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

1,014 篇 会议
15 篇 期刊文献
3 册 图书

馆藏范围

1,032 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

677 篇 工学
- 502 篇 计算机科学与技术...
- 421 篇 生物医学工程（可授...
- 223 篇 软件工程
- 123 篇 生物工程
- 84 篇 电气工程
- 63 篇 信息与通信工程
- 41 篇 光学工程
- 41 篇 仪器科学与技术
- 37 篇 电子科学与技术（可...
- 29 篇 控制科学与工程
- 22 篇 化学工程与技术
- 7 篇 机械工程
- 7 篇 动力工程及工程热...
- 7 篇 安全科学与工程
409 篇 医学
- 379 篇 临床医学
- 174 篇 基础医学(可授医学...
- 149 篇 药学(可授医学、理...
- 25 篇 公共卫生与预防医...
- 21 篇 特种医学
248 篇 理学
- 189 篇 生物学
- 79 篇 数学
- 41 篇 物理学
- 21 篇 统计学（可授理学、...
- 17 篇 化学
- 11 篇 系统科学
39 篇 管理学
- 24 篇 图书情报与档案管...
- 21 篇 管理科学与工程(可...
- 9 篇 工商管理
10 篇 农学
- 9 篇 作物学
6 篇 法学
- 6 篇 社会学
3 篇 教育学

主题

585 篇 cardiology
144 篇 electrocardiogra...
100 篇 heart
68 篇 computational mo...
50 篇 myocardium
44 篇 heart rate varia...
42 篇 diseases
42 篇 mathematical mod...
40 篇 hospitals
39 篇 abstracts
34 篇 heart rate
33 篇 medical computin...
33 篇 image segmentati...
33 篇 biomedical imagi...
33 篇 atrial fibrillat...
33 篇 biomedical compu...
31 篇 bioelectric pote...
30 篇 biomedical engin...
29 篇 feature extracti...
27 篇 monitoring

机构

7 篇 emory univ dept ...
6 篇 big data and int...
6 篇 georgia inst tec...
6 篇 school of comput...
5 篇 dei university o...
5 篇 ciber bbn zarago...
5 篇 warsaw univ tech...
5 篇 zaragoza
4 篇 a*star institute...
4 篇 national heart c...
4 篇 scientific compu...
4 篇 computing in car...
4 篇 institute for re...
4 篇 scientific compu...
4 篇 mit inst med eng...
4 篇 ningbo institute...
3 篇 univ british col...
3 篇 institute of rad...
3 篇 univ manchester ...
3 篇 institute for me...

作者

12 篇 yi su
11 篇 laguna pablo
10 篇 clifford gari d.
9 篇 liang zhong
8 篇 zhang henggui
8 篇 silva ikaro
7 篇 j.k.-j. li
7 篇 zhang qinhu
6 篇 van huffel sabin...
6 篇 soo-kng teo
6 篇 novakova marie
6 篇 sabine van huffe...
6 篇 si yong yeo
6 篇 mark roger g.
5 篇 j. kedem
5 篇 pavel jurak
5 篇 filip plesinger
5 篇 rolf krause
5 篇 rik willems
5 篇 josef halamek

语言

1,015 篇 英文
13 篇 其他
12 篇 中文
1 篇 土耳其文

检索条件"任意字段=Conference on Computing in Cardiology"

共 1032 条记录，以下是1-10 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：

computing in cardiology conference, CinC 2018

Computing in Cardiology Conference, CinC 2018

引用

45th computing in cardiology conference, CinC 2018

ISBN: (纸本)9781728109589

The proceedings contain 286 papers. The topics discussed include: TWA simulator: a graphical user interface for t-wave alternans;development and validation of an in silico rabbit Purkinje cell action potential model: a step towards a drug safety testing tool;a multiscale intracranial pressure signal simulator;effect of segmentation variation on ECG imaging;R-peak detection in Holter ECG signals using non-negative matrix factorization;an efficient algorithm based on wavelet transform to reduce powerline noise from electrocardiograms;fibroblasts induce calcium alternans when coupled to cardiomyocytes: a simulation study;a method to suppress chest compression artifact enhancing capnography-based ventilation guidance during cardiopulmonary resuscitation;and impact of signal preprocessing on the inverse localization of the origin of ventricular pacing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Early Detection and Diagnosis of Cardiac Disorders using Machine Learning Techniques 6

Early Detection and Diagnosis of Cardiac Disorders using Mac...

引用

6th International conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Sudhakar, G. Perumalla, Subhadra Saturi, Rajesh Vulapula, Sridhar Reddy Raju, Yeligeti Saturi, Swapna CMR College of Engineering and Technology Kandlakoya Medchal Road HyderabadTS- 501 401 India Department of CSE Vignana Bharathi Institute of Technology Aushapur Ghatkesar near HPCL Telangana Hyderabad501301 India Department of IT Vignana Bharathi Institute of Technology Aushapur Ghatkesar near HPCL Telangana Hyderabad501301 India Vignana Bharathi Institute of Technology Aushapur Ghatkesar near HPCL Telangana Hyderabad501301 India Department of CSE Kakatiya Institute of Technology and Science Telangana Warangal India

ISBN: (纸本)9798331522667

Heart disease (HD) is a wide spread health concern that impacts a significant number of people worldwide. Common symptoms of heart disease (HD) include swollen feet, weakness in the body, and shortness of breath. The recent advancements in ML has made significant role in transforming different diagnostic approaches Due to a variety of factors, current diagnostic methods are not very effective in early detection. To offer a practical approach for identifying cardiac disease, scientists are trying to increase precision and execution time. Heart disease diagnosis and treatment can be challenging in areas with limited access to modern medical professionals and equipment. With the right diagnosis and care, many lives can be saved. According to estimations from the European Society of cardiology, HD affects 26 million individuals. Nowadays, a higher proportion of individuals globally have heart failure, a complex clinical *** is the primary tool used by hospitals and cardiac centres to evaluate and diagnose heart failure in its early stages. One may think of the ECG as a standard tool. Early identification of heart disease is a major priority for medical services. This paper mainly highlights improving cardiac care through early diagnostic contributing to better patient outcomes and reduced health risks. © 2025 IEEE.

关键词： cardiology

来源：评论

学校读者我要写书评

暂无评论

A Breathable 1.1 oz Ripstop Nylon (BRN) Based Wearable Dry ECG Sensors A Pilot Study 5th

A Breathable 1.1 oz Ripstop Nylon (BRN) Based Wearable Dry E...

引用

5th EAI International conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

作者： Sriraam, Natarajan Akash, S. Vishal Heggade, Nitu R. Arun, Uma Prakash, Varsha Prakash, V.S. Center for Medical Electronics and Computing Department of Medical Electronics Engineering Ramaiah Institute of Technology Bangalore Bangalore India Department of Medical Electronics Engineering Ramaiah Institute of Technology Bangalore Bangalore India Department of Cardiology Ramaiah Medical College and Hospital Bangalore India

ISBN: (纸本)9783031770777

Selection of conductive polymer towards the design of dry bio potential sensor for physiological signal recordings is quite challenging. Such dry sensors are much essential for long term continuous monitoring applications. This specific study suggests a development of breathable 1.1 oz Ripstop nylon (BRN) based dry electrocardiogram (ECG) sensors for continuous recording cardiac rhythms. Bipolar limb lead configurations, Lead 1, Lead 2 and Lead 3 configurations were considered towards recording of ECG signals and its performance was assessed in terms of signal quality, ECG fidelity point tests, fidelity characteristics Ripstop based dry sensors found to yield comparable results with that of standard Ag/AgCl wet sensors for shorter duration recordings assessment and better per-formance while using it for longer duration (72 hrs) recordings setup. This pilot study has been carried out with 82 healthy volunteers and the proposed system has to be validated with larger group before the procedure can be introduced to the clinical routine. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Rayon

来源：评论

学校读者我要写书评

暂无评论

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study 59

Synthetic Time Series Data Generation for Healthcare Applica...

引用

59th Annual conference on Information Sciences and Systems, CISS 2025

作者： Jamshidi, Ainaz Arif, Muhammad Kalhoro, Sabir Ali Gelbukh, Alexander University of Maryland Baltimore County Dept. of Information Systems United States Colorado State University Dept. of Information Systems United States North Dakota State University Dept. of Electrical Engineering United States Instituto Politécnico Nacional Dept. of Computing Research Center Mexico

ISBN: (纸本)9798331513269

The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy. Specifically, synthesizing realistic phonocardiogram (PCG) signals offers significant potential as a cost-effective and efficient tool for cardiac disease pre-screening. Despite its potential, the synthesis of PCG signals for this specific application received limited attention in research. In this study, we employ and compare three state-of-the-art generative models from different categories - WaveNet, DoppelGANger, and DiffWave - to generate high-quality PCG data. We use data from the George B. Moody PhysioNet Challenge 2022. Our methods are evaluated using various metrics widely used in the previous literature in the domain of time series data generation, such as mean absolute error and maximum mean discrepancy. Our results demonstrate that the generated PCG data closely resembles the original datasets, indicating the effectiveness of our generative models in producing realistic synthetic PCG data. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs, in order to address the current scarcity of abnormal data. We hope to improve the robustness and accuracy of diagnostic tools in cardiology, enhancing their effectiveness in detecting heart murmurs. © 2025 IEEE.

关键词： cardiology

来源：评论

学校读者我要写书评

暂无评论

Adaptive Sliding Window-Based Identification of Significant Non-invasive Clinical Features for Prompt Cardiovascular Disease Diagnosis: An Indian Dataset Case Study 3rd

Adaptive Sliding Window-Based Identification of Significant ...

引用

3rd International conference on Intelligent Systems and Sustainable computing, ICISSC 2023

作者： Maini, Ekta Maini, Baljeet Marwaha, Dheeraj Department of Computer Science and Engineering Malla Reddy University Telangana Hyderabad500100 India Department of Pediatrics MMIMSR Mullana Haryana Ambala133203 India Amazon Development Centre Telangana Hyderabad5000032 India

ISBN: (纸本)9789819783540

The majority of deaths worldwide are caused by cardiovascular illnesses or CVDs. In developing nations like India, the largest obstacles to early identification of CVDs are a lack of doctors, an absence of authorized hospitals and diagnostic laboratories in rural regions, and high medical costs. The absence of infrastructure and the high cost of angiography tests make them impractical for diagnosing CVDs in rural locations. These factors cause CVDs to go undiagnosed, which ultimately raises death rates. Developing an affordable and user-friendly screening tool is essential for the early detection of cardiovascular diseases. In a tertiary hospital in India, this study used the adaptive sliding window method to identify the most important noninvasive clinical attributes for developing the CVD prediction system. To create prediction models, different combinations of clinical characteristics and four classification methods—support vector machines, random forests, k-nearest neighbor, and logistic regression—were used. After assessing the prediction models’ sensitivity, specificity, and accuracy, twelve important clinical features were found. A machine learning (ML) prediction system that was created with these important clinical characteristics achieved 93.8% accuracy, 92.8% sensitivity, and 94.6% specificity. This system was set up in the cloud to provide easy internet connectivity. Due to its affordability, effectiveness, and ease of use, the machine learning model developed in this work is effective in the early identification of cardiovascular diseases (CVDs) in rural areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： cardiology

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Performance Evaluation and Effectiveness of Data-Driven Heart Disease Diagnosis

A Novel Approach for Performance Evaluation and Effectivenes...

引用

International conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Islam, Md Aminul Nag, Anindya Das, Ayontika Faruque, Jobaer Shuvo, Shabbir Ahmed Nur, Abdullah Hafez Rahman, Md Habibur School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom Department of Computer Science and Engineering Adamas University Kolkata India IRD Dhaka Bangladesh Offenburg University of Applied Sciences Offenburg Germany International Islamic University Chittagong Sonaichhari Bangladesh Department of Mathematics University of Chittagong Chittagong Bangladesh

ISBN: (纸本)9789819795062

Cardiovascular disease (CVD) has emerged as a prominent cause of mortality globally during the past few decades. To receive medical care for heart illness in a clinic or medical center, it is necessary to undergo costly diagnostic tests. Implementing machine learning (ML) algorithms presents a viable approach to monitoring the health of individuals with cardiac conditions, offering a potential solution to mitigate the financial burden of medical expenses. This study employed ten ML algorithms to predict heart disease, which distinguishes itself from others by incorporating three distinct cross-validation approaches and three sample techniques, enhancing our findings’ accuracy. Three different cross-validation strategies have been implemented in this study: holdout cross-validation, tenfold cross-validation, and stratified tenfold cross-validation. Each technique has been applied to every sample technique used in the study. DTC, RFC, KNN, and XGB algorithms exhibited the most favorable outcomes. Our recommended model is the RF Classifier implemented with near-miss under-sampling out of the four models considered. The model under consideration demonstrates the maximum level of accuracy, specifically 97.54%, 97.11%, and 99%, while employing the Extra Tree, Random Forest Classifier, and XGB. The ML model's performance was assessed using metrics like accuracy, error rate, precision, recall, F1-score, ROC AUC curve etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： cardiology

来源：评论

学校读者我要写书评

暂无评论

Predictive Cardiovascular Health Assessment with Flask and Machine Learning Models

Predictive Cardiovascular Health Assessment with Flask and M...

引用

2025 International conference on Intelligent Control, computing and Communications, IC3 2025

作者： Mahesh, Kurma Aishwarya, Paladi Nithin, Sabavath Hariharan, Shanmugasundaram Vaishnavi, Bandar Vasantha, S.V. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331527495

Cardiovascular Health Forecaster Employing Flask is a cutting-edge online application that combines Python for its backend and Flask for its frontend. It anticipates heart disease risk with a smooth integration of Flask for data input and Python for machine learning, making it perfect for both users and healthcare professionals. As heart attacks take one life every minute, prediction automation becomes essential. Information science handles large amounts of data in healthcare, tackling the difficulty of predicting coronary heart disease. making use of a dataset on cardiac illness from the UCI AI repository. The AI algorithms-Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests-are used in this study to predict and classify the risk of cardiovascular illness. The study shows Light GBM's remarkable 90% accuracy, making it a relative report on algorithm performance. © 2025 IEEE.

关键词： cardiology

来源：评论

学校读者我要写书评

暂无评论

Early Detection and Diagnosis of Cardiac Disorders using Machine Learning Techniques

Early Detection and Diagnosis of Cardiac Disorders using Mac...

引用

Mobile computing and Sustainable Informatics (ICMCSI), International conference on

作者： G Sudhakar Subhadra Perumalla Rajesh Saturi Sridhar Reddy Vulapula Yellgeti Raju Swapna Saturi Computer Science and Engineering CMR College of Engineering and Technology Hyderabad India Department of CSE Vignana Bharathi Institute of Technology Hyderabad Telangana India Department of IT Vignana Bharathi Institute of Technology Hyderabad Telangana India Department of CSE Kakatiya Institute of Technology and Science Warangal Telangana

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

关键词： Heart Hospitals Europe Machine learning Electrocardiography Medical diagnosis Informatics Standards Mobile computing Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

A Simple and Effective Regional Contrastive Learning Method for 3D Medical Images 25

A Simple and Effective Regional Contrastive Learning Method ...

引用

Proceedings of the 2025 5th International conference on Bioinformatics and Intelligent computing

作者： Jin Liu Jiaqi Liu Huifang Tang School of Computer University of South China Hengyang Hunan China The First Affiliated Hospital Department of Cardiology Hengyang Medical School University of South China Hengyang Hunan China The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases Hengyang Medical School University of South China Hengyang Hunan China

ISBN: (纸本)9798400712203

3D medical images can visualize the internal structure of human organs, which have significant advantages over 2D medical images. However, the annotations of 3D medical images are more difficult to obtain compared to 2D medical images, which makes it challenging to capture complete spatial information in 3D medical images. To address this challenge, we propose a simple but effective self-supervised learning method that utilizes the similarity between medical images for region comparison and learns the organ correspondence information from different images but between the same regions. We pre-trained our method on the UK Biobank's cardiac short-axis nuclear magnetic resonance dataset. Our method performs well on three downstream task-related datasets.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

A Data Augmentation Method for Disease Diagnosis Based on Single Cell Omics Data 2nd

A Data Augmentation Method for Disease Diagnosis Based on Si...

引用

2nd International conference on Applied Intelligence, ICAI 2024

作者： Zhuang, Han Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology Shanghai East Hospital School of Medicine Tongji University Shanghai200123 China

ISBN: (纸本)9789819619061

Single cell omics data has enabled bioinformatics methods to identify cell type, infer gene regulatory network and beyond. Besides these great advances, there is active research about how to predict patients’ diagnosis based on cell predicted disease status. In this study, we propose a computational method consisting of a resampling method and an AI model to perform disease diagnosis with single cell omics data. According to our analysis, our proposed method outperforms baseline methods on three public datasets and is effective for disease diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： disease diagnosis resampling single cell omics

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104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：