Research Publications

Prediction of Heart Disease using Low Density Lipoprotein

LNCS Series, Springer [SCOPUS] | December 2020

Developed an innovative glucometer-based system employing fully homomorphic encryption schemes (Gorti's and Carmichael's encryption) to predict heart disease risk while preserving patient privacy. The system was implemented using JavaScript and successfully presented at the 26th International Conference on Advanced Computing and Communications (ADCOM 2020).

Key Technologies: Homomorphic Encryption, JavaScript, Edge Computing

Impact: Privacy-preserving healthcare prediction system


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Preserving Healthcare Data Security and Privacy using Carmichael's Theorem

Mary Ann Liebert, Inc., Big Data • August 2021

Advanced research on preserving healthcare data security using Carmichael's theorem-based homomorphic encryption and modified enhanced homomorphic encryption schemes specifically designed for edge computing systems. This work addresses critical privacy concerns in distributed healthcare data processing.

Key Technologies: Carmichael's Theorem, Enhanced Homomorphic Encryption, Edge Computing

Impact: Secure healthcare data processing in distributed systems


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Technical Projects

Blockchain-based Hypotension Prediction

Blockchain • Edge Computing • Healthcare

Secure prediction system for hypotension in edge computing environments using blockchain technology.

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Landscape Image Processing

Python • Machine Learning • Computer Vision

Machine learning project for landscape image classification and processing.

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Pharmacy Management Systems

Full Stack • SQL/NoSQL • Web Development

Complete pharmacy management solutions with both SQL and NoSQL implementations.

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Heart Disease Prediction System

JavaScript • Homomorphic Encryption • Machine Learning

Privacy preserving prediction of heart disease using low density lipoprotein from edge devices.

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