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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Subbarayalu Ramamurthy, Lavanya
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Computer Science & IT Electrical & Electronics Engineering

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Homomorphic encryption, privacy-preserving feature extraction, and decentralized architecture for enhancing privacy in voice authentication Murugesan, Kathiresh; Subbarayalu Ramamurthy, Lavanya; Palanisamy, Boopathi; Chandrasekar, Yamini; Shanmugam, Kavitha Masagoundanpudhur; Nithya, Balluru Thammaiahshetty Adishankar; Thiyagaraja, Velumani; Muniappan, Ramaraj
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2150-2160

Abstract

This paper introduces a novel framework designed to bolster privacy protections within automated voice authentication systems, addressing mounting concerns as voice-based authentication grows in prominence. The widespread adoption of these systems has underscored apprehensions regarding the storage and processing of sensitive voice biometric data without adequate safeguards. To mitigate these risks, a modified framework is proposed, aiming to enhance privacy without compromising authentication accuracy and efficiency. Three key techniques are implemented to address these challenges. Firstly, advanced encryption methods are employed for secure voice data storage and transmission, through the homomorphic encryption to enable authentication processing on encrypted data. Secondly, a privacy-preserving feature extraction method is introduced, transforming raw voice inputs into irreversible representations to shield original biometric information. Additionally, the framework incorporates differential privacy mechanisms, adding controlled noise to aggregated voice data to prevent individual identification within large datasets. A user-centric consent and control model is proposed, empowering individuals to manage their voice profiles and authentication settings. Experimental findings demonstrate that the framework achieves enhanced authentication accuracy while markedly reducing privacy risks compared to conventional systems. This contribution addresses the ongoing challenge of balancing security and privacy in biometric authentication technologies.
An optimized deep learning framework based on LEE for real time student performance prediction in educational data Muniappan, Ramaraj; Devi Devarajan, Sowmya; Subbarayalu Ramamurthy, Lavanya; Balakumar, Ayshwarya; Gunaseelan, Prathap; Palanisamy, Shyamala; Selvaraj, Srividhya; Sabareeswaran, Dhendapani; Bhaarathi, Ilango
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9773

Abstract

Predicting student performance in real-time remains a critical challenge in educational data mining (EDM), especially with large, noisy, and high-dimensional datasets. This study proposes an advanced deep learning framework that integrates learning entropy estimation (LEE) with models such as support vector machines (SVM), you only look once (YOLO), recurrent convolutional neural networks (RCNN), and artificial neural networks (ANN) to enhance feature selection and classification accuracy. The framework follows a systematic pipeline involving data preprocessing, LEE-based feature extraction, and model training on a real-time academic dataset comprising student demographics, attendance, and performance metrics. Among the proposed models, the LEE-based YOLO (LBYOLO) achieved the highest testing accuracy of 93% and the fastest execution time of 1.84 seconds, while the LEE-based ANN (LBANN) demonstrated consistent performance across precision, recall, and F1-score. The results confirm the superiority of deep learning methods over traditional machine learning techniques for educational prediction tasks. This approach enables early detection of at-risk students and supports timely, data-driven educational interventions. Future work will focus on adaptive learning systems and multi-platform student behavior analysis to support personalized education strategies.
Co-Authors Balakumar, Ayshwarya Bhaarathi, Ilango Chandrasekar, Yamini Devi Devarajan, Sowmya Gunaseelan, Prathap Muniappan, Ramaraj Murugesan, Kathiresh Nithya, Balluru Thammaiahshetty Adishankar Palanisamy, Boopathi Palanisamy, Shyamala Sabareeswaran, Dhendapani Selvaraj, Srividhya Shanmugam, Kavitha Masagoundanpudhur Thiyagaraja, Velumani