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

1,856 篇 会议
6 册 图书
1 篇 期刊文献

馆藏范围

1,863 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,364 篇 工学
- 1,248 篇 计算机科学与技术...
- 997 篇 软件工程
- 318 篇 生物工程
- 309 篇 信息与通信工程
- 189 篇 生物医学工程（可授...
- 160 篇 控制科学与工程
- 124 篇 电气工程
- 118 篇 光学工程
- 83 篇 机械工程
- 74 篇 化学工程与技术
- 64 篇 安全科学与工程
- 62 篇 网络空间安全
- 53 篇 电子科学与技术（可...
- 48 篇 仪器科学与技术
747 篇 理学
- 390 篇 数学
- 326 篇 生物学
- 166 篇 统计学（可授理学、...
- 150 篇 物理学
- 134 篇 系统科学
- 83 篇 化学
440 篇 管理学
- 250 篇 管理科学与工程(可...
- 224 篇 图书情报与档案管...
- 128 篇 工商管理
174 篇 医学
- 165 篇 临床医学
- 158 篇 基础医学(可授医学...
- 117 篇 药学(可授医学、理...
- 62 篇 公共卫生与预防医...
78 篇 教育学
- 78 篇 教育学
75 篇 法学
- 68 篇 社会学
50 篇 农学
- 49 篇 作物学
45 篇 经济学
4 篇 军事学

主题

326 篇 machine learning
128 篇 deep learning
77 篇 support vector m...
66 篇 machine learning...
55 篇 forecasting
46 篇 feature extracti...
37 篇 decision trees
36 篇 accuracy
35 篇 computational mo...
34 篇 computer vision
31 篇 predictive model...
30 篇 training
28 篇 data mining
28 篇 students
28 篇 convolutional ne...
26 篇 neural networks
22 篇 object detection
22 篇 artificial intel...
21 篇 sentiment analys...
20 篇 internet of thin...

机构

12 篇 chitkara univers...
7 篇 shenyang ligong ...
6 篇 uttaranchal inst...
5 篇 department of co...
5 篇 maharishi school...
5 篇 alibaba cloud bi...
4 篇 school of comput...
4 篇 college of compu...
4 篇 key laboratory o...
4 篇 lovely professio...
4 篇 jilin agricultur...
4 篇 uttaranchal univ...
4 篇 key laboratory o...
4 篇 department of co...
4 篇 master of comput...
4 篇 school of inform...
3 篇 school of comput...
3 篇 vivekananda glob...
3 篇 department of el...
3 篇 college of compu...

作者

4 篇 liu gang
4 篇 kumar rakesh
4 篇 waseemullah
4 篇 liu peng
3 篇 zhang tao
3 篇 kumar ajay
3 篇 rakesh kumar
3 篇 babu tina
3 篇 siddhant
3 篇 sharma arun
3 篇 faisal rehman
3 篇 wang xiaofei
3 篇 sharif hanan
3 篇 singh sunil kuma...
3 篇 rehman faisal
3 篇 tina babu
3 篇 yang wei
3 篇 sharma avinash
3 篇 prajwal
3 篇 vinay kukreja

语言

1,803 篇 英文
58 篇 其他
16 篇 中文

检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是361-370 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：

Extending Minimum Prediction Deviation as a Defence Against Adversarial Attacks 1

引用

3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Jois, Sruthi Gressel, Gilad Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri Vallikavu India

ISBN: (数字)9789819748600

ISBN: (纸本)9789819748594

machine learning can detect many types of cybersecurity attacks, adding a layer of security to various systems. However, machine learning is easily compromised by adversarial attacks, such as evasion attacks, model stealing, and data poisoning. The existence of adversarial machine learning raises the requirement for a robust defence. While many defences have been proposed, most have been broken shortly thereafter. Further, most defences are domain-specific and designed for a specific type of dataset or task. There is a need to find a suitable defence that prevents machine learning models from being exploited (Carlin et al. in On evaluating adversarial robustness, [1]). We build on previous work utilizing the Minimum Prediction Deviation (MPD), an uncertainty metric, to detect adversarial examples. Previously, MPD was shown to be very effective on a tabular phishing dataset (Santhosh et al. in AI 2022: advances in artificial intelligence: 35th Australasian joint conference, AI 2022, Springer, pp. 719–732, [2]). However, it was unknown if it would extend to other domains such as vision, malware, IDS, and non-binary classification tasks. In this work, we demonstrate that MPD can be used on multiple domains (IDS, malware, XSS, and computer vision), in a multi-class classification setting and with gradient and gradient-free attacks to detect adversarial examples. We demonstrate that MPD effectively detects 93% adversarial samples across all datasets (heterogeneous and homogeneous), with an average false positive of 2%. This approach showcases an effective defence mechanism to mitigate the issue of compromised machine learning, thus enhancing the security and reliability of various systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Research of Early Warning of Corporate Financial Crisis Based on machine learning Neural Network 3

Research of Early Warning of Corporate Financial Crisis Base...

引用

2022 3rd international conference on computer Information and Big Data applications, CIBDA 2022

作者： Zhao, Yanli Yang, Guang School of Business Administration Wuhan Business University Wuhan China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

ISBN: (纸本)9783800758760

In order to predict the financial crisis more accurately, based on kernel principal component analysis (KPCA) and Multilayer Perceptron (MLP), a financial crisis early warning model is proposed. Firstly, to eliminate random noise in massive financial data, the KPCA is used to extract the key information of financial data. Secondly, the MLP is adopted to predict the financial crisis. Finally, compared with other traditional machine learning financial early warning methods, our proposed method obtains the best prediction capability. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Prediction Framework Based on Iterative Optimization with Bayesian Hyperparameter Tuning 3

Breast Cancer Prediction Framework Based on Iterative Optimi...

引用

3rd international conference on Emerging Smart Technologies and applications, eSmarTA 2023

作者： Alsabry, Ayman Algabri, Malek Ahsan, Amin Mohamed Mosleh, Mogeeb A.A. Ahmed, Aqeel Abdullah Ali Qasem, Hamzah Sana'a University International University of Technology Twintech Department of Computer Science Sana'a Yemen Sana'a University Department of Computer Science Sana'a Yemen International University of Technology Twintech Department of Computer Science Sana'a Yemen International University of Technology Twintech Taiz University Sana'a Taiz Yemen

ISBN: (纸本)9798350305333

Breast cancer (BC) is a major health concern affecting women worldwide, and early detection is crucial for effective treatment and improved survival rates. In this study, we propose a novel BC prediction framework based on iterative optimization with Bayesian hyperparameter tuning applied to the Wisconsin Diagnostic Breast Cancer (WDBC) and Surveillance, Epidemiology, and End Results (SEER) datasets. Our approach employs various machine learning (ML) algorithms, including tree-based, support vector machine (SVM)-based, K-nearest neighbor (KNN)-based, tree-based ensemble, and artificial neural network (ANN)-based ML models. The results demonstrated that the optimized models generally outperformed their non-optimized counterparts. Notably, the optimized AdaBoost model achieved a remarkable performance with 100% accuracy, precision, recall, and F1-score on the WDBC dataset. The optimized GentleBoost model exhibited a high performance of 95.3% accuracy, 97.4% precision, 93.1% recall, 95.2% F1-score, and 0.99 area under the curve (AUC) on the SEER dataset. These findings highlight the potential of our proposed framework for enhancing BC prediction accuracy and robustness, paving the way for future research and clinical application. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of machine learning Algorithms for Photovoltaic Degradation Rate Prediction 3

A Comparative Study of Machine Learning Algorithms for Photo...

引用

3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Dhingra, Bhavya Tyagi, Shivam Tomar, Anuradha Netaji Subhas University of Technology Department of Electrical Engineering Delhi78 India Netaji Subhas University of Technology Department of Computer Science Delhi78 India Netaji Subhas University of Technology Department of Inst. and Control Engg. Delhi78 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Solar energy is the most versatile, harmless, and non-exhaustive energy present in nature because of this, the number of photovoltaic modules that have been integrated into the electrical grid is increasing every day. As a result, reliable forecasting of falling power output over the period of time is required for an acceptable return on investment made for these interactions, to estimate the power delivered to the power system by these photovoltaic modules, photovoltaic degradation rates must be known. In this study degradation rates of photovoltaic modules are estimated using the application of nine machine learning models and the effectiveness of these models is compared in order to determine which model is most efficient. All the models are tested on various evaluation metrics like mean absolute error, root mean squared error, and mean percentage error for an unbiased evaluation, and the run time of these models is also calculated and compared to determine the overall efficiency of the models. © 2022 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

AgriCare: An Android application for Detection of Paddy Diseases 3

AgriCare: An Android Application for Detection of Paddy Dise...

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Talreja, Rounak Jawrani, Varsha Watwani, Bhajan Sengupta, Sharmila Rohera, Prium Raghuwanshi, K.S. Vesit Dept. of Computer Engineering Mumbai India Agricultural Research Station Lonavala India

ISBN: (纸本)9781665494991

Agriculture is a significant part of India's economy being one of the main sources of living. Paddy plant is one of the most important food crops of India covering about one-fourth of the total cropped area. Although having the potential in the market, India's lower paddy crop productivity/hector and higher cost of production is a major concern for farmers. The low yield can be addressed by designing a system that helps farmers identify and predict diseases in the paddy crops. There are so many types of paddy diseases. It is very difficult to manually identify appropriate properties for distinguishing different types of crop diseases. So an android app will help farmers to identify and detect a paddy plant disease using images of the disease-infected paddy crop based on machine learning image processing techniques. © 2022 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Performance of Heuristics for Classifying Leftovers from Cutting Stock Problem 3rd

Performance of Heuristics for Classifying Leftovers from Cut...

引用

3rd international conference on Optimization, learning Algorithms and applications (OL2A)

作者： Bressan, Glaucia Maria da Silva, Esdras Battosti Pimenta-Zanon, Matheus Henrique da Silva Lizzi, Elisangela Aparecida Sakuray, Fabio Univ Tecnol Fed Parana UTFPR Dept Math Alberto Carazzai 1640 BR-86300000 Cornelio Procopio PR Brazil Univ Tecnol Fed Parana UTFPR Dept Elect Engn Alberto Carazzai 1640 BR-86300000 Cornelio Procopio PR Brazil Univ Tecnol Fed Parana UTFPR Dept Comp Sci Alberto Carazzai 1640 BR-86300000 Cornelio Procopio PR Brazil State Univ Londrina UEL Dept Comp Sci Rodovia Celso Garcia CidPr 445 Km 380CP 10011 BR-86057970 Londrina PR Brazil

ISBN: (纸本)9783031530357;9783031530364

The one-dimensional cutting stock problem is defined in the literature as a branch of the classic cutting stock optimization problem, involving one dimension in the cutting process, like cutting bars. The bar cutting optimization problem can generate leftovers - reusable- or losses - disposable. The objective of this paper is to compare the performance of OptimizationDistBSP and OptimizationTREE heuristics (proposed in [2]) for classifying leftovers or losses, from the cutting stock problem (specifically from cutting one-dimensional bars), using the dataset proposed in [3], since this dataset allows the application of machine learning methods, which are: Logistic Regression, Naive Bayes, Decision Tree and Random Forest, to classify the output data as leftover or loss. Results show that the OptimizationDistBSP and OptimizationTREE heuristics provide better performance in the classification task than the Greedy heuristic used in [2]. Thus, we can conclude that the heuristics can be applied in a more realistic problem, using bars of different sizes, and the dataset can be validated, providing good results for the classification using heuristics other than Greedy.

关键词： One-dimensional cutting stock problem Leftover classification machine learning Comparison of heuristics

来源：评论

学校读者我要写书评

暂无评论

application of artificial intelligence in sleep medicine 3

Application of artificial intelligence in sleep medicine

引用

3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Chen, QianFeng Hu, Maorong Jiangxi University of Traditional Chinese Medicines Nanchang330004 China The First Affiliated Hospital of Nanchang University Nanchang330006 China

ISBN: (纸本)9781510663473

Purpose of review ：Sleep is the life instinct of human beings. It is not only of great significance to the physical and mental health of individuals, but also can be used as a natural means of regulating, restoring and enhancing bodily functions. There is a prominent contradiction between health needs and the backward status of daily sleep health management, and there is an urgent need to develop theories and methods from sleep structure conversion mechanism to sleep quality monitoring and intervention. In the past few years, artificial intelligence (AI) technology has rapidly emerged in the field of sleep medicine. Research methods：The purpose of this article is to provide a brief overview of relevant terms, definitions and use cases of artificial intelligence in sleep medicine. Research result: AI has a variety of applications in sleep medicine, including sleep and respiratory event scoring in sleep labs, diagnosis and management of sleep disorders, and population health. Although still in its infancy, there are still challenges that hinder the ubiquity and broad clinical application of AI. Overcoming these challenges will help seamlessly integrate AI into sleep medicine and enhance clinical practice. Research conclusion: AI is a powerful tool in healthcare that can improve patient care, enhance diagnostic capabilities, and enhance the management of sleep disorders. However, before existing machine learning algorithms can be incorporated into sleep clinics, these artificial intelligence devices need to be regulated and standardized. © 2023 SPIE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

2022 3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

2022 3rd International Conference on Computer Vision, Image ...

引用

3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

ISBN: (纸本)9781665459112

The proceedings contain 253 papers. The topics discussed include: testing the feasibility of quantizing the progress of stroke patients’ rehabilitation with a computer vision method;recognition of surface defects of aluminum profiles based on convolutional neural network;a novel generative adversarial network simulating the complementary structure of DNA genetic information;an improved lightweight method of convolutional neural network for face recognition;a calculation method for line-of-sight stable attitude of networked optical transceiver based on depth feedforward neural network;a grapevine virus disease detection method based on convolution neural network;a predict method of measuring equipment operation performance based on improved local weighted partial least squares;research on multi-scale network computer modeling based on mobile vendor management;and identification method of dress pattern drawing based on machine vision algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A New Data Dimension Reduction Method Based on Convolution in the application of Authenticity Identification of Traditional Chinese Medicine LongGu 3

A New Data Dimension Reduction Method Based on Convolution i...

引用

3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Wu, Qiang Zhu, Yuting Shi, Wei Wang, Tianyan Huang, Yawei Jiang, Dongjing Liu, Xun Suzhou Vocational Health College JIANGSU Suzhou215009 China Xuzhou Medical University JIANGSU Xuzhou221000 China Jiangsu Provincial Hospital of Traditional Chinese Medicine JIANGSU Nanjing210004 China

As a commonly used Chinese herbal medicine in clinic, LongGu is often used as a powder, which is difficult to identify manually. In this study, unsupervised learning method was used to model and identify LongGu, calcining LongGu and LongGu counterfeits by infrared spectroscopy. After preprocessing the original data, the current commonly used principal component analysis method, which was used to reduce the data dimension, and then the KMeans algorithm was used to realize the classification and identification of the samples. The classification results were better in the theoretical samples, but not in the actual samples. Based on this problem, this paper proposes a convolution-based machine learning feature dimension reduction method for spectral data. Comparing with the data method of principal component analysis, the partial classification effect is obviously better on the theoretical data. The effect of identifying calcining LongGu and LongGu counterfeits in actual data has been significantly improved, and it also provides a reference for the application of machine learning technology in the field of spectral identification of traditional Chinese medicine. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

An Active learning application on Loan Default Prediction: Based on Forest Classifier Model 3

An Active Learning Application on Loan Default Prediction: B...

引用

3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Tong, Ronghao Franklin and Marshall College LancasterPA17603 United States

ISBN: (纸本)9781510663473

Under the global pandemic, the application of loans in various forms increases under the influence of economic gloom. Small loan agencies in regions with undeveloped credit systems often neglect background checks because it is costly and time-consuming. Lack of loan approval standards increases the occurrence of defaults. Ideally, a checker program that can make accurate default predictions with a small amount of data contributes to this dilemma. This paper shows a random forest classifier model can achieve 93.2% accuracy incorporating an active learning strategy with only 50 labeled data. Compared to the standard classifier model, the active learning strategy improves the efficiency by 7% with 50 labeled data and increases as much as 14.5% when provided with only 20 labeled data. The source data used in this paper comes from a dataset provided by Univ. AI simulates real-life personal financial records in India. © 2023 SPIE.

关键词： learning systems

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187页 << < 33 34 35 36 37 38 39 40 41 42 > >>

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：