AN An ica-ensemble learning approach for prediction of uwb nlos signals data classification

arXiv:2402.17808v1 Announce Type: cross
Abstract: Trapped human detection in search and rescue (SAR) scenarios poses a significant challenge in pervasive computing. This study addresses this issue by leveraging machine learning techniques, given their high accuracy. However, accurate identification of trapped individuals is hindered by the curse of dimensionality and noisy data. Particularly in non-line-of-sight (NLOS) situations during catastrophic events, the curse of dimensionality may lead to blind spots due to noise and uncorrelated values in detections. This research focuses on …

abstract accuracy arxiv challenge classification computing cs.it cs.lg data data classification detection eess.sp ensemble human identification issue machine machine learning machine learning techniques math.it prediction search study type