DEVO-DNFN A Novel Approach for Secure Medical Data Classification in the Cloud

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Suruchi Nannaware; Dr. Hiren Dand

DEVO-DNFN A Novel Approach for Secure Medical Data Classification in the Cloud

Authors: Suruchi Nannaware, Dr. Hiren Dand

Unique Paper ID: 175951
Volume: 11
Issue: 11
PageNo: 5274-5280

Keywords: cloud computing DNFN optimization Privacy preservation

Abstract:
In cloud Computing, storing and accessing sensitive information is a major concern. The paper tries to address these issues by providing the novel approach for classifying privacy preserved medical data, named DEVO. It integrates the two optimization algorithm namely, Energy Valley Optimizer (EVO) and Dingo Optimizer (DOX). Cloud simulation is the first step in the process, which gathers medical data from the dataset as an input. The privacy of data is then maintained in the cloud environment by using DEVO to create a privacy utility coefficient matrix using deep learning principles. The resulting privacy-preserved data is safely kept in the cloud and can only be retrieved by authorized parties with the same key. In addition, DEVO-DNFN uses Deep Neuro Fuzzy Network (DNFN) to classify medical data, which is then refined with DEVO. Assessment metrics like True Positive Rate (TPR), accuracy, and True Negative Rate (TNR) show encouraging results, having observed values of 0.912, 0.907, and 0.915, respectively. This comprehensive method addresses the important issues of privacy and data security in cloud-based medical data classification

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 5274-5280

DEVO-DNFN A Novel Approach for Secure Medical Data Classification in the Cloud

Available:https://ijirt.org/article?manuscript=175951

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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