优化实时视频流:将 5G、物联网和云计算与机器学习相结合

IF 0.9 Q4 TELECOMMUNICATIONS Internet Technology Letters Pub Date : 2024-06-07 DOI:10.1002/itl2.556
L. Srinivasan, Humaira Nishat, S. Shargunam, Deepak Kumar Nayak, K. Janani
{"title":"优化实时视频流:将 5G、物联网和云计算与机器学习相结合","authors":"L. Srinivasan, Humaira Nishat, S. Shargunam, Deepak Kumar Nayak, K. Janani","doi":"10.1002/itl2.556","DOIUrl":null,"url":null,"abstract":"In this research, we optimize live video broadcast performance by incorporating advanced technologies such as 5G, the Internet of Things (IoT), and cloud computing. Our approach utilizes the Random Forest classifier to categorize data, achieving a 99% precision rate. A comparative study demonstrates that our proposed technique outperforms RCNN and Mask‐RCNN methods in optimizing video streaming efficacy. We show that our method efficiently enhances video streaming quality by integrating machine learning technologies. The combination of 5G, IoT, and cloud computing creates a robust environment for delivering optimized Live video streaming to users. This research underscores the importance of leveraging cutting‐edge technology to address optimization challenges in modern video streaming systems, focusing on the real‐time optimization of video streams in contemporary technological environments.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing live video streaming: Integrating 5G, IoT, and cloud computing with machine learning\",\"authors\":\"L. Srinivasan, Humaira Nishat, S. Shargunam, Deepak Kumar Nayak, K. Janani\",\"doi\":\"10.1002/itl2.556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this research, we optimize live video broadcast performance by incorporating advanced technologies such as 5G, the Internet of Things (IoT), and cloud computing. Our approach utilizes the Random Forest classifier to categorize data, achieving a 99% precision rate. A comparative study demonstrates that our proposed technique outperforms RCNN and Mask‐RCNN methods in optimizing video streaming efficacy. We show that our method efficiently enhances video streaming quality by integrating machine learning technologies. The combination of 5G, IoT, and cloud computing creates a robust environment for delivering optimized Live video streaming to users. This research underscores the importance of leveraging cutting‐edge technology to address optimization challenges in modern video streaming systems, focusing on the real‐time optimization of video streams in contemporary technological environments.\",\"PeriodicalId\":100725,\"journal\":{\"name\":\"Internet Technology Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet Technology Letters\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.1002/itl2.556\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet Technology Letters","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.1002/itl2.556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

在这项研究中,我们结合了 5G、物联网(IoT)和云计算等先进技术,优化了视频直播性能。我们的方法利用随机森林分类器对数据进行分类,准确率达到 99%。对比研究表明,我们提出的技术在优化视频流效果方面优于 RCNN 和 Mask-RCNN 方法。我们的研究表明,我们的方法通过整合机器学习技术有效地提高了视频流的质量。5G、物联网和云计算的结合为向用户提供优化的实时视频流创造了一个强大的环境。这项研究强调了利用前沿技术解决现代视频流系统优化难题的重要性,重点关注当代技术环境下视频流的实时优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Optimizing live video streaming: Integrating 5G, IoT, and cloud computing with machine learning
In this research, we optimize live video broadcast performance by incorporating advanced technologies such as 5G, the Internet of Things (IoT), and cloud computing. Our approach utilizes the Random Forest classifier to categorize data, achieving a 99% precision rate. A comparative study demonstrates that our proposed technique outperforms RCNN and Mask‐RCNN methods in optimizing video streaming efficacy. We show that our method efficiently enhances video streaming quality by integrating machine learning technologies. The combination of 5G, IoT, and cloud computing creates a robust environment for delivering optimized Live video streaming to users. This research underscores the importance of leveraging cutting‐edge technology to address optimization challenges in modern video streaming systems, focusing on the real‐time optimization of video streams in contemporary technological environments.
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
CiteScore
3.10
自引率
0.00%
发文量
0
期刊最新文献
Issue Information Beyond passwords: A multi‐factor authentication approach for robust digital security A framework of survivability model virtualized wireless sensor networks for IOT‐assisted wireless sensor network Issue Information Abnormal behavior monitoring enhanced smart university stadium under the background of “Internet plus”
×
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