RETRACTED ARTICLE: Blockchain and ANFIS empowered IoMT application for privacy preserved contact tracing in COVID-19 pandemic

Aslam, Bakhtawar; Javed, Abdul Rehman; Chakraborty, Chinmay; Nebhen, Jamel; Raqib, Saira; Rizwan, Muhammad

doi:10.1007/s00779-021-01596-3

Cited by 47 publications

(23 citation statements)

References 36 publications

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“…We still do not have complete access to the factors that we are facing in this pandemic [15]- [17]. Moreover, other approaches like contact tracing, which have been applied in Australia, are claimed as "mission creep" because of the privacy concerns of people [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch

Saleem

Zeeshan

et al. 2023

IEEE Sensors J.

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Ambient intelligence plays a crucial role in healthcare situations. It provides a certain way to deal with emergencies to provide the essential resources such as nearest hospitals and emergency stations promptly to avoid deaths. Since the outbreak of Covid-19, several artificial intelligence techniques have been used. However, situation awareness is a key aspect to handling any pandemic situation. The situation-awareness approach gives patients a routine life where they are continuously monitored by caregivers through wearable sensors and alert the practitioners in case of any patient emergency. Therefore, in this paper, we propose a situation-aware mechanism to detect Covid-19 systems early and alert the user to be self-aware regarding the situation to take precautions if the situation seems unlikely to be normal. We provide Belief-Desire-Intention intelligent reasoning mechanism for the system to analyze the situation after acquiring the data from the wearable sensors and alert the user according to their environment. We use the case study for further demonstration of our proposed framework. We model the proposed system by temporal logic and map the system illustration into a simulation tool called NetLogo to determine the results of the proposed system.

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Section: Introductionmentioning

confidence: 99%

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch

Saleem

Zeeshan

et al. 2023

IEEE Sensors J.

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“…Blockchain and Federated Learning (FL) are two emerging technologies that may be employed in ITI to solve privacy and security concerns while also limiting communication costs and latency problems. Blockchain is a digital ledger for record-keeping [10,11]. It is an decentralized and dispersed in nature with tamper-resistant and tamper-evident features [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

Javed

Hassan

Shahzad

et al. 2022

Sensors

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106

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The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.

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“…All the biomedical experts worldwide are relentlessly trying to control the disease and find possible cures for this viral infection. To control this pandemic situation, vaccination is the most effective strategy to prevent the spread of COVID-19 disease all around the world (10,11). Vaccination is the first and crucial step to stop the coronavirus outbreak.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

Jalil

Abbasi

Javed

et al. 2022

Front. Public Health

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The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

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RETRACTED ARTICLE: Blockchain and ANFIS empowered IoMT application for privacy preserved contact tracing in COVID-19 pandemic

Cited by 47 publications

References 36 publications

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

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