Dr. SELVAKUMARASAMY K
@saveethaengineering.com
PROFESSOR & ECE Department
Saveetha Institute of Medical and Technical Science
RESEARCH INTERESTS
Signal and Image Processing
Soft Computing Methods
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Prabhu M, Sathishkumar A, Sasi G, Lau Chee Yong, Shanker M C, and Selvakumarasamy K
Anapub Publications
Despite the global COVID-19 pandemic, public health professionals are also concerned about a possible new monkeypox epidemic. Similar to vaccinia, cowpox, and variola, the orthopoxvirus that causes monkeypox has two strands that are double-stranded. Many people have propagated the current pandemic through sexual means, particularly those who identify as bisexual or gay. The speed with which monkeypox was detected is the most important element here. In order to catch monkeypox before it infects more people, machine learning could be a huge help in making a quick and accurate diagnosis. Finding a solution is the driving force behind this project, which aims to develop a model for detecting monkeypox using deep learning and image processing. For optimal cluster selection during photo segmentation, the Chameleon Swarm Algorithm (CSA) employs K-means clustering. Examining the accuracy with which the Swin Transformer model identified instances of monkeypox was the driving force for this study. The proposed techniques are evaluated on two datasets: Kaggle Monkeypox Skin Lesion Dataset (MSLD) besides the Monkeypox Skin Image Dataset (MSID). We assessed the outcomes of various deep learning models using sensitivity, specificity, and balanced accuracy. Positive results from the projected process raise the possibility of its widespread application in monkeypox detection. This ingenious and cheap method can be put to good use in economically deprived communities that may not have access to proper laboratory facilities.
Thella Preethi Priyanka, R. Reji, Venkata Lalitha Narla, K. Selvakumarasamy, Javed Miya, and Yogeshwari V. Mahajan
Springer Science and Business Media LLC
S Markkandan, Kapil Aggarwal, K. Ashok, K Selvakumarasamy, Rajanish Kumar Kaushal, and Makarand Mohan Jadhav
Springer Science and Business Media LLC
K. Baranitharan, Dineshbabu V., Robert Concepción-Lázaro, Balamanigandan R., K. Selvakumarasamy, Mahaveerakannan R., and Mohammed Wasim Bhatt
Elsevier BV
M. Manimaran, Murali Dhar M S, Roger Norabuena-Figueroa, Mahaveerakannan R, S. Saraswathi, and K. Selvakumarasamy
Intelligence Science and Technology Press Inc.
Smart homes present a serious challenge for the aged and those with mobility issues due to the environment's inherent danger. Unwary people have the propensity to fall over when bending over in these settings. Here, they show two time-based reasoning models to identify incidents of potentially fatal falls that have not been accounted for (CM-I and CM-II). The ubiquitous use of IoT altimeter watches among the elderly provides a wealth of data that can be used by these algorithms to predict the likelihood of a fall based on categorization criteria. They compared actual and simulated data involving missteps, mishaps, and crashes to gauge the programmers’ performance. Results suggest that using such logic models to help healthcare providers determine if senior people living in smart homes have fallen is a potential field for future study. The CM-II model had the highest prediction accuracy of any model identified in the literature, at 0.98 when compared to the test parameter. Since the number of devices linked to the IoT can be quickly extended in contrast to the number of devices connected to conventional computers, the number of hacks aimed at the IoT has grown dramatically. There is no way to fix the issue that hacked IoT devices create until they figure out how to track down the source of the attacks. Pursuing a deeper understanding of the technologies, protocols, and architecture of IoT systems, as well as the potential consequences of using infected IoT devices, is the overarching goal of this study. There are many Internet of Things (IoT) systems vulnerable to cybercriminal manipulation, so this study also explores a range of machine learning and deep learning-based methods that can be used to detect such compromise.
K. Selvakumarasamy, S. Poornachandra, and R. Amutha
Springer Science and Business Media LLC
Deepak Kumar Nayak, S. Sheik Aalam, R. Murugan, K. Selvakumarasamy, and S. Rama Reddy
Oriental Scientific Publishing Company
DC/DC Converters play a vital role in biomedical imaging systems such as x-ray, ultra sound and Magnetic Resonance Imaging systems. These applications require low ripple voltage waveforms, low voltage spikes and variable output voltages. This paper presents a naturally clamped bidirectional secondary modulation based zero current switching (ZCS) current fed half-bridge DC/DC converter for biomedical imaging systems. The proposed bidirectional converter achieves zero current switching (ZCS) of the main switches at the primary side and zero voltage switching (ZVS) of the rectifier switches at the secondary side. In the proposed converter, the secondary modulation clamps the voltage across the main switches in the primary side naturally, so that voltage spike at the switch turn-off is not seen. The operation of the proposed converter is explained, analyzed, and the performance of the converter is implemented in MATLAB for a model of 20 kHz switching frequency and 20 W output power.
K. SELVAKUMARASAMY, S. POORNACHANDRA, and R. AMUTHA
Oriental Scientific Publishing Company
ECG is the electrical activity of the heart which uses for the various diagnostic purposes. Denoising is a process which helps to remove the noise from the original signal. Wavelet transform is suitable for non stationary signal such as ECG signal, EEG signal, PPG signal etc. The wavelet coefficients obtained by wavelet transform are made zero which in turns reduces the noise to zero level. Fuzzy logic is a structured, powerful problem solving technique that approximates a function through linguistic variables. A fuzzy membership function is an abstract which each point in the input variable is mapped to a membership value between 0 and 1.In this paper, noise is removed from an ECG signal by using subband adaptive shrinkage function using the fuzzy logic. The threshold value is selected by the fuzzy membership function for signal denoising. Among the other shrinkages functions, subband adaptive method is preferred as it produces good result by holding linearity at discontinuities. The parameters used for measuring the performance are signal to noise ratio (SNR), percentage root mean square difference (PRD), and mean square error (MSE).
K. Selvakumarasamy, S. Poorna Chandra, and R. Amutha
IEEE
ECG is the generally recognized biomedical signal for diagnostic purpose and it shows the electrical activity of the heart muscles. ECG is a non stationary signal where wavelet transform (WT) is useful for analyzing it. Denoising is the process of removing noise in order to preserve useful information. A comparative analysis of various wavelet shrinkage functions for ECG signal denoising such as soft and hard shrinkage functions, hyper shrinkage function, subband - adaptive shrinkage functions are discussed. The performance of the wavelet shrinkage functions are compared based on the signal to noise ratio (SNR) Value.
K. Selvakumarasamy and S. Poornachandra
IEEE
With the increasing growth of technology and the entrance into the digital world, we have to handle a enormous amount of information every time which often presents difficulties. So, the digital information must be stored and retrieved in a professional and valuable manner, in order for it to be put to practical use. Imaging requires large amount of memory to store the digitized data. Due to the transmission bandwidth constraint, images must be compressed before transmission and storage. The wavelet transform is more and more widely used in image processing algorithms. In this paper, we reviewed the different methods of image coding. We reviewed the different methods in terms of PSNR [Peak Signal to Noise Ratio] and MSE [Mean Square Error] which can be used to better understand the relationship between various methods and their features.