Rethinking densely connected convolutional networks for diagnosing infectious diseases. (2023)
Journal Article
PODDER, P., ALAM, F.B., MONDAL, M.R.H., HASAN, M.J., ROHAN, A. and BHARATI, S. 2023. Rethinking densely connected convolutional networks for diagnosing infectious diseases. Computers [online], 12(5), article 95. Available from: https://doi.org/10.3390/computers12050095

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. I... Read More about Rethinking densely connected convolutional networks for diagnosing infectious diseases..

Deep scattering spectrum germaneness for fault detection and diagnosis for component-level prognostics and health management (PHM). (2022)
Journal Article
ROHAN, A. 2022. Deep scattering spectrum germaneness for fault detection and diagnosis for component-level prognostics and health management (PHM). Sensors [online], 22(23), article 9064. Available from: https://doi.org/10.3390/s22239064

Most methodologies for fault detection and diagnosis in prognostics and health management (PHM) systems use machine learning (ML) or deep learning (DL), in which either some features are extracted beforehand (in the case of typical ML approaches) or... Read More about Deep scattering spectrum germaneness for fault detection and diagnosis for component-level prognostics and health management (PHM)..