机器学习增强盲人数学教学

L. Topin, Regina Barwaldt, Luciano M. Ribeiro, Danúbia Spídola, Andre Luis Castro de Freitas, M. Pias, M. Torres, Joelson Sartori
{"title":"机器学习增强盲人数学教学","authors":"L. Topin, Regina Barwaldt, Luciano M. Ribeiro, Danúbia Spídola, Andre Luis Castro de Freitas, M. Pias, M. Torres, Joelson Sartori","doi":"10.1109/FIE43999.2019.9028500","DOIUrl":null,"url":null,"abstract":"This summary refers to a complete research paper. In 2010, the World Health Organization (WHO) estimated that 19 million children under the age of 15 were visually impaired, with about 39 million blind and 246 million people with severe or moderate vision loss. These numbers suggest the potential size and potential impact of the visually impaired on a day-today basis. The situation has seen some improvement in recent years with increasing access to formal education, from primary school to higher education. As a result, the demand for innovative, technology-based assistance tools that enhance the user experience and the quality of education has increased. This work takes a step towards bridging a technological gap with the design and validation of a system of artificial neural networks, called as Deep Neural Networks (DNNs), capable of identifying the main Cartesian curves of the mathematics curriculum. The Cartesian set comprises 6 degrees of rational algebraic curves, in addition to a total of 42 conic curves, for this paper will be presented two sets of curves, parabolas and ellipses. The development of this gave some methodological steps. First, relevant artificial neural networks were investigated considering the needs of the user and the space of the problem — computer efficiency, loss rate and precision of the generated models used as selection metrics for each neural network tested. The selected network models were InceptionV3, MobileNETV2, VGG16, and VGG19. With average accuracy of 94% and 20% of mean loss, the selected network for the application was VGG16.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"16 1","pages":"1-8"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Towards Machine Learning for Enhanced Maths Teaching to the Blind\",\"authors\":\"L. Topin, Regina Barwaldt, Luciano M. Ribeiro, Danúbia Spídola, Andre Luis Castro de Freitas, M. Pias, M. Torres, Joelson Sartori\",\"doi\":\"10.1109/FIE43999.2019.9028500\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This summary refers to a complete research paper. In 2010, the World Health Organization (WHO) estimated that 19 million children under the age of 15 were visually impaired, with about 39 million blind and 246 million people with severe or moderate vision loss. These numbers suggest the potential size and potential impact of the visually impaired on a day-today basis. The situation has seen some improvement in recent years with increasing access to formal education, from primary school to higher education. As a result, the demand for innovative, technology-based assistance tools that enhance the user experience and the quality of education has increased. This work takes a step towards bridging a technological gap with the design and validation of a system of artificial neural networks, called as Deep Neural Networks (DNNs), capable of identifying the main Cartesian curves of the mathematics curriculum. The Cartesian set comprises 6 degrees of rational algebraic curves, in addition to a total of 42 conic curves, for this paper will be presented two sets of curves, parabolas and ellipses. The development of this gave some methodological steps. First, relevant artificial neural networks were investigated considering the needs of the user and the space of the problem — computer efficiency, loss rate and precision of the generated models used as selection metrics for each neural network tested. The selected network models were InceptionV3, MobileNETV2, VGG16, and VGG19. With average accuracy of 94% and 20% of mean loss, the selected network for the application was VGG16.\",\"PeriodicalId\":6700,\"journal\":{\"name\":\"2019 IEEE Frontiers in Education Conference (FIE)\",\"volume\":\"16 1\",\"pages\":\"1-8\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Frontiers in Education Conference (FIE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FIE43999.2019.9028500\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Frontiers in Education Conference (FIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FIE43999.2019.9028500","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

这个摘要指的是一篇完整的研究论文。2010年,世界卫生组织(世卫组织)估计,有1 900万15岁以下儿童视力受损,其中约有3 900万人失明,2.46亿人患有严重或中度视力丧失。这些数字显示了视障人士每天的潜在规模和潜在影响。近年来,随着越来越多的人接受从小学到高等教育的正规教育,这种情况有所改善。因此,对创新的、基于技术的辅助工具的需求增加了,这些工具可以提高用户体验和教育质量。这项工作通过设计和验证人工神经网络系统(称为深度神经网络(dnn)),在弥合技术差距方面迈出了一步,该系统能够识别数学课程中的主要笛卡尔曲线。笛卡尔集包括6次有理代数曲线,以及总共42条圆锥曲线,本文将介绍抛物线和椭圆两组曲线。这方面的发展给出了一些方法论步骤。首先,考虑用户需求和问题空间,对相关的人工神经网络进行研究,生成的模型的计算机效率、损失率和精度作为每个被测试神经网络的选择指标。选择的网络模型有InceptionV3、MobileNETV2、VGG16和VGG19。在平均准确率为94%,平均损失为20%的情况下,该应用程序选择的网络是VGG16。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Machine Learning for Enhanced Maths Teaching to the Blind
This summary refers to a complete research paper. In 2010, the World Health Organization (WHO) estimated that 19 million children under the age of 15 were visually impaired, with about 39 million blind and 246 million people with severe or moderate vision loss. These numbers suggest the potential size and potential impact of the visually impaired on a day-today basis. The situation has seen some improvement in recent years with increasing access to formal education, from primary school to higher education. As a result, the demand for innovative, technology-based assistance tools that enhance the user experience and the quality of education has increased. This work takes a step towards bridging a technological gap with the design and validation of a system of artificial neural networks, called as Deep Neural Networks (DNNs), capable of identifying the main Cartesian curves of the mathematics curriculum. The Cartesian set comprises 6 degrees of rational algebraic curves, in addition to a total of 42 conic curves, for this paper will be presented two sets of curves, parabolas and ellipses. The development of this gave some methodological steps. First, relevant artificial neural networks were investigated considering the needs of the user and the space of the problem — computer efficiency, loss rate and precision of the generated models used as selection metrics for each neural network tested. The selected network models were InceptionV3, MobileNETV2, VGG16, and VGG19. With average accuracy of 94% and 20% of mean loss, the selected network for the application was VGG16.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信