Work place: Department of ECE, Vignan's Foundation for Science, Technology & Research, Vadlamudi, Andhra Pradesh
E-mail: mssrukmini@gmail.com
Website: https://orcid.org/0009-0006-8283-2020
Research Interests:
Biography
M. S. S. Rukmini is a professor at Vignan's Foundation for Science, Technology & Research in Guntur, Andhra Pradesh. Her research interests lie in Very Large-Scale Integration (VLSI), Wireless Communication, the Internet of Things (IoT), and Embedded Systems. She has extensive experience in conducting research and has published several research papers in international conferences and journals. Dr Rukmini has made significant contributions to the field of VLSI, particularly in the design of high-performance and low-power circuits. In addition to her research work, Dr Rukmini teaches and guides students in various aspects of electronics and communications engineering. She has been instrumental in developing the curriculum and has supervised many graduate and postgraduate research projects.
By Nagaraju Sonti Rukmini M. S. S. Venkatesh Munagala
DOI: https://doi.org/10.5815/ijigsp.2025.02.03, Pub. Date: 8 Apr. 2025
This research presents a groundbreaking method using graph neural networks (GNN) for the accurate identification of COVID-19 through the analysis of respiratory sounds. The method utilizes advanced signal processing and machine learning techniques, including Fast Fourier Transforms (FFTs), Mel-spectrograms, and GNN methodology. FFTs are used as a preprocessing step to convert raw respiratory sound signals into frequency-domain representations, enhancing signal quality and isolating informative acoustic patterns. Mel-spectrograms are used to extract essential feature vectors for diagnostic classification, enhancing the model's ability to discern subtle patterns indicative of COVID-19 infection.
The GNN methodology feeds preprocessed audio features into a graph neural network architecture, which excels at capturing complex relationships and dependencies within data by modeling them as graphs. In this context, respiratory sound data is represented as a graph, with nodes corresponding to specific audio features and edges representing relationships between them. The GNN effectively learns to propagate information across the graph, enabling it to identify meaningful patterns indicative of COVID-19 infection. The research findings show that GNN surpasses convolutional neural network (CNN) in terms of accuracy, precision, recall, and F1 score, indicating significant progress in the application of GNN in medical diagnostics. The study provides a comprehensive examination of the possibilities of using advanced neural network techniques to transform disease detection and diagnosis, with a validation accuracy of up to 97% under rigorous constraints.
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