提出了用于预测、发现和提取人类活动的隐马尔可夫模型

M. Rahneshin, Homan Kashanian, Batool Sabeti Noghabi
{"title":"提出了用于预测、发现和提取人类活动的隐马尔可夫模型","authors":"M. Rahneshin, Homan Kashanian, Batool Sabeti Noghabi","doi":"10.15520/AJCEM.2017.VOL6.ISS1.72.PP12-16","DOIUrl":null,"url":null,"abstract":"In recent years, new functional bases were appeared, which presented new limitations and ways in the base of information extraction. The ability of human activities understanding will increase the power and the ability of predicting the human activities. In fact, the analysis of human activities throughout history was something that has attracted everyone’s attention. However, the automatic discovery of human activities causes to challenge human’s natural activities. In this article we will propose a model based on hidden Markov model or hidden Markov for understanding, discovering and predicting of human activities. We will produce the hidden Markov model to realize and discover human activities. The output of this compound model will be able to record the human activities so efficiently. We will test our model based on available popular dataset (CASAS),this dataset contains 12 different daily activities of human. Our model has useful efficiency up to 89 percent to present in the same smart homes.","PeriodicalId":173381,"journal":{"name":"Asian Journal of Current Engineering and Maths","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The presentation of hidden Markov model for forecasting, discovering and extracting the human activities\",\"authors\":\"M. Rahneshin, Homan Kashanian, Batool Sabeti Noghabi\",\"doi\":\"10.15520/AJCEM.2017.VOL6.ISS1.72.PP12-16\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, new functional bases were appeared, which presented new limitations and ways in the base of information extraction. The ability of human activities understanding will increase the power and the ability of predicting the human activities. In fact, the analysis of human activities throughout history was something that has attracted everyone’s attention. However, the automatic discovery of human activities causes to challenge human’s natural activities. In this article we will propose a model based on hidden Markov model or hidden Markov for understanding, discovering and predicting of human activities. We will produce the hidden Markov model to realize and discover human activities. The output of this compound model will be able to record the human activities so efficiently. We will test our model based on available popular dataset (CASAS),this dataset contains 12 different daily activities of human. Our model has useful efficiency up to 89 percent to present in the same smart homes.\",\"PeriodicalId\":173381,\"journal\":{\"name\":\"Asian Journal of Current Engineering and Maths\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-02-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asian Journal of Current Engineering and Maths\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15520/AJCEM.2017.VOL6.ISS1.72.PP12-16\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Current Engineering and Maths","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15520/AJCEM.2017.VOL6.ISS1.72.PP12-16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

近年来,新的功能库的出现,给信息提取库带来了新的局限和途径。人类活动理解能力的提高,将提高预测人类活动的能力和能力。事实上,对历史上人类活动的分析吸引了所有人的注意力。然而,人类活动的自动发现导致了对人类自然活动的挑战。在本文中,我们将提出一个基于隐马尔可夫模型或隐马尔可夫模型的模型来理解、发现和预测人类活动。我们将产生隐马尔可夫模型来实现和发现人类活动。这种复合模型的输出将能够如此有效地记录人类活动。Â我们将基于可用的流行数据集(CASAS)测试我们的模型,该数据集包含12种不同的人类日常活动。Â我们的模型在相同的智能家居中具有高达89%的有效效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The presentation of hidden Markov model for forecasting, discovering and extracting the human activities
In recent years, new functional bases were appeared, which presented new limitations and ways in the base of information extraction. The ability of human activities understanding will increase the power and the ability of predicting the human activities. In fact, the analysis of human activities throughout history was something that has attracted everyone’s attention. However, the automatic discovery of human activities causes to challenge human’s natural activities. In this article we will propose a model based on hidden Markov model or hidden Markov for understanding, discovering and predicting of human activities. We will produce the hidden Markov model to realize and discover human activities. The output of this compound model will be able to record the human activities so efficiently. We will test our model based on available popular dataset (CASAS),this dataset contains 12 different daily activities of human. Our model has useful efficiency up to 89 percent to present in the same smart homes.
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