Dr.S.Mohana
@saranathan.ac.in
Associate Professor
Saranathan College of Engineering
EDUCATION
B.E,M.E.Ph..D
RESEARCH INTERESTS
Data Mining
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
C. Shyamala, S. Mohana, M. Ambika, and K. Gomathi
Springer Science and Business Media LLC
Palaniappan Rajarajeswari, Chandrasekaran Shyamala, and Shivashankar Mohana
Wiley
S. Mohana, C. Shyamala, E. Shapna Rani, and M. Ambika
Informa UK Limited
S. A. Sahaaya Arul Mary, Lakshmi Kanthan Narayanan, S. Mohana, R. Senthamil Selvi, R. Karthik, and N. Ramya
Springer Nature Singapore
M. Mohan, Anuradha Patil, S. Mohana, P. Subhashini, Sumit Kushwaha, and S Manikandaprabu Pandian
IEEE
Denoising magnetic resonance images are traditionally done individually, introducing undesired artefacts like blurring. To solve this issue, this paper offers a unique adaptive interpolation architecture that simultaneously allows for image data augmentation, noise removal, and detail augmentation. The multi-tier kernel (MTK) algorithm adjusts the extrapolation weights based on mathematical features in magnetic resonance (MR) data. The MTK weight matrix is then adaptively sharpened, and a Rician bias corrective is used to reduce Rician noise and improve small details. After processing, the noise eliminates the bias produced by the asymmetric sources. The adaptive MTK, in this way, extends the zero ordering estimating methodology to higher regression power facilitating edge maintenance and detail restoration. Investigation outcomes using genuine and MR images (with/without noise) evidenced that the algorithm delivered good restoration outcomes than conventional deep-learning-based approaches but with fewer requirements and calculation burden.
S. Mohana and S. A. Sahaaya Arul Mary
IEEE
Availability of information in profusion in the internet and databases is common knowledge. It has to be viewed in the backdrop of chances for disclosure of such information by a third party. Privacy Preserving Data Mining (PPDM) is in use for maintaining the privacy of individuals. Numerous updated methods are available for the purpose. Evolutionary Algorithms (EA's) are able to provide effective solutions for real-world optimization problems. They find use in business practice too. This work has a proposal for the implementation of an EA using K-Anonymization; particle swam optimization (PSO), Ant colony optimization (ACO) and a Genetic Algorithm (GA). We herein propose Genetic algorithm and particle swam optimization work with the same data. The use of generalization of the original dataset is meant for achieving K-anonymity. A collection of people called “chromosomes” frame the populace which shows an aggregate solution for a characterized issue in the proposed GA. The achievement of good accuracy is obtained by GA optimization, recall and precision in comparison with K-Anonymization, PSO and ACO methods.
S. Mohana and S. A. Sahaaya Arul Mary
American Scientific Publishers