R
Ramakrishnan Swaminathan
Researcher at Indian Institute of Technology Madras
Publications - 76
Citations - 515
Ramakrishnan Swaminathan is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 9, co-authored 54 publications receiving 269 citations. Previous affiliations of Ramakrishnan Swaminathan include Indian Institutes of Technology.
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Imperceptibility-Robustness tradeoff studies for ECG steganography using Continuous Ant Colony Optimization
TL;DR: The novelty of the proposed approach is to use CACO in ECG Steganography, to identify Multiple Scaling Factors (MSFs) that will provide a better tradeoff compared to uniform Single Scaling Factor (SSF) and the results validate that the tradeoff curve obtained through MSFs is better than the tradeoffs obtained for any SSF.
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Deep Learning on 1-D Biosignals: a Taxonomy-based Survey
TL;DR: A large variability of research with respect to data, application, and network topology is demonstrated and future research is expected to focus on the standardization of deep learning architectures and on the optimization of the network parameters to increase performance and robustness.
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Laplace Beltrami eigen value based classification of normal and Alzheimer MR images using parametric and non-parametric classifiers
TL;DR: An attempt has been made to analyse the shape changes of Corpus Callosum (CC) using shape based Laplace Beltrami (LB) eigen value features and machine learning techniques, which seems to be clinically significant in the shape investigation of brain structures for AD diagnosis.
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Convolutional neural network based emotion classification using electrodermal activity signals and time-frequency features
TL;DR: The proposed method is found to be robust in handling the dynamic variation of EDA signals for different emotional states and outperformed most of the state-of-the-art methods.
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Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features
TL;DR: The proposed computer aided diagnostic approach is found to perform better as compared to the existing methods and can be of significant assistance to physicians at the point of care in resource constrained regions.