Analyzing the security of e-Health data based on a hybrid federated learning model

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliographic Details
Main Authors:	Shafin, Md. Mehtabul Islam, Akhter, Sabrin, Hasan, Mohammad Shafkat, Nasimuzzaman, Md., Prithul, Tamzeedur Rahman
Other Authors:	Zaman, Shakila
Format:	Thesis
Language:	English
Published:	Brac University 2023
Subjects:	Federated learning Machine learning e-Health care CNN MLP Random forest Logistic regression Medical informatics. Medical telematics.
Online Access:	http://hdl.handle.net/10361/19294

id	10361-19294
record_format	dspace
spelling	10361-192942023-08-06T21:02:01Z Analyzing the security of e-Health data based on a hybrid federated learning model Shafin, Md. Mehtabul Islam Akhter, Sabrin Hasan, Mohammad Shafkat Nasimuzzaman, Md. Prithul, Tamzeedur Rahman Zaman, Shakila Hossain, Dr. Muhammad Iqbal Department of Computer Science and Engineering, Brac University Federated learning Machine learning e-Health care CNN MLP Random forest Logistic regression Medical informatics. Medical telematics. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-45). This research aims to provide an approach for analyzing the security of the e-health care system through the use of federated learning and the pre-processing of distinct deep learning models. The infrastructure for e-healthcare services is being gradually deployed by the health sector. This method increased the safety of patients and doctors through a protected platform. As a result, it is going to replace the current health service. Even if this technology is becoming more and more widespread, a number of data security threats need to be tackled. In this research, a CNN and MLP architecture with a classification-focused approach using a number of pre trained feature extractors such as ResNet-50, VGG16, and Inception- v3 have been implemented. Additionally, various machine learning classification algorithms (such as Random Forest, and Logistic Regression) have been used to classify the images in order to compare how well they perform to a neural network approach. Federated learning has also been incorporated to increase healthcare data security as it does not transmit actual data but models. The objective is to develop a hybrid federated learning model to analyze the security of e-health data. The core premise is to utilize a methodology like federated learning, which enables a technique for creating machine learning models while safeguarding user privacy and can maintain e-health data security without transferring real-world data. Md. Mehtabul Islam Shafin Sabrin Akhter Mohammad Shafkat Hasan Md. Nasimuzzaman Tamzeedur Rahman Prithul B. Computer Science 2023-08-06T05:53:37Z 2023-08-06T05:53:37Z 2023 2023-01 Thesis ID: 19101088 ID: 18301098 ID: 19101077 ID: 19101051 ID: 18301289 http://hdl.handle.net/10361/19294 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 45 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Federated learning Machine learning e-Health care CNN MLP Random forest Logistic regression Medical informatics. Medical telematics.
spellingShingle	Federated learning Machine learning e-Health care CNN MLP Random forest Logistic regression Medical informatics. Medical telematics. Shafin, Md. Mehtabul Islam Akhter, Sabrin Hasan, Mohammad Shafkat Nasimuzzaman, Md. Prithul, Tamzeedur Rahman Analyzing the security of e-Health data based on a hybrid federated learning model
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Zaman, Shakila
author_facet	Zaman, Shakila Shafin, Md. Mehtabul Islam Akhter, Sabrin Hasan, Mohammad Shafkat Nasimuzzaman, Md. Prithul, Tamzeedur Rahman
format	Thesis
author	Shafin, Md. Mehtabul Islam Akhter, Sabrin Hasan, Mohammad Shafkat Nasimuzzaman, Md. Prithul, Tamzeedur Rahman
author_sort	Shafin, Md. Mehtabul Islam
title	Analyzing the security of e-Health data based on a hybrid federated learning model
title_short	Analyzing the security of e-Health data based on a hybrid federated learning model
title_full	Analyzing the security of e-Health data based on a hybrid federated learning model
title_fullStr	Analyzing the security of e-Health data based on a hybrid federated learning model
title_full_unstemmed	Analyzing the security of e-Health data based on a hybrid federated learning model
title_sort	analyzing the security of e-health data based on a hybrid federated learning model
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/19294
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Analyzing the security of e-Health data based on a hybrid federated learning model

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