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. 2022 Feb 11:2022:5038851.
doi: 10.1155/2022/5038851. eCollection 2022.

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data

S Hannah  1 , A J Deepa  2 , Varghese S Chooralil  3 , S BrillySangeetha  4 , N Yuvaraj  5 , R Arshath Raja  5 , C Suresh  6 , Rahul Vignesh  7 , YasirAbdullahR  8 , K Srihari  9 , Assefa Alene  10
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Free PMC article

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data

S Hannah et al. Biomed Res Int. .
Free PMC article

Retraction in

Abstract

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.

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Conflict of interest statement

There is no conflict of interest.

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Architecture diagram.
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Sparse autoencoder.
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Packet delivery rate (no unit).
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Throughput (Mbps).
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Delay (ms).
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Energy efficiency (%).
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