检索结果-内蒙古大学图书馆

4th International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2020

作者： Das, Swarupa Sarkar, Madhumita Nandi, Shovon B. P. Poddar Inst. of Management and Technology Department of Ece Kolkata India Bengal Institute of Technology Department of Ece Kolkata India

ISBN: (纸本)9781728192871

This paper primarily focuses on the generation of transmission code by Delta Modulation (DM) using Machine learning Algorithm. Here, models like Logistic Regression and Support Vector Classification have been applied separately on both Sinusoidal and Triangular waves. The outputs have been analysed and compared with that of the default theoretical output and also with the outputs generated by an already existing method. Additionally, we have examined the yield of Matlab and Machine learning both. Finally, the efficiency of the process is measured in case of both the waveforms. © 2020 IEEE.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

UO_4to @ TAG-it 2020: Ensemble of machine learning methods 7

UO_4to @ TAG-it 2020: Ensemble of machine learning methods

引用

7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2020

作者： Herold, Maria Fernanda Artigas Castro, Daniel Castro Computer Science Department Universidad de Oriente Santiago de Cuba Cuba

This paper describes the proposal presented in the TAG-it author profiling task from EVALITA 2020 for sub-task 1. The main objective is to predict gender and age of some blog users by their posts, as well as topic they wrote about. Our proposal uses an ensemble of machine learning algorithms with three of the most used classifiers and language model of the n-grams of characters represented in a Bag of Word. To face this task we presented two different strategies aimed at finding the best possible results. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

META-learning WITHOUT MEMORIZATION 8

META-LEARNING WITHOUT MEMORIZATION

引用

8th International Conference on learning Representations, ICLR 2020

作者： Yin, Mingzhang Tucker, George Zhou, Mingyuan Levine, Sergey Finn, Chelsea UT Austin United States Google Research Brain Team UC Berkeley United States Stanford United States

The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks. However, most meta-learning algorithms implicitly require that the meta-training tasks be mutually-exclusive, such that no single model can solve all of the tasks at once. For example, when creating tasks for few-shot image classification, prior work uses a per-task random assignment of image classes to N-way classification labels. If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes. This requirement means that the user must take great care in designing the tasks, for example by shuffling labels or removing task identifying information from the inputs. In some domains, this makes meta-learning entirely inapplicable. In this paper, we address this challenge by designing a meta-regularization objective using information theory that places precedence on data-driven adaptation. This causes the meta-learner to decide what must be learned from the task training data and what should be inferred from the task testing input. By doing so, our algorithm can successfully use data from non-mutually-exclusive tasks to efficiently adapt to novel tasks. We demonstrate its applicability to both contextual and gradient-based meta-learning algorithms, and apply it in practical settings where applying standard meta-learning has been difficult. Our approach substantially outperforms standard meta-learning algorithms in these settings. © 2020 8th International Conference on learning Representations, ICLR 2020. All rights reserved.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of fundus retinal image using EGODD algorithm

引用

EAI Endorsed Transactions on Pervasive Health and Technology 2022年第5期8卷

作者： Subha, K. Mohideen, S. Kother Sri Ram Nallamani Yadava College of Arts and Science Tamil Nadu Tirunelveli India

The developed device is tested for optical disc identification in the retinal image of the fundus. The theme chosen was interesting, as optical disc detection plays a critical role in retinal image processing for recognizing other fundus structures and is essential for identifying eye-related diseases. Because human visual vision has been little researched for optical disc detection and an effort to extract data analytics based on eye eyes, the proposed study first discovers how human perception functions for optical disc detection using a bottom-up visual focus paradigm. Eye-view data as the user conducts basic target-search tasks are obtained from separate user groups made up of experts and no expert groups. Extensive data processing has been carried out to extract eye gazing characteristics such as fixation and using computer approach mark regions in the fundus' retinal picture. Segregated labeled data was used to build up a piece of top-down information to bend the search map to the target area. The resulting Eye-based Optical Disk Detection System (EGODD) was tested through normal machine learning algorithms. This proposed framework has been tested and approved for optical disc detection;however, it can also be applied to other applications. © 2023 Subha K. et al.,.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

New way of detecting vibration of mechanical systems by explainable deep learning 49

New way of detecting vibration of mechanical systems by expl...

引用

49th International Congress and Exposition on Noise Control Engineering, INTER-NOISE 2020

作者： Huh, Hyunsuk Lee, Soo Young Lee, Seungchul Sun, Kyung Ho Jung, Joon Ha 77 Cheongam-ro Nam-gu Pohang Gyeongbuk37673 Korea Republic of 156 Gajeongbuk-ro Yuseong-gu Daejeon Chungnam34103 Korea Republic of

ISBN: (纸本)9788994021362

Nowadays, AI technology is rapidly developing and can be applied to various tasks. Even in the mechanical engineering field, many tasks are automated, become very convenient, and its performance is often improved thanks to AI. Therefore, many industries also try to apply this kind of advanced AI technology for machine health monitoring tasks to reduce costs. In this research, we suggest a novel way to detect abnormal vibration and failure by deep learning algorithms. In the past, most industries diagnose abnormal vibration of equipment based on an expert's experience or vibration-related sensor readings. Unlike what industry typically does with a vibration sensor in the past, we propose a new way of detecting vibration and its regime by image-based pattern recognition deep learning algorithm and interpretable AI technology. We can establish a cost-efficient system, so reduce the cost and resources by using the camera only. We demonstrate the proposed algorithm with vibrating cantilever and pumps. © Proceedings of 2020 International Congress on Noise Control Engineering, INTER-NOISE 2020. All rights reserved.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

IMEXT text summarizer using deep learning 1

引用

5th International Conference on Computing in Engineering and Technology,ICCET 2020

作者： Menaka, R. Thaker, Jeet Ketan Bhushan, Rounak Karthik, R. School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9789811540295

ISBN: (纸本)9789811540288

This research focuses on the problem of creating a short summary for every page in a physical book. This will enable students, professors, lawyers and others to get the most important points of every page in the book in a summarized form. Motivated by the fact that users do not have the time to read lengthy materials, the research aims to make reading a less tedious activity by summarizing long paragraphs into short sentences. In this research, we model the summarizer using deep learning algorithms using bi-RNNs and attention mechanisms. Furthermore, a hardware model is made to turn and capture images of pages from a book automatically using raspberry pi. © Springer Nature Singapore Pte Ltd. 2020.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

CDN-hosted Domain Detection with Supervised Machine learning through DNS Records 20

CDN-hosted Domain Detection with Supervised Machine Learning...

引用

3rd International Conference on Information Science and System, ICISS 2020

作者： Li, Hailing He, Longtao Zhang, Hui Zhang, Kai Li, Xiaoqian He, Chenghai Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences CNCERT/CC Beijing China Institute of Information Engineering Chinese Academy of Sciences CNCERT/CC Beijing China CNCERT/CC Beijing China

ISBN: (纸本)9781450377256

Content delivery network (CDN) has become a critical infrastructure in the Internet and DNS-based request routing mechanism is widely used in CDNs. The current methods of CDN detection mainly use information such as hostname keywords, keywords in HTTP error message, PTR records and the public posted IP ranges. However, the application scope of these methods is limited. Considering the fact that CDN-hosted sites show some characteristics during the domain name resolution process, a novel machine learning algorithm for CDN-hosted site detection is proposed in this paper and three categories of features related to IPs, domains and TTLs are extracted from the DNS records. Machine learning classifiers are trained on a labeled dataset and the experimental results show that the method can achieve good precision and F1 score. Based on the feature evaluation, the IP and TTL related features are found to be more useful. © 2020 ACM.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A Machine learning Approach for Hot Topic Detection in News 7th

A Machine Learning Approach for Hot Topic Detection in News

引用

7th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2018

作者： Tran, Tat-Huy Le, Tan-Hung Tran, Thi-Thu-Huyen Hanoi University of Science and Technology Hanoi Viet Nam Hung Yen University of Technology and Education Hải Dương Viet Nam

ISBN: (纸本)9789813291850

We explore the related problems of topic detection within the stream of news sources collected by newspapers aggregators. In this paper, we focus on evaluating the effectiveness of the collaboration between preprocessing techniques and document clustering techniques and propose to use Pearson product-moment correlation coefficient to address the relation between keywords for retrieving topics behind keywords. The proposed approach is an experiment on over 10,000 articles which has been labeled manually and the experimental result is superior to the state-of-the-art methods with respects to the preciseness. © 2020, Springer Nature Singapore Pte Ltd.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Research on Online learning Resource Recommendation Method Based on Wide & Deep and Elmo Model 2

Research on Online Learning Resource Recommendation Method B...

引用

2019 2nd International Symposium on Big Data and Applied Statistics, ISBDAS 2019

作者： Liu, Jie Zhang, Hong Liu, Zihui College of Computer and Information Science Southwest University ChongQing China

In order to improve the accuracy of online learning platform recommendation for learner learning resources and to alleviate the cold start problem, this paper proposes an online learning resource recommendation method based on Wide&Deep and Elmo model. The method uses the real learning data set of a university network and educational learning platform, and uses Wide&Deep to deeply explore the deep features of learner characteristics and course content features under the condition of high-dimensional data sparseness, that is, automatic learning combination features, the learner-course feature vector is constructed as the input of the recommendation algorithm. In addition, for the learner's text feature, it will use the ELMo language model to pre-train the feature vector to improve the recommendation accuracy. CCS Concepts •Computing methodologiesCognitive science • Theory of computationOnline learning algorithms. © Published under licence by IOP Publishing Ltd.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A Model for Classifying Network Traffic Using Reinforcment learning

A Model for Classifying Network Traffic Using Reinforcment L...

引用

2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020

作者： Cheskidov, P. Varkentin, V. Shults, A. Chelyabinsk Russia

ISBN: (纸本)9781728169514

Recently, more attention has been paid to the task of traffic classification. This is due to the fact that, in the era of active development of digital systems, it is necessary to pay particular attention to the issues of application security and the protection of user data). There are many different algorithms and approaches with their own advantages and disadvantages, which differ from each other in processing speed, field of applicability and accuracy of the final results. One of the most actively developing areas at the moment is the use of various machine learning algorithms. This direction also has some disadvantages. In particular, there is a problem in the classification of completely new traffic, which was not during training, the need for periodic retraining when changing the characteristics of network protocols or other important parameters for any particular task. This article describes a network traffic classification model using reinforcement learning methods. © 2020 IEEE.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：