文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

凯利-珀泽尔为新冠患者数据分析确保通信安全并采用折刀相关分类法。

Cayley-Purser secured communication and jackknife correlative classification for COVID patient data analysis.

作者信息

Sekaran Ramesh, Munnangi Ashok Kumar, Ramachandran Manikandan, Khishe Mohammad

机构信息

Department of Computer Science and Engineering, JAIN (Deemed-to-be University), Bangalore, Karnataka, 562112, India.

Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College (Autonomous), Vijayawada, Andhra Pradesh, India.

出版信息

Sci Rep. 2025 Feb 7;15(1):4666. doi: 10.1038/s41598-025-88105-1.


DOI:10.1038/s41598-025-88105-1
PMID:39920299
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11806013/
Abstract

Internet of Medical Things (IoMT) is a group of medical devices that connect the healthcare information technology to minimize the redundant hospital visit and healthcare system troubles. IoMT connect the patients to the doctor and transmit the medical data over the network. The spread of corona virus has put the people at high risk. Due to increasing number of cases and its stress on health professionals, IoMT technology is used in many healthcare centers. But, the security level and data classification accuracy was not improved by existing methods during the data communication. In order to solve these issues, Cayley-Purser Cryptographic Secured Communication based Jackknife Correlative Data Classification (CPCSC-JCDC) method is designed. The key objective of CPCSC-JCDC method is to collect the patient information through IoMT devices and send to the doctor in more secured manner. Initially in CPCSC-JCDC method, the patient data is collected. After the data collection process, the data gets encrypted with help of public key of the patient by using cayley-purser cryptosystem. After the encryption process, the data is sent to the doctor. The doctor receives and decrypts the patient data by using their private key. After decryption process, the doctor analyses the patient data and classifies the data as emergency case or normal case by using jackknife correlation function. This helps to minimize the patient readmission rate and increase the patient satisfaction level. Experimental evaluation is carried out by Novel Corona Virus 2019 dataset using different metrics like data classification accuracy, data classification time and security level. The evaluation result shows that CPCSC-JCDC method improves the security level as well as accuracy and minimizes the time consumption during data communication than existing works.

摘要

医疗物联网(IoMT)是一组连接医疗信息技术的医疗设备,旨在尽量减少不必要的医院就诊和医疗系统故障。IoMT将患者与医生连接起来,并通过网络传输医疗数据。新冠病毒的传播使人们面临高风险。由于病例数量不断增加及其给医护人员带来的压力,IoMT技术在许多医疗中心得到应用。但是,在数据通信过程中,现有方法并未提高安全级别和数据分类准确性。为了解决这些问题,设计了基于凯莱 - 珀塞尔加密安全通信的留一法相关数据分类(CPCSC - JCDC)方法。CPCSC - JCDC方法的主要目标是以更安全的方式通过IoMT设备收集患者信息并发送给医生。在CPCSC - JCDC方法中,首先收集患者数据。在数据收集过程之后,使用凯莱 - 珀塞尔密码系统借助患者的公钥对数据进行加密。加密过程之后,将数据发送给医生。医生使用其私钥接收并解密患者数据。解密过程之后,医生分析患者数据,并使用留一法相关函数将数据分类为紧急情况或正常情况。这有助于尽量降低患者再次入院率并提高患者满意度。使用新型冠状病毒2019数据集,通过数据分类准确性、数据分类时间和安全级别等不同指标进行实验评估。评估结果表明,与现有工作相比,CPCSC - JCDC方法提高了安全级别以及准确性,并在数据通信过程中减少了时间消耗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/d66f0e34b870/41598_2025_88105_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/ed0477edee6e/41598_2025_88105_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/c0847ca44f82/41598_2025_88105_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/a1ec471afbfd/41598_2025_88105_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/3a64c7475106/41598_2025_88105_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/b561d6098705/41598_2025_88105_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/8a8e860fbb4d/41598_2025_88105_Figb_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/d66f0e34b870/41598_2025_88105_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/d79b7ed10129/41598_2025_88105_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/d66f0e34b870/41598_2025_88105_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/ed0477edee6e/41598_2025_88105_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/c0847ca44f82/41598_2025_88105_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/a1ec471afbfd/41598_2025_88105_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/3a64c7475106/41598_2025_88105_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/b561d6098705/41598_2025_88105_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/8a8e860fbb4d/41598_2025_88105_Figb_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/d66f0e34b870/41598_2025_88105_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/d79b7ed10129/41598_2025_88105_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de94/11806013/d66f0e34b870/41598_2025_88105_Fig7_HTML.jpg

相似文献

[1]
Cayley-Purser secured communication and jackknife correlative classification for COVID patient data analysis.

Sci Rep. 2025-2-7

[2]
Intelligent two-phase dual authentication framework for Internet of Medical Things.

Sci Rep. 2025-1-12

[3]
Biserial Miyaguchi-Preneel Blockchain-Based Ruzicka-Indexed Deep Perceptive Learning for Malware Detection in IoMT.

Sensors (Basel). 2021-10-27

[4]
Prospect of Internet of Medical Things: A Review on Security Requirements and Solutions.

Sensors (Basel). 2022-7-24

[5]
An Overview on Security and Privacy of Data in IoMT Devices: Performance Metrics, Merits, Demerits, and Challenges.

Stud Health Technol Inform. 2022-11-3

[6]
Blockchain and IPFS Integrated Framework in Bilevel Fog-Cloud Network for Security and Privacy of IoMT Devices.

Comput Math Methods Med. 2021

[7]
ESHA-256_GBGO: a high-performance and optimized security framework for internet of medical thing.

Sci Rep. 2025-3-20

[8]
Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring.

Sensors (Basel). 2024-4-27

[9]
The role of blockchain to secure internet of medical things.

Sci Rep. 2024-8-8

[10]
A secured internet of robotic things (IoRT) for long-term care services in a smart building.

J Supercomput. 2023

本文引用的文献

[1]
A machine learning forecasting model for COVID-19 pandemic in India.

Stoch Environ Res Risk Assess. 2020

[2]
Clinical characteristics of patients diagnosed with COVID-19 in Beijing.

Biosaf Health. 2020-6

[3]
A case-based reasoning framework for early detection and diagnosis of novel coronavirus.

Inform Med Unlocked. 2020

[4]
Data analytics for novel coronavirus disease.

Inform Med Unlocked. 2020

[5]
Evaluation and prediction of COVID-19 in India: A case study of worst hit states.

Chaos Solitons Fractals. 2020-10

[6]
Data-driven modelling and prediction of COVID-19 infection in India and correlation analysis of the virus transmission with socio-economic factors.

Diabetes Metab Syndr. 2020

[7]
COVID-19 diagnostic approaches: different roads to the same destination.

Virusdisease. 2020-6

[8]
Estimation of the probable outbreak size of novel coronavirus (COVID-19) in social gathering events and industrial activities.

Int J Infect Dis. 2020-7-4

[9]
Novel corona virus disease (COVID-19) awareness among the dental interns, dental auxiliaries and dental specialists in Saudi Arabia: A nationwide study.

J Infect Public Health. 2020-5-29

[10]
Predicting COVID-19 in China Using Hybrid AI Model.

IEEE Trans Cybern. 2020-5-8

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索