Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Machine learning based condition monitoring of a DC-link capacitor in a Back-to-Back converter(Institute of Electrical and Electronics Engineers Inc., 2022) RAJENDRAN, S.; Jena, D.; Diaz-D, M.; Devi, V.S.K.The utilization of power electronic converters has increased, and the significance of continuous operation is essential in various applications. Therefore, proper condition monitoring (CM) is vital for power converters to eradicate unpredictable maintenance. However, the existing CM techniques may require additional sensors or injection of controlled voltage to the converters. The following machine learning algorithms, such as a K-nearest neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes (NB), have been proposed to monitor the condition of the dc-link capacitor in a Back-to-Back (BTB) converter. The dc-link voltage is measured, and a wavelet decomposition is employed for the feature extraction. Moreover, the performance index evaluates the efficacy of the different classifiers. Further, different datasets have been considered for the evaluation of the classifiers. From this analysis, it is found that the SVM classifier performs better than others. © 2022 IEEE.Item Critical Review on Heart Disease Prediction: A Machine Learning Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Mahapatro, S.R.; Mahapatra, R.K.; Shet, N.S.V.; Prusty, S.B.; Satapathi, G.S.; Manjukiran, B.; Reddy, G.; Chandana, O.; Divya, N.; DImri, P.The heart is the second-most significant organ in the human body after the brain, which is the most significant organ. All of the body's organs are nourished and the blood is circulated. In the medical field, it might be difficult to anticipate the development of heart diseases. Data analytics is crucial for developing predictions based on new information, and it helps hospitals predict diseases. Every year, cardiovascular diseases account for more than 31 % of all fatalities globally. Different Machine learning algorithms are in this paper to predict heart disease. It presents a general overview of the previous work and offers insight into the current algorithm. © 2023 IEEE.Item Machine Learning Aided Signal Detection in Underwater Wireless Optical Communication for IoUT applications(Institute of Electrical and Electronics Engineers Inc., 2024) Kavitha, K.; Angayarkanni, V.; Paramanandham, N.; Yogarajan, G.; Krishnan, P.This study explores the effectiveness of the Machine Learning (ML) algorithms in addressing channel impairments in underwater Wireless Optical Communication (UWOC) systems employing On-Off Keying (OOK) modulation. The simulation takes into account underwater challenges such as signal attenuation, scattering, and absorption by using the Gamma-Gamma distribution to model fading and scintillation effects. The ML algorithm's performance is assessed by comparing its bit error rate (BER) against the signal-to-noise ratio (SNR) in comparison to the ideal scenario with perfect channel state information (CSI). The simulation covers various underwater scenarios, including ocean water, harbor water, clear water, coastal water, and oligotrophic water, showcasing the algorithm's adaptability in diverse environmental conditions. The results indicate that the SVM algorithm closely approaches the BER performance achieved with CSI, demonstrating its potential to improve communication reliability in UWOC systems under realistic channel conditions. This study provides valuable insights into the application of machine learning for signal detection in UWOC, offering prospects for enhanced underwater communication performance. © 2024 IEEE.