine Learning for Comprehensive Drone Activity Classification 2

RF-Based Lightweight Machine Learning for Comprehensive Dron...

引用

2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Alqodah, Mohammed A. Tahsin, Mahdia Omari, Mohammad H. Matalgah, Mustafa M. Harrison, David University of Mississippi Dep. of Electrical & Computer Engineering UniversityMS United States University of Mississippi Dept. of Computer and Information Science UniversityMS United States

ISBN: (纸本)9798331529529

The rapid rise of commercially available drones, driven by miniaturization and affordability, has introduced potential security risks alongside numerous societal benefits. While drones serve various industries, their unregulated use necessitates robust detection and classification methods. This paper proposes a novel, efficient model for classifying unwanted drone activity. We leverage statistical and time-domain features extracted from radio frequency (RF) signals and machine learning (ML) classifiers. This approach aims to not only detect drones but also distinguish their models and operational modes. Utilizing the DroneRF dataset, our feature selection strategy prioritizes informative signals in the low-band spectrum, achieving {100%} accuracy in both drone detection and drone model identification tasks. Furthermore, our hybrid model, combining time-domain and frequency-domain features, achieves an accuracy of {97.66%} in classifying different flight modes. This demonstrates the effectiveness of this comprehensive feature extraction approach for drone activity classification. © 2024 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Sign Language Detection Using Action Recognition LSTM Deep Learning Model 2

Sign Language Detection Using Action Recognition LSTM Deep L...

引用

2nd IEEE International Conference on Networks, Multimedia and information Technology, NMITCON 2024

作者： Snehalatha, N. Mohana Kumar, S. Kavitha, H. Nisha, S. Gowda, Puttabore Krishna Prasad, Sj Jss Academy of Technical Education Dept. of Computer Science and Engineering Bangalore India Bangalore India Siddaganga Institute of Technology Dept. of Information Science Tumukur India Bangalore India Ramaiah Institute of Technology Dept. of Me Bangalore India Visvesvaraya College of Engineering Dept. of Ec University Bangalore India

ISBN: (纸本)9798350372892

This project aims to develop a system that can recognize sign language gestures in real-time using computer vision techniques. The system is designed to bridge the communication gap between individuals with different hearing abilities by enabling accurate and effective communication through sign language. To achieve this, an image datasets is created, consisting of labeled images of sign language gestures for alphabets, digits, or other signs. This datasets is used to train a deep neural network using transfer learning techniques. Transfer learning involves leveraging the knowledge gained from pre-trained models, such as VGG-16 and ResNet, to accelerate the training process and improve the accuracy of the model. The trained deep neural network is then deployed in a real- time setting where it processes video feeds or live camera feeds to recognize and interpret sign language gestures. The system is designed to be user-friendly, with an intuitive interface that can be easily used by individuals with hearing difficulties or those who do not understand sign language The accuracy of the system is evaluated using various performance metrics such as accuracy, precision, recall, and F1 score. The system is tested in different environments, including noisy and low-light settings, to ensure that it can recognize sign language gestures accurately. Overall, this work demonstrates the potential of computer vision and deep learning techniques to improve communication for individuals with hearing difficulties. It also highlights the importance of developing accessible technologies that can enable effective communication and improve the quality of life for individuals with disabilities © 2024 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Operationalizing AI Ethics - A Quantifiable Framework for Implementation and Assessment 3

Operationalizing AI Ethics - A Quantifiable Framework for Im...

引用

3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Kumar, Devesh Singla, Bhawani Singh Choudhary, Rahul Kumar Nit Hamirpur Dept. of Computer Science & Engineering Hamirpur India Nit Hamirpur Dept. of Mathematics & Computing Hamirpur India Nit Hamirpur Dept. of Electrical Engineering Hamirpur India

ISBN: (纸本)9798331521691

This research presents a framework for operationalizing ethical principles in artificial intelligence systems through quantifiable metrics and adaptive implementation strategies. The investigation addresses the critical gap between theoretical frameworks and practical implementation by introducing systematic methodologies for measuring and enforcing ethical compliance. Based on extensive testing across healthcare, financial services, and public sector applications, the framework substantially outperforms traditional approaches, delivering significant improvements in both compliance rates and stakeholder acceptance. By establishing standardized yet flexible approaches to ethical AI implementation, the framework enables effective deployment across diverse global contexts while maintaining consistent ethical standards. The research provides organizations with practical tools and methodologies for ethical AI deployment that accommodate varied organizational needs and cultural requirements, advancing the development of responsible AI systems. Through comprehensive case studies, the approach demonstrates marked improvements in ethical compliance while achieving high levels of stakeholder engagement, establishing a new benchmark for ethical AI implementation. © 2025 IEEE.

关键词： artificial intelligence ethics compliance metrics cross-cultural deployment cultural adaptation ethical assessment implementation frameworks stakeholder engagement

来源：评论

学校读者我要写书评

暂无评论

Development of a Dempster-Shafer Model for Diagnosing Mental Health Disorders on Social Media 7

Development of a Dempster-Shafer Model for Diagnosing Mental...

引用

7th International Seminar on Research of information Technology and Intelligent Systems, ISRITI 2024

作者： Nasyuha, Ayahri Hadi Hadi Ilyas, Ananda Andriyani, Widyastuti Subagyo, Aloysius Agus Universitas Teknologi Digital Indonesia Faculty of Information Technology Dept. of Information System Yogyakarta Indonesia Universitas Dharmawangsa Faculty Of Engineering And Computer Science Dept. of Information System Medan Indonesia Universitas Teknologi Digital Indonesia Faculty of Information Technology Dept. of Master of Information Technology Yogyakarta Indonesia Universitas Teknologi Digital Indonesia Faculty of Information Technology Dept. of Accounting Information System Yogyakarta Indonesia

ISBN: (纸本)9798331519643

The rapid proliferation of social media has been paralleled by an increase in mental health concerns, often reflected in users' online behaviors and text posts. Traditional methods for diagnosing mental health disorders do not effectively leverage this vast amount of data, nor do they address the ambiguities and uncertainties inherent in natural language processing of social media content. A novel diagnostic model based on the Dempster-Shafer evidence theory is developed specifically for analyzing social media text. The model aims to enhance the precision of mental health disorder diagnoses by incorporating probabilistic reasoning and handling uncertainty in the data more effectively than traditional binary classification methods. It integrates linguistic cues, sentiment analysis, and behavioral indicators extracted from user posts to assess the likelihood of various mental health conditions. We collected a dataset comprising social media posts tagged with mental health keywords, verified by clinical annotations. The Dempster-Shafer model was trained with features derived from text analytics, including sentiment scores, thematic elements, and engagement metrics. The model's performance was evaluated against standard classification models through metrics such as accuracy, sensitivity, and specificity. The Dempster-Shafer model demonstrated superior capability in handling the uncertainty and imprecision of social media data, showing an improvement in diagnostic accuracy by 15% over traditional models. The findings suggest that applying Dempster-Shafer theory in mental health diagnostics can effectively utilize the nuanced and sparse data available on social media, offering a promising tool for early identification and intervention in mental health issues. © 2024 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Double Layer Message Security for 3D Object Using Mathematical Sequences and Secret Keys

Double Layer Message Security for 3D Object Using Mathematic...

引用

2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Alkhamese, Aya Elgawalby, Hewayda Eid, Ahmad Hanafy, Ibrahim Awad, Wael Port Said University Faculty of Engineering Dept. Physics and Engineering Mathematics Port Said Egypt Port Said University Faculty of Engineering Electrical Engineering Department Port Said Egypt Port Said University Faculty of Science Dept. Mathematics and Computer Science Port Said Egypt Damietta University Faculty of Computers and Artificial Intelligence Dept. Computer Science Damietta Egypt

ISBN: (纸本)9798350326062

It's It's been so crucial lately to emphasize data security applying as the world depends on data exchange extensively almost in all domains. Steganography is one of the main techniques used to ensure information security in storage and during communication. We accomplish this novel technique by using two main layers. In the first layer, the secret message is compressed using Gzip or encrypted using Advanced Encryption Standard (AES). In the next layer, the binary representation of the secret message is embedded into vertices of x, y, and z-coordinates of the 3D object using the Gray code sequence, Recamán's sequence, and Lucas sequence. This binary representation of secret message is embedded using the Least Significant Bit (LSB) technique according to the value of the used sequence. Different performance measurements are computed and evaluated to ensure that the 3D object quality remains preserved such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), embedding rate (ER), Normalized Hausdorff Distance (NHD) and maximum embedding capacity. The proposed scheme is compared with its counterparts in the literature. Outstanding numerical results are achieved, showing the superiority of the proposed schemes in the terms of performance, capacity, and security. © 2023 IEEE.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Architecture development of middleware and API for IOT-based Red Onion farming

Architecture development of middleware and API for IOT-based...

引用

2023 International Seminar on Application for Technology of information and Communication, iSemantic 2023

作者： Steven, Alvin Suharnawi Alzami, Farrikh Saputra, Filmada Ocky Megantara, Rama Aria Andono, Pulung Nurtantio Universitas Dian Nuswantoro Dept. of Information Systems Indonesia Universitas Dian Nuswantoro Dept. of Information Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Indonesia

ISBN: (纸本)9798350339215

IoT plays a crucial role in transforming the agricultural industry by offering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural community benefits immensely from IoT is due to its capacity to gather real-time data straight from their fields. Farming requires considerable attention, and wireless sensor networks enable farmers to deploy IoT devices throughout various parts of their farm so they can obtain information on soil moisture levels, temperature, humidity, among other essential parameters that are integral for effective agriculture management. This gathered data empowers farmers with accurate weather forecasts through machine learning techniques which assist them in making informed decisions related to planting or harvesting of crops - vital components critical for successful outcomes towards better yields. Moreover, middleware architectures/APIs developed specifically for IOT-based red onion farming utilize cloud computing services wherein these enlightening patterns generated get stored safely using analytics tools & automated systems. These intensify insights derived as it simplifies accessing important metrics aiding ease while analyzing historical trends advantageously helping make prudent future course corrective measures concerning planting/harvesting schedules based entirely on solid empirical evidence combined with artificial intelligence ensuring unprecedented efficiency gains and higher returns than previously achievable without technology guidance. © 2023 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Lightweight Attention Mechanism Via Knowledge Distillation For Vein Recognition 3

Lightweight Attention Mechanism Via Knowledge Distillation F...

引用

3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Chan, Hung-Tse Chang, Yung-Wei Hsia, Chih-Hsien Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Taipei Fuhsing Private School Taipei Taiwan Dept. of Computer Science and Information Engineering National Ilan University Yilan Taiwan

ISBN: (纸本)9798350333862

Internal biometric identification systems based on convolutional neuron network (CNN) is precise but there are disadvantages of demanding enormous data and endless calculating. Therefore, it is difficult for CNN to be used widely. To solve the problem, we present a light CNN with a channel shuffle that automatically balances channel attention and spatial attention. Then, the teacher-student training strategy is used to solve the insufficient generalization ability of light CNN and enhance the accuracy as the model focused on the program's information. The experiment results showed that the proposed light CNN model increased the accuracy by 14.35% without additional parameters based on the public dataset FVUSM. © 2023 IEEE.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

An Efficient Approach to Deal with Cyber Bullying using Machine Learning: A Systematic Review 2

An Efficient Approach to Deal with Cyber Bullying using Mach...

引用

2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Naveen Kumar, M.R. Vishwachetan, D. Mushtaq Ahmed, D.M. Dayananda, K.J. Manasa, K.J. Nanda, K.V. Vidyavardhaka College of Engineering Information Science and Engineering Mysuru India Ramaiah Institute of Technology Information Science and Engineering Bengaluru India Gsss Ssfgc Dept. of Computer Application Mysuru India MUSoE Dept. of Aiml Manasagangothri Mysuru India Sri Siddhartha Institute of Technology Compter Application Tumkur India

ISBN: (纸本)9798350328202

The prominence of social media platforms like Facebook, WhatsApp, and Instagram have given rise to Cyber crime, notably in the form of Cyber bullying. This unique manifestation of harassment involves individuals exploiting social networks to engage in harmful behaviors such as posting toxic comments or sharing embarrassing content, often causing severe reputation damage to victims, particularly among teenagers. This Paper gives a comprehensive survey of technical approach to tackle this escalating issue, employing advanced machine learning algorithms such as Support Vector Machines (SVM), Convolution Neural Networks (CNN), and J48. The objective is to review models capable of accurately detecting Cyber bullying, thereby contributing to the creation of robust systems that promote a safer online environment, focusing on safeguarding vulnerable demographics like teenagers and also possible future direction. © 2024 IEEE.

关键词： Support vector machines

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 << < 29 30 31 32 33 34 35 36 37 38 > >>

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：