Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Muniappan, Ramaraj
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases Muniappan, Ramaraj; Thangavel, Thiruvenkadam; Manivasagam, Govindaraj; Sabareeswaran, Dhendapani; Thangarasu, Nainan; Jothish, Chembath; Ilango, Bhaarathi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2803-2813

Abstract

In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.
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.
Optimization techniques applied on image segmentation process by prediction of data using data mining techniques Muniappan, Ramaraj; Selvaraj, Srividhya; Vanathi Gurusamy, Rani; Thiyagarajan, Velumani; Sabareeswaran, Dhendapani; Prasanth, David; Krithika, Varadharaj; Ilango, Bhaarathi; Subramanian, Dhinakaran
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.pp2161-2171

Abstract

The research work presents an enhanced method that combines rule-based color image segmentation with fuzzy density-based spatial clustering of applications with noise (FDBSCAN). This technique enhances super-pixel robustness and improves overall image quality, offering a more effective solution for image segmentation. The study is specifically applied to the challenging and novel task of predicting the age of tigers from camera trap images, a critical issue in the emerging field of wildlife research. The task is fraught with challenges, particularly due to variations in image scale and thickness. Proposed methods demonstrate that significant improvements over existing techniques through the broader set of parameters of min and max to achieve superior segmentation results. The proposed approach optimizes segmentation by integrating fuzzy clustering with rule-based techniques, leading to improved accuracy and efficiency in processing color images. This innovation could greatly benefit further research and applications in real-world scenarios. Additionally, the scale and thickness variations of the present barracuda panorama knowledge base offer many advantages over other enhancement strategies that have been proposed for the use of these techniques. The experiments show that the proposed algorithm can utilize a wider range of parameters to achieve better segmentation results.
Co-Authors Balakumar, Ayshwarya Bhaarathi, Ilango Chandrasekar, Yamini Devi Devarajan, Sowmya Gunaseelan, Prathap Ilango, Bhaarathi Jothish, Chembath Krithika, Varadharaj Manivasagam, Govindaraj Murugesan, Kathiresh Nainan, Thangarasu Nithya, Balluru Thammaiahshetty Adishankar Palanisamy, Boopathi Palanisamy, Shyamala Prasanth, David Raja, Joe Arun Sabareeswaran, Dhendapani Selvaraj, Srividhya Shanmugam, Kavitha Masagoundanpudhur Subbarayalu Ramamurthy, Lavanya Subramanian, Dhinakaran Sumbramanian, Dhinakaran Thangarasu, Nainan Thangavel, Thiruvenkadam Thiyagaraja, Velumani Thiyagarajan, Velumani Vanathi Gurusamy, Rani