Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Annapurna, Bala
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Secured and cloud-based electronic health records by homomorphic encryption algorithm Annapurna, Bala; Geetha, Gaddam; Madhiraju, Priyanka; Kalaiselvi, Subbarayan; Sushith, Mishmala; Ramadevi, Rathinasabapathy; Pandey, Pramod
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1152-1161

Abstract

This uses homomorphic encryption in cloud-based platforms to improve electronic health records (EHR) security and accessibility. Protecting sensitive medical data while enabling data processing and analysis is the main goal. The study examines how homomorphic encryption protects EHR data privacy and integrity. Its main purpose is to reduce risks of unauthorized access and data breaches to build trust between healthcare professionals and patients in digital healthcare. The research uses homomorphic encryption to safeguard cloud EHR storage and transmission. Results will highlight the algorithm's influence on data security and computing efficiency, revealing its potential use in healthcare to protect patient privacy and meet regulatory requirements. Results from dataset of patient health metrics show in the 1st instance sample data for 5 instances with ages between 57 to 88, blood pressure (BP) values from 33 to 85, glucose values from 5 to 99, and heart rate values from 24 to 88. In another study of 5 patients, cholesterol levels ranged from 10 to 80 mg/dL, body mass index (BMI) from 10 to 96 kg/m², smoking status from 14 to 79, and medication adherence from 6 to 78%.
Modern machine learning and deep learning algorithms for preventing credit card frauds Kumar, Indurthi Ravindra; Hameed, Shaik Abdul; Annapurna, Bala; Paladugu, Rama Krishna; Narayana Reddy, Veeramreddy Surya; Kaveti, Kiran Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1673-1680

Abstract

Credit card fraud poses a significant threat to financial institutions and consumers, particularly in the context of online transactions. Conventional rule-based systems often struggle to keep pace with the evolving tactics of fraudsters. This research paper investigates the application of advanced machine learning and deep learning algorithms for credit card fraud detection. By reviewing existing methodologies and addressing the challenges associated with fraud detection, we explore the potential of stateof-the-art techniques in enhancing detection accuracy and efficiency. Key aspects such as transaction data analysis, feature engineering, model evaluation metrics, and practical implementations are discussed. The findings underscore the importance of leveraging advanced algorithms to combat fraudulent activities effectively, thereby safeguarding the integrity of online transactions.
Co-Authors Geetha, Gaddam Hameed, Shaik Abdul Kalaiselvi, Subbarayan Kaveti, Kiran Kumar Kumar, Indurthi Ravindra Madhiraju, Priyanka Narayana Reddy, Veeramreddy Surya Paladugu, Rama Krishna Pandey, Pramod Ramadevi, Rathinasabapathy Sushith, Mishmala