ArticlePersonal and ubiquitous computing2021
Blockchain and ANFIS empowered IoMT application for privacy preserved contact tracing in COVID-19 pandemic.
Article in Personal and ubiquitous computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 46 citations in OpenAlex.
- A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects.Complex & intelligent systems · 2023Article
- A Blockchain-Based Life-Cycle Environmental Management Framework for Hospitals in the COVID-19 Context.Engineering (Beijing, China) · 2023Article
- Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch.IEEE sensors journal · 2023Article
- COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies.International journal of environmental research and public health · 2022Article
- Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2022Review
- Blockchain and cloud computing-based secure electronic healthcare records storage and sharing.Frontiers in public health · 2022Article
- A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan.Computational intelligence and neuroscience · 2022Article
- Determinants of mental and financial health during COVID-19: Evidence from data of a developing country.Frontiers in public health · 2022Article
- Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network.Computational and mathematical methods in medicine · 2022Article
- Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics.Pattern recognition letters · 2021Article
- Machine Learning Assisted Cervical Cancer Detection.Frontiers in public health · 2021Article
- COVIDSAVIOR: A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks.Frontiers in public health · 2021Article
- COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques.Frontiers in public healthArticle
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Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.