Implementation and Analysis of Novel Iris Monitoring System using Prewitt Algorithm in comparing with Sobel Algorithms by Signal-to-Noise Ratio

D. R. D. Varma, R. Priyanka
{"title":"Implementation and Analysis of Novel Iris Monitoring System using Prewitt Algorithm in comparing with Sobel Algorithms by Signal-to-Noise Ratio","authors":"D. R. D. Varma, R. Priyanka","doi":"10.1109/ICTACS56270.2022.9988712","DOIUrl":null,"url":null,"abstract":"The novel performance analysis of prewitt algorithm for iris monitoring in comparison with the sobel to improve the Signal to Noise Ratio (SNR) for improving strength of the signal using. Materials and Methods: The 40 samples were collected using the g power clinical calculator. G1 as the prewitt algorithm with 20 samples and g2 as the sobel algorithm with 20 samples. 80% of power is prescribed for pretest and the acceptable error of 0.05 were used to identify the number of samples. Results: The prewitt algorithm has achieved the predominant performance accuracy of 94.0% when compared to the sobel algorithm with 87.85% of accuracy. The prewitt algorithm has the implication of ($\\mathrm{p} < 0.05$) with the sobel algorithm. Conclusion: The prewitt algorithm is implified greater accuracy when compared with the sobel algorithm.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTACS56270.2022.9988712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The novel performance analysis of prewitt algorithm for iris monitoring in comparison with the sobel to improve the Signal to Noise Ratio (SNR) for improving strength of the signal using. Materials and Methods: The 40 samples were collected using the g power clinical calculator. G1 as the prewitt algorithm with 20 samples and g2 as the sobel algorithm with 20 samples. 80% of power is prescribed for pretest and the acceptable error of 0.05 were used to identify the number of samples. Results: The prewitt algorithm has achieved the predominant performance accuracy of 94.0% when compared to the sobel algorithm with 87.85% of accuracy. The prewitt algorithm has the implication of ($\mathrm{p} < 0.05$) with the sobel algorithm. Conclusion: The prewitt algorithm is implified greater accuracy when compared with the sobel algorithm.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于Prewitt算法的新型虹膜监测系统的实现与分析,并通过信噪比与Sobel算法进行比较
分析了新颖的prewitt算法用于虹膜监测的性能,并与sobel算法进行了比较,以提高信号的信噪比(SNR),用于提高信号的强度。材料与方法:采用g功率临床计算器采集40例标本。G1为20个样本的prewitt算法,g2为20个样本的sobel算法。规定80%的功率进行预测,采用0.05的可接受误差来识别样本数。结果:prewitt算法的准确率为94.0%,sobel算法的准确率为87.85%。prewitt算法与sobel算法具有($\mathrm{p} < 0.05$)的含义。结论:与sobel算法相比,prewitt算法具有更高的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Suicidal Ideation Detection on Social Media: A Machine Learning Approach Artificial Intelligence Techniques to Predict the Infectious Diseases: Open Challenges and Research Issues Brain Tumor Classification by Convolutional Neural Network FDR: An Automated System for Finding Missing People Autism Spectrum Disorder Detection using theDeep Learning Approaches
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1