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Blockchain and ANFIS empowered IoMT application for privacy preserved contact tracing in COVID-19 pandemic
Personal and Ubiquitous Computing Pub Date : 2021-07-22 , DOI: 10.1007/s00779-021-01596-3
Bakhtawar Aslam 1 , Abdul Rehman Javed 2 , Chinmay Chakraborty 3 , Jamel Nebhen 4 , Saira Raqib 1 , Muhammad Rizwan 1
Affiliation  

Life-threatening novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), also known as COVID-19, has engulfed the world and caused health and economic challenges. To control the spread of COVID-19, a mechanism is required to enforce physical distancing between people. This paper proposes a Blockchain-based framework that preserves patients’ anonymity while tracing their contacts with the help of Bluetooth-enabled smartphones. We use a smartphone application to interact with the proposed blockchain framework for contact tracing of the general public using Bluetooth and to store the obtained data over the cloud, which is accessible to health departments and government agencies to perform necessary and timely actions (e.g., like quarantine the infected people moving around). Thus, the proposed framework helps people perform their regular business and day-to-day activities with a controlled mechanism that keeps them safe from infected and exposed people. The smartphone application is capable enough to check their COVID status after analyzing the symptoms quickly and observes (based on given symptoms) either this person is infected or not. As a result, the proposed Adaptive Neuro-Fuzzy Interference System (ANFIS) system predicts the COVID status, and K-Nearest Neighbor (KNN) enhances the accuracy rate to 95.9% compared to state-of-the-art results.



中文翻译:


区块链和 AFIS 赋能 IoMT 应用程序,用于在 COVID-19 大流行中保护隐私的接触者追踪



危及生命的新型严重急性呼吸综合征冠状病毒(SARS-CoV-2),也称为 COVID-19,已席卷全球并造成健康和经济挑战。为了控制 COVID-19 的传播,需要一种机制来加强人与人之间的物理距离。本文提出了一种基于区块链的框架,该框架可以在支持蓝牙的智能手机的帮助下追踪患者的接触者的同时保持患者的匿名性。我们使用智能手机应用程序与拟议的区块链框架进行交互,以使用蓝牙对公众进行联系人追踪,并将获得的数据存储在云上,卫生部门和政府机构可以访问该数据以执行必要且及时的行动(例如,隔离四处走动的感染者)。因此,所提出的框架可以帮助人们通过受控机制开展日常业务和日常活动,确保他们免受感染和暴露者的伤害。智能手机应用程序足以在快速分析症状后检查他们的新冠病毒状态,并观察(根据给定的症状)此人是否被感染。因此,所提出的自适应神经模糊干扰系统 (ANFIS) 系统可以预测 COVID 状态,并且与最先进的结果相比,K 最近邻 (KNN) 将准确率提高到 95.9%。

更新日期:2021-07-22
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