N-Sanitization: A semantic privacy-preserving framework for unstructured medical datasets

Iwendi, Celestine; Moqurrab, Syed Atif; Anjum, Adeel; Khan, Sangeen; Mohan, Senthilkumar; Srivastava, Gautam

doi:10.1016/j.comcom.2020.07.032

Cited by 72 publications

(45 citation statements)

References 36 publications

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“…Metamask chrome extension was used to set up the Ethereum wallet for creating smart contracts 65‐69 . Ropsten Test Network on Ethereum was used to deploy the Proof of concept.…”

Section: System Framework: Materials and Methodsmentioning

confidence: 99%

Securing logistics system and supply chain using Blockchain

Kumar

Abhishek

Ghalib

et al. 2020

Appl Stoch Models Bus & Ind

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Past international trade practices have been associated with opaque information flows that have hindered traceability and created hurdles in hassle‐free trade. Blockchain and allied technologies have been investigated as a panacea for the problems faced by supply chain and logistics industry. Network analysis also uncovered twenty types of legal and antisocial entities operating on bitcoin and provided a path for uncovering these antisocial entities. However, earlier works of literature have focused on limited aspects of a typical supply chain such as monitoring assets, securing traceability, data integrity negligence, and data access. To overcome such drawbacks, this article proposes permissioned Blockchain with relevant processes and functions to obtain a holistic framework for securing the supply chain and logistic operations. The efficacy of the proposed framework was demonstrated in the case study. Comparative study was performed with the existing frameworks where the proposed framework was found to be better at four counts compared on the basis of data integrity in supply chain, decentralized decision making and traceability. It was found that critical loopholes in a current supply chain can be overcome using the proposed framework.

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“…Metamask chrome extension was used to set up the Ethereum wallet for creating smart contracts 65‐69 . Ropsten Test Network on Ethereum was used to deploy the Proof of concept.…”

Section: System Framework: Materials and Methodsmentioning

confidence: 99%

Securing logistics system and supply chain using Blockchain

Kumar

Abhishek

Ghalib

et al. 2020

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

show abstract

“…Authors in [ 34 ] trained ANFIS to predict human brain activity so that it can be used for real cases [ 42 ]. Authors in [ 17 , 22 , 43 ] also focused on enhancing the privacy of the individuals’ medical information. Backtracking Search algorithm (BSA) and ANFIS model are used for simulating the Ontario electricity price accurately.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

Self Cite

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Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

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“…Iwendi et al, ( 31 ) utilized COVID-19 patients' geographical and demographic data to predict the severity of cases, recovery, and death. In ( 32 ) a semantic privacy framework that uses sensitive and semantically related terms to sanitize healthcare documents was proposed, and ( 33 ) uses deep learning to detect and sanitize social media comments. In ( 34 ), the authors discussed the concepts of an incentive approach for COVID-19 planning using Blockchain Technology.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

Kumar

Khan

Din

et al. 2021

Front. Public Health

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Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.

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N-Sanitization: A semantic privacy-preserving framework for unstructured medical datasets

Cited by 72 publications

References 36 publications

Securing logistics system and supply chain using Blockchain

Securing logistics system and supply chain using Blockchain

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

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