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

7,176 篇 会议
48 篇 期刊文献
39 册 图书

馆藏范围

7,263 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,995 篇 工学
- 4,142 篇 计算机科学与技术...
- 2,540 篇 软件工程
- 1,097 篇 信息与通信工程
- 817 篇 控制科学与工程
- 686 篇 生物工程
- 656 篇 电气工程
- 450 篇 生物医学工程（可授...
- 309 篇 交通运输工程
- 290 篇 机械工程
- 252 篇 安全科学与工程
- 250 篇 仪器科学与技术
- 207 篇 建筑学
- 206 篇 动力工程及工程热...
- 187 篇 光学工程
- 166 篇 化学工程与技术
- 155 篇 土木工程
1,938 篇 理学
- 1,078 篇 数学
- 719 篇 生物学
- 446 篇 统计学（可授理学、...
- 309 篇 物理学
- 253 篇 系统科学
- 163 篇 化学
1,211 篇 管理学
- 687 篇 管理科学与工程(可...
- 604 篇 图书情报与档案管...
- 319 篇 工商管理
437 篇 医学
- 379 篇 临床医学
- 300 篇 基础医学(可授医学...
- 188 篇 药学(可授医学、理...
245 篇 教育学
- 244 篇 教育学
215 篇 法学
- 199 篇 社会学
123 篇 农学
113 篇 经济学
50 篇 文学
7 篇 艺术学
5 篇 军事学

主题

571 篇 machine learning
470 篇 deep learning
268 篇 training
253 篇 data mining
239 篇 data models
219 篇 feature extracti...
216 篇 accuracy
186 篇 support vector m...
178 篇 predictive model...
140 篇 neural networks
135 篇 machine learning...
133 篇 computational mo...
132 篇 reinforcement le...
111 篇 convolutional ne...
105 篇 real-time system...
93 篇 artificial intel...
92 篇 forecasting
89 篇 learning systems
88 篇 transfer learnin...
86 篇 big data

机构

69 篇 chitkara univers...
17 篇 school of electr...
15 篇 computer science...
14 篇 harbin engineeri...
13 篇 graphic era hill...
12 篇 building civil a...
12 篇 uttaranchal inst...
12 篇 computer science...
11 篇 university of ch...
10 篇 school of electr...
10 篇 school of comput...
10 篇 graphic era deem...
10 篇 school of electr...
10 篇 yonsei univ dept...
9 篇 wuhan university...
9 篇 univ chinese aca...
9 篇 school of inform...
8 篇 school of remote...
7 篇 university of sa...
7 篇 microsoft resear...

作者

17 篇 kukreja vinay
15 篇 vinay kukreja
13 篇 yin hujun
12 篇 liu yang
12 篇 corchado emilio
11 篇 mehta shiva
10 篇 shiva mehta
10 篇 hujun yin
10 篇 cho sung-bae
9 篇 notsu akira
9 篇 silva catarina
9 篇 alhajj reda
7 篇 ksieniewicz pawe...
7 篇 hammer barbara
7 篇 zhang byoung-tak
7 篇 wozniak michal
7 篇 hussain abir
7 篇 li jun
7 篇 soubra hassan
7 篇 huang de-shuang

语言

5,866 篇 英文
1,386 篇 其他
52 篇 中文
1 篇 西班牙文
1 篇 俄文

检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7263 条记录，以下是1251-1260 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：

A Multi-input Deep learning Model to Classify COVID-19 Pneumonia Severity from Imaging and Clinical data 11th

A Multi-input Deep Learning Model to Classify COVID-19 Pneum...

引用

11th international Work-conference on Bioinformatics and Biomedical engineering (IWBBIO)

作者： Lizzi, Francesca Brero, Francesca Fantacci, Maria Evelina Lascialfari, Alessandro Paterno, Gianfranco Postuma, Ian Oliva, Piernicola Scapicchio, Camilla Retico, Alessandra Natl Inst Nucl Phys Pisa Italy Univ Pavia Dept Phys Pavia Italy Univ Pisa Dept Phys Pisa Italy Natl Inst Nucl Phys Pavia Italy Natl Inst Nucl Phys Ferrara Italy Univ Sassari Dept Chem Phys Math & Nat Sci Sassari Italy Natl Inst Nucl Phys Cagliari Italy

ISBN: (纸本)9783031646355;9783031646362

the study of multi-input models that can process heterogeneous data is a challenge at the frontier of machine learning. We implemented a multi-modal approach aiming at exploiting both imaging and clinical information of patients to predict the severity of their outcome. As a specific use case, we developed a fully automated algorithm to predict the outcome of COVID-19 patients based on chest X-Ray images and clinical data, provided by the AI4COVID Challenge (1589 subjects). the system can distinguish between severe cases, those who needed intensive care or died, and mild ones. the system is composed of three Convolutional Neural Networks (CNN) for pre-processing, lung segmentation (U-Net architecture), and outcome classification. the first CNN is devoted to recognize the gray-level encoding needed to standardize the images. the U-Net for lung segmentation has been trained using two datasets collected for Tuberculosis screening. We achieved a Dice Similarity Coefficient (DSC) equal to 0.96 +/- 0.03. this was needed to focus the final classifier on evaluating features within the lung. Without the careful selection of the lung, in fact, the prediction strongly depended on features outside the lung district (e.g. ECG cables, respiratory masks). the outcome classifier is a multi-input CNN made of two branches joined at the bottom. the first branch is a ResNet that takes the segmented images as input, while the second branch is a Multi-Layer Perceptron (MLP) that takes in the preprocessed clinical parameters. We obtained an AUC equal to 0.84 and an accuracy equal to 76%. We also computed the saliency maps with the gradCAM and the feature importance to obtain a reasonable explanation of the classifier. this method based on data aggregation and on merging clinical and imaging information can be applied also to domains different from COVID-19 patients.

关键词： Multi-input models AI Chest X-Ray COVID-19

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Machine learning Approach in Detecting Coronary Heart Disease 11th

A Comprehensive Machine Learning Approach in Detecting Coron...

引用

11th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2023

作者： Prasanna, ElaproluSai Anuradha, T. Swetha, Vara Pragathi, Jangam Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India

ISBN: (纸本)9789819967018

Heart disease is on the rise, among addition to affecting the elderly heart disease is becoming more prevalent in young people. Not only India, but the entire world is impacted by this disease. A recent study found that at least 500,000 open heart surgeries are carried out annually and that the main causes of heart disease include high blood pressure, smoking, inactivity, etc. Diagnosing the heart disease early on leads to potentially avoid a serious issue. Many researchers are working on machine learning techniques for early diagnosis of disease. Non-invasive diagnose methods are used for diagnosing heart disease without opening the skin. this paper proposes a machine learning methods to accurately predict whether a person having heart disease or not. Supervised machine learning techniques were used for prediction and Django Framework for designing web page. An easy to use user interface was designed by using Django Framework as front end, where we can check the status of having coronary heart disease. At the back end, machine learning algorithms will predict based on the data entered by the user. Our approach attained a maximum of 98% accuracy. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Promoting Security and Trust on Social Networks: Explainable Cyberbullying Detection Using Large Language Models in a Stream-Based Machine learning Framework 11

Promoting Security and Trust on Social Networks: Explainable...

引用

11th IEEE international conference on Social Networks Analysis, Management and Security, SNAMS 2024

作者： Garcia-Mendez, Silvia De Arriba-Perez, Francisco Information Technologies Group-atlanTTic University of Vigo Vigo Spain

ISBN: (纸本)9798331518349

Social media platforms enable instant and ubiquitous connectivity and are essential to social interaction and communication in our technological society. Apart from its advantages, these platforms have given rise to negative behaviors in the online community, the so-called cyberbullying. Despite the many works involving generative Artificial Intelligence (AI) in the literature lately, there remain opportunities to study its performance apart from zero/few-shot learning strategies. Accordingly, we propose an innovative and real-Time solution for cyberbullying detection that leverages stream-based Machine learning (ML) models able to process the incoming samples incrementally and Large Language Models (LLMS) for feature engineering to address the evolving nature of abusive and hate speech online. An explainability dashboard is provided to promote the system's trustworthiness, reliability, and accountability. Results on experimental data report promising performance close to 90 % in all evaluation metrics and surpassing those obtained by competing works in the literature. Ultimately, our proposal contributes to the safety of online communities by timely detecting abusive behavior to prevent long-lasting harassment and reduce the negative consequences in society. © 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

Design and Implementation of an intelligent Monitoring System for Internet of things Aquaculture Based on ZigBee Technology 11

Design and Implementation of an Intelligent Monitoring Syste...

引用

11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Fu, Yuanfang Liu, Yingying Wang, Danping Liu, Kaiwen Li, Xinyu Xu, Jiaxin Wu, Zilong School of Information Engineering Shenyang University Shenyang China School of Mechanical Engineering Shenyang University Shenyang China

ISBN: (纸本)9798350333664

According to the impact of marine pollution on mariculture, an intelligent monitoring system for mariculture water quality based on ZigBee is designed in this study. the system includes a data acquisition part, a data transmission part and a user-side early warning part. It is composed of data terminals and multiple data acquisition points, which detect the temperature, PH value, oxygen solubility and other related parameters of water quality. the data of each collection point is transmitted to the ZigBee coordinator node through the ZigBee network. the coordinator node monitors the collected data value and makes a risk warning according to the early warning value. GPRS is used to upload the data to the server in real time, so that the staff can find and handle the abnormalities in time, and an intelligent monitoring system based on the Internet of things is utilized. © 2023 IEEE.

关键词： intelligent monitoring system Internet of things mariculture ZigBee

来源：评论

学校读者我要写书评

暂无评论

Deep learning for Predicting Invasive Ductal Carcinoma in Histopathological Tissue Images 11

Deep Learning for Predicting Invasive Ductal Carcinoma in Hi...

引用

11th international conference on Bio Signals, Images, and Instrumentation, ICBSII 2025

作者： Appavu, Narenthirakumar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai 602105 India

ISBN: (纸本)9798331513863

Nearly 80% of instances of breast cancer are invasive ductal carcinoma (IDC), the most prevalent and aggressive kind of the disease. Improving patient outcomes requires early detection and accurate diagnosis, but conventional diagnostic techniques rely on pathologists' manual tissue analysis, which is labour-intensive and subject to error. In order to improve diagnostic efficiency and accuracy, this study investigates the use of sophisticated convolutional neural networks (CNN to automate IDC identification and categorisation in histopathology pictures. In contrast to traditional methods, our method optimises extraction of features and classification by integrating a hybrid neural networks architecture that combines transfer learning and attention mechanisms. Our approach classifies tissues patches into three groups: healthy, IDC, as well as various types of breast cancer subtypes, using a dataset of samples of tissues from 162 IDC patients. To enhance generalisation over a range of histopathological abnormalities, the suggested method makes use of a refined CNN architecture, automatic data augmentation, and intensive preprocessing. Our approach performs better than conventional models, providing increased reliability in cancer detection, according to the performance assessment using F1-score with equal accuracy. this method makes use of cutting-edge deep learning developments to produce a more effective and precise diagnostic tool, which is especially advantageous in clinical settings with limited resources. © 2025 IEEE.

关键词： and Balanced Accuracy Diagnostic Workflow Optimisation IDC Localisation Pathology Automation PyTorch Framework Tissue Slice Classification

来源：评论

学校读者我要写书评

暂无评论

Deep Fake Detection for Preventing Audio and Video Frauds Using Advanced Deep learning Techniques 6

Deep Fake Detection for Preventing Audio and Video Frauds Us...

引用

6th IEEE international conference on Recent Advances in intelligent Computational Systems, RAICS 2024

作者： Vaidya, Anusha O. Dangore, Monika Borate, Vishal Kisan Raut, Nutan Mali, Yogesh Kisan Chaudhari, Ashvini Trinity Academy of Engineering E & T C Department Pune India G.H Raisoni Institute of Engineering and Management Aiml Department India Dr D Y Patil College of Engineering & Innovation Computer Engineering Department Pune India G.H Raisoni College of Engineering and Management Aiml Department India Nmims Center for Distance & Online Education Pune India

ISBN: (纸本)9798350381689

Deepfakes allow for the automated gen- eration of fake video content, often accomplished through the use of generative adversarial networks. To address the increasing issue of deepfakes, this study focuses on constructing a model that incorporates advanced techniques. the researchers combined the ResNeXt, Long Short-Term Memory (LSTM), and ResNet architectures, selecting them based on their effectiveness in handling complex visual data and capturing temporal dependencies. Prior to detection, the dataset underwent pre-processing using the Multi-Task Cascaded Convolutional Neural Network (MTCNN), which facilitated the accurate extraction of facial regions. Importantly, the study evaluated the model across three diverse and significant datasets: the Face Forensics++ dataset (FF-DF), the Celeb-DF dataset, and the Facebook Deepfake Detection Challenge (DFDC) dataset. this comprehensive evaluation en- sured the model's ability to generalize and its suitability for real-world scenarios, as demonstrated by its exceptional detection accuracy. the combination of models employed in this study yielded highly accurate results and remained robust in the face of evolving deepfake technology. © 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

A Deep learning-Based Approach for Generating 3D Models of Fluid Arts 11th

A Deep Learning-Based Approach for Generating 3D Models of F...

引用

11th international conference on Arts and Technologies Interactivity and Game Creation

作者： Cong, Hung Mai Trang, Mai Xuan Yamada, Akihiro Takashi, Suzuki Tosa, Naoko Nakatsu, Ryohei Osaka Univ Osaka Japan Phenikaa Univ Fac Comp Sci Hanoi Vietnam Toppan Inc Tokyo Japan Kyoto Univ Disaster Prevent Res Inst Kyoto Japan

ISBN: (纸本)9783031289927;9783031289934

this paper explores a method for creating 3D models of video artwork based on a fluid phenomenon called the Sound of Ikebana artwork. A process of using multiple generative adversarial networks (GANs) to reconstruct and predict the shape of the fluid artworks from two-dimensional reference photos was proposed. this is an extension of our previous efforts with Wasserstein GAN enhancements to predict the shape of the unmapped part and correct the texture. the experiment's results show that our process can reconstruct 3D arts without having large amount of 3D training data.

关键词： 3D fluid art Sound of Ikebana DIB-R network GAN variants

来源：评论

学校读者我要写书评

暂无评论

DELVING INTO SEMANTIC SCALE IMBALANCE

DELVING INTO SEMANTIC SCALE IMBALANCE

引用

11th international conference on learning Representations, ICLR 2023

作者： Ma, Yanbiao Jiao, Licheng Liu, Fang Li, Yuxin Yang, Shuyuan Liu, Xu Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education School of Artificial Intelligence Xidian University Xi'an710071 China

Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is used to measure the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed natural and medical datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias. Furthermore, we look ahead to future challenges in the field. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Online Teaching Strategies to Motivate Students' Active Participation Based on Personalized SPOC Teaching Mode 11

Online Teaching Strategies to Motivate Students' Active Part...

引用

11th international conference on Information and Education Technology, ICIET 2023

作者： Dou, W.N. Beijing University of Civil Engineering and Architecture School of Human Department of Foreign Languages Beijing China

ISBN: (纸本)9781665465489

Compared with the traditional teaching mode, online distance teaching mode breaks the barriers of time and space so that more people have access to education, which makes Massive Open Online Courses (MOOCs) popular all over the world, especially in China. However, MOOCs bring more challenges to teaching in the meantime. Students' participation in online learning becomes one of the most prominent problems, which is hard to deal with compared with traditional face-To-face teaching mode. So with the integration of the advantages of MOOCs and traditional teaching mode, Small Private Online Course(SPOC) teaching mode is more and more favored by educators. Guided by the 5C Framework theory, this paper aims to advance effective online-Teaching strategies in order to motivate students' active participation in online learning process under SPOC mode. Based on the analyses of qualitative and quantitative data, the results show that teachers can improve students' participation in online courses by 'the Optimized Combination' from three dimensions: interactive teaching platforms, teaching content carriers and teaching task difficulty, which provide a new perspective for improving the effectiveness of online teaching. © 2023 IEEE.

关键词： Teaching

来源：评论

学校读者我要写书评

暂无评论

Classification and Grading for Power Sensitive data Based on Hybrid data Distribution learning 11

Classification and Grading for Power Sensitive Data Based on...

引用

11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Huang, Xiuli Zhai, Yujia Shi, Congcong Jiang, Yiwen Zhang, Shijun State Grid Smart Grid Research Institute Co. LTD. State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China Big Data Center of State Grid Corporation of China Beijing China State Grid Jibei Electric Power Co. LTD. Beijing China

ISBN: (纸本)9798350333664

Power industry data contain various sensitive information, and the leakage of such data will cause critical damage to both the company and the country. However, the unbalanced and various data types make the traditional data classification and grading method fail to achieve enough performance. To better classify and grade the power sensitive data, we propose a Hybrid data Distribution learning Network, which simultaneously learns the distribution of multiple data types, including Character-based, Text-based, and Numerical data. Furthermore, we use an optimized nonlinear Support Vector Machine model to detect the grade of power data based on the learned data distribution. the experimental results show that our method outperforms other baselines and achieves a precision of 0.994, a recall of 0.985, and an F1 of 0.989. the visualization results also show that our method effectively learns the distribution of multiple types of power data. © 2023 IEEE.

关键词： classification and grading hybrid data distribution learning network nonlinear SVM model sensitive power data

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 << < 122 123 124 125 126 127 128 129 130 131 > >>

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：