Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1155/2022/5038851 |
RETRACTED: Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data (Retracted Article) | |
Hannah, S.; Deepa, A. J.; Chooralil, Varghese S.; BrillySangeetha, S.; Yuvaraj, N.; Arshath Raja, R.; Suresh, C.; Vignesh, Rahul; YasirAbdullahR; Srihari, K.; Alene, Assefa | |
通讯作者 | Srihari, K |
来源期刊 | BIOMED RESEARCH INTERNATIONAL
![]() |
ISSN | 2314-6133 |
EISSN | 2314-6141 |
出版年 | 2022 |
卷号 | 2022 |
英文摘要 | 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. |
类型 | Article ; Retracted Publication |
语种 | 英语 |
开放获取类型 | Green Published, gold |
收录类别 | SCI-E |
WOS记录号 | WOS:000777267900004 |
WOS类目 | Biotechnology & Applied Microbiology ; Medicine, Research & Experimental |
WOS研究方向 | Biotechnology & Applied Microbiology ; Research & Experimental Medicine |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/391988 |
推荐引用方式 GB/T 7714 | Hannah, S.,Deepa, A. J.,Chooralil, Varghese S.,et al. RETRACTED: Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data (Retracted Article)[J],2022,2022. |
APA | Hannah, S..,Deepa, A. J..,Chooralil, Varghese S..,BrillySangeetha, S..,Yuvaraj, N..,...&Alene, Assefa.(2022).RETRACTED: Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data (Retracted Article).BIOMED RESEARCH INTERNATIONAL,2022. |
MLA | Hannah, S.,et al."RETRACTED: Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data (Retracted Article)".BIOMED RESEARCH INTERNATIONAL 2022(2022). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。