Fuzzy-Based Hybrid Approach for Security Impact Evaluation in Healthcare Web Applications

被引:0
作者
Chaudhary, Jitendra Kumar [1 ]
Arthi, A. [2 ]
Shalini, S. [3 ]
Gunasundari, C. [4 ]
Sharma, Abhishek [5 ]
Sahu, Dillip Narayan [6 ]
机构
[1] Graph Era Hill Univ Bhimtal Campus, Sch Comp, Uttrakhand, India
[2] Karpagam Coll Engn, Comp Sci & Technol, Coimbatore 641032, Tamil Nadu, India
[3] JNN Inst Engn, Dept Math, 90 Ushaa Garden, Chennai, Tamil Nadu, India
[4] Roever Engn Coll, Dept Comp Sci & Engn, Perambalur, Tamil Nadu, India
[5] Natl Inst Adv Mfg Technol, Ranchi, Bihar, India
[6] Gangadhar Meher Univ, Dept MCA, Sambalpur, Odisha, India
来源
2024 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND APPLIED INFORMATICS, ACCAI 2024 | 2024年
关键词
Fuzzy; Security; Healthcare; Hybrid;
D O I
10.1109/ACCAI61061.2024.10602272
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to connect patients, technology, and healthcare facilities, as well as to efficiently and carefully address the changing needs of medical ecosystems, the smart medical system is evolving into a medical policy service that makes use of wearables, online services, and mobile devices. This article examines some of the challenges that users must overcome in order to adopt intelligent medical technology more quickly and gain access to constant healthcare. The essay examines the integration of a fuzzy-based hybrid strategy to produce intelligently designed health solutions. The patient can access medical services, including monitoring, medication management, and emergency readiness, from anywhere at any time with the help of the intelligent healthcare management system. The adaptive neuro-fuzzy inference system (ANFIS) is a tool that this study suggests using to identify security concerns and evaluate them while developing online applications. First, the security risk concerns associated with developing web applications for healthcare have been identified in this paper. The ANFIS technique has since been used to assess these security issues. Additionally, a fuzzy regression model is suggested by this study.
引用
收藏
页数:5
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