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

523 篇 会议
138 篇 期刊文献
1 册 图书

馆藏范围

662 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

318 篇 工学
- 229 篇 计算机科学与技术...
- 198 篇 软件工程
- 81 篇 信息与通信工程
- 65 篇 控制科学与工程
- 48 篇 生物工程
- 44 篇 电气工程
- 35 篇 生物医学工程（可授...
- 32 篇 电子科学与技术（可...
- 31 篇 网络空间安全
- 27 篇 安全科学与工程
- 25 篇 动力工程及工程热...
- 23 篇 土木工程
- 19 篇 机械工程
- 19 篇 光学工程
- 19 篇 交通运输工程
188 篇 理学
- 78 篇 数学
- 57 篇 物理学
- 55 篇 生物学
- 24 篇 统计学（可授理学、...
- 19 篇 系统科学
- 17 篇 化学
- 16 篇 海洋科学
101 篇 管理学
- 67 篇 管理科学与工程(可...
- 41 篇 图书情报与档案管...
- 31 篇 工商管理
50 篇 医学
- 40 篇 基础医学(可授医学...
- 40 篇 临床医学
- 30 篇 公共卫生与预防医...
- 18 篇 药学(可授医学、理...
31 篇 法学
- 28 篇 社会学
18 篇 农学
12 篇 经济学
8 篇 教育学
3 篇 文学

主题

73 篇 accuracy
36 篇 convolutional ne...
29 篇 deep learning
28 篇 computational mo...
27 篇 feature extracti...
26 篇 real-time system...
25 篇 training
22 篇 support vector m...
22 篇 predictive model...
22 篇 machine learning
22 篇 data models
19 篇 internet of thin...
16 篇 optimization
16 篇 data mining
13 篇 security
13 篇 artificial intel...
13 篇 technological in...
12 篇 scalability
12 篇 medical services
12 篇 random forests

机构

16 篇 lovely professio...
14 篇 maharishi school...
12 篇 centre of interd...
12 篇 department of ma...
10 篇 department of ma...
8 篇 school of comput...
8 篇 department of ma...
8 篇 master of comput...
7 篇 master program o...
7 篇 radiology techni...
7 篇 prince shri venk...
6 篇 department of co...
6 篇 department of co...
6 篇 department of co...
5 篇 school of comput...
5 篇 kongu engineerin...
5 篇 department of co...
5 篇 school of comput...
5 篇 department of co...
5 篇 department of co...

作者

7 篇 bens pardamean
7 篇 thotad puneeth n...
7 篇 asha v.
7 篇 jayadurga r.
7 篇 hiroshi hashimot...
7 篇 pundru chandra s...
7 篇 v. asha
6 篇 pardamean bens
6 篇 r. jayadurga
6 篇 puneeth n. thota...
6 篇 binju saju
6 篇 lin jason
6 篇 ginni nijhawan
6 篇 vijayakumar p.
6 篇 tsai chia-wei
6 篇 yang chun-wei
5 篇 j. relin francis...
5 篇 ali albawi
5 篇 singh kamred udh...
5 篇 yasuhiro ohyama

语言

493 篇 英文
166 篇 其他
5 篇 中文
3 篇 日文
1 篇 德文

检索条件"机构=Master of Technology-Computer Science and Engineering"

共 662 条记录，以下是41-50 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：

Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML 2

Mobile-Based Classification and Detection of Diabetic Retino...

引用

2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

来源：评论

学校读者我要写书评

暂无评论

The Risks & Realities of Picking an IoT OS

The Risks & Realities of Picking an IoT OS

引用

computer Applications (ICCA), IEEE Conference on

作者： Pyae Sone Hein Zar Zar Linn Master of Engineering in Computer Science Myanmar Institute of Information Technology Faculty of Computer Science Myanmar Institute of Information Technology

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

Internet of Things is no longer a new topic. Long ago, the network of things was a rare thing which could be seen only in offices, industries, and labs. Nowadays it is approaching becoming a ubiquitous appliance as a commonplace in everyday environments. Homes and public places such as traffic and city parks have changed to adapt with things within the ability to connect to a network. As a result, even commoners are becoming familiar with IoT. Researchers lay the new technologies into IoT networks. Developers drive them into industry and practitioners carry the aesthetic essence by carrying on the practice among people. In doing so, choosing the correct operating system for the correct device is a step not to be skipped as the learning curve grows up to advanced. This study aims to aid the practitioners by discussing the risks and realities concerned with the role of OS in IoT devices or systems.

关键词： Industries Job shop scheduling Systematics Operating systems Urban areas Switches Systems engineering and theory Real-time systems Internet of Things Stress

来源：评论

学校读者我要写书评

暂无评论

Camera-Only Perception System for Traffic Jam Assistance

Camera-Only Perception System for Traffic Jam Assistance

引用

2023 International Automatic Control Conference, CACS 2023

作者： Huang, Xiao-Wei Chen, Xiu-Zhi Chen, Yen-Lin National Taipei University of Technology Master Program in Artificial Intelligence Technology Taipei Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350306354

Traditional perception systems for TJA (Traffic Jam Assistance) are mostly implemented by fusing images with radar or lidar. As computer vision techniques become more powerful, cameras can almost replace the need for radar and lidar in perception tasks, which reduces the hardware cost of the system. In this research, we propose a camera-only perception system for TJA, which is able to provide the information of the vehicles ahead and the drivable area. The proposed system has been evaluated through real-world scenario sequences, and proved that it achieves high robustness, which is highly possible to be adopted for TJA development. © 2023 IEEE.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Robust Object Detection Model for UAV Application

Robust Object Detection Model for UAV Application

引用

2023 International Automatic Control Conference, CACS 2023

作者： Ku, Chun Chen, Xiu-Zhi Chen, Yen-Lin National Taipei University of Technology Master Program in Artificial Intelligence Technology Taipei Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350306354

Not only common issues on object detection task need to be deal with, for Unmanned Aerial Vehicle (UAV) applications, small object is one of the critical problems that needs to be solved. YOLOv7 is a powerful network architecture that provides high efficiency and accuracy object detection results. This paper adopts YOLOv7 as an object detection model for two different kinds of targets, one is vehicle, and the other is ocean flotsam. By training the model with open datasets and fine-tuning the model with self-collected datasets, we prove through sequences collected from real-world scenarios that YOLOv7 is able to provide robust and accurate object detection results, including vehicles and ocean flotsam, with real-time efficiency. Based on such experimental result, we confirmed that YOLOv7 can be the baseline for object detection model development. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Boosting-Based Machine Learning Approaches for Diabetes Prediction using Indian Demographic and Health Survey Data 3

Boosting-Based Machine Learning Approaches for Diabetes Pred...

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India

ISBN: (纸本)9798350364569

Traditional diagnostic techniques are time-consuming and prone to error. Performance is added however time and cost are reduced by using computer-aided diagnostic methods. This study aims to calculate how healthy boosting-based machine learning classifiers perform in automatically identifying diabetes in the Indian context. For this experiment, Indian demographic and health survey 2021 data on women aged 19 to 79 are considered. This data includes clinical, anthropometric, and biochemical components. 12,103 of the 706,504 responders who tested positive for diabetic testing were considered. The predictive exploration-based boosting machine learning models are adopted, such as adaptive boosting, categorical boosting, extreme gradient boosting, gradient boosting, and light gradient boosting models. Kernel Principal Component Analysis is employed for feature extraction. The Extreme Gradient Boosting model demonstrated strong performance on the diabetes dataset both before and after applying Kernel PCA. The metrics including accuracy, F1-score, precision, and recall for the XGBoost model showed improvements: accuracy increased from 81% to 83%, F1-score from 81% to 82%, precision from 81% to 88%, and recall from 81% to 76%.In contrast, Adaptive Boosting performed poorly, while Categorical Boosting, Gradient Boosting, and Light Gradient Boosting models achieved moderate performance levels. Overall, the model attained optimal performance across metrics such as accuracy, precision, Area Under the Curve (AUC), and recall. © 2024 IEEE.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

HealthPredictor: Accurate Health Insights at Your Fingertips 3

HealthPredictor: Accurate Health Insights at Your Fingertips

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sajjan, Mahantesh M. Ammanagi, Nausheen Pujari, Laxmi Kamatar, Vishwanath S Belavatgi, Meenaz K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580 027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi580 027 India

ISBN: (纸本)9798350364569

The "HealthPredictor: Accurate Health Insights at Your Fingertips"system transforms healthcare by predicting diseases based on user symptoms. Utilizing advanced machine learning models such as Decision Tree, SVM, Random Forest, and Logistic Regression, it assesses the likelihood of various illnesses. The system compares symptoms to a comprehensive dataset to provide accurate predictions, aiding in early detection. Users can consult with specialist doctors through an integrated chat feature, ensuring quick access to medical advice. By continuously learning from historical data, the system enhances its predictive capabilities, identifying new health patterns and trends. This proactive approach empowers users with timely information and personalized consultations, leading to better health outcomes. © 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

Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

引用

2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches 4

An Efficient Model for Early Detection of Maize Leaf Disease...

引用

4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. K.L.E.Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K.L.E.Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, Data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Design and Development of Cryptocurrency Price Prediction (CPP) Using ARIMA-LSTM

Design and Development of Cryptocurrency Price Prediction (C...

引用

2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Saha, Rahul Kumar Wankhade, Sanjana Mujawar, Maaz Jeswani, Ravi Tambavekar, Janhvi Mangal, Mayank University of Mumbai Computer Science Engineering Department Mumbai India University of Mumbai Department of Master in Management Studies Mumbai India University of Mumbai Department of Information Technology Mumbai India

ISBN: (纸本)9798350388800

Cryptocurrency price prediction is all about the digital currency called as cryptocurrency this software helps the new user to raise the understanding level regarding the cryptocurrency. Cryptocurrency like Bitcoin, Ethereum and etc. Which helps the new user to invest in the cryptocurrency without any fear the user can get his prediction about the desire cryptocurrency the stats of the specific cryptocurrency can be explained with the help of the different graphs. In this software four types of the algorithm are being used Python, CNN, TensorFlow, LSTM which helps to find out the best prediction level which decreases the level of loses to the users. As per the new cryptocurrency bill 2021 the government officially taking steps into the cryptocurrency and the government can issue their own cryptocurrency issued by Reserve Bank of India. Which leads the opportunity for the new investor are software will help that new investor to get a detailed explanation regarding the cryptocurrency. © 2024 IEEE.

关键词： Bitcoin Cryptocurrency Deep Learning Economics Neural Network Prediction Technical Trade

来源：评论

学校读者我要写书评

暂无评论

Enhanced Audio-Visual Speech Enhancement via Attention-based Sparse Graph Convolutional Neural Networks with Fire Hawk Optimization 3

Enhanced Audio-Visual Speech Enhancement via Attention-based...

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Hebri, Dheeraj Joon, Rakesh Kumar Mohan, M. Ramapraba, P.S. Dhulipudi, Venkata Satish Narasimharao, Jonnadula Department of Master of Computer Applications Srinivas Institute of Technology Karnataka Mangalore574143 India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana 124104 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Chennai641407 India Department of Electrical and Electronics Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering BVC Institute of Technology and Science Andhra Pradesh 533221 India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad501401 India

ISBN: (纸本)9798331532420

Audio-Visual Speech Enhancement (AVSE) has the vision to enhance speech quality and requite intelligibility by using both audio and video inputs, which is quite useful in noisy conditions. This approach acknowledges the fact that modalities such as lip movement may be equally informative of the auditory signals as the actual sound in cases where audio signals are distorted. In this research, an innovation approach to the AVSE is presented, which involves several state-of-the-art techniques. The data preprocessing step uses Z-score Min-Max Normalization to normalize the input data to help enhance the feature extraction process. Differentiable adaptive short-time Fourier transform (DA-STFT) is employed as the method of audio feature extraction that provides key temporal information. In the feature extraction process, a local directional structural pattern (LDSP) is utilized for the extraction of visual features since it examines the moving patterns on the face effectively. The fusion of data is done using a quadruplet depth-wise separable fusion convolutional neural network (QDSFCNN), which enhances the amalgamation of the audio and visual cues. Classification is done using an attention-based sparse graph convolutional neural network, known as ASGCNN, since the focus is on the relevant stipulations. Finally, to achieve the highest performance level, the fire hawk optimizer (FHO) is used for tuning parameters in the presented model. This combined approach presents advanced developments in AVSE, which provide improved speech quality in difficult conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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67页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：