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

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.

Xehetasun bibliografikoak
Egile Nagusiak:	Shafin, Md. Mehtabul Islam, Akhter, Sabrin, Hasan, Mohammad Shafkat, Nasimuzzaman, Md., Prithul, Tamzeedur Rahman
Beste egile batzuk:	Zaman, Shakila
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2023
Gaiak:	Federated learning Machine learning e-Health care CNN MLP Random forest Logistic regression Medical informatics. Medical telematics.
Sarrera elektronikoa:	http://hdl.handle.net/10361/19294

Antzeko izenburuak

eHealth research, theory, development : multi-disciplinary approach /
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A hybrid FL-Enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
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A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
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Federated ensemble-learning for transport mode detection in vehicular edge network
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Machine learning approach for ECG analysis and predicting different heart diseases
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SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
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Speech emotion detection using supervised, unsupervised and feature selection algorithms
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Forecasting stock market prices using advanced tools of machine learning
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Comparison of machine learning techniques to predict cardiovascular disease
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Translational informatics in smart healthcare /
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A hybrid rumor detection model derived from a comparative study of supervised approaches
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Comparative analysis and implementation of credit risk prediction through distinct machine learning models
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Amazon product reviews sentiment analysis using supervised learning algorithms
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EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence
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Fortifying federated learning: security against model poisoning attacks
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A secured federated learning system leveraging confidence score to identify retinal disease
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Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach
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An advanced hospital rating system using machine learning and natural language processing
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Predicting regional accents of Bengali language using deep learning
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Identification of fake news using machine learning in distributed system
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A feature selection approach to determine obesity using machine learning method
nork: Antor, Shahidul Alam, et al.
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Social media trend analysis to predict the success of products using deep learning technique
nork: Khan, Farden Ehsan, et al.
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A semi-supervised federated learning approach leveraging pseudo-labeling for Knee Osteoarthritis severity detection
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An enhanced CNN model for classifying skin cancer
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ResInvolution: an involution-ResNet fused global spatial relation leveraging model for histopathological image analysis under federated learning environment
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A counseling system to predict the study path for freshmen
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Prediction of coronary heart diseases using supervised machine learning algorithms
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Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning
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Personalization in federated recommendation system using SVD++ with explainability
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Detecting online recruitment fraud by using machine learning
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Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
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